bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibiotic–Drug Interactions in the Intensive Care Unit: A Literature Review
Authors: Radkowski Paweł , Derkaczew Maria , Mazuchowski Michał , Moussa Annas , Podhorodecka Katarzyna , Dawidowska-Fidrych Justyna , Braczkowska-Skibińska Małgorzata , Synia Daria , Śliwa Karol , Wiszpolska Marta , Majewska Marta
Published: May 2024

Interactions between drugs are a common problem in Intensive Care Unit patients, as they mainly have a critical condition that often demands the administration of multiple drugs simultaneously. Antibiotics are among the most frequently used medications, as infectious diseases are often observed in ICU patients. In this review, the most important antibiotic–drug interactions, based on the pharmacokinetic and pharmacodynamic mechanisms, were gathered together and described. In particular, some of the most important interactions with main groups of antibacterial drugs were observed in patients simultaneously prescribed oral anticoagulants, NSAIDs, loop diuretics, and valproic acid. As a result, the activity of drugs can be increased or decreased, as dosage modification might be necessary. It should be noted that these crucial interactions can help predict and avoid negative consequences, leading to better patient recovery. Moreover, since there are other factors, such as fluid therapy or albumins, which may also modify the effectiveness of antibacterial therapy, it is important for anaesthesiologists to be aware of them.
One Health Landscape of Antimicrobial Resistance in Bacteria Isolated from Virginia between 2007–2021
Authors: Kim Jimin , Ndegwa Eunice
Published: May 2024

The emergence of antimicrobial-resistant (AMR) bacteria has become a critical global One Health issue, mainly attributed to the extensive use of antimicrobial agents in human and agricultural settings. Regional and local AMR surveillance data is essential for implementing awareness and mitigation strategies. This article assesses AMR frequency in 1604 bacterial isolates consisting of Escherichia coli (E. coli) and Salmonella spp. isolated from diverse sources in Virginia, including farm animals, wildlife, environment, and food samples from 2007 to 2021. The results are based on the Kirby–Bauer disc diffusion assessment method of susceptibility to select antimicrobial agents, spanning nine distinct categories approved by the US Food and Drug Administration for clinical use. Streptomycin (STR) and tetracycline (TCY) exhibited the highest frequency of resistance in E. coli (39.1%) and Salmonella (25.2%), respectively. Multidrug resistance (MDR) was evident in 6.6% of E. coli and 10.9% of Salmonella isolates. Notably, 51% of E. coli and 36% of Salmonella isolates demonstrated resistance to more than one antimicrobial. None of the tested antimicrobials guaranteed effectiveness against the bacteria isolated from the surveyed sources and regions. The study found heightened MDR and distinct AMR patterns in bacteria isolated from food products compared to other sampled sources. These findings are vital for comprehending the current AMR landscape, prompting the development of strategies to mitigate the emergence of AMR bacteria, and advocating prudent antimicrobial use from a One Health perspective.
Evaluation of Surveillance Strategies of Antimicrobial Consumption in Animals
Authors: Ching Carly , Zaman Muhammad H. , Wirtz Veronika J.
Published: May 2024

The aim of this paper is to explore and assess various strategies for monitoring antimicrobial consumption (AMC) in animals, within the context of the One Health approach. Recent studies have shed light on the limited surveillance and data collection for AMC in animals. Using the United States Center for Disease Control and Prevention Policy Analytical Framework, we assess global, national, and farm-level surveillance strategies on public health impact and feasibility using evidence from primary, secondary, and grey literature. From this, we identify key policy mechanisms that support the adoption of surveillance while providing specific recommendations. We find that a global strategy, though valuable for benchmarking and policy guidance, faces participation and data visibility challenges. National-level surveillance offers direct inputs into national action plans but struggles with data uniformity and comparability. Farm-level surveillance, while resource-intensive, provides the most granular data for informing specific interventions. We advocate for a multi-faceted approach to AMC surveillance, emphasizing that legal mandates and financial incentives are crucial for encouraging surveillance participation, along with international cooperation for enhancing participation and data quality. Drawing parallels with public reporting challenges in other sectors can provide valuable lessons on how to address data collection, analysis, and reporting barriers.
Detection of KPC-216, a Novel KPC-3 Variant, in a Clinical Isolate of Klebsiella pneumoniae ST101 Co-Resistant to Ceftazidime-Avibactam and Cefiderocol
Authors: Giufrè Maria , Errico Giulia , Del Grosso Maria , Pagnotta Michela , Palazzotti Bernardetta , Ballardini Milva , Pantosti Annalisa , Meledandri Marcello , Monaco Monica
Published: May 2024

Background: Carbapenemase-producing Klebsiella pneumoniae (CP-KP) represents a global threat to public health, with limited antimicrobial therapeutic options. In this study, we analyzed a ceftazidime/avibactam (CAZ-AVI)-resistant K. pneumoniae isolate obtained from a patient previously exposed to CAZ-AVI expressing a novel K. pneumoniae carbapenemase (KPC)-3 variant. Methods: Antimicrobial susceptibility testing was performed using reference broth microdilution. Whole-genome sequencing (WGS) was performed using Illumina and Nanopore Technologies. Short- and long-reads were combined with Unicycler. Assemblies were investigated for multilocus sequence typing (MLST), antimicrobial resistance genes, porins, and plasmids. Results: The K. pneumoniae isolate (KP_RM_1) was resistant to CAZ-AVI, expanded-spectrum cephalosporins, amikacin, ertapenem, and cefiderocol (FDC) but was susceptible to tigecycline, colistin, trimethoprim/sulfamethoxazole, meropenem–vaborbactam, and imipenem–relebactam. WGS revealed that the KP_RM_1 genome is composed of a single chromosome of 5 Mbp and five circular plasmids. Further analysis showed the presence of novel blaKPC-216 located on a 72 kb plasmid. KPC-216 differs from KPC-3 by a Lysin (K) insertion at position 168 (+K168). Conclusions: We report the identification of a new KPC-3 variant associated with CAZ-AVI resistance. The KPC variants associated with CAZ-AVI resistance should be determined to promptly inform clinicians and start the appropriate antimicrobial therapy.
Antibiofilm Strategies in Neonatal and Pediatric Infections
Authors: Kosmeri Chrysoula , Giapros Vasileios , Serbis Anastasios , Balomenou Foteini , Baltogianni Maria
Published: May 2024

Biofilm-related infections pose significant challenges in neonatal and pediatric care, contributing to increased morbidity and mortality rates. These complex microbial communities, comprising bacteria and fungi, exhibit resilience against antibiotics and host immune responses. Bacterial species such as Enterococcus faecalis, Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis commonly form biofilms on medical devices, exacerbating infection risks. Neonates and children, particularly those in intensive care units, are highly susceptible to biofilm-associated infections due to the prolonged use of invasive devices, such as central lines and endotracheal tubes. Enteral feeding tubes, crucial for neonatal nutritional support, also serve as potential sites for biofilm formation, contributing to recurrent microbial contamination. Moreover, Candida species, including Candida pelliculosa, present emerging challenges in neonatal care, with multi-drug resistant strains posing treatment complexities. Current antimicrobial therapies, while important in managing infections, often fall short in eradicating biofilms, necessitating alternative strategies. The aim of this review is to summarize current knowledge regarding antibiofilm strategies in neonates and in children. Novel approaches focusing on biofilm inhibition and dispersal show promise, including surface modifications, matrix-degrading enzymes, and quorum-sensing inhibitors. Prudent use of medical devices and exploration of innovative antibiofilm therapies are imperative in mitigating neonatal and pediatric biofilm infections.
Analysis of the Naturally Aged Scented Components of Montien Boonma’s House of Hope
Authors: Stephens Catherine H. , Biggs Kyna , Poh Soon Kai , Zycherman Lynda
Published: May 2024

Analysis of the scented components of the art installation House of Hope by Montien Boonma, including eight bags of unlabeled or poorly labeled powder and five strands of aromatic beads, was completed to facilitate its display at the Museum of Modern Art (MoMA). Though an olfactory experience is central to the piece, limited information was available concerning the origin of the object’s scent. Identification of the aromas from the powders and beads, some of which were nearly 30 years old, was accomplished through visual assessment, attenuated total reflectance–Fourier-transform infrared spectroscopy (ATR-FTIR), and static headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GCMS) and confirmed using controls. Challenges included spices that had lost some of their potency or become cross-contaminated. The contents of five of the eight bags of powder were successfully identified as black pepper (two bags), clove, turmeric, and white sandalwood (Santalum album). All beads contained nutmeg, peppermint, ginger, and turmeric, while licorice root, thyme, cardamom, and clove were noted in some. The beads were bound using pine honey, a unique type of honey produced by bees that feed on aphid excretions. Identifying the scented components informs current and future installations so that the artist’s original intent is more closely approximated.
Zero Carbon Emissions Due to Ultra-High RES Penetration in Interconnected Island
Authors: Karapidakis Emmanuel , Mozakis Ioannis , Nikologiannis Marios , Tsikalakis Antonios
Published: May 2024

European islands have been leading the charge in renewable energy innovation. Yet, the intermittent nature of sources like solar and wind poses challenges such as grid saturation and frequency variations. Limited interconnection with mainland grids exacerbates these issues, necessitating backup from conventional power sources during low-production periods. Until 2021, Crete operated independently, but new infrastructure now integrates it with the Greek mainland grid, facilitating swift energy transfers. This integration enables surplus power from Crete’s solar and wind systems to be transmitted to the mainland and vice versa. However, reliance on remote power production exposes the island to market fluctuations and distant disruptions, impacting electricity production. Storage technologies offer a solution, enhancing renewable energy penetration while reducing carbon emissions. Green hydrogen, a rising storage method, shows promise in offsetting carbon emissions. Its clean-burning nature minimizes environmental impact and reduces reliance on costly and harmful conventional sources. This study aims to evaluate the feasibility of achieving carbon-neutral electricity production in Crete, Greece, using hydrogen storage to offset annual carbon emissions in a financially viable and sustainable manner. Hydrogen’s clean-burning properties reduce environmental impact and lessen dependence on expensive and environmentally harmful conventional sources. The methodology prioritizes the independence of the Cretan electricity system, utilizing electrolysis to produce green hydrogen and proton-exchange membrane (PEM) fuel cells for energy generation. It investigates the optimal expansion of renewable energy systems, including photovoltaic (PV) and wind turbine (WT) parks, alongside the installation of hydrogen storage, under specific assumptions. This proposed installation aims to achieve both island independence and profitability, requiring an additional expansion in PV capacity of 2.13 GW, WT capacity of 3.55 GW, and a hydrogen system with electrolyzer and fuel cell capacities totaling 278.83 MW each, along with a hydrogen tank capacity of 69.20 MWh. The investment entails a capital expenditure (CAPEX) of 6,516,043,003.31 EUR for a nearly zero net present value (NPV) over 20 years. However, carbon neutrality cannot be attained through this optimal solution alone, as relying solely on carbon sequestration from olive groves, the primary crop cultivated on the island, is insufficient as a carbon sink method. The annual net carbon emissions from electricity production, island transport, residential heating, and carbon sequestration are estimated at 94,772.22 tCO2.
Contextual Hypergraph Networks for Enhanced Extractive Summarization: Introducing Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES)
Authors: Onan Aytuğ , Alhumyani Hesham
Published: May 2024

Extractive summarization, a pivotal task in natural language processing, aims to distill essential content from lengthy documents efficiently. Traditional methods often struggle with capturing the nuanced interdependencies between different document elements, which is crucial to producing coherent and contextually rich summaries. This paper introduces Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES), a novel framework designed to address these challenges through an advanced hypergraph-based approach. MCHES constructs a contextual hypergraph where sentences form nodes interconnected by multiple types of hyperedges, including semantic, narrative, and discourse hyperedges. This structure captures complex relationships and maintains narrative flow, enhancing semantic coherence across the summary. The framework incorporates a Contextual Homogenization Module (CHM), which harmonizes features from diverse hyperedges, and a Hypergraph Contextual Attention Module (HCA), which employs a dual-level attention mechanism to focus on the most salient information. The innovative Extractive Read-out Strategy selects the optimal set of sentences to compose the final summary, ensuring that the latter reflects the core themes and logical structure of the original text. Our extensive evaluations demonstrate significant improvements over existing methods. Specifically, MCHES achieves an average ROUGE-1 score of 44.756, a ROUGE-2 score of 24.963, and a ROUGE-L score of 42.477 on the CNN/DailyMail dataset, surpassing the best-performing baseline by 3.662%, 3.395%, and 2.166% respectively. Furthermore, MCHES achieves BERTScore values of 59.995 on CNN/DailyMail, 88.424 on XSum, and 89.285 on PubMed, indicating superior semantic alignment with human-generated summaries. Additionally, MCHES achieves MoverScore values of 87.432 on CNN/DailyMail, 60.549 on XSum, and 59.739 on PubMed, highlighting its effectiveness in maintaining content movement and ordering. These results confirm that the MCHES framework sets a new standard for extractive summarization by leveraging contextual hypergraphs for better narrative and thematic fidelity.
A Preprocessing Technique Using Diffuse Reflectance Spectroscopy to Predict the Soil Properties of Paddy Fields in Korea
Authors: Shin Juwon , Kim Dae-Cheol , Cho Yongjin , Yang Myongkyoon , Cho Woo-Jae
Published: May 2024

In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky–Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R2 = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R2 = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing.
Seismic Performance of Moment-Resisting–Eccentrically Braced Dual Frame Equipped with Detachable Links
Authors: Tartaglia Roberto , D’Aniello Mario , Maddaloni Giuseppe , Landolfo Raffaele
Published: May 2024

Moment-resisting frames (MRFs) and eccentrically braced frames (EBFs) can be effectively combined in multi-story steel buildings. In fact, MRFs provide redundancy and ductility, while EBFs provide initial and post-yield stiffness with high energy dissipation capacity. In addition, if detachable links are designed as dissipative fuses, it is possible to activate their almost contemporary yielding and prevent the collapse of structures during severe earthquakes, ensuring easy repair and rehabilitation measures. The seismic responses of dual frames with MRFs and EBFs are investigated by means of pushover analyses. Different buildings were designed, varying the number of floors (two, four, and eight stories), the number of MRF spans (zero, one, and two), and the length of the short links (0.8 Mp/Vp and 1.6 Mp/Vp). The obtained results confirmed the superior behavior of dual frames with respect to simple EBFs. In addition, it was observed that the frames with the shorter links exhibited higher resistance and rigidity but smaller overall displacement capacity because of the anticipated failure of the links.
Fiber-Optic System for Monitoring Pit Collapse Prevention
Authors: Neshina Yelena , Mekhtiyev Ali , Kalytka Valeriy , Kaliaskarov Nurbol , Galtseva Olga , Kazambayev Ilyas
Published: May 2024

Currently, there are many enterprises involved in extracting and processing of primary raw materials. The danger of working in this industry consists in the formation of cracks in rocks of the pit side slopes, which can lead to destruction. This article discusses the existing systems for monitoring the pit collapse prevention. The most promising is the use of systems with fiber-optic sensors. However, use of these systems is associated with some difficulties due to high costs, low noise immunity, and in some cases, the requirement for additional equipment to improve the reliability of measurements. A completely new method of processing the data from a fiber-optic sensor that simplifies the design and reduces the cost of the device is proposed considering the experience of previous developments. The system uses artificial intelligence, which improves the data processing. The theoretical part is dedicated to the development of foundations, and the analysis of the nonlinear properties of the physical and mathematical model of optical processes associated with the propagation of an electromagnetic wave in a fiber-optic material was developed. The results of experimental and theoretical applied research, which are important for the development of fiber-optic systems for monitoring the pit collapse prevention, are presented. The dependences of optical losses and the number of pixels on the dis-placement were obtained. The accuracy of the method corresponds to the accuracy of the device by which it is calibrated and is 0.001 mm. The developed hardware-software complex is able to track the rate of changing the derivative of the light wave intensity in time, as well as changing the shape of the spot and transition of pixels from white to black.
Branch-Transformer: A Parallel Branch Architecture to Capture Local and Global Features for Language Identification
Authors: Li Zeen , Liu Shuanghong , Fang Zhihua , He Liang
Published: May 2024

Currently, an increasing number of people are opting to use transformer models or conformer models for language identification, achieving outstanding results. Among them, transformer models based on self-attention can only capture global information, lacking finer local details. There are also approaches that employ conformer models by concatenating convolutional neural networks and transformers to capture both local and global information. However, this static single-branch architecture is difficult to interpret and modify, and it incurs greater inference difficulty and computational costs compared to dual-branch models. Therefore, in this paper, we propose a novel model called Branch-transformer (B-transformer). In contrast to traditional transformers, it consists of parallel dual-branch structures. One branch utilizes self-attention to capture global information, while the other employs a Convolutional Gated Multi-Layer Perceptron (cgMLP) module to extract local information. We also investigate various fusion methods for integrating global and local information and experimentally validate the effectiveness of our approach on the NIST LRE 2017 dataset.
Optical Power Limiter for Charged-Coupled Devices Protection Based on Dye-Doped Nematic Liquid Crystals
Authors: Klus Bartłomiej Wojciech , Kwaśny Michał , Karpierz Mirosław Andrzej , Laudyn Urszula Anna
Published: May 2024

This paper develops an optical power limiter (OPL) utilizing dye-doped nematic liquid crystals (NLCs) in a twisted nematic configuration designed to protect charged-coupled devices from intense light damage. The device harnesses the intrinsic optical properties of NLCs, enhanced by dye doping, to control light transmission without external electric fields. Placed between two crossed polarizers, the NLC cell exploits both reorientational and thermal nonlinearities to reduce the activation thresholds and enhance responsiveness to fluctuating light intensities. The experiments employ a continuous-wave green laser, chosen for its peak interference in the visual field and alignment with CCD camera sensitivities, emphasizing the practical relevance of the OPL in the military and aviation sectors. The results indicate that integrating plastic polarizers and strategically adjusting thermal nonlinearity significantly lowers the operational threshold of the limiter, effectively counteracting high-intensity light exposure while allowing safe light levels. This approach offers effective CCD protection and demonstrates the potential for broad wavelength applications. The developed NLC-based OPL represents a significant advancement in dynamic light management technologies, promising extensive industrial applications.
Hematological and Hemorheological Parameters of Blood Platelets as Biomarkers in Diabetes Mellitus Type 2: A Comprehensive Review
Authors: Zvetkova Elissaveta , Ivanov Ivan , Koytchev Eugeni , Antonova Nadia , Gluhcheva Yordanka , Alexandrova-Watanabe Anika , Kostov Georgi
Published: May 2024

Diabetes mellitus type 2 (DM2) is a hypercoagulable state with enhanced platelet (PLT) activation and increased clotting factor production. Simultaneously, the fibrinolytic cell system is inhibited due to the formation of clots with high fibrinolysis resistance. The stages of PLT “activation” have been well characterized microscopically, morphometrically, and nanomechanically using a light microscope, transmission electron microscope (TEM), scanning electron microscope (SEM), and atomic force microscope (AFM). Thrombocytes in an “activated” (procoagulant) state play a central role in two main biological processes: hemostasis and vascular vessel repair. Enhanced PLT reactivity in diabetic patients is considered a “pro-thrombotic” state. PLT hematometric indices are higher in retrospective and prospective studies, such as PLTs (count), MPV (mean platelet volume), PDW (platelet distribution width), PCR (platelet crit), and the PLTs/Ly ratio. The platelet indices MPV and PDW are higher in people with diabetes who have chronic vascular complications, and are statistically significant. PLT parameters/indices are useful biomarkers in the early diagnosis and prognosis of DM2. Precise studies of PLT activation state during DM2 may be useful for new diabetes (DM2) treatment strategies and effective therapeutic agents. Researchers have observed an association between MPV and medications such as insulin, metformin, and sulfonylureas using the blood glucose concentration attached to hemoglobin (HbA1c values) as markers of glycemic control in patients with diabetes. Computational modeling of PLT activation in DM2 is also a controlling factor for thrombocyte distribution and margination in blood vessels, both of which are associated with micro- and macrovascular disease in DM2. PLT-derived microRNAs (miRNAs) are novel molecular biomarkers for the diagnosis and prognosis of DM2, insulin resistance, and diabetes complications. Anti-platelet agents and natural plant products may also be effective in the prevention and secondary treatment of micro- and macrovascular complications in type 2 diabetes mellitus. To determine new ways of diagnosing, treating, predicting, and managing DM2 and its related vascular complications, we propose monitoring a combination of hematological, hemorheological, and hemostatic parameters (indices), which merit future studies.
ROI-Binarized Hyperbolic Region Segmentation and Characterization by Using Deep Residual Convolutional Neural Network with Skip Connection for GPR Imaging
Authors: Zhang Hua , Dai Qianwei , Feng Deshan , Wang Xun , Zhang Bin
Published: May 2024

Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images.
Optimization of Cutting Parameters for Energy Efficiency in Wire Electrical Discharge Machining of AISI D2 Steel
Authors: González-Rojas Hugo Orlando , Miranda-Valenzuela José Carlos , Calderón-Najera Juan de Dios
Published: May 2024

Improving energy efficiency in manufacturing processes is a critical global concern for the industry. Manufacturers strive to enhance energy efficiency across all manufacturing operations to remain competitive globally, aiming to reduce production times without compromising product quality. While there has been significant research characterizing energy efficiency and surface roughness in conventional processes like turning or milling, studies on unconventional manufacturing techniques are limited. This study focuses on optimizing a wire electrical discharge machining (WEDM) process to minimize energy consumption while maintaining surface roughness. Various cutting parameters, such as pulse on-time, pulse off-time, servo voltage, wire tension, wire speed, and wire voltage, were evaluated. Experiments were conducted using Taguchi’s methodology with a L27 orthogonal array, employing AISI D2 steel plates of 19 mm and 25 mm thickness as the machining material. The research identified that optimal parameters for reducing energy consumption and improving surface roughness included a pulse on-time of 10 s, pulse off-time of 11 s, servo voltage of 44 V, wire tension of 50 g-force, wire speed of 7 m per minute, and wire voltage of 9 volts. This combination led to an 8% reduction in energy consumption and a 1% enhancement in surface roughness compared to baseline values.
A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
Authors: Botezatu Adrian-Paul , Burlacu Adrian , Orhei Ciprian
Published: May 2024

The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. In the domain of road damage detection, this study explores a range of deep learning methods, particularly focusing on one-stage and two-stage detectors. These methodologies, including notable ones like YOLO and SSD for one-stage detection and Faster R-CNN for two-stage detection, are critically analyzed for their efficacy in identifying various road damages under diverse conditions. The review provides insights into their comparative advantages, balancing between real-time processing and accuracy in damage localization. For road surface classification, the paper investigates the classification techniques based on both environmental conditions and material road composition. It highlights the role of different convolutional neural network architectures and innovations at the neural level in enhancing classification accuracy under varying road and weather conditions. The main finding of this work is that it offers a comprehensive overview of the current state of the art, showcasing significant strides in utilizing deep learning for road analysis in autonomous vehicle systems. The study concludes by underscoring the importance of continued research in these areas to further refine and improve the safety and efficiency of autonomous driving.
Autochthonous or Allochthonous, the Prehistoric Pottery of Cueva de Los Postes
Authors: Lattao Virginia , Garcês Sara , Gomes Hugo , Rosina Pierluigi , Collado Hipólito
Published: May 2024

Cueva de Los Postes is located in the southern part of the Spanish Extremadura region, in the Fuentes de León municipality. This study analyzed pottery found during archaeological excavations in Cueva de Los Postes. The aim was to determine whether the raw materials had common or separate origins and determine whether or not the origin of the pottery raw material was regional. In this regard, several pottery fragments were found in different phases (Neolithic and Copper Age) of the Holocene occupation of Cueva de Los Postes. These were analyzed using ATR-FTIR (Fourier Transform Infrared spectroscopy). The analyzed samples demonstrated no strong differences in their composition. In order to identify a possible origin for the kaolinite of the analyzed pottery, six samples of clay sediments were taken from inside and around the cave and subjected to ATR-FTIR. This was done to compare their compositions and check for a possible correlation with the pottery. The analyzed pottery, from different stratigraphic units, shows homogeneity in raw material. This finding, and the analyses made of the regional sediments, confirm that the raw material was regional and that there was a continuous use of it.
Region-Selective Corrosion for the Fabrication of Tilted Microfiber Bragg Gratings: A Candidate for the Monitoring of Buildings’ Health
Authors: Que Yufei , Li Jin
Published: May 2024

Optical fiber gratings can be appropriately packaged and integrated for the real-time monitoring of the structural health of buildings or composite-material films. In this work, a tilted fiber Bragg grating at the micron scale was proposed, designed, and optimized via simulation model analysis using OptiFDTD software. The effects of the grating inclination, grating period, and grating length on the transmission spectrum of the tilted microfiber Bragg grating (TMFBG) were studied. The transmission spectrum’s responses to different refractive indices were simulated and compared. A TFBG was uniformly etched with hydrofluoric acid based on the chemical etching method, and several TMFBGs with different diameters were prepared. The refractive index-sensing characteristics of the TMFBGs with different structures were studied. It was found that the sensitivity of the etched TFBG was greatly improved from 0.964 nm/RIU to 6.368 nm/RIU for the higher-order cladding mode, and from 0.294 nm/RIU to 2.353 nm/RIU for the lower-order cladding mode, being approximately six times and eight times higher, respectively.
Upper Airway Collapsibility during Sleep Endoscopy with a Titratable Mandibular Advancement Simulator in Obstructive Sleep Apnea Patients
Authors: Cameli Matteo , Stipa Chiara , Pelligra Irene , Ippolito Daniela Rita , Sorrenti Giovanni , Alessandri-Bonetti Giulio , Incerti Parenti Serena
Published: May 2024

Drug-induced sleep endoscopy (DISE) has been progressively used to determine the individual patient responsiveness to therapy with a mandibular advancement device (MAD) for obstructive sleep apnea (OSA). This retrospective cohort study compared the general and polygraphic characteristics, as well as the sites, degrees, and patterns of upper airway collapse, in patients who responded to advancement with a titratable mandibular advancement (TMA) simulator during DISE—referred to as responders—to those in non-responders. The sample included 335 OSA patients (307 males) with a mean age of 49.98 (SD = 9.88) years, and a mean AHI of 34.14 (SD = 18.61). Once the TMA simulator customized to the patient’s dental arches was inserted and the examination was performed at 0%, 25%, 50%, and 75% of the patient’s range of antero-posterior mandibular excursion, the simulator was removed and the upper airway behavior was studied in the baseline situation. Without TMA simulator non-responders had a higher percentage of oropharyngeal complete latero-lateral and complete concentric velopharyngeal collapse. With TMA simulators, there was a significant difference between responders and non-responders in individual obstructive sites at velopharyngeal, oropharyngeal, and epiglottis levels, while at the tongue level, responders and non-responders showed the same response tendency. If confirmed in future prospective studies, these results suggest that the presence of complete latero-lateral obstruction at the oropharynx level and complete circular obstruction at the velopharynx level could be adverse phenotypes for MAD treatment outcomes in OSA patients and MAD treatment should not be considered in these patients (at least as a single therapy).
The Development of the Special Brazilian Jiu-Jitsu Fitness Test: Takedown Zone (SBJJFT-TZ), Gi Formula
Authors: Wąsacz Wojciech , Rydzik Łukasz , Šimenko Jožef , Kędra Andrzej , Błach Wiesław , Ambroży Tadeusz
Published: May 2024

Background: There is a consensus among combat sports researchers regarding the important role of evaluating the comprehensive special fitness and performance of athletes. This is due to the specific structure of tasks performed in these sports as they require a comprehensive and integrated function of broadly understood physical fitness. The present study aimed to develop and check the Special Brazilian Jiu-Jitsu Fitness Test: Takedown Zone (SBJJFT-TZ), Gi formula’s reliability. The major task of this sport-specific tool is to illustrate the comprehensive special fitness and performance of professional Brazilian jiu-jitsu athletes. Methods: The study covered 27 BJJ athletes (age in years: 25.36 ± 2.99; height: 175.04 ± 5.70 cm; weight: 76.56 ± 8.59 kg; BMI: 24.96 ± 2.30; 6.33 ± 2.51 years of training) with a high sports skill level. A coaches’ ranking of the athletes studied according to sports achievement was developed. An experimental approach to the problem was employed, with the SBJJFT-TZ assessment, including throws and specialized locomotion, performed on two dates (seven days apart). The test parameters were recorded and, using a specialized formula, an index showing comprehensive special fitness was calculated. To verify the test’s validity and reliability, using statistical procedures, the results were comparatively analyzed (Student’s t-test for the dependent variables), and the relationships between rankings were examined (Pearson’s r linear correlation). The level of statistical significance was set at p < 0.05. Results: The analyses showed statistically significant relationships with very high strength between coach ranking and SBJJFT-TZ parameters for both dates (R = 0.73–0.88; p < 0.001). A similar trend of significant correlations was noted between the first and second SBJJFT-TZ dates (R = 0.96–0.98; p < 0.001), and the strength of the coexistence was almost complete. The test-retest procedure for SBJJFT-TZ showed similar levels for the parameters studied, without significant variation (p > 0.05) but with a weak effect profile (d = 0.03–0.06). Internal variation in the variables was very low (HR CV < 10%) and moderate (Throws and Index CV = 20.82–32.25%). Significantly different throwing performance between sets was shown, with an advantage in the first round of work compared to the second, and a moderate effect (p < 0.05; d = 0.38–0.39). Conclusions: The identification of relationships, the comparison, and the test-retest procedure showed the high application value of SBJJFT-TZ. The proposed tool provides a reliable cross-sectional evaluation of BJJ athletes’ special fitness and performance in the stand-up fighting plane, as well as at the moment of achieving an advantage over the opponent, and favors implementation in field conditions.
Exploring the Spatio-Temporal Evolutionary Characteristics of Paomo Restaurants in Xi’an’s Central Urban Area through POI Data Analysis
Authors: Yang Dawei , Xu Ping , Yang Xiaojuan
Published: May 2024

This study explores the spatial and temporal evolution of Paomo restaurants in Xi’an’s central urban area from 2012 to 2023, utilizing point-of-interest (POI) data and advanced analytical techniques. Employing methods such as nearest neighbor distance, kernel density estimation, and band set statistical analysis, we have systematically analyzed the shifting patterns and critical factors influencing the distribution of these restaurants. The study’s findings reveal a significant outward expansion from the city center to the periphery, marked by substantial spatial agglomeration and the emergence of multiple distinct cores. This distribution shift is largely driven by urban policy initiatives and market dynamics. Key factors identified include accessibility to transportation, residential living standards, and the availability of commercial services. These factors highlight the substantial impact of urban spatial reorganization and socioeconomic factors on the specialty catering sector. The insights from this study provide valuable implications for urban planning and policy-making, suggesting targeted strategies for the spatial arrangement of specialty catering services to enhance urban layouts and promote sustainable economic development.
Experimental Study on Pore Structure Evolution of Unloaded Rock Mass during Excavation of Reservoir Slope under Dry–Wet Cycle
Authors: Chen Lili , Chen Xingzhou , Gong Sheng , Li Zhenhan , Su Zhenkun
Published: May 2024

There is a long sequence of periodic characteristics of reservoir water storage and discharge in large hydropower stations. The unloaded rock mass formed by blasting and excavation in the reservoir slope of the reservoir fluctuation zone is not only subjected to the penetration erosion caused by the change of the water level of the reservoir slope, but also the dry–wet cycle caused by the reservoir water storage and discharge. There is an obvious process of crack derivation and pore structure expansion, and the subsequent strength degradation breeds reservoir slope risks, which is one of the important factors restricting the operation safety of power stations. To study the pore structure evolution law of unloaded rock mass in reservoir slope excavation of reservoir fluctuation zones, the dry–wet cycle test simulating the periodic storage and discharge environment was carried out with samples of equal unloading amount obtained by indoor triaxial unloading test. The variation law of mesoscopic parameters such as wave velocity, mass, and nuclear magnetic resonance spectrum under dry–wet cycle was compared and analyzed, and the physical and mechanical mechanism of the pore structure evolution of the unloaded specimen under dry–wet cycles was explored. The results show that: (1) With the increase of dry–wet cycles, the evolution of wave velocity and dry mass of unloaded samples has obvious stage characteristics, which generally presents a rapid change in the early stage, moderate in the middle stage, and gradually stable in the late stage; (2) nuclear magnetic resonance (NMR) shows that the number of macropore structures in unloaded samples increases gradually with the dry–wet cycles; (3) the smaller the initial confining pressure, the larger the first peak area and the peak value of unloaded samples, and the spectral area corresponding to each peak under low confining pressure is significantly larger than that under medium and high confining pressure; (4) the unloading amount affects the overall proportion of macropores in the sample, which determines the deterioration process and evolution law of the mesostructure of the sample under dry–wet cycles.
A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
Authors: Zentani Ahmed , Almaktoof Ali , Kahn Mohamed T.
Published: May 2024

Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging management while ensuring safety and performance. Challenges and future trends in EV fast charging are thoroughly explored, highlighting infrastructure limitations, standardization efforts, battery technology advancements, and energy optimization through smart grid solutions and bidirectional chargers. The paper advocates for global collaboration to establish universal standards and interoperability among charging systems to facilitate widespread EV adoption. Future research areas include faster charging, infrastructure improvements, standardization, and energy optimization. Encouragement is given for advancements in battery technology, wireless charging, battery swapping, and user experience enhancement to further advance the EV fast charging ecosystem. In summary, this paper offers valuable insights into the current state, challenges, and future directions of EV fast charging, providing a comprehensive examination of technological advancements and emerging trends in the field.
Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features
Authors: Kurek Jarosław , Świderska Elżbieta , Szymanowski Karol
Published: May 2024

The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields makes selecting the right model for sequence classification very important. This research aims to show the distinct capabilities and performance nuances of LSTM and 1-D CNN models, leveraging their inherent strengths in understanding temporal dependencies and feature extraction, respectively. Through a series of experiments, the study unveils that while both models demonstrate competencies in handling sequence data, the 1-D CNN model, with its superior feature extraction capabilities, achieved the best performance, boasting an accuracy of 94.5% on the test dataset. The insights gained from this comparison not only help to understand LSTM and 1-D CNN models better, but also open the door for future improvements in using neural networks for complex sequence classification challenges.
Fragility Analysis of Step-Terrace Frame-Energy Dissipating Rocking Wall Structure in Mountain Cities
Authors: Yang Youfa , Jia Yingwei , Jin Hongshen
Published: May 2024

Rocking walls can control the overall deformation pattern and the distribution of plastic energy dissipation in structures, suppressing the occurrence of weak layers. In the case of step-terrace frame structures, issues such as severe lateral stiffness irregularities, abrupt changes in floor-bearing capacity, and concentrated deformation in upper ground layers exist. To improve the yielding and failure modes of step-terrace frame structures in mountainous regions, this paper proposes a structural system combining step-terrace frame structures with energy dissipation rocking walls attached to their bottoms, aiming to control the yielding mechanism of the structure, further reduce the seismic response, limit residual deformation, and propose a structural system of step-terrace frame structures with buckling-restrained braces (BRBs) and energy dissipation rocking walls. Two sets of numerical models for step-terrace frame structures with different numbers of dropped layers and spans were established. Through simulating low-cycle repeated loading tests on step-terrace frame structures, the rationality of the models and parameters was verified. Incremental dynamic analysis (IDA) was employed to systematically investigate the vulnerability of step-terrace frame structures with energy dissipation rocking walls under different dropped layer and span configurations. This investigation covered aspects such as IDA curve clusters, percentile curves, seismic demand models, fragility functions, failure state probabilities, vulnerability indices, collapse resistance factors, and safety margins. The results indicated that the change in dropped layer numbers had a far greater impact on the vulnerability of step-terrace frame structures with energy dissipation rocking walls than the change in dropped span numbers. Under seismic excitations with the same peak ground acceleration (PGA), rocking walls can limit the depth of structural plasticity development, reduce the dispersion of peak responses, and lower the probability of exceeding various performance levels, thereby exhibiting good collapse resistance. The addition of buckling-restrained braces (BRBs) can further enhance the seismic performance and collapse resistance of the rocking wall frame structure. By analyzing the correlation between seismic intensity measures and peak structural responses, the validity of using peak ground acceleration as a scaling indicator for incremental dynamic analysis (IDA) has been verified.