bluuebunny/crossref_metadata_2025_split huggingface dataset:
Blockchain integration for in-vehicle CAN bus intrusion detection systems with ISO/SAE 21434 compliant reporting
Authors: Andreica Tudor , Musuroi Adrian , Anistoroaei Alfred , Jichici Camil , Groza Bogdan 
Published: April 2024

AbstractThe development of Intrusion Detection Systems (IDS) for in-vehicle buses has gained a lot of momentum in recent years as the number of reported vulnerabilities and the degree of interconnectivity for modern vehicles are on the rise. Since intrusion detection is resource consuming, it can be performed on computationally capable Android head units that are now present inside vehicles. Moreover, these units are connected to the internet, which enables the use of more complex algorithms that run in cloud environments. In this work we develop one such approach: an IDS that consists of a locally installed copy, running on head units, and a centralized instance of it that runs in the cloud and monitors traffic for groups of similar vehicles. Additionally, the centralized instance is part of a cloud service for intrusion detection which is continuously updated with the most recent types of attacks. The classification results of the cloud-based service are further analyzed by an incident response team which confirms the presence of known attacks, analyzes new types of attacks and assesses their impact. The output of this activity is stored on the Blockchain as ISO/SAE 21434 compliant reports, ensuring the transparency and traceability of the reported incidents.
Assessing the impact of climate change on water requirement and yield of sugarcane over different agro-climatic zones of Tamil Nadu
Authors: Guhan V. , Annadurai K. , Easwaran S. , Marimuthu M. , Balu D. , Vigneswaran S. , Navinkumar C. 
Published: April 2024

AbstractThe DSSAT CANEGRO model was calibrated and verified using field experimental data from five Tamil Nadu Agroclimatic Zones (1981–2022). The genetic coefficients of the sugarcane cultivar (CO-86032) were calculated. R2 obtained between measured and simulated stalk fresh mass was 0.9 with the nRMSE (0.01) and RMSE (1.6) and R2 between measured and simulated sucrose mass was 0.9 with the nRMSE (0.16) and RMSE (1.2). For yield R2 obtained between measured and simulated was 0.9 with the nRMSE (0.01) and RMSE (1.6). As a result, the CANEGRO model may be used to mimic the phenology and yield features of the sugarcane cultivar in Tamil Nadu's Agro Climatic Zones. Temperature increases in Agro Climatic Zones resulted in varying yield reductions, with 2 °C increases causing a 3% loss, 3 °C increases 5%, and 4 °C increases 9%. The Water Requirement rose throughout all of the ACZ due to the high temperature, but to differing degrees. A 2 °C increase often results in an average 4% increase in the WR. 3 °C rise in temperature increased WR to 9% and WR rose by 13% when the temperature was raised by 4 °C.
A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions
Authors: Barbieri Marco , Hooijmans Melissa T. , Moulin Kevin , Cork Tyler E. , Ennis Daniel B. , Gold Garry E. , Kogan Feliks , Mazzoli Valentina 
Published: April 2024

AbstractThis work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin’s concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies.
Simple and rapid determination of tartrazine in fake saffron using the metal organic framework (Fe SA MOF@CNF) by HPLC/PDA
Authors: salehi Ali , Shariatifar Nabi , Jahed-Khaniki Gholamreza , Sadighara Parisa , Hozoori Mohammad 
Published: April 2024

AbstractThe present study of a novel metal–organic framework containing Fe single atoms doped on electrospun carbon nanofibers (Fe SA-MOF@CNF) based on dispersive micro solid phase extraction (D-μ-SPE) using HPLC–PDA for detection tartrazine in fake saffron samples was designed. The Fe SA-MOF@CNF sorbent was extensively characterized through various techniques including N2 adsorption–desorption isotherms, X-ray diffraction (XRD), scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. The specific area of surface of the sorbent was 577.384 m2/g. The study variables were optimized via the central composite design (CCD), which included a sorbent mass of 15 mg, a contact time of 6 min, a pH of 7.56, and a tartrazine concentration of 300 ng/ml. Under the optimum condition, the calibration curve of this method was linear in the range of 5–1000 ng/mL, with a correlation coefficient of 0.992. The LOD and LOQ values were ranged 0.38–0.74 and 1.34–2.42 ng/ml, respectively. This approach revealed significant improvements, including high extraction recovery (98.64), recovery rates (98.43–102.72%), and accuracy (RSDs < 0.75 to 3.6%). the enrichment factors were obtained in the range of 80.6–86.4 with preconcentration factor of 22.3. Consequently, the D-μ-SPE method based on synthesized Fe SA-MOF@CNF could be recommended as a sustainable sorbent for detecting tartrazine in saffron samples.
Microbial induced wettability alteration with implications for Underground Hydrogen Storage
Authors: Boon Maartje , Buntic Ivan , Ahmed Kadir , Dopffel Nicole , Peters Catherine , Hajibeygi Hadi 
Published: April 2024

AbstractCharacterization of the microbial activity impacts on transport and storage of hydrogen is a crucial aspect of successful Underground Hydrogen Storage (UHS). Microbes can use hydrogen for their metabolism, which can then lead to formation of biofilms. Biofilms can potentially alter the wettability of the system and, consequently, impact the flow dynamics and trapping mechanisms in the reservoir. In this study, we investigate the impact of microbial activity on wettability of the hydrogen/brine/rock system, using the captive-bubble cell experimental approach. Apparent contact angles are measured for bubbles of pure hydrogen in contact with a solid surface inside a cell filled with living brine which contains sulphate reducing microbes. To investigate the impact of surface roughness, two different solid samples are used: a “rough” Bentheimer Sandstone sample and a “smooth” pure Quartz sample. It is found that, in systems where buoyancy and interfacial forces are the main acting forces, the impact of biofilm formation on the apparent contact angle highly depends on the surface roughness. For the “rough” Bentheimer sandstone, the apparent contact angle was unchanged by biofilm formation, while for the smooth pure Quartz sample the apparent contact angle decreased significantly, making the system more water-wet. This decrease in apparent contact angle is in contrast with an earlier study present in the literature where a significant increase in contact angle due to microbial activity was reported. The wettability of the biofilm is mainly determined by the consistency of the Extracellular Polymeric Substances (EPS) which depends on the growth conditions in the system. Therefore, to determine the impact of microbial activity on the wettability during UHS will require accurate replication of the reservoir conditions including surface roughness, chemical composition of the brine, the microbial community, as well as temperature, pressure and pH-value conditions.
Response accuracy of ChatGPT 3.5 Copilot and Gemini in interpreting biochemical laboratory data a pilot study
Authors: Kaftan Ahmed Naseer , Hussain Majid Kadhum , Naser Farah Hasson 
Published: April 2024

AbstractWith the release of ChatGPT at the end of 2022, a new era of thinking and technology use has begun. Artificial intelligence models (AIs) like Gemini (Bard), Copilot (Bing), and ChatGPT-3.5 have the potential to impact every aspect of our lives, including laboratory data interpretation. To assess the accuracy of ChatGPT-3.5, Copilot, and Gemini responses in evaluating biochemical data. Ten simulated patients' biochemical laboratory data, including serum urea, creatinine, glucose, cholesterol, triglycerides, low-density lipoprotein (LDL-c), and high-density lipoprotein (HDL-c), in addition to HbA1c, were interpreted by three AIs: Copilot, Gemini, and ChatGPT-3.5, followed by evaluation with three raters. The study was carried out using two approaches. The first encompassed all biochemical data. The second contained only kidney function data. The first approach indicated Copilot to have the highest level of accuracy, followed by Gemini and ChatGPT-3.5. Friedman and Dunn's post-hoc test revealed that Copilot had the highest mean rank; the pairwise comparisons revealed significant differences for Copilot vs. ChatGPT-3.5 (P = 0.002) and Gemini (P = 0.008). The second approach exhibited Copilot to have the highest accuracy of performance. The Friedman test with Dunn's post-hoc analysis showed Copilot to have the highest mean rank. The Wilcoxon Signed-Rank Test demonstrated an indistinguishable response (P = 0.5) of Copilot when all laboratory data were applied vs. the application of only kidney function data. Copilot is more accurate in interpreting biochemical data than Gemini and ChatGPT-3.5. Its consistent responses across different data subsets highlight its reliability in this context.
Toward the eco-friendly cosmetic cleansing assisted by the micro-bubbly jet
Authors: Kang Yeeun , Park Jooyeon , Park Hyungmin 
Published: April 2024

AbstractWhile numerous types of chemical cosmetic cleansers have been presented, those with sensitive skin may still experience some irritation while using them. Moreover, the environmental issue of chemical agents has been documented repeatedly. To address these, we suggest the potential application of a micro-sized bubble-laden water jet to cleanse the cosmetics without (or less) using chemical detergents. We devised a venturi-type nozzle with a mesh and air holes capable of generating massive fine bubbles. By testing with the foundation and lip tint (known to be highly adhesive) coated on the synthetic leather and artificial skin surfaces, we measured that the cleansing performance of the bubbly jet is much better (even without the chemical agent) than the single-phase liquid jet. As a mechanism for enhanced removal, it is understood that the greater kinetic energy of the jet due to the acceleration of the effective liquid–air mixture flow and the direct bubble-cosmetic collisions play essential roles. We believe that the present results will spur the development of environment-friendly cleaning methods.
Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions
Authors: Zaghba Layachi , Borni Abdelhalim , Benbitour Messaouda Khennane , Fezzani Amor , Alwabli Abdullah , Bajaj Mohit , Dost Mohammadi Shir Ahmad , Ghoneim Sherif S. M. 
Published: April 2024

AbstractThis paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.
Numerical simulation study on the evolution law of mechanical properties of different metamorphic coals after heat treatment
Authors: Zhang Xun , Lai Hongyu , Huang Ge , Lu Bing , Liang Huimin 
Published: April 2024

AbstractIn order to study the effect of temperature on the structure and mechanical properties of coal with different metamorphic degree. Three coal samples with varying degrees of metamorphism were chosen for analysis. The discrete element software PFC2D is used to simulate the heat treatment and compression of coal. The findings indicate that during the heating process, low-order coal exhibits noticeable thermal cracks at an early stage, while thermal crack development in middle-order coal is concentrated in the later stages. In contrast, high-order coal demonstrates a more stable macroscopic structure. The strength and stiffness of low rank coal show the lowest value and decrease significantly within 135 °C. However, the strength and stiffness of medium rank coal decrease significantly after 135 °C. The changes of mechanical properties and damage modes of coal caused by thermal damage are often ignored, which may lead to the deviation of design and research results from the actual situation. Therefore, this study is of great significance to the prevention and control of coal mine disasters.
Mutational signature-based identification of DNA repair deficient gastroesophageal adenocarcinomas for therapeutic targeting
Authors: Prosz Aurel , Sahgal Pranshu , Huffman Brandon M. , Sztupinszki Zsofia , Morris Clare X. , Chen David , Börcsök Judit , Diossy Miklos , Tisza Viktoria , Spisak Sandor , Likasitwatanakul Pornlada , Rusz Orsolya , Csabai Istvan , Cecchini Michael , Baca Yasmine , Elliott Andrew , Enzinger Peter , Singh Harshabad , Ubellaker Jessalyn , Lazaro Jean-Bernard , Cleary James M. , Szallasi Zoltan , Sethi Nilay S. 
Published: April 2024

AbstractHomologous recombination (HR) and nucleotide excision repair (NER) are the two most frequently disabled DNA repair pathways in cancer. HR-deficient breast, ovarian, pancreatic and prostate cancers respond well to platinum chemotherapy and PARP inhibitors. However, the frequency of HR deficiency in gastric and esophageal adenocarcinoma (GEA) still lacks diagnostic and functional validation. Using whole exome and genome sequencing data, we found that a significant subset of GEA, but very few colorectal adenocarcinomas, show evidence of HR deficiency by mutational signature analysis (HRD score). High HRD gastric cancer cell lines demonstrated functional HR deficiency by RAD51 foci assay and increased sensitivity to platinum chemotherapy and PARP inhibitors. Of clinical relevance, analysis of three different GEA patient cohorts demonstrated that platinum treated HR deficient cancers had better outcomes. A gastric cancer cell line with strong sensitivity to cisplatin showed HR proficiency but exhibited NER deficiency by two photoproduct repair assays. Single-cell RNA-sequencing revealed that, in addition to inducing apoptosis, cisplatin treatment triggered ferroptosis in a NER-deficient gastric cancer, validated by intracellular GSH assay. Overall, our study provides preclinical evidence that a subset of GEAs harbor genomic features of HR and NER deficiency and may therefore benefit from platinum chemotherapy and PARP inhibitors.
Klebsiella pneumoniae peptide hijacks a Streptococcus pneumoniae permease to subvert pneumococcal growth and colonization
Authors: Lux Janine , Portmann Hannah , Sánchez García Lucía , Erhardt Maria , Holivololona Lalaina , Laloli Laura , Licheri Manon F. , Gallay Clement , Hoepner Robert , Croucher Nicholas J. , Straume Daniel , Veening Jan-Willem , Dijkman Ronald , Heller Manfred , Grandgirard Denis , Leib Stephen L. , Hathaway Lucy J. 
Published: April 2024

AbstractTreatment of pneumococcal infections is limited by antibiotic resistance and exacerbation of disease by bacterial lysis releasing pneumolysin toxin and other inflammatory factors. We identified a previously uncharacterized peptide in the Klebsiella pneumoniae secretome, which enters Streptococcus pneumoniae via its AmiA-AliA/AliB permease. Subsequent downregulation of genes for amino acid biosynthesis and peptide uptake was associated with reduction of pneumococcal growth in defined medium and human cerebrospinal fluid, irregular cell shape, decreased chain length and decreased genetic transformation. The bacteriostatic effect was specific to S. pneumoniae and Streptococcus pseudopneumoniae with no effect on Streptococcus mitis, Haemophilus influenzae, Staphylococcus aureus or K. pneumoniae. Peptide sequence and length were crucial to growth suppression. The peptide reduced pneumococcal adherence to primary human airway epithelial cell cultures and colonization of rat nasopharynx, without toxicity. We identified a peptide with potential as a therapeutic for pneumococcal diseases suppressing growth of multiple clinical isolates, including antibiotic resistant strains, while avoiding bacterial lysis and dysbiosis.
A neural speech decoding framework leveraging deep learning and speech synthesis
Authors: Chen Xupeng , Wang Ran , Khalilian-Gourtani Amirhossein , Yu Leyao , Dugan Patricia , Friedman Daniel , Doyle Werner , Devinsky Orrin , Wang Yao , Flinker Adeen 
Published: April 2024

AbstractDecoding human speech from neural signals is essential for brain–computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity and high dimensionality. Here we present a novel deep learning-based neural speech decoding framework that includes an ECoG decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable speech synthesizer that maps speech parameters to spectrograms. We have developed a companion speech-to-speech auto-encoder consisting of a speech encoder and the same speech synthesizer to generate reference speech parameters to facilitate the ECoG decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Our experimental results show that our models can decode speech with high correlation, even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. Finally, we successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with deficits resulting from left hemisphere damage.
Current inequality and future potential of US urban tree cover for reducing heat-related health impacts
Authors: McDonald Robert I. , Biswas Tanushree , Chakraborty T. C. , Kroeger Timm , Cook-Patton Susan C. , Fargione Joseph E. 
Published: April 2024

AbstractExcessive heat is a major and growing risk for urban residents. Here, we estimate the inequality in summertime heat-related mortality, morbidity, and electricity consumption across 5723 US municipalities and other places, housing 180 million people during the 2020 census. On average, trees in majority non-Hispanic white neighborhoods cool the air by 0.19 ± 0.05 °C more than in POC neighborhoods, leading annually to trees in white neighborhoods helping prevent 190 ± 139 more deaths, 30,131 ± 10,406 more doctors’ visits, and 1.4 ± 0.5 terawatt-hours (TWhr) more electricity consumption than in POC neighborhoods. We estimate that an ambitious reforestation program would require 1.2 billion trees and reduce population-weighted average summer temperatures by an additional 0.38 ± 0.01 °C. This temperature reduction would reduce annual heat-related mortality by an additional 464 ± 89 people, annual heat-related morbidity by 80,785 ± 6110 cases, and annual electricity consumption by 4.3 ± 0.2 TWhr, while increasing annual carbon sequestration in trees by 23.7 ± 1.2 MtCO2e yr−1 and decreasing annual electricity-related GHG emissions by 2.1 ± 0.2 MtCO2e yr−1. The total economic value of these benefits, including the value of carbon sequestration and avoided emissions, would be USD 9.6 ± 0.5 billion, although in many neighborhoods the cost of planting and maintaining trees to achieve this increased tree cover would exceed these benefits. The exception is areas that currently have less tree cover, often the majority POC, which tend to have a relatively high return on investment from tree planting.