bluuebunny/crossref_metadata_2025 huggingface dataset:
Tailored Pre-Operative Antibiotic Prophylaxis to Prevent Post-Operative Surgical Site Infections in General Surgery
Authors: Vierra Mason , Rouhani Ravari Mohsen , Soleymani Sardoo Fatemeh , Shogan Benjamin D.
Published: January 2024

The average American today undergoes three inpatient and two outpatient surgical procedures during one’s life, each of which carries with it a risk of post-operative infection. It has long been known that post-operative infections cause significant morbidity in the immediate peri-operative period, but recent evidence suggests that they can have long-term consequences as well, increasing a patient’s risk of infectious complications in unrelated surgeries performed months or even years later. While there are several theories on the origin of this association, including bacterial colonization of a post-operative infectious wound site, antimicrobial resistance from curative courses of antibiotics, subclinical immunosuppression, or the creation of an inflammatory “pathobiome” following an infectious insult, it is ultimately still unclear why patients who experience a single post-operative infection seem to be at a significantly higher risk of experiencing subsequent ones. Regardless, this association has significant implications for the routine use of pre-operative antibiotic prophylaxis. Indeed, while the prescription of antibiotics pre-operatively has dramatically reduced the rate of post-operative infections, the chosen prophylaxis regimens are typically standardized according to national guidelines, are facing increasing antimicrobial resistance patterns, and have been unable to reduce the risk of post-operative infection to acceptably low levels for certain surgeries. As a result, some clinicians have speculated that tailoring pre-operative antibiotic prophylaxis according to a patient’s prior infectious and operative history could improve efficacy and further reduce the rate of post-operative infections. The purpose of this review is to describe the evidence for the link between multiple post-operative infections and explore the efficacy of individualized pre-operative prophylaxis.
Chrysoeriol Improves the Early Development Potential of Porcine Oocytes by Maintaining Lipid Homeostasis and Improving Mitochondrial Function
Authors: Wang Chao-Rui , Yuan Xiu-Wen , Ji He-Wei , Xu Yong-Nan , Li Ying-Hua , Kim Nam-Hyung
Published: January 2024

Our previous study established that chrysoeriol (CHE) can reduce reactive oxygen species (ROS) accumulation, apoptosis, and autophagy in vitro culture (IVC) of porcine embryos. However, the role of CHE in oocyte maturation and lipid homeostasis is unclear. Herein, we aimed to elucidate the effect of CHE on porcine oocyte competence in vitro maturation (IVM) and subsequent embryo development. The study chooses parthenogenetic activated porcine oocytes as the research model. The study revealed that the cumulus expansion index and related gene expressions are significantly elevated after supplementing 1 μM CHE. Although there were no significant differences in nuclear maturation and cleavage rates, the blastocyst formation rate and total cell numbers were significantly increased in the 1 μM CHE group. In addition, CHE improved the expression of genes related to oocyte and embryo development. ROS was significantly downregulated in all CHE treatment groups, and intracellular GSH (glutathione) was significantly upregulated in 0.01, 0.1, and 1 μM CHE groups. The immunofluorescence results indicated that mitochondrial membrane potential (MMP) and lipid droplet (LD), fatty acid (FA), ATP, and functional mitochondria contents significantly increased with 1 μM CHE compared to the control. Furthermore, CHE increased the expression of genes related to lipid metabolism, mitochondrial biogenesis, and β-oxidation.
The Hepatokine RBP4 Links Metabolic Diseases to Articular Inflammation
Authors: Pazos-Pérez Andrés , Piñeiro-Ramil María , Franco-Trepat Eloi , Alonso-Pérez Ana , Guillán-Fresco María , Crespo-Golmar Antía , López-Fagúndez Miriam , Aranda Javier Conde , Bravo Susana Belen , Jorge-Mora Alberto , Gómez Rodolfo
Published: January 2024

Objectives: This study investigates the role of retinol binding protein 4 (RBP4) in an articular context. RBP4, a vitamin A transporter, is linked to various metabolic diseases. Methods: Synovial fluid RBP4 levels were assessed in crystalline arthritis (CA) patients using ELISA. RBP4’s impact on articular cell types was analysed in vitro through RT-PCR and flow cytometry. Proteomic analysis was conducted on primary human osteoarthritis chondrocytes (hOACs). Results: Synovial fluid RBP4 concentrations in CA patients correlated positively with glucose levels and negatively with synovial leukocyte count and were elevated in hypertensive patients. In vitro, these RBP4 concentrations activated neutrophils, induced the expression of inflammatory factors in hOACs as well as synoviocytes, and triggered proteomic changes consistent with inflammation. Moreover, they increased catabolism and decreased anabolism, mitochondrial dysfunction, and glycolysis promotion. Both in silico and in vitro experiments suggested that RBP4 acts through TLR4. Conclusions: This study identifies relevant RBP4 concentrations in CA patients’ synovial fluids, linking them to hypertensive patients with a metabolic disruption. Evidence is provided that RBP4 acts as a DAMP at these concentrations, inducing robust inflammatory, catabolic, chemotactic, and metabolic responses in chondrocytes, synoviocytes, and neutrophils. These effects may explain RBP4-related metabolic diseases’ contribution to joint destruction in various rheumatic conditions like CA.
Telomere Maintenance Is Associated with Type 2 Diabetes Remission in Response to a Long-Term Dietary Intervention without Non-Weight Loss in Patients with Coronary Heart Disease: From the CORDIOPREV Randomized Controlled Trial
Authors: Ojeda-Rodriguez Ana , Alcala-Diaz Juan F. , Rangel-Zuñiga Oriol Alberto , Arenas-de Larriva Antonio P. , Gutierrez-Mariscal Francisco M. , Torres-Peña Jose D. , Mora-Ortiz Marina , Romero-Cabrera Juan L. , Luque Raul M. , Ordovas Jose M. , Perez-Martinez Pablo , Delgado-Lista Javier , Yubero-Serrano Elena M. , Lopez-Miranda Jose
Published: January 2024

In order to evaluate whether telomere maintenance is associated with type 2 diabetes remission, newly diagnosed type 2 diabetes patients without glucose-lowering treatment (183 out of 1002) from the CORDIOPREV study (NCT00924937) were randomized to consume a Mediterranean or low-fat diet. Patients were classified as Responders, those who reverted from type 2 diabetes during the 5 years of dietary intervention (n = 69), and Non-Responders, who did not achieve diabetes remission by the end of the follow-up period (n = 104). We found no differences in diabetes remission between the two diets, and we determined telomere length (TL) by measuring qPCR, telomerase activity using the TRAP assay, and direct redox balance based on the ratio of reduced glutathione (GSH) to oxidized glutathione (GSSH) via colorimetric assay. Responders exhibited higher baseline TL in comparison with Non-Responders (p = 0.040), and a higher TL at baseline significantly predicted a higher probability of type 2 diabetes remission (OR 2.13; 95% CI, 1.03 to 4.41). After the dietary intervention, Non-Responders showed significant telomere shortening (−0.19, 95% CI −0.32 to 0.57; p = 0.005). Telomere shortening was significantly pronounced in type 2 diabetes patients with a worse profile of insulin resistance and/or beta-cell functionality: high hepatic insulin resistance fasting, a high disposition index (−0.35; 95% CI, −0.54 to −0.16; p < 0.001), and a low disposition index (−0.25; 95% CI, −0.47 to −0.01; p = 0.037). In addition, changes in TL were correlated to the GSH/GSSG ratio. Responders also showed increased telomerase activity compared with baseline (p = 0.048), from 0.16 (95% CI, 0.08 to 0.23) to 0.28 (95% CI, 0.15 to 0.40), with a more marked increase after the dietary intervention compared with Non-Responders (+0.07; 95% CI, −0.06–0.20; p = 0.049). To conclude, telomere maintenance may play a key role in the molecular mechanisms underlying type 2 diabetes remission in newly diagnosed patients. However, further larger-scale prospective studies are necessary to corroborate our findings.
Assessing the Impact of Sensor Orientation on Accelerometer-Derived Angles: A Systematic Analysis and Proposed Error Reduction
Authors: McClintock Frederick A. , Callaway Andrew J. , Clark Carol J. , Williams Jonathan M.
Published: January 2024

Accelerometers have been widely used for motion analysis. The effect of initial sensor orientation (ISO) on the derived range of motion (ROM) is currently unexplored, limiting clarity in understanding error. This two-step study systematically explored the effect of ISO on the error of accelerometer-derived range of motion (ROM) and the effect of a proposed correction algorithm. Accelerometer data were used to compute peak and through-range ROM across a range of ISO and movement angular velocities up to 148° s−1 compared to an optoelectronic gold-standard. Step 1 demonstrated that error increased linearly with increasing ISO offsets and angular velocity. Average peak ROM RMSE at an ISO of 20° tilt and twist was 5.9° for sagittal motion, and for an ISO of 50° pitch and 20° twist, it was 7.5° for frontal plane ROM. Through-range RMSE demonstrated errors of 7–8° for similar ISOs. Predictive modeling estimated a 3.2° and 3.7° increase in peak and through-range sagittal plane error for every 10° increase in tilt and twist ISO. Step 2 demonstrated error reduction utilizing mathematical correction for ISO, resulting in <1° mean peak error and <1.2° mean through-range ROM error regardless of ISO. Accelerometers can be used to measure cardinal plane joint angles, but initial orientation is a source of error unless corrected.
Acceptability of Remote Monitoring in Assisted Living/Smart Homes in the United Kingdom and Associated Use of Sounds and Vibrations—A Systematic Review
Authors: Tong Ki , Attenborough Keith , Sharp David , Taherzadeh Shahram , Deepak-Gopinath Manik , Vseteckova Jitka
Published: January 2024

The ageing of populations is increasing pressure on health and social care systems. Potentially, assistive technologies are a way to support the independence of older adults in their daily activities. Among existing assistive technologies, ambient sensing technologies have received less attention than wearable systems. Moreover, there has been little research into cheaper technologies capable of using multiple modalities. A systematic review of the acceptability of assisted living or smart homes in the United Kingdom and the simultaneous use of sounds and vibrations in remote monitoring of assisted living or smart homes will inform and encourage the use of digital monitoring technologies. The acceptability of sensing technologies depends on whether there is any social stigma about their use, for example, the extent to which they invade privacy. The United Kingdom studies reviewed suggest a lack of measurements of the perceived efficacy or effectiveness of the monitoring devices. The primary use of vibration or acoustic technologies has been for detecting falls rather than monitoring health. The review findings suggest the need for further exploration of the acceptability and applicability of remote monitoring technologies, as well as a need for more research into the simultaneous use of sounds and vibrations in health monitoring.
Conversion of a Small-Size Passenger Car to Hydrogen Fueling: 0D/1D Simulation of EGR and Related Flow Limitations
Authors: Irimescu Adrian , Vaglieco Bianca Maria , Merola Simona Silvia , Zollo Vasco , De Marinis Raffaele
Published: January 2024

Hydrogen is seen as a prime choice for complete replacement of gasoline so as to achieve zero-emissions energy and mobility. Combining the use of this alternative fuel with a circular economy approach for giving new life to the existing fleet of passenger cars ensures further benefits in terms of cost competitiveness. Transforming spark ignition (SI) engines to H2 power requires relatively minor changes and limited added components. Within this framework, the conversion of a small-size passenger car to hydrogen fueling was evaluated based on 0D/1D simulation. One of the methods to improve efficiency is to apply exhaust gas recirculation (EGR), which also lowers NOx emissions. Therefore, the previous version of the quasi-dimensional model was modified to include EGR and its effects on combustion. A dedicated laminar flame speed model was implemented for the specific properties of hydrogen, and a purpose-built sub-routine was implemented to correctly model the effects of residual gas at the start of combustion. Simulations were performed in several operating points representative of urban and highway driving. One of the main conclusions was that high-pressure recirculation was severely limited by the minimum flow requirements of the compressor. Low-pressure EGR ensured wider applicability and significant improvement of efficiency, especially during partial-load operation specific to urban use. Another benefit of recirculation was that pressure rise rates were predicted to be more contained and closer to the values expected for gasoline fueling. This was possible due to the high tolerance of H2 to the presence of residual gas.
Effects of La-Co Co-Substitution on the Structural and Magnetic Properties of SrM Hexaferrites Prepared by Solid-State Reaction
Authors: Lee Kanghyuk , Kang Young-Min , Yoo Sang-Im
Published: January 2024

The effects of La-Co co-substitution on the structural and magnetic properties of strontium M-type hexaferrites (SrM) were carefully investigated for the Sr1−xLaxFe12−xCoxO19 (0.0 ≤ x ≤ 0.3) polycrystalline samples prepared by solid-state reaction in air. Without the use of sintering additives, La-Co co-substituted SrM single phases could be obtained from polycrystalline bulk samples by employing proper processing conditions. The lattice parameter a initially increases with increasing x from 0.0 to 0.1 but gradually decreases with increasing x to 0.20, and then remains almost unaltered up to x = 0.3; the lattice parameter c monotonously decreases with increasing x from 0.0 to 0.25, but it turns to an increase when x = 0.3. The reduction in c/a ratios and Vcell values with increasing x up to x = 0.25 are obviously attributable to the decreasing size effect resulting from La3+ substitution at the Sr2+ site, which surpasses the increasing size effect due to Co2+ and Fe2+ occupancy at the Fe3+ site. Meanwhile, with increasing x from 0.0 to 0.3, while the saturation magnetization (MS) continuously decreases from 75.90 to 72.07 emu/g, the magnetic anisotropy field (Ha) increases from 20.1 to 24.7 kOe, leading to an increase in the intrinsic coercivity (Hci) from 2.68 to 3.99 kOe. The gradual increase in Ha with x, probably caused by a gradual decrease in the crystallographic symmetry, is inversely proportional to the variation in the c/a ratios up to x = 0.25 as usual except when x = 0.3, of which deviation needs further study.
Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems
Authors: Zhao Di , Ding Zhenyu , Li Wenjie , Zhao Sen , Du Yuhong
Published: January 2024

With the rapid advancement of industrial automation and artificial intelligence technologies, particularly in the textile industry, robotic technology is increasingly challenged with intelligent path planning and executing high-precision tasks. This study focuses on the automatic path planning and yarn-spool-assembly tasks of textile robotic arms, proposing an end-to-end planning and control model that integrates deep reinforcement learning. The innovation of this paper lies in the introduction of a cascaded fuzzy reward system, which is integrated into the end-to-end model to enhance learning efficiency and reduce ineffective exploration, thereby accelerating the convergence of the model. A series of experiments conducted in a simulated environment demonstrate the model’s exceptional performance in yarn-spool-assembly tasks. Compared to traditional reinforcement learning methods, our model shows potential advantages in improving task success rates and reducing collision rates. The cascaded fuzzy reward system, a core component of our end-to-end deep reinforcement learning model, offers a novel and more robust solution for the automated path planning of robotic arms. In summary, the method proposed in this study provides a new perspective and potential applications for industrial automation, especially in the operation of robotic arms in complex and uncertain environments.
TGN: A Temporal Graph Network for Physics Prediction
Authors: Yue Miaocong , Liu Huayong , Chang Xinghua , Zhang Laiping , Li Tianyu
Published: January 2024

Long-term prediction of physical systems on irregular unstructured meshes is extremely challenging due to the spatial complexityof meshes and the dynamic changes over time; namely, spatial dependence and temporal dependence. Recently, graph-based next-step prediction models have achieved great success in the task of modeling complex high-dimensional physical systems. However, due to these models ignoring the temporal dependence, they inevitably suffer from the effects of error accumulation. To capture the spatial and temporal dependence simultaneously, we propose a temporal graph network (TGN) to predict the long-term dynamics of complex physical systems. Specifically, we introduce an Encode-Process-Decode architecture to capture spatial dependence and create low-dimensional vector representations of system states. Additionally, a temporal model is introduced to learn the dynamic changes in the low-dimensional vector representations to capture temporal dependence. Our model can capture spatiotemporal correlations within physical systems. On some complex long-term prediction tasks in fluid dynamics, such as airfoil flow and cylinder flow, the prediction error of our method is significantly lower than the competitive GNN baseline. We show accurate phase predictions even for very long prediction sequences.
Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models
Authors: Sui Yiping , Zhang Lei , Sun Zhipeng , Yi Weixun , Wang Meng
Published: January 2024

In the process of coal mining, one of the main reasons for the high labor intensity of workers and the frequent occurrence of casualties is the low level of intelligence of coal mining equipment. As the core equipment in the process of coal mining, the intelligence level of shearers directly affects the safety production and mining efficiency of coal mines. Coal and rock recognition technology is the core technology used to realize the intelligentization of shearers, which is an urgent technical problem to be solved in the field of coal mining. In this paper, coal seam images, rock stratum images, and coal–rock mixed-layer images of a coal mining area are taken as the research object, and key technologies such as the construction of a sample image library, classification and recognition, and semantic segmentation are studied by using the relevant theoretical knowledge of artificial neural network models. Firstly, the BP neural network is used to classify and identify coal seam images, rock stratum images, and coal–rock mixed-layer images, so as to distinguish which of the current mining targets of a shearer is the coal seam, rock stratum, or coal–rock mixed layer. Because different mining objectives will lead to different working modes of a shearer, it is necessary to maintain normal power to cut coal when encountering a coal seam, to stop working when encountering rock stratum, and to cut coal along the boundary between a coal seam and rock stratum when encountering a coal–rock mixed stratum. Secondly, the DeepLabv3+ model is used to perform semantic segmentation experiments on the coal–rock mixed-layer images. The purpose is to find out the distribution of coal and rocks in the coal–rock mixed layer in the coal mining area, so as to provide technical support for the automatic adjustment height of the shearer. Finally, the research in this paper achieved a 97.16% recognition rate in the classification and recognition experiment of the coal seam images, rock stratum images, and coal–rock mixed-layer images and a 91.2% accuracy in the semantic segmentation experiment of the coal–rock mixed-layer images. The research results of the two experiments provide key technical support for improving the intelligence level of shearers.
Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019
Authors: Lemos Angelo , Andrade Laiza , Franklin Larissa , Bezerra Diego , Ghisolfi Renato , Maita Rosio , Nobre Paulo
Published: January 2024

In this study, we investigate the circulation and chemical processes associated with the deposition of the largest oil spill that reached the northeast coast of Brazil during the second half of 2019. Using the Oil Spill Contingency And Response model (OSCAR), we performed both deterministic and probabilistic simulations of oil spills from tanker ships that were present in the sea in the region at the time. The study used a dataset comprising the latitudinal distribution of oil sightings along the coast between 31 August and 2 December 2019 (box plot analysis) provided by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). The total amount of oil that reached the coast during this period (approximately 5000 tons) and the date and location of the first sighting (30 August, in the southern part of the state of Paraíba (PB)) were also used as parameters to assess the results of the 31 simulations conducted for ships en route near the area of interest between July and August 2019. The results indicate that a leak having occurred through a mobile source is the most plausible hypothesis for explaining the observed temporal–spatial arrival of the oil leaks along the Brazilian coastline. We suggest that prevention, monitoring, and international cooperation are essential for reducing the risks of future environmental accidents of the kind analyzed in this study and to protect the environment and communities affected.
Development of a Time Series E-Commerce Sales Prediction Method for Short-Shelf-Life Products Using GRU-LightGBM
Authors: Chen Yong , Xie Xian , Pei Zhi , Yi Wenchao , Wang Cheng , Zhang Wenzhu , Ji Zuzhen
Published: January 2024

Effective sales prediction for e-commerce would assist retailers in developing accurate production and inventory control plans, which would further help them to reduce inventory costs and overdue losses. This paper develops a systematic method for e-commerce sales prediction, with a particular focus on predicting the sales of products with short shelf lives. The short-shelf-life product sales prediction problem is poorly addressed in the existing literature. Unlike products with long shelf lives, short-shelf-life products such as fresh milk exhibit significant fluctuations in sales volume and incur high inventory costs. Therefore, accurate prediction is crucial for short-shelf-life products. To solve these issues, a stacking method for prediction is developed based on the integration of GRU and LightGBM. The proposed method not only inherits the ability of the GRU model to capture timing features accurately but also acquires the ability of LightGBM to solve multivariable problems. A case study is applied to examine the accuracy and efficiency of the GRU-LightGBM model. Comparisons among other sales prediction methods such as ARIMA and SVR are also presented. The comparative results show that the GRU-LightGBM model is able to predict the sales of short-shelf-life products with higher accuracy and efficiency. The selected features of the GRU-LightGBM model are also useful due to their interpretability while developing sales strategies.
Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications
Authors: Abdallah Boubaker , Khriji Sabrine , Chéour Rym , Lahoud Charbel , Moessner Klaus , Kanoun Olfa
Published: January 2024

LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for Industrial IoT (IIoT) applications. Experimental measurements consider factors like vegetation and infrastructure, introducing mobility to replicate NLoS conditions. Utilizing an open-source LoRa Physical Layer (PHY) Software-Defined Radio (SDR) prototype developed with GNU Radio, we assess communication reliability through metrics such as Block Error Rate (BLER), Signal-to-Noise-Interference-plus-Noise Ratio (SINR), and data rate. The study reveals the estimated overall reliability of the LoRa signal at 90.23%, emphasizing specific configuration details. This work contributes to the broader field of LoRa communication, encompassing hardware, software, protocols, and management, enhancing our understanding of LoRa’s dependability in challenging IIoT environments.
A Comparative Study of Oleuropein Extraction from Wild Olive Leaves (Olea europea subsp. oleaster, Hoffmanns. & Link), Its Gastrointestinal Stability, and Biological Potential
Authors: Soldo Barbara , Bilušić Tea , Giacometti Jasminka , Ljubenkov Ivica , Čikeš Čulić Vedrana , Bratanić Andre , Bošković Perica , Šola Ivana , Ilić Krunoslav
Published: January 2024

Olive leaves are the richest source of phenolic compounds, particularly oleuropein, which has many beneficial effects on human health. This study compares the effect of three extraction techniques: ultrasonic-solvent extraction, microwave-assisted extraction, and supercritical CO2 extraction of freeze-dried wild olive leaves (Olea europea subps. oleaster, Hoffmanns. & Link) on oleuropein concentration in obtained extracts. The extract with the highest concentration of oleuropein was obtained after low-frequency ultrasound solvent extraction (WOLE-S) (71.97 µg/mL). Oleuropein from that extract showed low stability after simulated gastric digestion with human gastric juices (35.66%) and high stability after 60 min of intestinal phase (65.97%). The highest antiproliferative activity of WOLE-S and pure oleuropein was detected against T24 cancer cells. The antiproliferative activity of WOLE-S against MD-MBA-123, A549, and A1235 cells was moderate, while the activity of oleuropein was high. WOLE-S significantly down-regulated the expression of IL-6 in A549 cells, indicating a possible anti-inflammatory effect in lung cancer cells. The expression of IL-8 was slightly reduced by treating the cells with WOLE-S, while the expression of IL-10 was not altered by the treatment with WOLE-S or oleuropein.
Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
Authors: Wu Runbing , Zhu Xinghui , Yi Zeqian , Zou Zhuoyang , Liu Yi , Zhu Lei
Published: January 2024

Unsupervised cross-modal hashing is a topic of considerable interest due to its advantages in terms of low storage costs and fast retrieval speed. Despite the impressive achievements of existing solutions, two challenges remain unaddressed: (1) Semantic similarity obtained without supervision is not accurate enough, and (2) the preservation of similarity structures lacks effectiveness due to the neglect of both global and local similarity. This paper introduces a new method, Multi-Grained Similarity Preserving and Updating (MGSPU), to tackle these challenges. To overcome the first challenge, MGSPU employs a newly designed strategy to update the semantic similarity matrix, effectively generating a high-confidence similarity matrix by eliminating noise in the original cross-modal features. For the second challenge, a novel multi-grained similarity preserving method is proposed, aiming to enhance cross-modal hash code learning by learning consistency in multi-grained similarity structures. Comprehensive experiments on two widely used datasets with nine state-of-the-art competitors validate the superior performance of our method in cross-modal hashing.
Exploring the Cognitive Neural Basis of Factuality in Abstractive Text Summarization Models: Interpretable Insights from EEG Signals
Authors: Zhang Zhejun , Zhu Yingqi , Zheng Yubo , Luo Yingying , Shao Hengyi , Guo Shaoting , Dong Liang , Zhang Lin , Li Lei
Published: January 2024

(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This research examined four fact extraction techniques: dependency relation, named entity recognition, part-of-speech tagging, and TF-IDF, in order to explore their correlation with human EEG signals. Representational Similarity Analysis (RSA) was applied to gauge the relationship between language models and brain activity. (3) Results: Named entity recognition showed the highest sensitivity to EEG signals, marking the most significant differentiation between factual and non-factual words with a score of −0.99. The dependency relation followed with −0.90, while part-of-speech tagging and TF-IDF resulted in 0.07 and −0.52, respectively. Deep language models such as GloVe, BERT, and GPT-2 exhibited noticeable influences on RSA scores, highlighting the nuanced interplay between brain activity and these models. (4) Conclusions: Our findings emphasize the crucial role of named entity recognition and dependency relations in fact extraction and demonstrate the independent effects of different models and TOIs on RSA scores. These insights aim to refine algorithms to reflect human text processing better, thereby enhancing ATS models’ factual integrity.
Innovative Quality Assessment of Pavement Subgrades Using the Glegg Impact Soil Tester
Authors: Hodasova Katarina , Musuta Juraj , Decky Martin , Kudelcikova Maria
Published: January 2024

This article presents the case study of our research in the field of innovative methods of pavement subgrade quality control using the CIST (Clegg Impact Soil Tester) device. The CIST device developed by Dr Clegg from the University of Western Australia measures soil compaction indirectly using the CBR value. The value is evaluated based on the deceleration rate of a falling 4.5 kg weight moving in a vertical guide roller. In Europe, for the assessment of the mechanical efficiency (bearing capacity) of cohesive soils in the pavement subgrade, priority is given to indirect assessment methods especially using the laboratory determination of CBR (Californian Bearing Ratio) and directly through the implementation of a static plate load test (SPLT). This article reports the long-term results of our research in the field of verification and validation of an innovative CIST device, which minimizes the time, space, and economic disadvantages of SPLT. This article presents the results of determining the field of applicability of the CIST device for cohesive soils, the correlation dependencies (CD) of the CBR values determined by the CIST device, and, according to STN 72 1016, the CD of the impact dynamic deformation modulus Evd from the CIV (Clegg Impact Value). We consider the most important results of our long-term research to be a recognition of the ability of CIST to assess the quality of cohesive soils up to a compression value of 40 mm, corresponding to a CBR of 2.2% and a modulus of subgrade deformation of 20 MPa. A very strong correlation dependence of CBRClegg [%] on the moisture content of clayey soils in the interval from 5 to 19% was also observed. The presented knowledge led to the creation of relevant documents for the credible implementation of the CIST device in the system approach for assessing the quality of the pavement subgrade.
Study on High-Precision Tension Control Technology for a Cold-Rolling Pilot Mill with Hydraulic Tension
Authors: Wang Guiqiao , Gao Yang , Ding Jingguo , Sun Jie
Published: January 2024

Tension control in cold-rolling pilot mills (CRPMs) with hydraulic tension is subject to extraneous forces resulting from positional disturbances, strong coupling, and time-varying characteristics, making it challenging to achieve ideal control results using the existing control methods. Therefore, in this study, a mathematical model of tension is first derived and then used to analyze the properties and difficulties associated with tension control. Second, a hydraulic servo-control system based on servovalves and proportional pressure relief valves is developed. In this system, redundant feedforward flow is generated by a servovalve according to the rolling schedule. The surplus flow is absorbed by a proportional pressure relief valve in the closed-loop control of tension. Third, simulation analysis is performed. Under severe friction disturbance (maximum 0.2 kN), and with the wide range of forward and backward slip, an accuracy of ±0.27 kN in tension control can still be achieved using the proposed control strategy, thereby demonstrating its effectiveness. Moreover, it has obvious advantages over existing control methods. Finally, an experimental study of tension is carried out in the cold-rolling mill with a maximum tension capacity of 50 kN, achieving ±0.2 kN tension control precision in the multipass rolling process.
Fail-Safe Topology Optimization Using Damage Scenario Filtering
Authors: Sun Wuhe , Zhang Yong , Liu Yunfei , Cheng Kai , Cheng Fei
Published: January 2024

Within the framework of isotropic materials, this paper introduces an efficient topology optimization method that incorporates fail-safe design considerations using a penalty function approach. Existing methods are either computationally expensive or overlook fail-safe requirements during optimization. This approach not only achieves optimized structures with fail-safe characteristics, but also significantly enhances the computational efficiency of fail-safe topology optimization. In this method, the minimization of worst-case compliance serves as the optimization objective, employing the Kreisselmeier–stein Hauser function to approximate the non-differentiable maximum operator. A sensitivity analysis, derived through the adjoint method, is utilized, and a universal fail-safe optimization criterion is developed to update the design variables. During the optimization process for fail-safe strategies, a density-based filtering method is applied, effectively reducing damage scenarios. Finally, the effectiveness and computational efficiency of this method are validated through several numerical examples.
Shear Performance of the Interface of Sandwich Specimens with Fabric-Reinforced Cementitious Matrix Vegetal Fabric Skins
Authors: Gil Lluís , Mercedes Luis , Mendizabal Virginia , Bernat-Maso Ernest
Published: January 2024

The utilization of the vegetal fabric-reinforced cementitious matrix (FRCM) represents an innovative approach to composite materials, offering distinct sustainable advantages when compared to traditional steel-reinforced concrete and conventional FRCM composites employing synthetic fibers. This article introduces a design for sandwich solutions based on a core of extruded polystyrene and composite skins combining mortar as a matrix and diverse vegetal fabrics as fabrics such as hemp and sisal. The structural behavior of the resulting sandwich panel is predominantly driven by the interaction between materials (mortar and polyurethane) and the influence of shear connectors penetrating the insulation layer. This study encompasses an experimental campaign involving double-shear tests, accompanied by heuristic bond-slip models for the potential design of sandwich solutions. The analysis extends to the examination of various connector types, including hemp, sisal, and steel, and their impact on the shear performance of the sandwich specimens. The results obtained emphasize the competitiveness of vegetal fabrics in achieving an effective composite strength comparable to other synthetic fabrics like glass fiber. Nevertheless, this study reveals that the stiffness of steel connectors outperforms vegetal connectors, contributing to an enhanced improvement in both stiffness and shear strength of the sandwich solutions.
Research on the Stability and Water Isolation of Waterproof Coal Pillars between Adjacent Working Faces under the Influence of Water Ponding Goaf—A Case Study
Authors: Gu Wei , Xu Dalong , Wang Yunqing , Miao Kuo , Yao Sumeng , Zhang Hao , Han Zhenfei
Published: January 2024

Retaining a waterproof coal pillar is an important measure to defend against water inrush accidents in mining areas and guarantee the safe mining of the next working face. In this paper, the mechanical model of the coal pillar is established and the calculation formula of the waterproof coal pillar width is derived. Then, the development of the water-conducting fracture zone of the overlying rock layer under different coal pillar widths is analyzed using numerical simulation and finally, the integrity of the coal pillar is detected using the geophysical survey method. The main conclusions are as follows: (1) According to the mechanical failure characteristics of the coal pillar, it can be divided into the plastic zone, elastic zone, and water pressure damage zone. The mechanical calculation model for each zone was established, and the formula for calculating the width of the waterproof coal pillar was obtained. (2) Numerical simulation was employed to investigate the development condition of the water conducting fracture zone in the overlying rock strata under the actual width of the waterproof coal pillar; the simulation results indicated that the water conducting fracture zone of two working faces was not connected, which can effectively prevent the accumulation of water in the 2303 goaf. (3) On-site geophysical surveys determined that the influence of water-logged goaf on the coal pillar is between 5 to 15 m; the integrity of the waterproof coal pillar is good, which effectively prevents water accumulation in the previous working face goaf and ensures safe mining in the next working face.
Interdecadal Change in the Covariability of the Tibetan Plateau and Indian Summer Precipitation and Associated Circulation Anomalies
Authors: Wei Xinchen , Liu Ge , Nan Sulan , Qian Tingting , Zhang Ting , Mao Xin , Feng Yuhan , Zhou Yuwei
Published: January 2024

This study investigates the interdecadal change in the covariability between the Tibetan Plateau (TP) east–west dipole precipitation and Indian precipitation during summer and primarily explores the modulation of atmospheric circulation anomalies on the covariability. The results reveal that the western TP precipitation (WTPP), eastern TP precipitation (ETPP), and northwestern Indian precipitation (NWIP) have covariability, with an in-phase variation between the WTPP and NWIP and an out-of-phase variation between the WTPP and ETPP. Moreover, this covariability was unclear during 1981–2004 and became significant during 2005–2019, showing a clear interdecadal change. During 2005–2019, a thick geopotential height anomaly, which tilted slightly northward, governed the TP, forming upper- and lower-level coupled circulation anomalies (i.e., anomalous upper-level westerlies over the TP and lower-level southeasterlies and northeasterlies around the southern flank of the TP). As such, the upper- and lower-tropospheric circulation anomalies synergistically modulate the summer WTPP, ETPP, and NWIP, causing the covariability of summer precipitation over the TP and India during 2005–2019. The upper- or lower-level circulation anomalies cannot independently result in significant precipitation covariability. During 1981–2004, the upper- and lower-level circulation anomalies were not strongly coupled, which caused precipitation non-covariability. The sea surface temperature anomalies (SSTAs) in the western North Pacific (WNP) and tropical Atlantic (TA) may synergistically modulate the upper- and lower-level coupled circulation anomalies, contributing to the covariability of the WTPP, ETPP, and NWIP during 2005–2019. The modulation of the WNP and TA SSTs on the coupled circulation anomalies was weaker during 1981–2004, which was therefore not conducive to this precipitation covariability. This study may provide valuable insights into the characteristics and mechanisms of spatiotemporal variation in summer precipitation over the TP and its adjacent regions, thus offering scientific support for local water resource management, ecological environment protection, and social and economic development.
Estimation of the Concentration of XCO2 from Thermal Infrared Satellite Data Based on Ensemble Learning
Authors: Gong Xiaoyong , Zhang Ying , Fan Meng , Zhang Xinxin , Song Shipeng , Li Zhongbin
Published: January 2024

Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv).
The Impact of Trees on the UHI Effect and Urban Environment Quality: A Case Study of a District in Pisa, Italy
Authors: Frosini Greta , Amato Agnese , Mugnai Francesca , Cinelli Fabrizio
Published: January 2024

As the urban heat island effect has become a worldwide phenomenon commonly affecting densely built-up areas, public administrations need efficient strategies to mitigate its impact on human well-being and public health. The aim of this study was to define a replicable method to estimate the ecosystem services provided by public street trees as a supporting tool in the decision-making process of urban greenery management. We compared three street arrangements characteristic of a residential district in Pisa, Italy: (1) with large trees, (2) with small trees, and (3) without trees. First, the software i-Tree Eco was used to assess the benefits of public trees located in the case-study area when provided with the three scenarios. Second, the comparison was held on the field, and we collected data with a wet bulb globe temperature meter in order to evaluate the differences in pedestrian thermal comfort among the street arrangements. The results confirmed the importance of urban vegetation, as it has major impacts on carbon sequestration and storage, pollution removal, air humidity and quality, and shade, given bigger trees and canopy sizes. The loss of ecosystem services compared to the presence of large trees varied between 40% and 50% (no trees) and 30% and 40% (small trees).
Applicability Analysis of Three Atmospheric Radiative Transfer Models in Nighttime
Authors: He Jiacheng , Zhang Wenhao , Liu Sijia , Zhang Lili , Liu Qiyue , Gu Xingfa , Yu Tao
Published: January 2024

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.
Numerical Reconstruction of the Source in Dynamical Boundary Condition of Laplace’s Equation
Authors: Koleva Miglena N. , Vulkov Lubin G.
Published: January 2024

In this work, we consider Cauchy-type problems for Laplace’s equation with a dynamical boundary condition on a part of the domain boundary. We construct a discrete-in-time, meshless method for solving two inverse problems for recovering the space–time-dependent source and boundary functions in dynamical and Dirichlet boundary conditions. The approach is based on Green’s second identity and the forward-in-time discretization of the non-stationary problem. We derive a global connection that relates the source of the dynamical boundary condition and Dirichlet and Neumann boundary conditions in an integral equation. First, we perform time semi-discretization for the dynamical boundary condition into the integral equation. Then, on each time layer, we use Trefftz-type test functions to find the unknown source and Dirichlet boundary functions. The accuracy of the developed method for determining dynamical and Dirichlet boundary conditions for given over-determined data is first-order in time. We illustrate its efficiency for a high level of noise, namely, when the deviation of the input data is above 10% on some part of the over-specified boundary data. The proposed method achieves optimal accuracy for the identified boundary functions for a moderate number of iterations.