bluuebunny/crossref_metadata_2025 huggingface dataset:
Coronary Vessel Segmentation in X-ray Angiography Images Using Edge-Based Tracking Method
Authors: Lalinia Mehrshad , Sahafi Ali
Published: June 2024

Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.
The effects of self-leadership and mindfulness training on leadership development: a systematic review
Authors: Tenschert Julia , Furtner Marco , Peters Mike
Published: June 2024

Investment in leadership development programs (LDPs) does not reliably increase leaders’ competence in core socioemotional skills related to self-management, self-awareness, and relationship-building with employees. Training programs focused on self-leadership, in combination with mindfulness practices, have the potential to address this gap. However, robust research that assesses the suitability and efficacy of such programs is lacking. In this article, the results of a systematic review of the literature on self-leadership and mindfulness in the context of LDPs are reported. A total of 52 articles were selected from an initial pool of 284 articles, subjected to textual analysis, and coded in terms of the reported impact levels for all of the examined training programs. This study revealed that training in self-leadership competencies and skills improved stress resilience, job performance and satisfaction, and positive attitudes and increased leaders’ abilities to organize and motivate their teams. Mindfulness training was strongly linked to stress reduction and self-regulation as well as to enhanced sleep and reduced burnout. Mindfulness also appeared to improve job performance and emotional regulation and to increase the ability to establish positive relationships with employees.
An integrated groundwater vulnerability and artificial recharge site suitability assessment using GIS multi-criteria decision making approach in Kayseri region, Turkey
Authors: Mouhoumed Rachid Mohamed , Ekmekcioğlu Ömer , Özger Mehmet
Published: June 2024

Groundwater resources worldwide face significant challenges that require urgent implementation of sustainable measures for effective long-term management. Managed aquifer recharge (MAR) is regarded as one of the most promising management technologies to address the degradation of groundwater resources. However, in urban aquifers, locating suitable areas that are least vulnerable to contamination for MAR implementation is complex and challenging. Hence, the present study proposes a framework encapsulating the combined assessment of groundwater vulnerability and MAR site suitability analysis to pinpoint the most featured areas for installing drywells in Kayseri, Turkey. To extrapolate the vulnerable zones, not only the original DRASTIC but also its multi-criteria decision-making (MCDA)–based modified variants were evaluated with regard to different hydrochemical parameters using the area under the receiver operating characteristic (ROC) curve (AUC). Besides, the fuzzy analytical hierarchy process (FAHP) rationale was adopted to signify the importance level of criteria and the robustness of the framework was highlighted with sensitivity analysis. In addition, the decision layers and the attained vulnerability layer were combined using the weighted overlay (WOA). The findings indicate that the DRASTIC-SWARA correlates well with the arsenic (AUC = 0.856) and chloride (AUC = 0.648) and was adopted as the vulnerability model. Groundwater quality parameters such as chloride and sodium adsorption ratio, as well as the vadose zone thickness, were found to be the most significant decision parameters with importance levels of 16.75%, 14.51%, and 15.73%, respectively. Overall, 28.24% of the study area was unsuitable for recharge activities with high to very high vulnerability, while the remaining part was further prioritized into low to high suitability classes for MAR application. The proposed framework offers valuable tool to decision-makers for the delineation of favorable MAR sites with minimized susceptibility to contamination.
The Gap in the Knowledge Argument
Authors: Montero Barbara
Published: June 2024

Alter (The Matter of Consciousness: From the Knowledge Argument to Russellian Monism, GB: Oxford University Pres, 2023) argues for something surprising: despite being widely rejected by philosophers, including Frank Jackson himself, Jackson’s knowledge argument succeeds. Alter’s defense of Jackson’s argument is not only surprising; it’s also exciting: the knowledge argument, if it’s sound, underscores the power of armchair philosophy, the power of pure thought to arrive at substantial conclusions about the world. In contrast, I aim to make a case for something unsurprising and unexciting: that the knowledge argument does not succeed, or, even less far-reaching, that Alter’s defense of it is not persuasive. Mine is a classic file-drawer thesis, but what it has going for it is that it’s true, or so I think, and hope to illustrate why you should too.
Stereo reconstruction from microscopic images for computer-assisted ophthalmic surgery
Authors: Peter Rebekka , Moreira Sofia , Tagliabue Eleonora , Hillenbrand Matthias , Nunes Rita G. , Mathis-Ullrich Franziska
Published: June 2024

Purpose

This work presents a novel platform for stereo reconstruction in anterior segment ophthalmic surgery to enable enhanced scene understanding, especially depth perception, for advanced computer-assisted eye surgery by effectively addressing the lack of texture and corneal distortions artifacts in the surgical scene.

Methods

The proposed platform for stereo reconstruction uses a two-step approach: generating a sparse 3D point cloud from microscopic images, deriving a dense 3D representation by fitting surfaces onto the point cloud, and considering geometrical priors of the eye anatomy. We incorporate a pre-processing step to rectify distortion artifacts induced by the cornea’s high refractive power, achieved by aligning a 3D phenotypical cornea geometry model to the images and computing a distortion map using ray tracing.

Results

The accuracy of 3D reconstruction is evaluated on stereo microscopic images of ex vivo porcine eyes, rigid phantom eyes, and synthetic photo-realistic images. The results demonstrate the potential of the proposed platform to enhance scene understanding via an accurate 3D representation of the eye and enable the estimation of instrument to layer distances in porcine eyes with a mean average error of 190 $$\upmu {\hbox {m}}$$

μ
m

, comparable to the scale of surgeons’ hand tremor.

Conclusion

This work marks a significant advancement in stereo reconstruction for ophthalmic surgery by addressing corneal distortions, a previously often overlooked aspect in such surgical scenarios. This could improve surgical outcomes by allowing for intra-operative computer assistance, e.g., in the form of virtual distance sensors.
On the U-Fe-C Isothermal Section at 1100 °C
Authors: Henriques Margarida I. Sousa , Silva André , Havela Ladislav , Gonçalves António Pereira
Published: June 2024

The energy crisis and climate change have promoted a growing interest in non-fossil sources, such as nuclear, with uranium carbides being seen as potential fuel candidates for Generation IV nuclear reactors. However, the need of accurate thermophysical data for the fuel and its compatibility with core materials during the extreme fission conditions is still an issue. Here a study of the ternary uranium-iron-carbon system performed at 1100 °C using powder x-ray diffraction and Scanning Electron Microscopy coupled with Energy Dispersive Spectrometry is presented. The U-Fe-C isothermal section is characterized by two ternary compounds, thirteen 3-phase regions and five 2-phase regions. UFeC2 and ~ U11Fe12C18 were confirmed to be present at 1100 °C and crystallize in structures related to the binary uranium carbides. UFeC2 crystallizes in an original structure type, a distorted variant of the UCoC2 structure, with space group P4/n and a = 3.503(5) Å and c = 7.405(5) Å lattice parameters. ~ U11Fe12C18, has a crystal structure related to the Th11Ru12C18 structure-type (space group I$$\overline{4 }$$

4
¯

3m) with the lattice parameter a ≈ 10 Å. Furthermore, an island of a α-UC2-based phase with 32U:4Fe:64C composition was found in the 1100 °C isothermal section, indicating the inclusion of Fe in the α-UC2 binary compound.
Patient-specific placental vessel segmentation with limited data
Authors: Sarwin Gary , Lussi Jonas , Gervasoni Simone , Moehrlen Ueli , Ochsenbein Nicole , Nelson Bradley J.
Published: June 2024

A major obstacle in applying machine learning for medical fields is the disparity between the data distribution of the training images and the data encountered in clinics. This phenomenon can be explained by inconsistent acquisition techniques and large variations across the patient spectrum. The result is poor translation of the trained models to the clinic, which limits their implementation in medical practice. Patient-specific trained networks could provide a potential solution. Although patient-specific approaches are usually infeasible because of the expenses associated with on-the-fly labeling, the use of generative adversarial networks enables this approach. This study proposes a patient-specific approach based on generative adversarial networks. In the presented training pipeline, the user trains a patient-specific segmentation network with extremely limited data which is supplemented with artificial samples generated by generative adversarial models. This approach is demonstrated in endoscopic video data captured during fetoscopic laser coagulation, a procedure used for treating twin-to-twin transfusion syndrome by ablating the placental blood vessels. Compared to a standard deep learning segmentation approach, the pipeline was able to achieve an intersection over union score of 0.60 using only 20 annotated images compared to 100 images using a standard approach. Furthermore, training with 20 annotated images without the use of the pipeline achieves an intersection over union score of 0.30, which, therefore, corresponds to a 100% increase in performance when incorporating the pipeline. A pipeline using GANs was used to generate artificial data which supplements the real data, this allows patient-specific training of a segmentation network. We show that artificial images generated using GANs significantly improve performance in vessel segmentation and that training patient-specific models can be a viable solution to bring automated vessel segmentation to the clinic.
Insights from telesurgery expert conference on recent clinical experience and current status of remote surgery
Authors: Rocco Bernardo , Moschovas Marcio Covas , Saikali Shady , Gaia Giorgia , Patel Vipul , Sighinolfi Maria Chiara
Published: June 2024

Remote surgery provides opportunity for enhanced surgical capabilities, wider healthcare reach, and potentially improved patient outcomes. The network reliability is the foundation of successful implementation of telesurgery. It relies on a robust, high-speed communication network, with ultra-low latency. Significant lag has been shown to endanger precision and safety. Furthermore, the full-fledged adoption of telerobotics demands careful consideration of ethical challenges too. A deep insight into these issues has been investigated during the first Telesurgery Consensus Conference that took place in Orlando, Florida, USA, on the 3rd and 4th of February, 2024. During the Conference, the state of the art of remote surgery has been reported from robotic systems displaying telesurgery potential. The Hinotori, a robotic-assisted surgery platform developed by Medicaroid, experienced remote surgery as pre-clinical testing only; the Edge Medical Company, Shenzen, China, reported more than one hundred animal and 30 live human surgeries; the KanGuo reported human telesurgical cases performed with distances more than 3000 km; the Microport, China, collected more than 100 human operations at a distance up to 5000 km. Though, several issues—cybersecurity, data privacy, technical malfunctions — are yet to be addressed before a successful telesurgery implementation. Expanding the discussion to encompass ethical, financial, regulatory, and legal considerations is essential too. The Telesurgery collaborative community is working together to address and establish the best practices in the field.
Relationship between aging and excess body fat with markers of inflammation, skeletal muscle mass and strength in Mexican community-dwelling people
Authors: Mendoza-Núñez Víctor Manuel , Aguilar-Curiel Jimena Valeria , Castillo-Martínez Lilia , Rodríguez-García Wendy Daniella , Vaquero-Barbosa Nayeli , Rosado-Pérez Juana , Arista-Ugalde Taide Laurita
Published: June 2024

Introduction

Aging is accompanied by changes in body composition, such as an increase in fat mass (FM), a decrease in skeletal muscle mass index (SMMI) and muscle strength, combined with a chronic inflammatory process (CI).

Objective

Determine the relationship between age and excess body fat with markers of chronic inflammation, skeletal muscle mass and strength.

Methods

A cross-sectional alitical study was carried out in a convenience sample of adults 45 to 59 years old (n = 100) and older adults 60 to 74 years old (n = 133). All participants had their body composition measured with an impedance meter. They were subsequently divided into two groups: (i) with excess fat (WEF), (ii) without excess fat (NEF), in order to relate excess fat and age with inflammation, muscle mass and strength.

Results

NEF adults and older adults had similar values of SMMI (9.1 ± 1.5 vs. 8.8 ± 1.3, p > 0.05) and strength (28 ± 8 vs. 27 ± 8.6, p > 0.05). Likewise, WEF adults showed significantly lower values than NEF adults in the SMMI (7.9 ± 0.8 vs. 9.1 ± 1.5, p < 0.05) and strength (28 ± 8 vs. 22 ± 5, p < 0.001). Also, WEF older adults presented significantly lower values in the SMMI (15.9 ± 1.8 vs. 22.8 ± 5.1, p < 0.05) and strength (17.9 ± 4.8 vs. 27 ± 8.6, p < 0.001).

Conclusions

Our findings suggest that excess fat mass is a risk factor that has a significantly greater influence than aging per se on the index of skeletal muscle mass and strength.
AI Through Ethical Lenses: A Discourse Analysis of Guidelines for AI in Healthcare
Authors: Arbelaez Ossa Laura , Milford Stephen R. , Rost Michael , Leist Anja K. , Shaw David M. , Elger Bernice S.
Published: June 2024

While the technologies that enable Artificial Intelligence (AI) continue to advance rapidly, there are increasing promises regarding AI’s beneficial outputs and concerns about the challenges of human–computer interaction in healthcare. To address these concerns, institutions have increasingly resorted to publishing AI guidelines for healthcare, aiming to align AI with ethical practices. However, guidelines as a form of written language can be analyzed to recognize the reciprocal links between its textual communication and underlying societal ideas. From this perspective, we conducted a discourse analysis to understand how these guidelines construct, articulate, and frame ethics for AI in healthcare. We included eight guidelines and identified three prevalent and interwoven discourses: (1) AI is unavoidable and desirable; (2) AI needs to be guided with (some forms of) principles (3) trust in AI is instrumental and primary. These discourses signal an over-spillage of technical ideals to AI ethics, such as over-optimism and resulting hyper-criticism. This research provides insights into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.
Empirical comparison of assembly complexity assessment approaches in manufacturing
Authors: Capponi Matteo , Mastrogiacomo Luca , Franceschini Fiorenzo
Published: June 2024

Investigating assembly complexity is a continuing concern within the manufacturing field, as it may impact the quality and costs of products. A growing body of literature is aimed at developing novel and reliable methods to assess assembly complexity. The first aim of this paper is to provide an extensive literature review, identifying the main approaches to this problem. Three main approaches emerged: product-centred approach, information-centred approach and system-centred approach. A second goal is to conduct a comparative analysis of these approaches, highlighting their advantages and limitations and providing guidance for process and product designers seeking to control assembly complexity. For each approach, a representative method was chosen, analysed in detail, and applied to five different real products. As concluding findings it was observed that product-centred approaches provide a more objective analysis, focusing strictly on product features. On the other hand, information-centred approaches focus on quantity and variety of information handled by assembly operators, thus proposing a valuable link between complexity and effort required. Finally, system-centred approaches provide a holistic assessment of assembly complexity which is typically only qualitative.
The Clinicopathological Significance of Tumor Cell Subtyping in Appendiceal Neuroendocrine Tumors: A Series of 135 Tumors
Authors: Mete Ozgur , Dodington David W. , Shen Daniel L. , Asa Sylvia L.
Published: June 2024

Appendiceal neuroendocrine tumors (NETs) are common and often are identified as incidental lesions at the time of appendectomy. The guidelines for management are based on tumor size, degree of invasion, and the Ki67 proliferation index. Most small bowel NETs are composed of serotonin-producing EC-cells, but there are multiple other neuroendocrine cell types. In the rectum, there are L-cell tumors that express peptide YY (PYY), glucagon-like peptides (GLPs), and pancreatic polypeptide (PP); they are thought to have a better prognosis than serotonin-producing tumors. We investigated whether the appendix has distinct neuroendocrine tumor types based on cell type and whether that distinction has clinical significance. We collected 135 appendiceal NETs from the pathology archives of UHN Toronto and UHCMC (Cleveland). We analyzed the expression of biomarkers including CDX2, SATB2, PSAP, serotonin, glucagon (that detects GLPs), PYY, and pancreatic polypeptide (PP) and correlated the results with clinicopathologic parameters. Immunohistochemistry identified three types of appendiceal NETs. There were 75 (56%) classified as EC-cell tumors and 37 (27%) classified as L-cell tumors; the remaining 23 (17%) expressed serotonin and one of the L-cell biomarkers and were classified as mixed. EC-cell tumors were significantly larger with more extensive invasion involving the muscularis propria, subserosa, and mesoappendix compared with L-cell tumors. Mixed tumors were intermediate in all of these parameters. Both EC-cell and mixed tumors had lymphatic and/or vascular invasion while L-cell tumors had none. Unlike EC-cell NETs, L-cell tumors were not associated with lymph node metastasis. Tumor type correlated with pT stage and the only patient with distant metastatic disease in this series had an EC-cell tumor. Our study confirms that appendiceal NETs are not a homogeneous tumor population. There are at least three types of appendiceal NET, including EC-cell, L-cell, and mixed tumors. This information is important for surveillance of patients, as monitoring urinary 5HIAA levels is only appropriate for patients with serotonin-producing tumors, whereas measurement of GLPs and/or PP is more appropriate for patients with L-cell tumors. Our data also show that tumor type is of significance with EC-cell tumors exhibiting the most aggressive behavior.
Hepatic-associated vascular morphological assessment to predict overt hepatic encephalopathy before TIPS: a multicenter study
Authors: Chen Xiaoqiong , Huang Mingsheng , Yu Xiangrong , Chen Jinqiang , Xu Chunchun , Jiang Yunzheng , Li Yiting , Zhao Yujie , Duan Chongyang , Luo Yixin , Zhang Jiawei , Lv Weifu , Li Qiyang , Luo Junyang , Dong Dandan , An Taixue , Lu Ligong , Fu Sirui
Published: June 2024

Background

To provide patients the chance of accepting curative transjugular intrahepatic portosystemic shunt (TIPS) rather than palliative treatments for portal hypertension-related variceal bleeding and ascites, we aimed to assess hepatic-associated vascular morphological change to improve the predictive accuracy of overt hepatic encephalopathy (HE) risks.

Methods

In this multicenter study, 621 patients undergoing TIPS were subdivided into training (413 cases from 3 hospitals) and external validation datasets (208 cases from another 3 hospitals). In addition to traditional clinical factors, we assessed hepatic-associated vascular morphological changes using maximum diameter (including absolute and ratio values). Three predictive models (clinical, hepatic-associated vascular, and combined) were constructed using logistic regression. Their discrimination and calibration were compared to test the necessity of hepatic-associated vascular assessment and identify the optimal model. Furthermore, to verify the improved performance of ModelC−V, we compared it with four previous models, both in discrimination and calibration.

Results

The combined model outperformed the clinical and hepatic-associated vascular models (training: 0.814, 0.754, 0.727; validation: 0.781, 0.679, 0.776; p < 0.050) and had the best calibration. Compared to previous models, ModelC−V showed superior performance in discrimination. The high-, middle-, and low-risk populations displayed significantly different overt HE incidence (p < 0.001). Despite the limited ability of pre-TIPS ammonia to predict overt HE risks, the combined model displayed a satisfactory ability to predict overt HE risks, both in the low- and high-ammonia subgroups.

Conclusion

Hepatic-associated vascular assessment improved the predictive accuracy of overt HE, ensuring curative chances by TIPS for suitable patients and providing insights for cirrhosis-related studies.

Graphical Abstract
N6-methyladenosine modification and post-translational modification of epithelial–mesenchymal transition in colorectal cancer
Authors: Wang Yingnan , Chen Yufan , Zhao Miaomiao
Published: June 2024

Colorectal cancer is a leading cause of cancer-related mortality worldwide. Traditionally, colorectal cancer has been recognized as a disease caused by genetic mutations. However, recent studies have revealed the significant role of epigenetic alterations in the progression of colorectal cancer. Epithelial–mesenchymal transition, a critical step in cancer cell metastasis, has been found to be closely associated with the tumor microenvironment and immune factors, thereby playing a crucial role in many kinds of biological behaviors of cancers. In this review, we explored the impact of N6-methyladenosine and post-translational modifications (like methylation, acetylation, ubiquitination, SUMOylation, glycosylation, etc.) on the process of epithelial–mesenchymal transition in colorectal cancer and the epigenetic regulation for the transcription factors and pathways correlated to epithelial-mesenchymal transition. Furthermore, we emphasized that the complex regulation of epithelial-mesenchymal transition by epigenetics can provide new strategies for overcoming drug resistance and improving treatment outcomes. This review aims to provide important scientific evidence for the prevention and treatment of colorectal cancer based on epigenetic modifications.
On quantum computing for artificial superintelligence
Authors: Grabowska Anna , Gunia Artur
Published: June 2024

Artificial intelligence algorithms, fueled by continuous technological development and increased computing power, have proven effective across a variety of tasks. Concurrently, quantum computers have shown promise in solving problems beyond the reach of classical computers. These advancements have contributed to a misconception that quantum computers enable hypercomputation, sparking speculation about quantum supremacy leading to an intelligence explosion and the creation of superintelligent agents. We challenge this notion, arguing that current evidence does not support the idea that quantum technologies enable hypercomputation. Fundamental limitations on information storage within finite spaces and the accessibility of information from quantum states constrain quantum computers from surpassing the Turing computing barrier. While quantum technologies may offer exponential speed-ups in specific computing cases, there is insufficient evidence to suggest that focusing solely on quantum-related problems will lead to technological singularity and the emergence of superintelligence. Subsequently, there is no premise suggesting that general intelligence depends on quantum effects or that accelerating existing algorithms through quantum means will replicate true intelligence. We propose that if superintelligence is to be achieved, it will not be solely through quantum technologies. Instead, the attainment of superintelligence remains a conceptual challenge that humanity has yet to overcome, with quantum technologies showing no clear path toward its resolution.
Ethnopharmacological study on Adenosma buchneroides Bonati inhibiting inflammation via the regulation of TLR4/MyD88/NF-κB signaling pathway
Authors: Shi Yuru , Zhang Xiaoqian , Pei Shengji , Wang Yuhua
Published: June 2024

Adenosma buchneroides Bonati, also known as fleagrass, is an important medicinal plant used by the Akha (Hani) people of China for treating inflammation-related skin swelling, acne, and diarrhoea, among other conditions. In this study, we aimed to evaluate the anti-inflammatory activities and explore the molecular mechanisms of fleagrass on treating skin swelling and acne. The results demonstrated that fleagrass inhibited the enzymatic activities of 5-LOX and COX-2 in vitro, and decreased the release of NO, IL-6, TNF-α, and IL-10 in the LPS-induced RAW264.7 macrophages. The levels of proteins associated with the nuclear factor-kappa B (NF-κB) pathway were examined by western blotting and immunofluorescence, demonstrating that fleagrass downregulated the expression of TLR4, MyD88, NF-κB/p65, and iNOS and blocked the nuclear translocation of NF-κB/p65. Furthermore, fleagrass exhibited acute anti-inflammatory activity in paw oedema models. The results confirm that fleagrass exhibits remarkable anti-inflammatory activity and can be used in alleviating inflammation, suggesting that fleagrass has the potential to be a novel anti-inflammatory agent.

Graphical Abstract
Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making
Authors: Albahri A. S. , Hamid Rula A. , Alzubaidi Laith , Homod Raad Z. , Zidan Khamis A. , Mubark Hassan , Shayea Ghadeer Ghazi , Albahri O. S. , Alamoodi A. H.
Published: June 2024

This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.
A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis
Authors: Wang Youming , Chen Lisha
Published: June 2024

The Capsule Network (CapsNet) has been shown to have significant advantages in improving the accuracy of bearing fault identification. Nevertheless, the CapsNet faces challenges in identifying the type of bearing fault under nonstationary and noisy conditions. These challenges arise from the distinctive nature of its dynamic routing algorithm and the use of fixed single-scale kernels. To address these challenges, a multi-scale spatial–temporal capsule network (MSCN) based on sequence encoding is proposed for bearing fault identification under nonstationary and noisy environments. A spatial–temporal sequence encoding module focuses on feature correlations at various times and positions. Dilated convolution-based multiscale capsule layer (MCaps) is designed to capture spatial–temporal features at different scales. MCaps establishes connections between various layers, enhancing the comprehension and interpretation of spatial–temporal features. Furthermore, the Bhattacharyya coefficient is introduced into the dynamic routing to compare the similarity between capsules. The validity of the model is verified through comparative experiments, and the results show that MSCN has significant advantages over traditional methods.
Bayesian network structure learning with a new ensemble weights and edge constraints setting mechanism
Authors: Liu Kaiyue , Zhou Yun , Huang Hongbin
Published: June 2024

Bayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of complex samples of a joint distribution is currently a challenging combinatorial problem. Due to the growing volume and complexity of data, some Bayesian structure learning algorithms are ineffective and lack the necessary precision to meet the required needs. In this paper, we propose a new PCCL-CC algorithm. To ensure the accuracy of the network structure, we introduce the new ensemble weights and edge constraints setting mechanism. In this mechanism, we employ a method that estimates the interaction between network nodes from multiple perspectives and divides the learning process into multiple stages. We utilize an asymmetric weighted ensemble method and adaptively adjust the network structure. Additionally, we propose a causal discovery method that effectively utilizes the causal relationships among data samples to correct the network structure and mitigate the influence of Markov equivalence classes (MEC). Experimental results on real datasets demonstrate that our approach outperforms state-of-the-art methods.
Geochemistry of Cenozoic coals from Sarawak Basin, Malaysia: implications for paleoclimate, depositional conditions, and controls on petroleum potential
Authors: Asiwaju Lanre , Mustapha Khairul Azlan , Abdullah Wan Hasiah , Sia Say Gee , Hakimi Mohammed Hail
Published: June 2024

Forty Tertiary coals from Mukah-Balingian and Merit-Pila coalfields of the Sarawak Basin, Malaysia were investigated using bulk and molecular geochemical techniques such as proximate analysis, gas chromatography-mass spectrometry, elemental analyser, isotope ratio mass spectrometry, and inductively coupled plasma mass spectrometry to reconstruct their paleovegetation, paleoclimate, and environments of deposition. In addition, principal component analysis (PCA) of selected geochemical parameters was carried out to determine the controlling influences on the petroleum potential of the humic coals. δ13C values and the abundance of terpenoids imply the predominant contribution of angiosperms to the paleoflora. Bimetal proxies (Sr/Ba, Sr/Cu, and C-value), and δD values are generally suggestive of a warm and humid climate during the accumulation of the paleopeats. However, n-alkane proxies (Pwax, Paq, n-C23/n-C29, etc.) and polycyclic aromatic hydrocarbons (PAHs) distribution suggest that Balingian coals accumulated under relatively drier and strongly seasonal paleoclimate in the Late Pliocene. When compared with published global average abundances, the investigated coals are mostly depleted in major oxides and trace elements, suggesting peat accumulation in freshwater-influenced environments. Nonetheless, higher (> 0.5 wt%) total sulfur content in some Mukah-Balingian coals suggests some degree of epigenetic marine influence. Furthermore, the low to moderately-high ash contents of the Sarawak Basin coals indicate the presence of ombrotrophic and rheotrophic peat deposits. PCA result of selected geochemical proxies suggests that source input, paleoflora, and marine incursions are not major controlling influences on the petroleum potential. However, climatic, and depositional conditions appear to slightly influence the petroleum potential of the studied humic coals.
Challenges and Solutions to Meet the Euro 7 NOx Emission Requirements for Diesel Light-Duty Commercial Vehicles
Authors: Kossioris Theodoros , Maurer Robert , Sterlepper Stefan , Günther Marco , Pischinger Stefan
Published: June 2024

The improvement of air quality requires a further reduction of pollutant emissions, especially in urban areas. The Euro 7 regulations aim at the development of a new generation of internal combustion engine vehicles capable of achieving ultra-low pollutant emissions under demanding, real-world operating conditions. They introduce new technical challenges in the holistic design of a vehicle’s powertrain and emission control system. To identify these, four real-world Euro 7 driving scenarios are investigated, covering demanding urban, highway and mountain driving situations. Technical solutions are then presented to address these challenges and ensure compliance with the Euro 7 emission requirements as set out in the latest regulation proposal of the European Commission. The study focuses on the NOx emissions of an N1 Class III light commercial vehicle with 3.5 t mass and a P2 diesel mild-hybrid powertrain. To ensure emission compliance, a Euro 6e exhaust gas aftertreatment system with enlarged catalysts is combined with NOx raw emission improvements. For low-load cold starts, a 4-kW electric heater in the exhaust system is considered in addition to a 2-l DOC and a 6-l DPF with SCR coating. For high-load cycles with high raw emissions, a 10-l underfloor SCR is considered to ensure the necessary deNOx performance.
Augmented degree correction for bipartite networks with applications to recommender systems
Authors: Leinwand Benjamin , Pipiras Vladas
Published: June 2024

In recommender systems, users rate items, and are subsequently served other product recommendations based on these ratings. Even though users usually rate a tiny percentage of the available items, the system tries to estimate unobserved preferences by finding similarities across users and across items. In this work, we treat the observed ratings data as partially observed, dense, weighted, bipartite networks. For a class of systems without outside information, we adapt an approach developed for dense, weighted networks to account for unobserved edges and the bipartite nature of the problem. The approach begins with clustering both users and items into communities, and locally estimates the patterns of ratings within each subnetwork induced by restricting attention to one community of users and one community of items community. The local fitting procedure relies on estimating local sociability parameters for every user and item, and selecting the function that determines the degree correction contours which best models the underlying data. We compare the performance of our proposed approach to existing methods on a simulated data set, as well as on a data set of joke ratings, examining model performance in both cases at differing levels of sparsity. On the joke ratings data set, our proposed model performs better than existing alternatives in relatively sparse settings, though other approaches achieve better results when more data is available. Collectively, the results indicate that despite struggling to pick up subtler signals, the proposed approach’s recovery of large scale, coarse patterns may still be useful in practical settings where high sparsity is typical.
Field effectiveness of some essential oil emulsions against the whitefly Bemisia tabaci (GENN.) that infest potato plants in Egypt
Authors: Wahba Trandil F. , Abd-Elatef Enas Adel , Wahba Mona N.
Published: June 2024

Whitefly, Bemisia tabaci, nymphs and sucking sap adults, is one of the most damaging pests of potato, Solanum tuberosum. Excessive use of pesticides causes environmental pollution and the death of beneficial insects, so it is necessary to search for safer controlling alternatives. An experiment was carried out during seasons 2020–2021 and 2021–2022, under field conditions in Egypt. The objective of this study was to identify the main constituent compounds of peppermint and eucalyptus essential oils prepare coarse emulsion (CE) and nanoemulsion (NE) of both peppermint and eucalyptus essential oils and evaluate their efficiency against the whitefly, B. tabaci, in potato cultivars and the effect of these emulsions on the chemical components of potato leaves. The results revealed that the toxicity of the emulsions decreased with increasing time after spraying. The second spray which took place at a 10-day interval was more effective than the first spray. During the season of 2021, three days following the second spray, the tested emulsions' toxicity was highly effective. The reduction percentages of nymph for peppermint coarse emulsion (PCE), peppermint nanoemulsion (PNE), eucalyptus coarse emulsion (ECE), eucalyptus nanoemulsion (ENE) and imidacloprid were 82.64, 84.14, 73.26, 72.72 and 89.03, but they were 78.58, 83.27, 77.85, 69.97 and 89.26 compared with 2022. A moderately positive correlation was identified between temperature, specific humidity, wind speed and the effectiveness of the emulsions. This indicates that higher levels of temperature, humidity and wind speed were associated with increased efficacy of the emulsions. The study of the phytochemicals (total soluble protein, total carbohydrate, total phenolic contents and peroxidase activity). PCE and ECE achieved a slight decrease in protein levels. Also, ECE increased total plant carbohydrates. All treatments did not affect the phenolic compounds of potato leaf plants except ENE, which caused an increase in phenolic compounds. All treatments decreased the nitrogen plants' contents, while PCE, PNE, and ENE increased the potassium content. All treatments increase the activity of peroxidase (POX) compared with untreated plants. The formulations PNE and ENE might be an interesting alternative for integrated pest management of B. tabaci nymphs.
Super-Resolution by Localized Plasmonic Structured Illumination Microscopy Using Self-Assembled Nanoparticle Substrates
Authors: Guan Yizhao , Masui Shozo , Kadoya Shotaro , Michihata Masaki , Takahashi Satoru
Published: June 2024

Structured illumination microscopy (SIM), an advanced super-resolution methodology, transcends the traditional diffraction limit inherent in optical imaging. This technique utilizes standing-wave illumination generated through the interplay of two obliquely incident light waves. The intrinsic resolution constraint of SIM, traditionally pegged at half the wavelength because of the standing wave’s periodicity, has the potential for enhancement by integrating high spatial frequency illumination patterns, particularly when sourced in the near-field of plasmonic nanostructures. The present study introduces and computationally validates a novel, easily fabricated substrate composed of self-assembled gold nanoparticles designed explicitly for generating these high spatial frequency patterns. Addressing the necessity for diverse patterns in reconstructing super-resolution imagery within plasmonic SIM, this research conducted extensive numerical simulations of nanoparticle arrays under varying illumination scenarios. This undertaking affirmed the feasibility of manipulating high-frequency patterns. Super-resolution reconstruction was actualized by applying Blind-SIM techniques, which verified its effectiveness. This innovative approach notably achieved a resolution threshold of 60 nm, markedly exceeding the conventional 150 nm diffraction barrier and surpassing the 75 nm resolution typically observed in standard SIM applications.
Test for high-dimensional outliers with principal component analysis
Authors: Nakayama Yugo , Yata Kazuyoshi , Aoshima Makoto
Published: June 2024

We herein consider a test of outlier detection for high-dimensional, low-sample-size (HDLSS) data. Although outlier detection is a fundamental problem, it has not been extensively studied in the HDLSS setting. We derive asymptotic properties of the first principal component scores with outliers. We consider high-dimensional outlier detection by applying the asymptotic properties to the Grubbs test, a well-known method for testing outliers. Our results indicate that the test statistic provides preferable performance for both the size and power. Using this test procedure, we propose an algorithm to identify multiple outliers. We present an investigation of the theoretical properties of a sure independent screening and it can achieve complete identification of the outliers with high accuracy. Finally, we investigate the performance for both numerical studies and real data analyses as compared to available outlier detection methods in HDLSS settings. The proposed method exhibits superiority in terms of not only correctly detecting outliers, but also identifying a number of false identifications.
Growth modulation response in vertebral body tethering depends primarily on magnitude of concave vertebral body growth
Authors: Louer Craig R. , Upasani Vidyadhar V. , Hurry Jennifer K. , Nian Hui , Farnsworth Christine L. , Newton Peter O. , Parent Stefan , El-Hawary Ron ,   Pediatric Spine Study Group (PSSG)
Published: June 2024

Purpose

There is variability in clinical outcomes with vertebral body tethering (VBT) partly due to a limited understanding of the growth modulation (GM) response. We used the largest sample of patients with 3D spine reconstructions to characterize the vertebra and disc morphologic changes that accompany growth modulation during the first two years following VBT.

Methods

A multicenter registry was used to identify idiopathic scoliosis patients who underwent VBT with 2 years of follow-up. Calibrated biplanar X-rays obtained at longitudinal timepoints underwent 3D reconstruction to obtain precision morphological measurements. GM was defined as change in instrumented coronal angulation from post-op to 2-years.

Results

Fifty patients (mean age: 12.5 ± 1.3yrs) were analyzed over a mean of 27.7 months. GM was positively correlated with concave vertebra height growth (r = 0.57, p < 0.001), 3D spine length growth (r = 0.36, p = 0.008), and decreased convex disc height (r = − 0.42, p = 0.002). High modulators (patients experiencing GM > 10°) experienced an additional 1.6 mm (229% increase) of mean concave vertebra growth during study period compared to the Poor Modulators (GM < − 10°) group, (2.3 vs. 0.7 mm, p = 0.039), while convex vertebra height growth was similar (1.3 vs. 1.4 mm, p = 0.91).

Conclusion

When successful, VBT enables asymmetric vertebra body growth, leading to continued postoperative coronal angulation correction (GM). A strong GM response is correlated with concave vertebral body height growth and overall instrumented spine growth. A poor GM response is associated with an increase in convex disc height (suspected tether rupture). Future studies will investigate the patient and technique-specific factors that influence increased growth remodeling.
Spatial transcriptomics: a new frontier in cancer research
Authors: Huang Siyuan , Ouyang Linkun , Tang Junjie , Qian Kun , Chen Xuanwei , Xu Zijie , Ming Jingsi , Xi Ruibin
Published: June 2024

Tumor research is a fundamental focus of medical science, yet the intrinsic heterogeneity and complexity of tumors present challenges in understanding their biological mechanisms of initiation, progression, and metastasis. Recent advancements in single-cell transcriptomic sequencing have revolutionized the way researchers explore tumor biology by providing unprecedented resolution. However, a key limitation of single-cell sequencing is the loss of spatial information during single-cell preparation. Spatial transcriptomics (ST) emerges as a cutting-edge technology in tumor research that preserves the spatial information of RNA transcripts, thereby facilitating a deeper understanding of the tumor heterogeneity, the intricate interplay between tumor cells and the tumor microenvironment. This review systematically introduces ST technologies and summarizes their latest applications in tumor research. Furthermore, we provide a thorough overview of the bioinformatics analysis workflow for ST data and offer an online tutorial (https://github.com/SiyuanHuang1/ST_Analysis_Handbook). Lastly, we discuss the potential future directions of ST. We believe that ST will become a powerful tool in unraveling tumor biology and offer new insights for effective treatment and precision medicine in oncology.