bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Investigation of the Nano-Fin Effect (nFE) During Thin Film Evaporation From Nanopores Using Temperature Nano-Sensors
Authors: Shafer Juliet , Lee Jonghyun , Banerjee Debjyoti
Published: February 2024

Recent advances in micro/nano-fabrication has enabled the deployment of nanostructured surfaces, nanochannels, and nanoporous membranes for development of new generation thermal management devices with remarkable potential for heat transfer enhancement. Anomalous heat transfer has been reported in studies involving heaters with nanostructured surfaces. For example, nanofins with lower thermal conductivity values can cause higher levels of enhancement in heat flux values, especially during phase change (such as for boiling on heaters with nanostructured surfaces). In addition, confinement of fluid in nanopores can also result in anomalous properties. This is manifest in anomalous production curves during hydraulic fracturing operations in oil and gas applications. A transport model that resolves these conundrums is termed as the “nanoFin Effect (nFE)”. nFE is governed by interfacial phenomena, i.e., the formation of thermal impedances in parallel circuit configuration: (a) interfacial thermal resistance (also known as “Kapitza resistance”); (b) thermal capacitor; and (c) thermal diode (that form at the interface between each nanoparticle and the surface adsorbed thin-film of solvent molecules). nFE (i.e., primarily the interfacial thermal diode effect) also leads to preferential trapping of ions on the surface adsorbed thin film of solvent molecules leading to very high concentration gradients causing drastic reduction in corrosion.

The motivation of this study was to explore nFE during thin film evaporation from nanopores. The methods used in this study include mounting a nano-thermocouple array (also termed as Thin Film Thermocouples or “TFT”) on a hot plate and observing the transient response recorded by the TFT array when a small liquid droplet (of fixed mass or volume) is dispensed on to an isotropic AAO membrane containing nanopores. In this study, two different pore sizes were explored: 200 nm and 10 nm. The experiments were performed using acetone and isopropyl alcohol droplets for four different temperature settings of the heated membrane (containing the nanopores).
Multiphase Modeling of Droplet-Based 3D Printing: Predicting Printability, Resolution and Shape Fidelity in Additive Manufacturing Processes
Authors: Shah Rauf G. , Mohan Ram V.
Published: February 2024

The multi-phase flow during the evolution of droplets in the printing process is modeled using the Volume of Fluid (VOF) method. It involves solving basic Navier-Stokes and Continuity equations for an incompressible flow with two or more immiscible phases on a finite volume grid. An indicator function keeps track of the interfaces and calculates the surface tension forces. A grid independence study showed convergence, indicating the efficacy of the solutions. The computational model agreed well with the experimental data and captured the impact, spreading, and recoiling of the droplet impinging on a solid surface.

The computational model also validated the droplet’s interaction with hydrophilic surface for constant and dynamic contact angles. Relevant non-dimensional numbers (Re, We, Oh) are also considered to study the interplay of different forces associated with droplet impact on a solid surface. The final quality of the printing depends on the droplet dynamics associated with the deposition. This phenomenon is governed by body forces (Surface Tension, gravity), contact angle, dissipative forces due to motion, and material properties. Computational studies give an insight into the overall process performance and the final print quality for different process conditions and material properties.
Investigating Patterns of Donor and Recipient Sports of Talent Transfer Paralympians
Authors: Green Adeline , Mulcahy Rory , Fleischman David , MacDonald Luke , Kean Bridie
Published: February 2024

Talent transfer has enabled elite athletes to be successful in another sport, with great potential in para-sport. Previous research suggests that similarities between donor and recipient sports may facilitate talent transfer; however, this remains unclear in para-sport. This study investigated patterns between donor and recipient sports’ characteristics, identifying the impact on talent transfer in para-sport. An Australian case study utilizing secondary data of 38 Australian Paralympians who competed at the Paralympic Games from 2000 through 2020 was analyzed. Results demonstrated that similarities between sports were not significantly associated with successful talent transfers between Paralympic sports. Understanding patterns associated with successful Paralympic talent transfers offers a foundation of knowledge for designing and developing future talent-transfer pathways and research. Based on this study, it is recommended that sport administrators and practitioners explore greater opportunity for talent transfer in para-sport, rather than limiting talent-transfer opportunities based on athletes’ donor sports.
Fostering Motivation in Physical Education to Promote Learning and Achievement
Authors: Chen Senlin , Solmon Melinda
Published: February 2024

Research exploring motivation has yielded a robust body of evidence to guide efforts to improve teaching and learning in physical education (PE). We begin by defining motivation and achievement within the context of PE. Given the extensive and diverse motivational perspectives, we purposely selected three widely studied theories in PE, achievement goal theory, self-determination theory, and interest theory, as the focus of our review. These theories have guided many investigations and the development of pedagogical practice over the past 3 decades. We elaborate on these theories and then synthesize recently published intervention studies to provide an interpretive analysis of the literature. This analysis has enabled us to identify gaps that need to be addressed in future research and efforts to improve practice. We conclude with the proposition calling for innovative, theory-driven, evidence-based research and practice to foster adaptive student motivation for optimal educational, behavioral, and health outcomes in K–12 PE.
The role of private healthcare sector actors in health service delivery and financing policy processes in low-and middle-income countries: a scoping review
Authors: Sriram Veena , Yilmaz Volkan , Kaur Simran , Andres Chloei , Cheng Michael , Meessen Bruno
Published: February 2024

The expansion of the private healthcare sector in some low-income and middle-income countries (LMICs) has raised key questions and debates regarding the governance of this sector, and the role of actors representing the sector in policy processes. Research on the role played by this sector, understood here as private hospitals, pharmacies and insurance companies, remains underdeveloped in the literature. In this paper, we present the results of a scoping review focused on synthesising scholarship on the role of private healthcare sector actors in health policy processes pertaining to health service delivery and financing in LMICs. We explore the role of organisations or groups—for example, individual companies, corporations or interest groups—representing healthcare sector actors, and use a conceptual framework of institutions, ideas, interests and networks to guide our analysis. The screening process resulted in 15 papers identified for data extraction. We found that the literature in this domain is highly interdisciplinary but nascent, with largely descriptive work and undertheorisation of policy process dynamics. Many studies described institutional mechanisms enabling private sector participation in decision-making in generic terms. Some studies reported competing institutional frameworks for particular policy areas (eg, commerce compared with health in the context of medical tourism). Private healthcare actors showed considerable heterogeneity in their organisation. Papers also referred to a range of strategies used by these actors. Finally, policy outcomes described in the cases were highly context specific and dependent on the interaction between institutions, interests, ideas and networks. Overall, our analysis suggests that the role of private healthcare actors in health policy processes in LMICs, particularly emerging industries such as hospitals, holds key insights that will be crucial to understanding and managing their role in expanding health service access.
BioMEL: a translational research biobank of melanocytic lesions and melanoma
Authors: Helkkula Teo , Christensen Gustav , Ingvar Christian , Isaksson Karolin , Harbst Katja , Persson Bertil , Ingvar Åsa , Hafström Anna , Carneiro Ana , Gaspar Viktoria , Jönsson Göran , Nielsen Kari
Published: February 2024

Introduction

Diagnosing invasive cutaneous melanoma (CM) can be challenging due to subjectivity in distinguishing equivocal nevi, melanoma in situ and thin CMs. The underlying molecular mechanisms of progression from nevus to melanoma must be better understood. Identifying biomarkers for treatment response, diagnostics and prognostics is crucial. Using biomedical data from biobanks and population-based healthcare data, translational research can improve patient care by implementing evidence-based findings. The BioMEL biobank is a prospective, multicentre, large-scale biomedical database on equivocal nevi and all stages of primary melanoma to metastases. Its purpose is to serve as a translational resource, enabling researchers to uncover objective molecular, genotypic, phenotypic and structural differences in nevi and all stages of melanoma. The main objective is to leverage BioMEL to significantly improve diagnostics, prognostics and therapy outcomes of patients with melanoma.

Methods and analysis

The BioMEL biobank contains biological samples, epidemiological information and medical data from adult patients who receive routine care for melanoma. BioMEL is focused on primary and metastatic melanoma, but equivocal pigmented lesions such as clinically atypical nevi and melanoma in situ are also included. BioMEL data are gathered by questionnaires, blood sampling, tumour imaging, tissue sampling, medical records and histopathological reports.

Ethics and dissemination

The BioMEL biobank project is approved by the national Swedish Ethical Review Authority (Dnr. 2013/101, 2013/339, 2020/00469, 2021/01432 and 2022/02421-02). The datasets generated are not publicly available due to regulations related to the ethical review authority.

Trial registration number

NCT05446155.
What factors in the workplace enable success in antimicrobial stewardship in paediatric intensive care? An exploration of antimicrobial stewardship excellence through thematic analysis of appreciative inquiry interviews with healthcare staff
Authors: Roche Emma , Jones Alison , Plunkett Adrian
Published: February 2024

Objectives

Antimicrobial resistance (AMR) is a major global health problem. Efforts to mitigate AMR prioritise antimicrobial stewardship (AMS) interventions. These interventions typically focus on deficiencies in practice and providing negative or normative feedback. This approach may miss opportunities to learn from success. We aimed to identify factors that enable success in AMS practices in the paediatric intensive care unit (PICU) by analysing the data obtained from interviews with staff members who had achieved success in AMS.

Design

Qualitative study design using thematic analysis of appreciative inquiry interviews with healthcare staff.

Setting

31-bedded PICU in the UK between January 2017 and January 2018.

Participants

71 staff who had achieved success in AMS in the PICU.

Results

Six themes were identified: (1) cultural factors including psychological safety, leadership and positive attitude are important enablers for delivering good clinical care; (2) ergonomic design of the physical environment and ready availability of tools and resources are key elements to support good practice and decision-making; (3) expertise and support from members of the multidisciplinary team contribute to good care delivery; (4) clarity of verbal and written communication is important for sharing mental models and aims of care within the clinical team; (5) a range of intrinsic factors influences the performance of individual HCPs, including organisation skill, fear of failure, response to positive reinforcement and empathetic considerations towards peers; (6) good clinical care is underpinned by a sound domain knowledge, which can be acquired through training, mentorship and experience.

Conclusion

The insights gained in this study originate from frontline staff who were interviewed about successful work-as-done. This strengths-based approach is an understudied area of healthcare, and therefore offers authentic intelligence which may be leveraged to effect tangible improvement changes. The methodology is not limited to AMS and could be applied to a wide range of healthcare settings.
Experiences and perspectives related to shared decision-making among outpatients with degenerative joint disease in Taiwan: a qualitative study
Authors: Chuang Yeu-Hui , Wang Chih-Chien , Hsiao Chih-Yin , Lu Chien-Yeh , Wu Jeng‑Cheng , Hou Wen-Hsuan
Published: February 2024

Objectives

Various treatment options are available for degenerative joint disease (DJD). During clinical visits, patients and clinicians collaboratively make decisions regarding the optimal treatment for DJD; this is the essence of shared decision-making (SDM). Here, we collated and assessed the SDM-related experiences and perspectives of outpatients with DJD in Taiwan.

Design

In-depth interviews and thematic analysis.

Setting

Primary care clinics of a regional teaching hospital in Taiwan, October 2021–May 2022.

Participants

21 outpatients with at least three visits for DJD and who were aware of SDM.

Results

Four main themes emerged in this study: first, equipping themselves with knowledge: outpatients obtained disease-related and treatment-related knowledge in various ways—seeking relevant information online, discussing with family and friends, learning from their own experiences or learning from professionals. Second, shared or not shared: physicians had different patterns for communicating with patients, particularly when demonstrating authority, performing mutual discussion, respecting patient preferences or responding perfunctorily. Third, seldom saying no to physician-prescribed treatment plans during clinical visits: most patients respected physicians’ professionalism; however, some patients rejected physicians’ recommendations indirectly, whereas some responded depending on their disease prognosis. Fourth, whose call?—participants decided to accept or reject a treatment plan independently or by discussing it with their families or by obeying their physicians’ recommendations.

Conclusions

In general, patients with DJD sought reliable medical information from various sources before visiting doctors; however, when having a conversation with patients, physicians dominated the discussion on treatment options. The patient–physician interaction dynamics during the SDM process determined the final medical decision, which was in accordance with either patients’ original autonomy or physicians’ recommendations. To alleviate medical paternalism and physician dominance, patients should be empowered to engage in medical decision-making and share their opinions or concerns with their physicians. Family members should also be included in SDM.
Ceragenin-coated endotracheal tubes for the reduction of ventilator-associated pneumonia: a prospective, longitudinal, cross-over, interrupted time, implementation study protocol (CEASE VAP study)
Authors: Symonds Nicola E , Meng Eric X M , Boyd John Gordon , Boyd Tracy , Day Andrew , Hobbs Hailey , Maslove David M , Norman Patrick A , Semrau Joanna S , Sibley Stephanie , Muscedere John
Published: February 2024

Background

Critically ill patients are at high risk of acquiring ventilator-associated pneumonia (VAP), which occurs in approximately 20% of mechanically ventilated patients. VAP results either from aspiration of pathogen-contaminated oropharyngeal secretions or contaminated biofilms that form on endotracheal tubes (ETTs) after intubation. VAP results in increased duration of mechanical ventilation, increased intensive care unit and hospital length of stay, increased risk of death and increased healthcare costs. Because of its impact on patient outcomes and the healthcare system, VAP is regarded as an important patient safety issue and there is an urgent need for better evidence on the efficacy of prevention strategies. Modified ETTs that reduce aspiration of oropharyngeal secretions with subglottic secretion drainage or reduce the occurrence of biofilm with a coating of ceragenins (CSAs) are available for clinical use in Canada. In this implementation study, we will evaluate the efficacy of these two types of Health Canada-licensed ETTs on the occurrence of VAP, and impact on patient-centred outcomes.

Methods

In this ongoing, pragmatic, prospective, longitudinal, interrupted time, cross-over implementation study, we will compare the efficacy of a CSA-coated ETT (CeraShield N8 Pharma) with an ETT with subglottic secretion drainage (Taper Guard, Covidien). The study periods consist of four alternating time periods of 11 or 12 weeks or a total of 23 weeks for each ETT. All patients intubated with the study ETT in each time period will be included in an intention-to-treat analysis. Outcomes will include VAP incidence, mortality and health services utilisation including antibiotic use and length of stay.

Ethics and dissemination

This study has been approved by the Health Sciences Research Ethics Board at Queen’s University. The results of this study will be actively disseminated through manuscript publication and conference presentations.

Trial registration number

NCT05761613.
Cosmic-ray boosted dark matter in Xe-based direct detection experiments
Authors: Maity Tarak Nath , Laha Ranjan
Published: February 2024

LUX-ZEPLIN (LZ) collaboration has achieved the strongest constraint on weak-scale dark matter (DM)-nucleon spin-independent (SI) scattering cross section in a large region of parameter space. In this paper, we take a complementary approach and study the prospect of detecting cosmic-ray boosted sub-GeV DM in LZ. In the absence of a signal for DM, we improve upon the previous constraints by a factor of $$\sim 2$$

∼
2

using the LZ result for some regions of the parameter space. We also show that upcoming XENONnT and future Darwin experiments will be sensitive to cross sections smaller by factors of $$\sim 3$$

∼
3

and $$\sim 10$$

∼
10

compared to the current LZ limit, respectively.
Lebesgue points of functions in the complex Sobolev space
Authors: Vigny Gabriel , Vu Duc-Viet
Published: February 2024

Let [Formula: see text] be a function in the complex Sobolev space [Formula: see text], where [Formula: see text] is an open subset in [Formula: see text]. We show that the complement of the set of Lebesgue points of [Formula: see text] is pluripolar. The key ingredient in our approach is to show that [Formula: see text] for [Formula: see text] is locally bounded from above by a plurisubharmonic function.
Pair production of scalar fields near the accelerating Reissner–Nordstrom black hole
Authors: Siahaan Haryanto M.
Published: February 2024

Accelerating space–time is commonly utilized to depict the formation of pairs of black holes. This paper studies the pair production of charged scalar particles in the vicinity of an accelerating Reissner–Nordstrom black hole’s event horizon. Essentially, we aim to investigate the production of particles that accompanies the creation of black hole pairs. In a near extremal state, this pair production manifests as a combination of Hawking radiation and the Schwinger effect. Under extremal conditions, only the latter effect remains. Furthermore, this research explores the influence of the acceleration parameter on the absorption cross section of scalar particles in proximity to the black hole.
Distributed Event-Triggered Algorithm with Network Independent Step-Size for Constraint-Coupled Optimization Problems
Authors: Chen Baitong , Yang Jianhua , Lu Wei , Pedrycz W. , Sun Changhai
Published: February 2024

In this study, we introduce a distributed algorithm that is specifically designed to address optimization problems featuring a decomposable objective function and equality constraints. To minimize the amount of communication required, we incorporate an event-triggered mechanism that enables information exchange only when variable values exceed predefined thresholds. Importantly, our proposed algorithm possesses a distinctive characteristic where the determination of step size is solely based on the properties of the objective function, regardless of the structure of the communication network. Even in situations where changes occur in the network structure, our algorithm remains valid without necessitating any updates to its step size. Assuming strong convexity and smoothness in local objective functions, along with appropriate event-triggered thresholds, our algorithm achieves a convergence rate that is linear. Several numerical experiments provide evidence supporting the effectiveness and superiority of our proposed approach.
The first and second crossings in low-lying rotational bands of Er isotopes: A systematic study
Authors: Shayestefar Mohadese , Kardan Azam
Published: February 2024

Essential understanding about the structure of rotating nuclei has come from the study on the band crossings which are often associated with the alignment of a pair of quasiparticles. In this work, the first and second discontinuities in the low-lying rotational bands of [Formula: see text]Er nuclei have been investigated systematically, for the first time, within the paired cranked Nilsson–Strutinsky–Bogoliubov (CNSB) framework. Our calculations show these irregularities are related to the high-j intruder [Formula: see text] neutron and [Formula: see text] proton alignment, which is consistent with the previous studies within the cranked shell model (CSM) approach. A comparison was made with the experimental data where available, and good agreement is observed.
Nonlinear Vibration and Stiffness Characteristics Analysis of Maglev Train Based on Cubic Displacement Control
Authors: Cao Shuaikang , Liu Canchang , Wang Can , Sun Liang , Wang Shuai
Published: February 2024

A control strategy combining cubic displacement feedback nonlinear control and proportional-differential (PD) linear control is used to control the vibration performance of the maglev system. The maglev system is divided into positive stiffness maglev system, quasi-zero stiffness maglev system and negative stiffness maglev system according to the linear stiffness value of maglev system. Firstly, an improved multi-scale method is used to analyze the vibration characteristics of suspension in the positive stiffness state of the maglev system. Secondly, the influence of control parameters on train vibration amplitude and vibration center displacement under quasi-zero stiffness is studied. Finally, the vibration characteristics of the train when the maglev system is in negative stiffness are analyzed by numerical simulation. The maglev system exhibits the worst vibration performance under negative stiffness compared with positive stiffness and quasi-zero stiffness. The suspension frame is easy to enter the chaotic motion state, and its vibration center is easy to deviate from the equilibrium position and produce large displacement when the maglev system is in the negative stiffness state. The control results show that the control strategy combining the cubic displacement feedback nonlinear control with the PD linear control can make the maglev system exhibit better vibration characteristics under positive and quasi-zero stiffness.
Coherent Upper Conditional Expectations Defined by Fractal Measures and the Probabilistic Representation of Quantum States
Authors: Doria Serena , Selmi Bilel
Published: February 2024

Coherent upper conditional expectations are introduced through fractal outer measures to consider conditioning events that have zero probability with respect to the initial probability, as fractal sets. The model can be applied when the probability is concentrated on sets with zero Lebesgue measure and the density does not exist. It can be applied to provide a probabilistic representation of quantum states in cases where the density probability is absent. However, when the density probability exists, the two probabilistic representations align. This new approach can have intriguing data analytics applications, ranging from addressing probabilistic challenges in quantum state representation in quantum physics and managing extreme events in financial markets to capturing rare occurrences with significant implications in biomedical research.
Adaptive Attitude Control for QUAVs Using Event-Triggered Control and Optimized Quantized Communication with Prescribed Performance
Authors: Sun Yunqiang
Published: February 2024

This paper proposes an appointed-time prescribed performance adaptive attitude controller with an event-triggered (ET) mechanism and optimized quantized communication for quadrotor unmanned aerial vehicles (QUAVs) in an uncertain environment. An appointed-time prescribed performance function is first designed to improve the convergence performance of attitude tracking errors considering different convergence time requirements and the convergence rate. In addition, to reduce the communication burden, a switching threshold ET mechanism and an optimized hybrid quantizer are adopted by using the K-means method. The ET mechanism not only involves the fixed threshold but also refers to the change rate of calculated control law flexibly and predictively. The optimized hybrid quantizer creatively classifies signals by their value and maximizes the advantage of each quantization method to reduce excessive quantization errors. It is shown that the proposed control scheme ensures that the closed-loop signals are uniformly ultimately bounded according to the Lyapunov method. Finally, the simulation experiments are implemented on a QUAV to demonstrate the effectiveness of the presented control scheme.
NUMERICAL SIMULATION OF BLOOD FLOW THROUGH NORMAL AND STENOTIC CAROTID ARTERY
Authors: LI GUOJIE , CHEN BIN , WANG XINKAI , NIE PENG
Published: February 2024

The carotid artery sinus is often the site of vascular diseases such as atherosclerosis, which may induce cerebral stroke. Individual differences in the carotid artery can lead to different possibilities for atherosclerosis, and the different hemodynamics of the carotid artery with stenosis can help to reveal the occurrence of atherosclerosis. In this work, the hemodynamics in the patient-specific carotid bifurcation are studied by using the modified Herschel–Bulkley model. Calculations show that the helical flow is presented in the healthy carotid sinus and disappears in the stenosed carotid sinus. The disappearance of helical flow in the carotid artery enhances the risk of atherosclerosis. The atherosclerotic lesion is more prone to occur in the distal portion of the carotid sinus than in the proximal portion where the oscillatory shear index (OSI) is also high. The presence of stenosis makes the atherosclerotic lesion develop toward the proximal portion. The disappearance of helical flow in the carotid artery is an important risk signal for the occurrence of atherosclerosis.
AN OPTIMIZED RESIDUAL NETWORK FOR EMOTION RECOGNITION BASED ON A MULTI-FEATURES FUSION TECHNOLOGY AND ELECTROENCEPHALOGRAPHY SIGNALS
Authors: JIN ENMAN , WANG QINYONG , CHEN PEIYU , PAN LU
Published: February 2024

Presently, a significant number of students enrolled in colleges and universities encounter mental health challenges, including academic stress and difficulties in interpersonal relationships. These factors contribute to a decline in mental well-being and necessitate prompt intervention and assistance. The automation of mental health identification in students greatly benefits from the implementation of intelligent emotion recognition systems. Through the analysis of students’ emotional states, educational institutions can gain a deeper understanding of students’ mental well-being, enabling them to promptly identify and address any issues and offer more complete care and support to students. This paper proposes a method for automated emotion recognition that is driven by data. The method is developed based on the provided background information. The proposed approach incorporates a feature fusion mechanism and an attention mechanism into the ResNet-34 model. This integration enhances the model’s capability to analyze intricate details and subsequently improves its classification performance when applied to electroencephalography (EEG) signals. This paper introduces several key innovations. First, an optimization technique is applied to the input component of the model, enabling multifeature fusion. Second, an attention mechanism is incorporated after the residual network module, enabling the model to prioritize parts that contribute to classification and enhance feature extraction. Finally, the network parameters are optimized using both softmax loss and center loss functions. The findings from the analysis of the sentiment EEG public data SJTU Emotion EEG Dataset (SEED) indicate that the proposed sentiment recognition approach not only enhances the classification performance of the model on the sentiment EEG data but also improves the stability of the results. This paper presents a novel approach that enables automatic and efficient recognition of students’ emotions on commonly used platforms. The findings of this study hold significant implications for mental health assessment and detection in real-life production settings, offering substantial reference value in this domain.
ENHANCING TONGUE REGION SEGMENTATION THROUGH SELF-ATTENTION AND TRANSFORMER BASED
Authors: SONG YIHUA , LI CAN , ZHANG XIA , LIU ZHEN , SONG NINGNING , ZHOU ZUOJIAN
Published: February 2024

As an essential component of traditional Chinese medicine diagnosis, tongue diagnosis has faced limitations in clinical practice due to its subjectivity and reliance on the experience of physicians. Recent advancements in deep learning techniques have opened new possibilities for the automated analysis and diagnosis of tongue images. In this paper, we collected 500 tongue images from various patients. These images were initially preprocessed and annotated, resulting in the dataset used for this experiment. This project is based on the previously proposed segmentation method using Harnessing Self-Attention and Transformer, which is divided into three key stages: feature extraction, feature fusion, and segmentation prediction. By organically combining these three key stages, our tongue region segmentation model is better equipped to handle complex tongue images and provides accurate segmentation results. The segmentation DICE coefficient reaches 0.953, which is of significant importance for the automation and objectivity of tongue diagnosis in traditional Chinese medicine.
BLADDER WALL SEGMENTATION AND CANCER ASSESSMENT USING ATTENTION U-NET WITH ADVERSARIAL MECHANISM AND TWO-LAYER LEVEL SETS
Authors: LUAN HAIYANG , MA BENTENG , SHI ZHANGZHEN , LIU YU
Published: February 2024

Improving the segmentation of bladder mucosa and serosa is a significant advancement for enhancing the diagnosis and treatment of bladder cancer, leading to a better understanding of the disease and improved decision-making accuracy for physicians. However, the primary aim of multi-region segmentation in bladder cancer is to facilitate the staging of the disease, specifically distinguishing between muscle-invasive bladder cancer and nonmuscle-invasive bladder cancer. This differentiation is crucial as urothelial cancers become increasingly aggressive as they infiltrate and penetrate the bladder wall. Yet, none of the existing segmentation methods have effectively addressed this critical issue. In our study, an attention U-Net with an adversarial mechanism is proposed, combining with two-layer level sets, to address these challenges. The attention U-Net with an adversarial mechanism captures the inner and outer boundaries of the bladder, including the tumor. Moreover, a two-layer level sets is proposed to further catching subpixels of the bladder wall and gets results with Dice score of 0.91. By calculating the convexity of the two-level sets, the two types of tumor are successfully classified, achieving an experimental accuracy of 0.88. This innovative approach significantly contributes to the field of bladder cancer segmentation and classification, providing valuable insights for improved diagnosis and treatment strategies.
APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF THYROID CANCER WITH ENHANCED COMPUTED TOMOGRAPHY
Authors: HAN NA , FAN JINRUI , CHEN DONGWEI , WANG YAPENG
Published: February 2024

Thyroid Cancer (TC) is a common malignant tumor, head and neck in the incidence of malignant tumors in the seventh, ranked fourth in the incidence in women. There are several methods to diagnose and recognize TC, such as ultrasonic, computed tomography (CT) and other means. While, it is an important role for CT examination in the diagnosis of TC, because it has the characteristics of objectivity, repeatability, and multi-dimensional imaging, and can clearly understand the scope and spatial characteristics of the lesion. CT has unique advantages in showing lymph nodes and distant metastases, such as coarse-walled or thick-walled ring calcification. The early diagnosis of TC mainly relies on manual labor, which is very inefficient. With the continuous development of information science, the construction of TC diagnosis and recognition models based on artificial intelligence (AI) has gradually become a research hotspot. At present, research on AI-based thyroid screening models mainly focuses on four aspects: first, thyroid region segmentation and image denoising based on image-omics; second, the establishment of a high-precision TC risk prediction model based on multi-omics data; third, screening of biomarkers of TC for clinical diagnosis; fourth, establish the early screening model of TC based on AI. This paper reviews the research status of the AI-based thyroid screening model based on the above four aspects. In addition, this paper also summarizes the main challenges faced by the current AI -based TC recognition and detection model and it proposes a new research idea for the future TC early screening research based on enhanced CT.
MSDRA-NET: A MULTI-SCALE ATTENTION-GUIDED NETWORK FOR MAGNETIC RESONANCE IMAGE RESTORATION
Authors: YOU XUEXIAO , CAO NING , WANG WEI
Published: February 2024

The acquisition and transmission of magnetic resonance (MR) images are frequently affected by random noise pollution, which hampers the diagnosis of diseases by doctors or automated systems. Hence, the search for advanced denoising methods is of great research interest, particularly in magnetic resonance imaging (MRI) denoising models, which are based on deep learning networks and achieve satisfactory results. However, the mining of noisy contextual information and effective information-guided transfer are often neglected in the denoising process, which leads to poor extraction of information at different scales, and poor retention of details. This greatly hinders the further development of MR image denoising methods. Here, we propose a denoising method, MSDRA-Net, for the mining and exploitation of different hierarchical noise features and construct a multi-scale dilated residual (MSDR) structure to transfer and retain noise information at different levels across the layer. Next, a contextual guidance attention (CGA) module guides and transfers contextual information, utilizing the features learnt from different layers of the model as weights. A reconstruction refinement block (RRB) is utilized to construct clean images from the obtained noise bias and the given noisy images. Experiments on simulated and clinical MRI data validated the effectiveness of our method, which demonstrated a superior performance compared to several state-of-the-art methods.
Reduction of photodynamic damage of blood vessels in the protected region by (–)-epigallocatechin gallate
Authors: Chen Tianlong , Shen Yi , Lin Li , Lin Huiyun , Song Xuejiao , Chen Defu , Li Buhong
Published: February 2024

Photodynamic therapy (PDT) has been increasingly used in the clinical treatment of neoplastic, inflammatory and infectious skin diseases. However, the generation of reactive oxygen species (ROS) may induce undesired side effects in normal tissue surrounding the treatment lesion, which is a big challenge for the clinical application of PDT. To date, (–)-Epigallocatechin gallate (EGCG) has been widely proposed as an antiangiogenic and antitumor agent for the protection of normal tissue from ROS-mediated oxidative damage. This study evaluates the regulation ability of EGCG for photodynamic damage of blood vessels during hematoporphyrin monomethyl ether (Hemoporfin)-mediated PDT. The quenching rate constants of EGCG for the triplet-state Hemoporfin and photosensitized 1O2 generation are determined to be [Formula: see text] M−1S−1 and [Formula: see text] M−1S−1, respectively. The vasoconstriction of blood vessels in the protected region treated with EGCG hydrogel after PDT is lower than that of the control region treated with pure hydrogel, suggesting an efficiently reduced photodamage of Hemoporfin for blood vessels treated with EGCG. This study indicates that EGCG is an efficient quencher for triplet-state Hemoporfin and 1O2, and EGCG could be potentially used to reduce the undesired photodamage of normal tissue in clinical PDT.
LiDAR Depth Cluster Active Detection and Localization for a UAV with Partial Information Loss in GNSS
Authors: Deng Chencheng , Wang Shoukun , Wang Junzheng , Xu Yongkang , Chen Zhihua
Published: February 2024

Accurate and robust state estimation is critical for the heterogeneous agent systems, particularly when considering the challenges posed by Unmanned Aerial Vehicles (UAVs) operating in perceptually-degraded environments where access to Global Navigation Satellite System (GNSS) signals is lost. We can, however, actively increase the amount of optimal localization available to UAV by augmenting them with a small number of more expensive, but less resource-constrained, heterogeneous agents. In this paper, we propose a novel detection, localization, and tracking framework for UAV based on LiDAR. First, we present an innovative approach that integrates range image projection and Depth Cluster of LiDAR point clouds with UAV technology. Subsequently, we devise a multidimensional feature probability detection and tracking evaluation function, enabling the detection, estimation, and active tracking of UAV movement. Finally, we conduct comprehensive experiments using heterogeneous agent systems to assess the effectiveness and robustness of the developed framework. The experiments reveal a minimum 20% reduction in running time and an average localization accuracy error of 1.98[Formula: see text]cm.
Derivation of discrete analog of Breeden–Litzenberger relation for risk-neutral density
Authors: Kumar Abhimanyu
Published: February 2024

This short paper derives a formula for the risk-neutral density function at discrete points over the strike range using Abel’s summation formula in terms of the arbitrage-free call price function in a European option contingent. The estimate of risk-neutral density is crucial from a risk management perspective and other crucial assessments.
Community Resilience to Global Climate Change-Related Disasters
Authors: Karunarathne Ananda Y.
Published: February 2024

Global climate change-related disasters have dramatically escalated throughout the world. Among a range of unexpected calamities, hydrometeorological disasters have come to the fore. This is because, torrential rains, mass flooding events, cyclones, storms, landslides (wet), droughts, heat waves and cold waves are very predominant. In addition to that, global warming, wild/forest fires, and melting down of glacier/ice covers of polar and nearby regions are very serious. This is because community resilience and ecological resilience are dramatically eroding and challenging. The existing situation is very serious due to the inherent nexus between community resilience and ecological resilience. In this context, the study aims to conduct a systematic literature review on the extant literature of resilience and to identify the cardinal variables that are appropriate to measure community resilience to the ongoing global climate change scenarios. Methodologically, the study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model to filter the appropriate literature (followed by the identifying, screening, and including process). The study considered the manuscripts and reports published in English. Accordingly, inappropriate literature works were excluded. Finally, the study considered 33 literature works on resilience out of 117. According to the analysis and results revealed, six cardinal resiliency aspects were identified, namely, Socioeconomic resilience, Demographic resilience, Health resilience, Sociopolitical resilience, Social capital resilience, and Ecological resilience. These key resiliency aspects will extremely be important for securing the lives and livelihoods of the human population from the ongoing climate change impasses/crisis.
Causal Probabilistic Spatio-Temporal Fusion Transformers in Two-Sided Ride-Hailing Markets
Authors: Wan Shixiang , Luo Shikai , Zhu Hongtu
Published: February 2024

In this work, we address the complex problem of multi-objective time series forecasting with a focus on predicting interdependent targets such as supply and demand in ride-hailing services. Traditional machine learning techniques approach the targets independently, while deep learning strategies may use joint learning with shared representations, both neglecting inter-target causal relationships and potentially compromising the models’ generalization capabilities. Our novel
CausalTrans
model introduces a framework to define and leverage the temporal causal interplay between supply and demand, incorporating both temporal and spatial causality into the forecasting process. Additionally, we enhance computational efficiency by introducing an innovative fast attention mechanism that reduces the time complexity from quadratic to linear without sacrificing performance. Our comprehensive experiments show that
CausalTrans
significantly surpasses contemporary forecasting methods, achieving up to a 15% reduction in error, thus setting a new benchmark in the field.
Multi-Content Interaction Network for Few-Shot Segmentation
Authors: Chen Hao , Yu Yunlong , Dong Yonghan , Lu Zheming , Li Yingming , Zhang Zhongfei
Published: February 2024

Few-Shot Segmentation (FSS) poses significant challenges due to limited support images and large intra-class appearance discrepancies. Most existing approaches focus on aligning the support-query correlations from the same layer of the frozen backbone while neglecting the bias between different tasks and different layers. In this article, we propose a Multi-Content Interaction Network (MCINet) to remedy these issues by fully exploiting and interacting with the different contextual information contained in distinct branches. Specifically, MCINet improves FSS from three perspectives: (1) boosting the query representations through incorporating the independent information from another learnable branch into the features from the frozen backbone, (2) enhancing the support-query correlations by exploiting both the same-layer and adjacent-layer features, and (3) refining the predicted results with a multi-scale mask prediction strategy. Experiments on three benchmarks demonstrate that our approach reaches state-of-the-art performances and outperforms the best competitors with many desirable advantages, especially on the challenging COCO dataset. Code will be released on GitHub (
https://github.com/chenhao-zju/mcinet
).
PMAlloc: A Holistic Approach to Improving Persistent Memory Allocation
Authors: Dang Zheng , He Shuibing , Zhang Xuechen , Hong Peiyi , Li Zhenxin , Chen Xinyu , Song Haozhe , Sun Xian-He , Chen Gang
Published: February 2024

Persistent memory allocation is a fundamental building block for developing high-performance and in-memory applications. Existing persistent memory allocators suffer from many performance issues. First, they may introduce repeated cache line flushes and small random accesses in persistent memory for their poor heap metadata management. Second, they use static slab segregation resulting in a dramatic increase in memory consumption when allocation request size is changed. Third, they are not aware of NUMA effect, leading to remote persistent memory accesses in memory allocation and deallocation processes. In this article, we design a novel allocator, named PMAlloc, to solve the above issues simultaneously. (1) PMAlloc eliminates cache line reflushes by mapping contiguous data blocks in slabs to interleaved metadata entries stored in different cache lines. (2) It writes small metadata units to a persistent bookkeeping log in a sequential pattern to remove random heap metadata accesses in persistent memory. (3) Instead of using static slab segregation, it supports slab morphing, which allows slabs to be transformed between size classes to significantly improve slab usage. (4) It uses a local-first allocation policy to avoid allocating remote memory blocks. And it supports a two-phase deallocation mechanism including recording and synchronization to minimize the number of remote memory access in the deallocation. PMAlloc is complementary to the existing consistency models. Results on six benchmarks demonstrate that PMAlloc improves the performance of state-of-the-art persistent memory allocators by up to 6.4× and 57× for small and large allocations, respectively. PMAlloc with NUMA optimizations brings a 2.9× speedup in multi-socket evaluation and is up to 36× faster than other persistent memory allocators. Using PMAlloc reduces memory usage by up to 57.8%. Besides, we integrate PMAlloc in a persistent FPTree. Compared to the state-of-the-art allocators, PMAlloc improves the performance of this application by up to 3.1×.
A Taxonomy for Learning with Perturbation and Algorithms
Authors: Yao Rujing , Wu Ou
Published: February 2024

Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert low weights on samples which are likely to be noisy or quite hard. This study summarizes another less-explored strategy, namely, perturbation. Various incarnations of perturbation have been utilized but it has not been explicitly revealed. Learning with perturbation is called perturbation learning and a systematic taxonomy is constructed for it in this study. In our taxonomy, learning with perturbation is divided on the basis of the perturbation targets, directions, inference manners, and granularity levels. Many existing learning algorithms including some classical ones can be understood with the constructed taxonomy. Alternatively, these algorithms share the same component, namely, perturbation in their procedures. Furthermore, a family of new learning algorithms can be obtained by varying existing learning algorithms with our taxonomy. Specifically, three concrete new learning algorithms are proposed for robust machine learning. Extensive experiments on image classification and text sentiment analysis verify the effectiveness of the three new algorithms. Learning with perturbation can also be used in other various learning scenarios, such as imbalanced learning, clustering, regression, and so on.