bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dualities for characteristic polynomial averages of complex symmetric and self dual non-Hermitian random matrices
Authors: Forrester Peter J 
Published: January 2025

Abstract
Ensembles of complex symmetric, and complex self dual random matrices are known to exhibit local statistical properties distinct from those of the non-Hermitian Ginibre ensembles. On the other hand, in distinction to the latter, the joint eigenvalue probability density function of these two ensembles are not known. Nonetheless, as carried out in the recent works of Liu and Zhang, Akemann et al and Kulkarni et al by considering averages of products of characteristic polynomials, analytic progress can be made. Here we show that an approach based on the theory of zonal polynomials provides an alternative to the diffusion operator or supersymmetric Grassmann integrations methods of these works. It has the advantage of not being restricted to a Gaussian unitary invariant measure on the matrix spaces. To illustrate this, as an extension, we consider averages of products and powers of characteristic polynomials for complex symmetric, and complex self dual random matrices subject to a spherical measure. In the case of powers, when comparing against the corresponding real Ginibre, respectively quaternion Ginibre averages with a spherical measure, one finds the qualitative feature of a decreasing (increasing) profile as the magnitude of the argument of the characteristic polynomial increases. This is analogous to the findings of the second two of the cited works in the Gaussian case.
Electrospun robust, biodegradable, bioactive, and nanostructured sutures to accelerate the chronic wound healing
Authors: Li Yiran , Xu Hongxing , Zhao Wenwen , Zhang Li , Wu Shaohua 
Published: January 2025

Abstract
The design and development of advanced surgical sutures with appropriate structure and abundant bio-functions are urgently required for the chronic wound closure and treatment. In this study, an integrated technique routine combining modified electrospinning with hot stretching process was proposed and implemented to fabricate poly(L-lactic acid) (PLLA) nanofiber sutures, and the Salvia miltiorrhiza Bunge-Radix Puerariae herbal compound (SRHC) was encapsulated into PLLA nanofibers during the electrospinning process to enrich the biofunction of as-generated sutures. All the PLLA sutures loading without or with SRHC were found to exhibit bead-free and highly-aligned nanofiber structure. The addition of SRHC was found to have no significant influences on the fiber morphology, diameter, and the crystallinity of as-prepared PLLA sutures. Importantly, all the SRHC-contained PLLA nanofiber sutures possessed excellent tensile and knot strength, which were of significant importance for the surgical suture applications. Besides, the antioxidant and anti-inflammatory properties of these sutures obviously enhanced with the increasing of SRHC concentration. Furthermore, the in vitro cell tests illustrated that the high fiber orientation of the sutures was able to efficiently induce the human dermal fibroblasts (HDFs) to migrate in a rapid manner, and the sutures loaded with high content of SRHC could significantly promote the attachment and proliferation of HDFs in comparison. The in vivo diabetic mouse model experiments revealed that all the as-developed PLLA sutures could effectively close the wound, but the PLLA sutures containing high content of SRHC could dramatically promote the wound healing with high quality by shortening the healing time, improving the collagen deposition, neovascularization, and the regeneration of hair follicles, especially compared with commercial polyester (PET) suture. This study offers a simple and easily-handling strategy to develop robust, biodegradable, bioactive, and nanostructured PLLA sutures, which shows huge potential for the treatment of hard-to-heal diabetic wounds.
A highly polarization-sensitive near-infrared photodetector based on two-dimensional germanane/α-CdS heterostructure
Authors: Chen Zhengwang , Linghu Jiajun , Zhang Qiang , Che Junwei , Wang Xuezhi , Wei Songrui 
Published: January 2025

Abstract
Near-infrared (NIR) photodetectors have been widely used in imaging, remote sensing, communications, and medical fields. Emerging trends in the next-generation NIR photodetectors emphasize small size, low power consumption, and high polarization-sensitivity, thus demanding novel material compositions. Based on first-principles calculations, this paper proposes the two-dimensional Germanane/α-CdS vertically stacked van der Waals heterostructure as a polarization-sensitive NIR photodetector with superior performance. This structure exhibits thermodynamic stability at ambient temperatures, a type II band alignment, a tunable sensitive wavelength spanning 849–2296 nm, strong photocurrent and anisotropy, and remarkable polarization sensitivity with an extinction ratio of 6.74 under a nominal bias voltage of 0.2 V.
Towards wafer-scale 2D material sensors
Authors: Steeneken Peter G , Soikkeli Miika , Arpiainen Sanna , Rantala Arto , Jaaniso Raivo , Pezone Roberto , Vollebregt Sten , Lukas Sebastian , Kataria Satender , Houmes Maurits J A , Álvarez-Diduk Ruslan , Lee Kangho , Suryo Wasisto Hutomo , Anzinger Sebastian , Fueldner Marc , Verbiest Gerard J , Alijani Farbod , Hoon Shin Dong , Malic Ermin , van Rijn Richard , Nevanen Tarja K , Centeno Alba , Zurutuza Amaia , van der Zant Herre S J , Merkoçi Arben , Duesberg Georg S , Lemme Max C 
Published: January 2025

Abstract
The unique properties of two-dimensional (2D) materials bring great promise to improve sensor performance and realise novel sensing principles. However, to enable their high-volume production, wafer-scale processes that allow integration with electronic readout circuits need to be developed. In this perspective, we review recent progress in on-chip 2D material sensors, and compare their performance to the state-of-the-art, with a focus on results achieved in the Graphene Flagship programme. We discuss transfer-based and transfer-free production flows and routes for complementary metal-oxide-semiconductor integration and prototype development. Finally, we give an outlook on the future of 2D material sensors, and sketch a roadmap towards realising their industrial and societal impact.
Polariton manipulation using soliton angle in 2D moiré superlattices
Authors: Wang Kaiyuan , Lv Xinyu , Wen Lu , Yu Geliang , Li Zhiqiang , Wang Lei 
Published: January 2025

Abstract
We theoretically study the propagation and interference of plasmon polaritons in soliton superlattices in minimally twisted bilayer graphene (mTBG) with arbitrary soliton angle, which characterizes the local strain direction. We show that the band structures and local optical conductivity across the soliton evolve systematically with soliton angle, providing an important and tunable degree of freedom for tuning polariton-soliton interactions. Consequently, the plasmon polariton interference profile near single solitons can be significantly and continuously modulated through the soliton angle. Furthermore, our simulation reveals that the polariton interference profile can be designed and manipulated by changing the soliton angle profiles. Our study demonstrates that the modulating soliton angle profile in mTBG provides a valuable pathway to controlling polaritons for nanophotonic applications.
Grey relational analysis to identify the ideal combination of impact toughness and hardness for biodegradable quenched-tempered 42CrMo4 steel
Authors: Laxmi Bhagya , P K Jayashree , Rao U Sathish , Hegde Ananda , Sharma Sathyashankara 
Published: January 2025

Abstract
This work investigated the effect of different types of tempering on the hardness, impact toughness and microstructure of normalized and biodegradable-quenched 42CrMo4 steel. Specimens were normalised initially then hardened using green oil quenchants such as Pinnay oil, Karanja seed oil and blended oil (Equal volume percentage of Pinnay and Karanja seed oil) then subjected to single stage tempering process. Surface response methodology was used to obtain optimised conditions from single stage tempering process to carry out other types of the tempering processes such as multiple and step tempering processes. Grey relational analysis was used to select the tempering process that yield best balanced property condition. The two steps tempering process with hardness of 36 HRC, impact toughness of 56 Joules and with grey relational grade of 0.7 was considered as the process to get optimal mix of hardness and impact toughness in 42CrMo4 steel.
A systematic review of the impact of carbon reduction measures on human comfort: a human factors and ergonomics perspective
Authors: Fang Mingzhu , Kong Yue , Hu Jie , Wang Lingyu , Yan Xingyun , Guo Meixing , Wang Min , Wang Hairui , Liu Pinkuan 
Published: January 2025

Abstract
This systematic review uniquely explores the impact of carbon reduction measures on human comfort from a human factors and ergonomics perspective, focusing on human comfort. An analysis of 30 papers identifies key environmental factors requiring attention in contemporary carbon reduction strategies and their implications for human comfort. The results reveal a growing emphasis on thermal comfort and air quality in carbon reduction retrofits, while other environmental factors such as light and noise receive insufficient attention. While optimal ventilation and local environmental adjustments can yield positive outcomes, adverse effects like significant indoor temperature fluctuations and heightened carbon dioxide levels may arise from certain modifications. Moreover, the findings from the analysis of human comfort assessment methods indicate a lack of focus on human comfort evaluation in carbon emission reduction initiatives, with predominant reliance on subjective questionnaires. Most studies superficially mention human comfort assessment without delving into comprehensive research or employing systematic evaluation techniques. The findings indicate that although the implementation of carbon reduction measures is extensively covered in academic literature, the limited literature addressing human comfort assessment post-implementation implies a need for further comprehensive exploration and development within the field. This study furnishes researchers, practitioners, and policymakers with a comprehensive insight into the current landscape and proposes prospective research avenues within the domain.
Utilizing generative AI for crack detection in the marble industry
Authors: Vrochidou Eleni , Sidiropoulos George K , Ouzounis Athanasios G , Tsimperidis Ioannis , Kalpakis Vassilis , Stamkos Andreas , Papakostas George A 
Published: January 2025

Abstract
Recently, deep learning algorithms have achieved great accuracies in multiple computer vision tasks by using considerable amounts of annotated data. In the case of marble surface crack detection, the formation of such a dataset is challenging. Currently, there are limited marble crack-related datasets, obstructing the development of robust inspection models for automated industrial quality control. To resolve this problem, we propose a generative Artificial Intelligence (AI) approach, reporting the first synthetic marble crack texture image dataset using four generative adversarial networks (GANs). Each of the final four sub-datasets includes images of different cracks on marble surfaces. The quality of the synthetic datasets is comparatively evaluated; quantitative and qualitative experimental results are provided, highlighting the most efficient generative method. Moreover, to verify the effectiveness and significance of this work, two deep learning segmentation models are used to assess the quality of the synthetic marble crack image datasets. Results indicate that different training strategies using synthetic data could boost the performance of segmentation by up to 6.55% compared to conventional data augmentation techniques. Thus, the first step towards deep learning-based automated marble crack segmentation is delivered, aiming for the simultaneous robotic resin application for healing cracks on-site, in marble-processing industrial settings.
Crack detection based on GMM-Wasserstein distance under variable temperature environment
Authors: Zhang Xiaozhen , Wang Tiantian , Yang Jinsong , Xie Jingsong , Peng Chang , Xue Yuan 
Published: January 2025

Abstract
The operation of high-speed trains in dynamic temperature environments presents significant challenges for crack detection in critical aluminum alloy components. It has been demonstrated that temperature fluctuations significantly impact the performance of Lamb wave-based structural health monitoring (SHM) systems, thereby compromising the reliability of damage detection protocols. In response to these challenges, a novel crack detection methodology is introduced, leveraging GMM-Wasserstein distance metrics to address variable temperature conditions. Two innovative information entropy measures are developed: Energy Singular spectral entropy (ESE) and Power Singular spectral entropy (PSE), which are utilized to characterize the complex interactions between Lamb wave signals and crack propagation under thermal variations. To enhance feature extraction robustness, an integrated approach combining Ensemble Empirical Mode Decomposition (EEMD) and Singular Spectrum Analysis (SSA) has been implemented, effectively mitigating temperature-induced signal perturbations. Additionally, a sophisticated GMM-based probability migration analysis framework incorporating Wasserstein distance metrics has been developed to quantify structural damage state evolution. The efficacy of the proposed methodology has been rigorously validated through comprehensive experimentation on Al6061 aluminum plates subjected to temperatures ranging from −40 °C to 80 °C. Experimental results indicate that the proposed method achieves a classification accuracy of 95.83% in crack detection, representing a significant improvement over conventional approaches.
Tests of thermal macroeconomic theory on simulated microeconomies
Authors: Luo Yihang , MacKay R S , Chater Nick 
Published: January 2025

Abstract
In this paper, we test predictions of a new theory of macroeconomics, called ‘thermal macroeconomics.’ The theory aims to apply the mathematical structure of classical thermodynamics, including analogues of temperature and entropy, to predict aspects of the aggregate behaviour of populations of economic agents without analysing their detailed interactions. We test the theory by comparing its predictions with the behaviour of a variety of simulated micro-economies in which goods and money can be exchanged between agents, confirming the predictions of the theory. The paper serves also to illustrate and make more tangible the predictions of thermal macroeconomics.
Quantitative assessment of PINN inference on experimental data for gravity currents flows
Authors: Delcey Mickaël , Cheny Yoann , Schneider Jean , Becker Simon , Kiesgen De Richter Sébastien 
Published: January 2025

Abstract
In this paper, we apply physics informed neural networks (PINNs) to infer velocity and pressure field from light attenuation technique (LAT) measurements for gravity current induced by lock-exchange. In a PINN model, physical laws are embedded in the loss function of a neural network, such that the model fits the training data but is also constrained to reduce the residuals of the governing equations. PINNs are able to solve ill-posed inverse problems training on sparse and noisy data, and therefore can be applied to real engineering applications. The noise robustness of PINNs and the model parameters are investigated in a 2 dimensions toy case on a lock-exchange configuration, employing synthetic data. Then we train a PINN with experimental LAT measurements and quantitatively compare the velocity fields inferred to particle image velocimetry measurements performed simultaneously on the same experiment. The results state that accurate and useful quantities can be derived from a PINN model trained on real experimental data which is encouraging for a better description of gravity currents.
Quantum resources of quantum and classical variational methods
Authors: Spriggs Thomas , Ahmadi Arash , Chen Bokai , Greplova Eliska 
Published: January 2025

Abstract
Variational techniques have long been at the heart of atomic, solid-state, and many-body physics. They have recently extended to quantum and classical machine learning, providing a basis for representing quantum states via neural networks. These methods generally aim to minimize the energy of a given ansatz, though open questions remain about the expressivity of quantum and classical variational ansätze. The connection between variational techniques and quantum computing, through variational quantum algorithms, offers opportunities to explore the quantum complexity of classical methods. We demonstrate how the concept of non-stabilizerness, or magic, can create a bridge between quantum information and variational techniques and we show that energy accuracy is a necessary but not always sufficient condition for accuracy in non-stabilizerness. Through systematic benchmarking of neural network quantum states, matrix product states, and variational quantum methods, we show that while classical techniques are more accurate in non-stabilizerness, not accounting for the symmetries of the system can have a severe impact on this accuracy. Our findings form a basis for a universal expressivity characterization of both quantum and classical variational methods.
Trait Domain Specifiers
Authors: Bach Bo , Clark Lee Anna 
Published: January 2025

Abstract
This chapter defines and introduces the trait domain specifiers, including the rationale for their inclusion in the ICD-11 classification of personality disorder. The distinction and continuity between adaptive “Big Five” traits and maladaptive ICD-11 traits are described, followed by an overview of the clinical utility of codifying maladaptive trait domain. Each trait domain is characterized, along with tentative clinical interpretations and implications. Finally, the chapter highlights that combinations of trait domains are more informative for clinical practice than single domains considered separately. Thus, a number of trait domain combinations are comprehensively characterized, including their possible interpretations and implications for clinical practice.
Instruments and Other Sources of Diagnostic Information
Authors: Bach Bo , Sellbom Martin 
Published: January 2025

Abstract
ICD-11 Clinical Descriptions and Diagnostic Requirements for Personality Disorders and Related Traits should essentially be useful based on available clinical information without having to apply additional measures or instruments. However, for confident clinical use and research purposes, in which inter-rater reliability is often valued, standardized measurement may be important. In the present chapter we therefore provide an overview of available instruments, such as the Diagnostic Interview for Personality Pathology based on ICD-11 (DIPP-11) and the Personality Disorder Severity ICD-11 (PDS-ICD-11) scale, along with trait inventories such as the Personality inventory for ICD-11 (PiCD) and the Five-Factor inventory for ICD-11 (FFiCD). Additionally, we provide an overview of other instruments and sources of information that may inform severity determination and trait specifiers, including implicit association and projective tests.
Psychopathy and Interpersonal Harm
Authors: Somma Antonella , Fossati Andrea 
Published: January 2025

Abstract
Psychopathy represents a personality disorder that is characterized by a pattern of attributes including affectiveand interpersonal features, and impulsive and antisocial behaviors. Although the rates of psychopathic individuals are particularly elevated in correctional settings, psychopathy can be observed also in clinical and community settings. This chapter provides a review of psychopathy models and major personality dimensions involved in its presentation. It specifically provides a detailed guide to using the ICD-11 dimensional model of personality disorders to capture essential features of psychopathy in clinical populations. Finally, a case vignette is provided to help the reader to translate theoretical descriptions of psychopathy-related dimensions and their ICD-11 counterparts into clinical observation of their manifestations.
Schema Therapy
Authors: Rafaeli Eshkol , Shuv-Ami Itay 
Published: January 2025

Abstract
This chapter introduces schema therapy (ST), an integrative theory- and evidence-based clinical approach for the treatment of personality disorders (PDs) and other enduring psychological difficulties. ST deliberately employs few technical terms, and instead relies on concepts that allow clinicians from diverse orientations to find common ground. Moreover, ST’s straightforward language helps therapists share their case conceptualization and treatment goals with their patients. To illustrate this, we introduce ST’s basic concepts and link them to the ICD-11 model of PDs. We define three early ST concepts (needs, schemas, and coping styles) and link these to ICD-11’s (dimensional) trait domains. We then expand on the ST concept of modes, which we link to ICD-11’s PD severity dimension, and note how it takes a further step toward attending to fluctuating states and within-person changes. The focus on modes also guides schema therapists’ differential use of therapeutic stances, on which we elaborate. These include limited reparenting, which helps address patients’ unmet primary needs, soothe their distressed or hyper-aroused child modes, help them return into a window of tolerance (Ogden & Minton, 2000), and (ultimately) serve as a model for their self-care (i.e., their Healthy Adult mode). They also include the dialectic stance of empathic confrontation, which helps address the ill effects of well-intentioned but often maladaptive coping modes (e.g., detachment, avoidance, hostility, dependence, perfectionism, etc.). We use two case examples to explore ST conceptualizations and interventions.
Psychoeducation and Case-formulation
Authors: Hutsebaut Joost , Kamphuis Jan H 
Published: January 2025

Abstract
Psychoeducation and case-formulation can be powerful clinical tools to engage a client in treatment by offering a destigmatizing and validating translation of current symptoms and problems into foci for treatment. This chapter will illustrate this translational process by offering a jargon-free reformulation of core features of the ICD-11 model for personality disorders. Using a case illustration, it will offer clinicians an experience-near translation of the general diagnostic requirements; the core impairments in self and interpersonal functioning; the emotional, cognitive, and behavioral manifestations of these impairments; and the trait domains. In addition, the chapter will illustrate how this diagnostic information can be used to identify treatment goals, and to reach consensus with a client on the goals and tasks of treatment, which we consider one of the main goals of an assessment.
Application to Children and Young People
Authors: Sharp Carla , Bo Sune , Chanen Andrew 
Published: January 2025

Abstract
In this chapter, we argue that the publication of the International Classification of Diseases 11th revision (ICD-11) paves the way for both the description of a developmental pathway for personality disorder, and the prevention, early identification, and treatment of personality challenges in young people. We propose a developmental pathway for personality disorder that integrates both personality functioning (maladaptive self and interpersonal functioning) and maladaptive trait domain. We then translate this developmental pathway into an ICD-11 informed “clinical staging” model for the prevention and treatment of personality disorder. We conclude that the ICD-11 holds much promise for prevention and early intervention, given its developmental relevance.