bluuebunny/crossref_metadata_2025 huggingface dataset:
Time-dependent gamma-ray emission from quasi-spherical explosions: the case of novae
Authors: Bednarek W , Witczak P
Published: January 2025

We consider a general model for a quasi-spherical explosion in which a part of the explosion energy is thermalized, forming an expanding photosphere around a compact object, and the second part of energy is taken by the expanding shell of material which forms a shock wave in the surrounding medium. Different types of particles (electrons, hadrons) can, in principle, be accelerated at the shock. They interact with the thermal radiation from the photosphere and also with the material at the shell. We determine the equilibrium spectra of particles in the shell as a function of time after explosion and calculate the time-dependent γ-ray spectra by taking into account the effects which are due to the anisotropy of the photo-spheric radiation field on the Inverse Compton (IC) process; we also include the absorption of IC γ-rays in the photosphere radiation. We conclude that, in principle, both leptonic and hadronic models can explain the GeV-TeV gamma-ray emission recently detected from the Nova RS Oph. However, the hadronic model, in comparison to the leptonic model, is more energetically demanding and requires much stronger magnetization of the nova shell.
Computational condensed matter science contributions to addressing water emerging contaminant pollution: a comprehensive review
Authors: Bordin José Rafael , Ferreira de Matos Jauris Carolina , Côrtes Patrick R B , Araújo Wanderson S , Moreira Luana S , Pereira dos Santos Alexsandra , Bitencourt Leão Mayara , Moraes Elizane E , Piotrowski Maurício J , Köhler Mateus H
Published: January 2025

The study of emerging contaminants (ECs) in water resources has garnered significant attention due to their potential risks to human health and the environment. This review examines the contribution from computational approaches, focusing on the application of machine learning (ML) and molecular dynamics (MD) simulations to understand and optimize experimental applications of ECs adsorption on carbon-based nanomaterials. Condensed matter physics plays a crucial role in this research by investigating the fundamental properties of materials at the atomic and molecular levels, enabling the design and engineering of materials optimized for contaminant removal. We provide a comprehensive discussion of various force fields (FFs) such as AMBER, CHARMM, OPLS, GROMOS, and COMPASS, highlighting their unique features, advantages, and specific applications in modeling molecular interactions. The review also delves into the development and application of reactive potentials like ReaxFF, which facilitate large-scale atomistic simulations of chemical reactions. Additionally, we explore how ML models, including sGDML and SchNet, significantly enhance the potential and refinement of classical models by providing high-level quantum descriptions at reduced computational costs. The integration of ML with MD simulations allows for the accurate parameterization of FFs, offering detailed insights into adsorption mechanisms. Through a qualitative analysis of various ML models applied to the study of ECs on carbon materials, we identify key physical and chemical descriptors influencing adsorption capacities. Despite these advancements, challenges such as the limited diversity of ECs studied and the need for extensive experimental validation persist. This review underscores the importance of interdisciplinary collaboration, particularly the contributions of condensed matter physics, in developing innovative materials and strategies to address the environmental challenges posed by ECs.
Physical origin and control of exciton spatial localization in high-κ MOene monolayers under external perturbations
Authors: Kishore Amal , Seksaria Harshita , De Sarkar Abir
Published: January 2025

Two-dimensional (2D) materials hold great promise for the next-generation optoelectronics applications, many of which, including solar cell, rely on the efficient dissociation of exciton into free charge carriers. However, photoexcitation in atomically thin 2D semiconductors typically produces exciton with a binding energy of ∼500 meV, an order of magnitude larger than thermal energy at room temperature. This inefficient exciton dissociation can limit the efficiency of photovoltaics. In this study, employing the first principles approach—DFT, GW + BSE, and analytical model, we demonstrate the role of asymmetric halogenation, dielectric environment, and magnetic field in 2D Ti2O MOene as an efficient strategy for regulating exciton binding energy (EBE) towards spontaneous exciton dissociation. Our study goes beyond the exciton ground state and quantifies the degree of spatial delocalization of exciton in excited states as well. We determine the quantitative impact of varying dielectric screening and magnetic field strength on EBE for different excited states (1 s, 2 s, 3 s, 4 s, and so on). Importantly, we reveal the significant role of orbital orientation (whether in-plane or out-of-plane) and symmetry (related to the angular momentum quantum number) in understanding the spatial localization of excitons and their binding energy. Additionally, a high dielectric constant in 2D MOene enables easier exciton dissociation, similar to that observed in 3D bulk semiconductors, while also harnessing the advantages of 2D materials. This makes it an effective material that combines the best of both 3D bulk and 2D structures. The study offers a promising strategy for designing next-generation optoelectronic devices.
Quantitative measurement of the Sauter mean diameter in dense fuel sprays using simultaneous planar-laser-induced-fluorescence/Mie scattering technique
Authors: Wu Guohua , Yu Xin , Peng Jiangbo , Yang Chaobo , Cao Zhen , Song Yingjie
Published: January 2025

The planar laser induced fluorescence (PLIF)/Mie scattering technique has been established as an effective method for measuring the Sauter mean diameter (SMD) distribution in dense fuel sprays. This technique typically utilized a 266 nm laser. However, the strong absorption of laser wavelengths ranging from 266 nm to 274 nm in dense fuel sprays affected the detection of Mie scattering signals. Therefore, it was proposed to use a 266 nm laser for fluorescence excitation and a 532 nm laser for Mie scattering radiation in dense fuel sprays. In this study, a look-up table was created by correlating the PLIF/Mie ratio with the SMD using the phase Doppler particle analyzer, reducing the SMDerror from 27% to 13%. Furthermore, the proposed method was successfully applied to investigate the atomization field of room temperature and atmospheric pressure in a dual-stage axial swirl combustion chamber. By comparing the time-averaged PLIF images and time-averaged Mie images, the vapor and liquid phases of the fuel in the spray were successfully imaged and identified. The results indicated that, under the same fuel flow, increasing the air mass flow led to a gradual increase in the spray cone angle inside the dual-stage axial swirl combustor. Additionally, at a constant fuel flow, increasing the air mass flow resulted in a more uniform droplet size distribution, thereby enhancing the atomization effect. The presented technique provides a reliable and accurate tool for studying fuel spray behavior under various operating conditions. This knowledge can contribute to the design of more efficient combustion systems and the optimization of fuel injection strategies.
Design and characterization of novel graphene-enhanced vapor chambers for lightweight and high-performance electronics cooling
Authors: Enmark Markus , Murugesan Murali , Zhang Hongfeng , Nilsson Torbjörn M J , Kallio Kai J , Kamal Arian , Liu Johan
Published: January 2025

The trend towards miniaturization of electronics and increasing transistor density in semiconductors requires more efficient cooling solutions. Vapor chambers are well established passive cooling devices that are used in a wide variety of electronics. Commercial vapor chambers are often made of high-density metals such as copper which can be a downside in lightweight applications such as laptops, smartphones, and tablets. In this study, different novel lightweight graphene-enhanced vapor chambers were built using graphene-assembled film with high thermal conductivity as envelope material. The thermal performance of the designed graphene-enhanced vapor chambers was characterized in a customized test rig and compared to a copper vapor chamber. One of the graphene-enhanced vapor chambers was shown to have 21.6% lower thermal resistance than that of a copper vapor chamber with the same design. A mass-based thermal resistance parameter was introduced as a figure of merit to account for the superior low density of the graphene-enhanced vapor chambers. The mass-based thermal resistance of the graphene-enhanced vapor chamber was seen to be 46.5% lower than that of the copper vapor chamber. The result of this study shows that replacing copper with graphene-assembled film as envelope in vapor chambers can both reduce thermal resistance and decrease the mass of the device. Hence, it is believed that graphene-enhanced vapor chambers have great potential for replacing conventional metal-based vapor chambers in lightweight and high-performance electronics and power module cooling applications in the future.
Exploration of an adaptive proton therapy strategy using CBCT with the concept of digital twins
Authors: Chang Chih-Wei , Tian Zhen , Qiu Richard L J , Scott Mcginnis H , Bohannon Duncan , Patel Pretesh , Wang Yinan , Yu David S , Patel Sagar A , Zhou Jun , Yang Xiaofeng
Published: January 2025

Objective. This study aims to develop a digital twin (DT) framework to achieve adaptive proton prostate stereotactic body radiation therapy (SBRT) with fast treatment plan selection and patient-specific clinical target volume (CTV) setup uncertainty. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes. This study seeks to address these uncertainties using DT concept to improve treatment quality. Approach. A retrospective study on two-fraction prostate proton SBRT was conducted, involving a cohort of 10 randomly selected patient cases from an institutional database (n = 43). DT-based treatment plans were developed using patient-specific CTV setup uncertainty, determined through machine learning predictions. Plans were optimized using pre-treatment CT and corrected cone-beam CT (cCBCT). The cCBCT was corrected for CT numbers and artifacts, and plan evaluation was performed using cCBCT to account for actual patient anatomy. The ProKnow scoring system was adapted to determine the optimal treatment plans. Main Results. Average CTV D98 values for original clinical and DT-based plans across 10 patients were 99.0% and 98.8%, with hot spots measuring 106.0% and 105.1%. Regarding bladder, clinical plans yielded average bladder neck V100 values of 29.6% and bladder V20.8 Gy values of 12.0cc, whereas DT-based plans showed better sparing of bladder neck with values of 14.0% and 9.5cc. Clinical and DT-based plans resulted in comparable rectum dose statistics due to SpaceOAR. Compared to clinical plans, the proposed DT-based plans improved dosimetry quality, improving plan scores ranging from 2.0 to 15.5. Significance. Our study presented a pioneering approach that leverages DT technology to enhance adaptive proton SBRT, potentially revolutionizing prostate radiotherapy to offer personalized treatment solutions using fast adaptive treatment plan selections and patient-specific setup uncertainty. This research contributes to the ongoing efforts to achieve personalized prostate radiotherapy.
Dual-domain Wasserstein Generative Adversarial Network with Hybrid Loss for Low-dose CT Imaging
Authors: Zhou Haichuan , Liu Wei , Zhou Yu , Song Weidong , Zhang Fengshou , Zhu Yining
Published: January 2025

Objective. Low-dose computed tomography (LDCT) has gained significant attention in hospitals and clinics as a popular imaging modality for reducing the risk of x-ray radiation. However, reconstructed LDCT images often suffer from undesired noise and artifacts, which can negatively impact diagnostic accuracy. This study aims to develop a novel approach to improve LDCT imaging performance. Approach. A dual-domain Wasserstein generative adversarial network (DWGAN) with hybrid loss is proposed as an effective and integrated deep neural network (DNN) for LDCT imaging. The DWGAN comprises two key components: a generator (G) network and a discriminator (D) network. The G network is a dual-domain DNN designed to predict high-quality images by integrating three essential components: the projection-domain denoising module, filtered back-projection-based reconstruction layer, and image-domain enhancement module. The D network is a shallow convolutional neural network used to differentiate between real (label) and generated images. To prevent the reconstructed images from becoming excessively smooth and to preserve both structural and textural details, a hybrid loss function with weighting coefficients is incorporated into the DWGAN. Main results. Numerical experiments demonstrate that the proposed DWGAN can effectively suppress noise and better preserve image details compared with existing methods. Moreover, its application to head CT data confirms the superior performance of the DWGAN in restoring structural and textural details. Significance. The proposed DWGAN framework exhibits excellent performance in recovering structural and textural details in LDCT images. Furthermore, the framework can be applied to other tomographic imaging techniques that suffer from image distortion problems.
Commissioning and operation of 11.74T human MRI magnet for Gachon university gil medical center, South Korea
Authors: Pittaluga S , Dormicchi O , Mauro L , Pizzigoni G , Ventura D , Jo Young Seung , Chung Jun-Young , Kim Woo-Kyung
Published: January 2025

Magnetic resonance imaging (MRI) systems with magnetic field strengths higher than 7 Tesla (T), offer exceptional image resolution and signal-to-noise ratio, enabling more detailed visualization of fine anatomical structures, especially for brain, musculoskeletal, and cardiovascular studies. Only a few of them are operating in the whole world. An 11.74 T magnet for human MRI has been commissioned at Gachon University Gil Medical Center (GUGMC) in South Korea in 2022 and has now exceeded two-year operational life at full field. An overview of its features, the commissioning test campaign, and the experience gained after operations is presented.
Constraints on brane geometry from massive vector KK modes*
Authors: Fu 付 Chun-E 春娥 , Guo 郭 Heng 恒 , Yang 杨 Ye-Hao 烨豪
Published: January 2025

We examine the gauge invariance of massive vector Kaluza-Klein (KK) modes within various 6D brane models. Our analysis reveals that additional constraints on the brane geometry are essential to maintain the gauge invariance of the massive vector KK modes. However, these conditions are not universally satisfied by brane solutions, leading to loss of gauge invariance. In instances in which the brane solutions align with the conditions, we compute the mass spectra of both vector and scalar KK modes and determine some resonances for the KK modes in one of the brane models. Our findings indicate that the presence of a single type of massive scalar KK mode will break the gauge invariance.
The impact of E × B shear on microtearing based transport in spherical tokamaks
Authors: Patel B.S. , Hardman M.R. , Kennedy D. , Giacomin M. , Dickinson D. , Roach C.M.
Published: January 2025

Electromagnetic microtearing modes (MTMs) have been observed in many different spherical tokamak (ST) regimes. Understanding how these and other electromagnetic modes nonlinearly saturate is likely critical in understanding the confinement of a high β ST. Equilibrium E × B sheared flows have sometimes been found to significantly suppress low β ion scale transport in both gyrokinetic simulations and in experiment. This work aims to understand the conditions under which E × B sheared flow impacts on the saturation of MTM simulations, as there have been examples where it does (Guttenfelder et al 2012 Phys. Plasmas
19 056119) and does not (Doerk et al 2012 Phys. Plasmas
19 055907) have a considerable effect. Two experimental regimes are examined from MAST and NSTX, on surfaces that have unstable MTMs. The MTM driven transport on a local flux surface in MAST is shown to be more resilient to suppression via E × B shear, compared to the case from NSTX where the MTM transport is found to be significantly suppressed. This difference in the response to flow shear is explained through the impact of magnetic shear,

s
^

, on the MTM linear growth rate dependence on ballooning angle, θ
0. At low

s
^

, the growth rate depends weakly on θ
0, but at higher

s
^

, the MTM growth rate peaks at θ
0 = 0, with regions of stability at higher θ
0. Equilibrium E × B sheared flows act to advect the θ
0 of a mode in time, providing a mechanism to reduce the linear drive and suppress the transport from modes where the growth rate is strongly peaked in θ
0 (Roach et al 2009 Plasma Phys. Control. Fusion
51 124020). Bicoherence analysis demonstrates that with the inclusion of E × B shear there is a more effective coupling between the linearly driven drift-waves responsible for transport and zonal modes, which enhances damping. The dependence of γ
MTM on θ
0 is in qualitative agreement with a recent theory (Hardman et al
Plasma Phys. Control. Fusion
65 045011) at low β when

q
∼
1

, but the agreement worsens at higher q where the theory breaks down. At higher

s
^

, MTMs drive more stochastic transport due a stronger overlap of magnetic islands centred on neighbouring rational surfaces, but equilibrium E × B shear acts to mitigate this. This is especially critical towards the plasma edge where

s
^

can be larger and where the total stored energy in the plasma is more sensitive to the local gradients. This work highlights the important role of the safety factor profile in determining the impact of equilibrium E × B shear on the saturation level of MTM turbulence.
The discrete analogue of the Gaussian
Authors: Chinta Gautam , Jorgenson Jay , Karlsson Anders , Smajlović Lejla
Published: January 2025

This paper illustrates the utility of the heat kernel on

Z

as the discrete analogue of the Gaussian density function. The heat kernel on

Z

is the two-variable function

K

Z

(
t
,
x
)
=

e

−
2
t

I

x

(
2
t
)

where

I

x

(
2
t
)

is a Bessel function, with variables

x
∈

Z

and

t
⩾
0

. Like its classic counterpart,

K

Z

(
t
,
x
)

appears in many mathematical and physical contexts and has a wealth of applications. Some of these applications will be reviewed here, and they concern Bessel integrals, trigonometric sums, hypergeometric functions and asymptotics of discrete models appearing in statistical and quantum physics. Moreover, we prove a new local limit theorem for sums of integer-valued random variables, obtain novel special values of the spectral zeta function of Bethe lattices, and provide a discussion on how

e

−
2
t

I

x

(
2
t
)

could be useful in differential privacy.
Dual-band topological Bott insulators in Ammann–Beenker-tiling square photonic quasicrystals
Authors: Zhang Yingfang , Lan Zhihao , Chen Weicheng , Li Jianqing
Published: January 2025

In this work, we study topological states in Ammann–Beenker-tiling photonic quasicrystals made of magneto-optical materials. While conventional topological states in photonic systems with crystalline symmetry are characterized by topological invariants associated with bulk Bloch bands in momentum space, photonic systems in quasicrystal geometries typically lack exact periodicity and translational symmetry. As a result, conventional topological invariants defined in momentum space for photonic crystals, such as Chern number, are not applicable for photonic quasicrystals. Instead, a topological invariant called Bott index defined in real space could be employed for characterizing the topological properties of photonic quasicrystals, which we term as topological Bott insulators. In specific, we investigate the topological properties of photonic quasicrystals made of gyromagnetic dielectric cylinders arranged in a two-dimensional Ammann–Beenker tiling quasicrystalline lattice and find that this system supports dual-band chiral topological edge states, where the topological nature of both bandgaps is unambiguously confirmed by explicit calculations of the Bott index. Our work not only provides new insights on topological states in photonic quasicrystals based on the Ammann–Beenker-tiling, the results may also offer promising potentials for robust multiband photonic devices and applications not constrained by crystalline symmetries.
Optimized strategies for vehicle detection in autonomous driving systems with complex dynamic characteristics
Authors: Qiu Chengqun , Tang Hao , Xu Xixi , Liang Ji , Ji Jie , Shen Yujie
Published: January 2025

High-precision road vehicle detection is a key requirement in the field of autonomous driving. In this paper, an optimization method based on YOLOv8n is proposed to improve the detection accuracy and generalization ability of existing deep learning target detection algorithms. The structure of YOLOv8n is comprehensively improved by upgrading Backbone to VanillaNet structure, optimizing the traditional PANet in the neck to Bi-FPN, and introducing the ASFF module in the head part. The model training is performed using a specially designed dataset based on the pictures collected from the car recorder. Comparative experimental results show that the optimized model improves 1.75%, 2.76%, 3.6%, and 4.7% in Precision, Recall, mAP0.5, and mAP0.5:0.95. While the accuracy is improved, the number of parameters only increases by 21.81M, the FLOPS only increases by 61.22B, and the computational complexity is still lower than that of the same series of YOLOv8m. In addition, the independent contributions of each improvement module are systematically analyzed through ablation experiments. In the driving test, the inference confidence and frame rate are used as indicators for the detection evaluation of road scenes. The test results show that the frame rate always stays above 38 FPS and reaches up to 56 FPS, and the detection confidence is no less than 0.69 in the face of diversified road targets, which meets the accuracy and real-time demand of the automatic driving detection system. The research in this paper provides a feasible and efficient solution to improve the target detection accuracy.
Lower extremity exoskeletons: A systematic review on design, control, and sensing
Authors: Htet Ye , Behera Bulbul , Maria Joseph Felix Orlando
Published: January 2025

Recently, the use of lower limb exoskeleton is growing from medicalrehabilitation to enhancing ability and strength of human. Leg exoskeleton assistthe patients with lower limb impairment or elderly people to stand up and rehabilitategait patterns. This paper provides an extensive overview of leg exoskeleton in termsof design consideration, control strategies, potential endangerment associated withexoskeleton use, and sensing system. A comparison study based on these topics areprovided with applications. Finally, perquisite challenges and possible solution in thisdirections are highlighted which will significantly help the researchers to develop nextgeneration lower limb exoskeleton.
An effective unsupervised image stitching method based on improved UDISNet
Authors: Wang Hongru , Zhang Jingtao , Dai Chaolei , Cheng Hu , Wang Jia
Published: January 2025

Addressing the challenges of artifacts, misalignment, and distortion in complex scenarios, which arise from difficulties in feature extraction from images, we proposed a novel unsupervised image stitching method based on Unsupervised Deep Image Stitching Net (UDISNet). This method improves the UDISNet-based unsupervised image stitching method from two aspects: image alignment and image reconstruction. In the image alignment stage, to solve the problem that the network cannot extract effective alignment information in large baseline scenarios, the Edge-Preserved Image Stitching Net(EPISNet) large baseline depth homography network is introduced and an AdaPool layer is used between each two convolution layers. In the image reconstruction stage, to address the problems of weak image edge information detection, artifacts, and misalignment, we employ the Edge-Preserved Deformation Net (EPDNet) image edge detection module and deepen the network. The comparative experimental results show that the RMSE, PSNR, and SSIM of our method are 1.93, 24.85, and 0.85, respectively, which are all superior to those of other state-of-the-art methods. Moreover, extensive experiments indicate that our method can effectively stitch both ground and water surface images, which means that it has a high generalization. Therefore, our work is effective and constructive.
Design and optimization of piezoelectric diaphragm for self-powered acoustic sensor
Authors: Sawane Mohini , Prasad Mahanth
Published: January 2025

A self-powered piezoelectric material-based sensor has miniaturization and mass production capabilities. The finite element analysis shows results prior to device fabrication, which discover potential defects in the design, improve sensor performance, and lead to cost-effective fabrication. This study reveals the trade-offs between sensitivity and frequency range of the proposed device and provides an optimized diaphragm design in terms of material choice and dimension. Regardless of the cavity and holes in the silicon substrate, the optimized design is applicable for any piezoelectric square shaped diaphragm-based acoustic sensor. The constraints of MEMS fabrication technique are considered while selecting a parameter range for the diaphragm. The obtained results from COMSOL Multiphysics with diaphragm area 12.25 mm2, ZnO (2.5 μm), and silicon layer (5 μm) shows a resonance frequency of 39 kHz and sensitivity of 1.17 mV at 1 kHz of frequency, results in higher sensitivity with maximum feasible frequency range for optimized design.
Analysis of existing techniques in human emotion and behavioral analysis using deep learning and machine learning models
Authors: Jinnuo Zhu , Goyal S B , Rajawat Anand Singh , Nassar Waked Hayyan , Ahmad Sultan , Randhawa Princy , Suresh Shilpa , Naik Nithesh
Published: January 2025

Artificial intelligence has become a pivotal force in the 21st-century information technology landscape, driving significant advancements in various fields. As AI continues to evolve, its capacity to understand and analyze human emotions and behaviors through machine learning has reached a new height. In practical applications, it is essential to intelligently capture the emotional information. Current research methods for studying human emotions and behaviors can be broadly classified into artificial and machine learning. Predominantly, emotion research leverages machine learning to enable AI to recognize facial expressions and perform emotion detection and feedback tasks. In the realm of human behavior research, vital structural information is extracted from limbs and skeletons for detailed analysis. Significant breakthroughs have been achieved by integrating AI technology with effective algorithms. However, existing detection mechanisms still suffer from varying degrees of error, primarily owing to imperfections in machine learning and algorithmic approaches to human emotions and behaviors, resulting in computational inaccuracies. This review explores the contributions and limitations of both machine and artificial learning in the current research landscape. This review proposes leveraging the working principles of wearable technology to develop a comprehensive detection framework for emotions and behaviors. This integrated approach aims to enhance the accuracy and reliability of AI in detecting human emotions and behaviors, thereby laying a solid foundation for future advancements in this field. By addressing the current challenges and refining methodologies, we can significantly improve AI’s effectiveness of AI in understanding and interacting with human emotional and behavioral patterns.
Robust

H
∞

-based prescribed time attitude tracking control of quadrotor UAVs
Authors: Wang Xuejiao , Chen Ming , Gai Yulin
Published: January 2025

A novel robust H
∞
prescribed time controller is constructed based on the backstepping method, and it is applied to the attitude tracking problem of quadrotor UAVs with disturbances for the first time. The proposed controller integrates Lyapunov stability theory, prescribed time control, robust H
∞
control and backstepping method, and it not only ensures that the attitude errors of the system converge to zero at given time, but also has H
∞
performance against external disturbances. In addition, compared with the traditional finite-time and fixed-time, another significant advantage of the proposed controller is that its convergence time does not depend on the initial conditions and any design parameters of the system. A rigorous proof of the prescribed time stability of the quadrotor system is derived, and the simulation results further demonstrate the effectiveness and superiority of the proposed method.
Tuning gut microbiota by advanced nanotechnology
Authors: Qi Yue , Wang Yueyi , Wang Xiaofei , Zheng Hao , Lu Yuan
Published: January 2025

Gut microbiota reveals fundamental mechanisms of health and disease, and its modulation has important applications in biomedicine. Traditional modulation methods (e.g. diet, antibiotics, and probiotics) suffer from drug resistance, poor targeting, and low efficiency. Nanotechnology has become an attractive option for the precise modulation of gut microbiota due to its targeting and controllability. This review will focus on research progress in nanotechnology to modulate gut microbiota, including the direct use of nanomaterials as antimicrobials, nano-drug delivery systems, and stimulus-responsive nanotechnology. In addition, the applications of nanotechnology to modulate gut microbiota are summarized in terms of healthcare, animal protection, and agricultural development. Finally, the challenges and corresponding solution strategies for nanotechnology modulation are reviewed, and the future development prospects for nanotechnology modulation are summarized. This review provides an important theoretical basis and practical reference for the development of gut microbiota modulation, and promotes the research and application of more precise and efficient microbiota community intervention strategies.
Ethics, Supervenience, and Ramsey Sentences
Authors: Williamson Timothy
Published: January 2025

The chapter identifies a logical error in the argument for the supervenience of evaluative properties on non-evaluative properties in Frank Jackson’s book From Metaphysics to Ethics. It also shows how Jackson’s attempt to provide a priori conceptual analyses of evaluative expressions in non-evaluative terms by the method of Ramsey sentences (the ‘Canberra plan’) depends on extremely implausible assumptions about the requirements of linguistic competence and the level of moral consensus needed to secure anything like the uniqueness constraint on the realization of the roles of moral expressions on which Ramsey’s account depends. An Afterword explains the mistakes in Jackson’s reply to the original critique.
Boghossian, Müller-Lyer, the Parrot, and the Nazi
Authors: Williamson Timothy
Published: January 2025

The chapter is a critique of Paul Boghossian’s defence of internalism about epistemic justification. His account implies that a consistent Nazi can be fully justified in his belief that he ought to kill innocent people, provided that the belief coheres with all his other beliefs and his ‘intuitions’, as it may do. Boghossian’s attempt to separate the link between justification for believing that one ought to perform an action and justification for performing it is shown to be unconvincing. The chapter also criticizes Boghossian’s attempt to show that seemings (including intuitions) are not inhibited dispositions to believe. He argues that parrots have perceptual seemings because they have been shown to be subject to Müller-Lyer illusions, but are not capable of belief. However, the experimentalists who tested the parrot in question also attributed belief-like states to it, so it does not help Boghossian’s case.
Epistemological Ambivalence
Authors: Williamson Timothy
Published: January 2025

The chapter explains how conflicts amongst the primary and derivative norms discussed in Chapters 9 and 10 can generate something like epistemic dilemmas. It is argued that virtually any initial non-trivial epistemic norm will potentially generate such dilemmas. Analogous conflicts are illustrated with systems for communicating information (in the information-theoretic sense). It is argued that the basic epistemic norm is better conceived as governing whole cognitive systems rather than individual beliefs, since only the former but not the latter can explain the epistemic defect in failing to generate beliefs when there is no epistemic obstacle to knowing. The basic norm on cognitive systems is to generate knowledge (on which the agent can then act). It is a functional norm: the function of the cognitive system is to generate such knowledge.
A Note on Weighing Reasons
Authors: Williamson Timothy
Published: January 2025

This short chapter explains why using a framework of reasons is problematic: the normal way of understanding how reasons combine is in terms of the metaphor of weighing reasons for and against something. That metaphor embodies an additive conception of how reasons combine. That conception has only heuristic value, since it is easy to construct cases where two reasons each of which strongly favours a given option combine to make a decisive case against that option. Attempts to finesse such examples sacrifice much of the utility of thinking in terms of reasons, which arguably depends on the weighing heuristic. High-profile cases of intersectionality also violate the additive model. Thinking explicitly in terms of probabilities is much more sensitive than thinking in terms of reasons to such subtleties.
On the Moral Objection to Coercion
Authors: White Stephen J
Published: January 2025

This paper addresses the question of what makes coercion wrong, when it is wrong. The central claim White defends is that the moral objection to coercion lies in the fact that the coercer influences the coercee by illegitimately imposing on her a responsibility she should not have to bear (for example, the responsibility to ensure the coercer does not physically assault her). He argues that this account helps explain why it often matters whether the coercer has any right to do what she is threatening to do; and that it illuminates the sense in which, when one submits to a coercive threat, one is not acting autonomously, despite the fact that one may be exercising one’s ordinary, fully intact capacity for rational choice.
The Relevance of Formative Circumstances to Blameworthiness
Authors: White Stephen J
Published: January 2025

Suppose a person has committed some heinous crime. And suppose we know this person experienced severe abuse and deprivation as a child. Many of us feel hesitant to blame this person fully for her crime. But how is the person’s childhood relevant? The account White offers appeals to a theory of blame and blameworthiness recently defended by T.M. Scanlon. For Scanlon, blame is a response made appropriate by an impairment of an interpersonal relationship. It follows from this that the kind of reaction (blame) that is appropriate in response to a person’s action depends on the nature of the relationship one has with the person. He argues here that horrific formative circumstances can make a difference to the fundamental moral relations a person has with others and thus to whether she is in any normal sense morally blameworthy for her actions.
Responsibility, Justice, and Solidarity
Authors: White Stephen J
Published: January 2025

This paper examines, first, how the concept of personal responsibility figures in different theories of distributive justice, and second, what the relation is between ideas of personal responsibility and solidarity in the context of an account of justice. White discusses luck egalitarianism as a representative attempt to make the theory of distributive justice sensitive to an independent conception of personal responsibility. After clarifying the structure of the luck egalitarian approach, he raises doubts about whether it is possible to develop an account of personal responsibility for outcomes that does not itself rely on a prior theory of justice. He then contrasts the structural role that personal responsibility plays in luck egalitarianism with the role it plays in Rawls’s theory of justice. Finally, he shows how these different roles in turn support very different accounts of the relation between ideals of personal responsibility and solidarity.
Practical Commitment and Knowledge of Intentional Action
Authors: White Stephen J
Published: January 2025

It is generally agreed that, as agents, our knowledge of our intentional actions is not based solely on observation or other forms of evidence. According to inferentialism, when an agent knows that she is or will be performing an intentional action, φ, what she knows without the help of observation or evidence is just that she intends or has decided to φ. Assuming relevant background information, she may then be in a position to infer that she will φ. By contrast, according to the “practical reasoning view,” an agent’s knowledge of what she does is grounded primarily in the practical reasoning that supports doing it in the first place. White attempts to provide some motivation for the practical reasoning view by arguing that the inferential view is incompatible with an intuitive conception of intention as a form of rational commitment to action.
Beyond Thanking Black Women
Authors: Crowder Chaya Y
Published: January 2025

This chapter begins by detailing the ways in which, after each Democratic electoral victory, it has become commonplace for the election to follow with numerous calls to thank Black women for being the backbone of the Democratic Party. Thanking Black women is rarely followed by action or policy in support of Black women. Even still, Black women are the most loyal Democratic voting-block in the United States. Therefore, understanding the formation of public opinion about issues that affect Black women is important. The chapter begins by defining and describing the significance of intersectionality and group consciousness and explaining the broader implications of bringing these concepts into conversation with one another for understanding race, gender, sexuality, and American political behavior. Finally, the chapter builds on the concepts of intersectionality and group consciousness to introduce the theory of intersectional solidarity informed by work in social psychology, political science, and Black feminist theory. Intersectional solidarity is defined as a political disposition that is characterized by awareness and distress over the oppression of Black women.
Intersectional Solidarity from Protest to Politics
Authors: Crowder Chaya Y
Published: January 2025

Chapter 5 expands upon the political issues that will be examined in relation to intersectional solidarity. In particular, the chapter examines how intersectional solidarity influenced issues that were salient in 2020. The Collaborative Multiracial Post-Election Survey allows the exploration of how intersectional solidarity influenced people’s motivation for supporting a range of issues including the Black Lives Matter Movement and the #MeToo Movement. Additionally, the chapter examines the relationship between intersectional solidarity and descriptive representation by exploring how likely people think it is that Congress represents the interest of people like them when more women, Black people, or Black women are elected to Congress.
Unequal Learning
Authors: Xiang Xin
Published: January 2025

Unequal Learning takes reader inside four schools and communities across China’s socioeconomic spectrum: a rural school hidden among tall mountains, a public school in an emerging city in an impoverished region, a low-cost private school serving rural migrants, and a prestigious metropolitan public school attracting the children of elite professionals and government officials in a prosperous metropolis. In these diverse communities, the author identifies four distinct paradigms of learning that exist in most modern societies across the globe: Learning in Family and Communal Endeavors, Learning through Formal Instruction, Learning in Organized Activities, and Learning through Play. Weaving global histories into riveting narratives about children’s day-to-day activities, the author elegantly sets forth the economic, political, and social structures that enable and constrain each paradigm of learning. The paradigm of learning framework provides a powerful tool to make sense of educational inequality in modern societies, which is far more complex than a number of intersecting achievement and attainment gaps. The differentiation of Learning through Formal Instruction and the exclusivity of Learning in Organized Activities results from and contributes to the widening gulf between the rising metropolitan elites and the popular classes in China. At the same time, the systematic devaluation of Learning in Family and Communal Endeavors reflects and reinforces the devaluation of human labor in global capitalism. Therefore, if we are at all serious about educational equity and justice, we need to fundamentally rethink whose knowledge and contribution counts as well as what good schools and good education look like.
Introduction
Authors: Kudelia Serhiy
Published: January 2025

The chapter introduces the main sites and themes of the book—the region of Donbas and its urban agglomerations, irredentism and secessionism, foreign intervention, and armed insurgency. It explains how a micro-level approach based on comparative process-tracing of developments in individual towns at the conflict outset allows to identify mechanisms through which armed militants, led by Russian agents, seized control over the region. It also positions the book within an existing literature on the causes of armed conflict in Donbas. It offers a new explanatory framework for the conflict by distinguishing between the two stages of the insurgency campaign, state founding and state building, and specifying the exact role that Russian and local actors played during each stage.
How Was Donbas Occupied?
Authors: Kudelia Serhiy
Published: January 2025

This chapter develops the book’s conceptual framework, which views insurgency as a form of state formation that requires establishing a coercive monopoly over a particular territory and building the institutions of a nascent state. The chapter then compares the extent to which separatists in Donbas managed to achieve these two objectives at the outset of the conflict in six urban agglomerations of the region. It demonstrates that the establishment of full or partial coercive control over towns by militants also increased the likelihood that they could gain administrative hold over the locality. They achieved this by encouraging the collaboration of local elites or forming parallel governance structures. The chapter introduces the concept of urban agglomeration density and explains its relevance for understanding the swift spread of separatist presence across Donbas. It describes in greater detail the location and other attributes of the urban centers and agglomerations in Donbas discussed further in the book.
Rivalry, Revolution, and Russia’s Intervention
Authors: Kudelia Serhiy
Published: January 2025

This chapter argues that the armed conflict in Donbas can be conceptualized as a foreign-led insurgency campaign that started as a result of external intervention from Russia. It argues that the intervention resulted from Moscow’s longtime hostility to the idea of Ukrainian independence, evident already during the breakup of the Soviet Union. The chapter traces how this hostility increased over time and centered on three key areas: disagreement over borders, Russia’s rejection of Ukraine’s alliance choices, and contestation over symbols, such as national language policy and historical memory politics. The chapter demonstrates how the outbreak of the Euromaidan revolution in 2013–2014 created an opening for Russia’s intervention, first in Crimea and later in Donbas.
The “Russian Spring” and Its Discontents
Authors: Kudelia Serhiy
Published: January 2025

This chapter shifts the analytical focus to internal dynamics in Donbas and traces the origins of separatist mobilization there in spring 2014. It examines the framing of anti-government protests in Kyiv by local elites and civic activists and explains how it led to the growing public demand for broader regional autonomy. It also demonstrates how anti-Maidan mobilization in the region produced counter-rallies in support of the unity of Ukraine in major towns of Donbas, which were subdued through physical assaults and coercive threats. As the chapter shows, although anti-Kyiv rallies also lost steam, the escalation of violence following the launch of the government’s “counter-terrorist operation” in mid-April created new grievances that favored the separatist state-building project.
From Sabotage to Resistance
Authors: Kudelia Serhiy
Published: January 2025

This chapter focuses on several towns in Donbas where local activists and municipal authorities created self-defense groups to protect their towns from militant threats. These actions usually preceded any major local separatist mobilization and required joint efforts of town residents and mayors. They also rested on the ability to project grass-roots coercive capacity, receive backing from powerful business actors and engage in coordination with Ukrainian armed units. The chapter shows that Ukraine’s sovereign presence remained or was quickly restored in those towns in Donbas where pro-Ukrainian forces could credibly threaten to use force, while local separatists lacked direct assistance from Russian mercenaries. The proximity of the Ukrainian armed forces or volunteer battalions to these towns had a further deterrent effect on local militants and compelled them to withdraw.
Experiencing War from Afar
Authors: Hurl-Eamon Jennine
Published: January 2025

Children enjoyed listening to martial music, celebrating victories, and watching military parades. They not only viewed soldiers from the sidelines; they also pretended to be them in play activities covering not only war’s glories but its horrors as well. Poor and rich youngsters alike found comfort in playing at soldiering, and servicemen of all ranks basked in children’s adoration. Adults also enjoyed watching youngsters drill and seeing them in uniform. The evidence suggests that the children—particularly those emulating officers—might take as much pleasure in this as their elders did in viewing them. Education efforts thus sought to harness juvenile war interests. Parents taught their offspring about battles and veterans shared their war stories to impart lessons in history, patriotism, reading, and even maths. The Army also pioneered efforts at mass education in its regimental schools and by training its personnel to teach using Bell’s monitorial system. Not every child fell prey to martial culture’s appeal, however; this chapter concludes with examples of young people who mocked, feared, or resisted servicemen.