bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of waste management policies based on village owned enterprises (Bumkal) (empirical study on Caturharjo Village)
Authors: Rechandy Christian Adhitya
Published: January 2025

The problem of waste is a serious thing to be handled in Yogyakarta and overcoming waste through its source is an interesting thing to research. The purpose of this study is to find out the waste management based on Village-Owned Enterprises (BUMKAL) in Caturharjo Village which has succeeded in building waste management through BUMKAL Catur Sejahtera. This research uses a qualitative approach by conducting direct interviews with stakeholders and the community in waste management in Caturharjo Village. The results of this study revealed that Caturharjo Village through Perkal Number 2 of 2022 concerning waste management using (1). Waste collection is not harmful, (2). Waste management and disposal are not hazardous, (3). Community- and community-based organic compost production, (4). Combined provider of facility supports services. In addition, the findings saw that Caturharjo Village also carried out pentahelix collaboration to solve waste in Caturharjo Village.
High-performance traffic volume prediction: An evaluation of RNN, GRU, and CNN for accuracy and computational trade-offs
Authors: Pranolo Andri , Saifullah Shoffan , Bella Utama Agung , Wibawa Aji Prasetya , Bastian Muhammad , Hardiyanti P Cicin
Published: January 2025

Predicting urban traffic volume presents significant challenges due to complex temporal dependencies and fluctuations driven by environmental and situational factors. This study addresses these challenges by evaluating the effectiveness of three deep learning architectures— Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—in forecasting hourly traffic volume on Interstate 94. Using a standardized dataset, each model was assessed on predictive accuracy, computational efficiency, and suitability for real-time applications, with Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), R2 coefficient, and computation time as performance metrics. The GRU model demonstrated the highest accuracy, achieving a MAPE of 2.105%, RMSE of 0.198, and R2 of 0.469, but incurred the longest computation time of 7917 seconds. Conversely, CNN achieved the fastest computation time at 853 seconds, with moderate accuracy (MAPE of 2.492%, RMSE of 0.214, R2 of 0.384), indicating its suitability for real- time deployment. The RNN model exhibited intermediate performance, with a MAPE of 2.654% and RMSE of 0.215, reflecting its limitations in capturing long-term dependencies. These findings highlight crucial trade- offs between accuracy and efficiency, underscoring the need for model selection aligned with specific application requirements. Future work will explore hybrid architectures and optimization strategies to enhance further predictive accuracy and computational feasibility for urban traffic management.
Comparison of irrigation performance assessment of D.I Cikahuripan according to irrigation network maintenance exploitation guidelines with fuzzy set theory
Authors: Hamka M. Noris , Putra Krisna , Fauzi Rizki , Saputri Utamy Sukmayu , Triono
Published: January 2025

Performance assessment of irrigation networks is an integral part of irrigation management. Irrigation managers conduct it and form the basis for policymaking in the irrigation area being evaluated. Performance assessment involves six aspects of irrigation networks, including physical infrastructure, crop productivity, operation and maintenance (O&M) support facilities, personnel organization, documentation, and Water User Farmer Association (P3A) organization. Currently, performance assessment, on some aspects, still relies on the experience of field officers or observers. This research aims to evaluate the performance of irrigation networks by applying fuzzy set theory to reduce subjectivity in accordance with the method used by the Balai, which refers to Permen PUPR No. 12/PRT/M/2015 concerning Guidelines for Exploitation and Maintenance of Irrigation Networks. Input data comes from the performance assessment of irrigation networks in the Cikahuripan Irrigation Area in 2023. The results showed that fuzzy analysis of the performance value of irrigation networks in the Cikahuripan Irrigation Area reached 70.17%, while the assessment carried out manually reached 67.57%. The comparison between the assessment using fuzzy set theory and manual methods shows a difference of 2.6%. This shows that the fuzzy set theory method can be used to evaluate the performance of irrigation systems.
Quantum theory of coherence and nonlinear optics
Authors: Peřina Jan
Published: January 2025

We review the beginning stage of quantum theory of coherence and research performed at the Palacký University in quantum and nonlinear optics related to quantum state reconstruction, generalized superposition of signal and quantum noise, quantum Zeno effect, nonlinear optical couplers and parametric down-conversion.
Polarization beating of random electromagnetic beams
Authors: Lajunen H. , Salaj J. , Setälä T.
Published: January 2025

We consider temporal interference of two stationary, quasi-monochromatic, partially polarized optical beams with different mean frequencies. We show that both the intensity and the polarization state, represented by the Stokes parameters, exhibit beating, i.e., periodic temporal variation with frequency specified by the difference of the mean frequencies. The contrasts, or visibilities, of the Stokes-parameter changes are characterized by the equal-time electromagnetic degree of coherence between the beams. If the beams are otherwise identical random processes, but with spectra centered at different frequencies, then the polarization modulation is characterized by the degree of polarization, consistently with a recent interferometric interpretation of this quantity. Our results provide insight into the role of polarization in beating of electromagnetic waves.
A Transformer-Based Pipeline for German Clinical Document De-Identification
Authors: Arzideh Kamyar , Baldini Giulia , Winnekens Philipp , Friedrich Christoph M. , Nensa Felix , Idrissi-Yaghir Ahmad , Hosch René
Published: January 2025

Objective Commercially available large language models such as Chat Generative Pre-Trained Transformer (ChatGPT) cannot be applied to real patient data for data protection reasons. At the same time, de-identification of clinical unstructured data is a tedious and time-consuming task when done manually. Since transformer models can efficiently process and analyze large amounts of text data, our study aims to explore the impact of a large training dataset on the performance of this task.

Methods We utilized a substantial dataset of 10,240 German hospital documents from 1,130 patients, created as part of the investigating hospital's routine documentation, as training data. Our approach involved fine-tuning and training an ensemble of two transformer-based language models simultaneously to identify sensitive data within our documents. Annotation Guidelines with specific annotation categories and types were created for annotator training.

Results Performance evaluation on a test dataset of 100 manually annotated documents revealed that our fine-tuned German ELECTRA (gELECTRA) model achieved an F1 macro average score of 0.95, surpassing human annotators who scored 0.93.

Conclusion We trained and evaluated transformer models to detect sensitive information in German real-world pathology reports and progress notes. By defining an annotation scheme tailored to the documents of the investigating hospital and creating annotation guidelines for staff training, a further experimental study was conducted to compare the models with humans. These results showed that the best-performing model achieved better overall results than two experienced annotators who manually labeled 100 clinical documents.
Decentering Coloniality: Epistemic Justice, Development Studies and Structural Transformation
Authors: Gebremariam Eyob Balcha
Published: January 2025

The debate on decolonising development studies (DS) can be more fruitful if we focus on epistemic issues. Since mainstream DS has been essentially Eurocentric, where coloniality is normalised, I suggest in this commentary that decolonising DS is best pursued by decentring coloniality. I use the epistemic justice framework to illustrate how both interpretative and testimonial injustices are common features of DS when we take the dominance of Eurocentric knowledge production in DS for granted. I adopt coloniality and empire as my analytical framework to show how current manifestations of colonialism influence the intertwined relations between knowledge, the knower and the relations of knowledge production. The piece then juxtaposes the widely accepted definitions of structural transformation (ST) with those explicitly focussing on the inherently colonial design of the global political economy. Then, I briefly discuss the “FrançAfrique” economic zone as a site of coloniality to demonstrate the limitations of the mainstream notions of ST.
Molecular dynamics analysis of microstructural deformation mechanisms in single crystal copper undergoing equal channel angular pressing
Authors: Yang Ping , Zhang Huaming , Luan Tianxin , Jin Ye
Published: January 2025

This study employs molecular dynamics simulations to analyze the crystal structure, lattice rotation, dislocations, twinning, shear strain, and volumetric strain in three copper workpieces during the equal channel angular pressing (ECAP) process. The workpieces, oriented as [100], [110], and [111], are aligned parallel to the Y-axis in the simulation, corresponding to the extrusion direction. The deformation of the three workpieces is primarily achieved through the interaction between twinning and dislocation slip. The [100] oriented workpiece activated multiple slip systems with high shear factors, leading to intense shear deformation. This caused different regions to experience varying strains, resulting in the most dispersed lattice rotation distribution. The intense deformation also generated the most deformation twins, and the interaction between deformation twins and dislocations was the strongest, further increasing the overall dislocation density, thereby causing the most severe grain fragmentation. The [111] oriented workpiece activated only one slip system, causing minimal shear deformation, fewer dislocation interactions, and uniform deformation. The deformation and grain fragmentation of the [110] oriented workpiece were intermediate between the other two orientations. This research provides theoretical insights for optimizing the ECAP process and enhancing copper performance.
Electrohydrodynamic behavior of droplets on the surface of epoxy resin under electric field
Authors: Shu Shengwen , Huang Wenhua , Zhan Zhaoxuan , Zhang Lina , Lin Yihong , Bian Zhiwen
Published: January 2025

Epoxy resins are susceptible to condensation in high humidity environments, leading to flashover discharges that compromise their insulation properties. Hence, investigating the electrohydrodynamic behaviors of droplets on epoxy resin surfaces is crucial for understanding and mitigating such phenomena. By analyzing the droplet dynamics through experiments, this study thoroughly explores the spreading and migration characteristics of droplets under the influence of varying AC voltages. Simulation and numerical models are developed to depict droplet motion and force dynamics, integrating principles of hydrodynamics and electrodynamics. The findings reveal that droplets spread in the direction of the electric field, with the extent of spreading increasing with the increase in voltage. This spreading is influenced by various factors, including the droplet’s position, volume, and surface tension. Primarily, the electric field at the droplet’s base generates a tangential force that determines its spreading. Furthermore, the droplet tends to move toward the nearer electrode, especially when a tip structure emerges on its surface, facilitating more rapid deformation in that direction.
Giant and flexible toroidal circular dichroism from planar chiral metasurface
Authors: Kang Shijie , Li Haitao , Fan Jiayu , Yu Jiusi , Qu Boyang , Chen Peng , Wu Xiaoxiao
Published: January 2025

Chirality, a fundamental concept describing an object cannot superpose with its mirror image, is crucial in optics and photonics and leads to various exotic phenomena, such as circular dichroism and optical activity. Recent findings reveal that besides electric and magnetic dipoles, toroidal dipoles, an elusive part of dynamic multipoles, can also significantly contribute to chirality. However, as toroidal dipoles are typically represented by solenoidal currents circulating on a three-dimensional (3D) torus, toroidal circular dichroism is usually observed in 3D intricate microstructures. Facing corresponding challenges in fabrication, integration, and application, it is generally difficult to employ toroidal circular dichroism in compact metasurfaces for flexible modulation of chiral interactions between electromagnetic waves and matter. To overcome these stringent challenges, we propose and experimentally demonstrate the giant toroidal circular dichroism in a bilayer metasurface that is comprised of only planar layers, effectively bypassing various restrictions imposed by 3D microstructures. With the introduction of a displacement, or bilayer offset, between the opposite layers, we experimentally achieve giant chiral responses with the intrinsic circular dichroism (CD) reaching 0.69 in measurements, and the CD can be quantitatively manipulated in a simple manner. The giant intrinsic chirality primarily originates from distinct excitations of in-plane toroidal dipole moments under circular polarized incidences, and the toroidal chiral response is quantitatively controlled by the bilayer offset. Therefore, our work provides a straightforward and versatile approach for the development of giant and flexible intrinsic chirality through toroidal dipoles with inherently planar layers, important for applications in communications, sensing, and chiroptical devices.
Numerical study on the surface evolution and flow dynamics in laser polishing of 100Cr6 steel based on the coupled capillary and thermocapillary flow
Authors: Fang Chuqiao , Dai Wei , Wu Xiao , Wang Sijin
Published: January 2025

Laser polishing (LP) has gained widespread interest in various engineering fields due to its high efficiency, precision, and non-contact processing capabilities. Despite its potential applications, most LP research primarily relies on experimental approaches, which are time-consuming, expensive, and may not systematically reveal the underlying mechanisms of laser–material interactions. Therefore, integrating numerical simulation techniques is crucial for enhancing our understanding and optimizing the LP process. This study develops a two-dimensional transient numerical model to simulate the superficial surface evolution during the LP of 100Cr6 steel. A moving laser beam with a top-hat energy density distribution was implemented, considering the microscopic flow mechanism of the molten pool through combined capillary and thermocapillary flows. A detailed analysis of temperature distributions, molten pool behaviors, and surface profiles was conducted to assess how various process parameters influenced the results. The simulation results indicated that an optimal surface roughness of approximately Ra 0.706 µm can be achieved using the optimized parameters (laser power: 400 W, scanning speed: 300 mm/s, and laser beam size: 300 μm). Additionally, the molten pool can form within a suitable temperature and time range for LP using these optimized parameters. The results also indicated that the LP surface roughness exhibits a V-shaped relationship with laser power, scanning speed, and laser beam size. This study highlights the critical role of thermal input in LP and discusses how adjusting laser process parameters effectively controls this input. It provides numerical simulation parameters and theoretical guidance for LP research, ultimately leading to desired polishing effects.
Kinetics, thermodynamics, and catalysis of the cation incorporation into GeO2, SnO2, and (SnxGe1−x)O2 during suboxide molecular beam epitaxy
Authors: Chen Wenshan , Egbo Kingsley , Kler Joe , Falkenstein Andreas , Lähnemann Jonas , Bierwagen Oliver
Published: January 2025

Rutile GeO2 is a promising ultra-wide bandgap semiconductor for future power electronic devices whose alloy with the wide bandgap semiconductor rutile-SnO2 enables bandgap engineering and the formation of heterostructure devices. The (SnxGe1−x)O2 alloy system is in its infancy, and molecular beam epitaxy (MBE) is a well-suited technique for its thin-film growth, yet it presents challenges in controlling the alloy composition and growth rate. To understand and mitigate this challenge, the present study comprehensively investigates the kinetics and thermodynamics of suboxide incorporation into GeO2, SnO2, and (SnxGe1−x)O2 during suboxide MBE (S-MBE), the latest development in oxide MBE using suboxide sources. We find S-MBE to simplify the growth kinetics, offering better control over growth rates than conventional MBE but without supporting cation-driven oxide layer etching. During binary growth, SnO incorporation is kinetically favored due to its higher oxidation efficiency and lower vapor pressure (limiting its loss by desorption) compared to those of GeO. In (SnxGe1−x)O2 growth, however, the GeO incorporation is preferred and the SnO incorporation is suppressed, indicating a catalytic effect, where SnO promotes GeO incorporation. The origin of this catalytic effect cannot be understood by comparing the binary kinetics or thermodynamics (cation–oxygen bond strengths), thus calling for further theoretical studies. Our experimental study provides guidance for controlling the growth rate and alloy composition of (SnxGe1−x)O2 in S-MBE, highlighting the impact of the substrate temperature and active oxygen flux besides that of the mere SnO:GeO flux stoichiometry. The results are likely transferable to further physical and chemical vapor deposition methods, such as conventional and hybrid MBE, pulsed laser deposition, mist-, or metalorganic chemical vapor deposition.
Global existence and large-time behavior of solutions to one-dimensional compressible Navier–Stokes system with outer pressure in the half-space
Authors: Han Xiaomin , Wu Yunshun , Zhang Rong
Published: January 2025

The outer pressure problem of the compressible Navier–Stokes system in the Lagrangian coordinate system describing the one-dimensional motion of a viscous heat-conducting prefect polytropic gas in half-space is studied. Both the specific volume and the temperature are proved to be bounded from below and above independently of both space and time, and as a direct consequence, the global existence and large-time behavior of strong solutions for the outer pressure problem in the half-space is also obtained.
MAGInet based on deep learning for magnetic multi-parameter inversion
Authors: Wen Wudi , Li Yi , Liu Zhongle
Published: January 2025

This manuscript introduces MAGInet, a novel deep learning framework designed for the magnetic multi-parameter inversion of complex structures. The architecture of MAGInet integrates a classifier and several solvers, where the classifier performs a preliminary categorization of magnetic field signals and the solvers execute a detailed regression analysis to predict multiple parameters of the structures. The operational sequence of MAGInet is as follows: magnetic field signals are initially fed into the classifier for classification, and the outcomes are subsequently channeled into the appropriate solver for multi-parameter forecasting. The underlying principle of MAGInet’s multi-parameter inversion is to establish a learned mapping between the magnetic field signals and the multi-magnetic parameters of complex structures, leveraging extensive training datasets. In this study, simulation experiments are conducted, with training datasets generated via finite element magnetic field modeling, which are then utilized to fine-tune the MAGInet model. The results of these experiments demonstrate that the accuracy of multi-parameter prediction for complex structures can reach up to 97.5% under zero-error conditions. Furthermore, the efficacy of the MAGInet inversion approach is contrasted with the traditional, deep, fully connected neural network methodologies. Comparative analyses reveal that MAGInet significantly outperforms traditional deep neural networks in terms of accuracy for predicting the magnetic multi-parameters of complex structures, showcasing superior performance.
Nonapoptotic role of EGL-1 in exopher production and neuronal health in
Caenorhabditis elegans
Authors: Wu Zheng , Cardona Eric A. , Cohn Jesse A. , Pierce Jonathan T.
Published: January 2025

While traditionally studied for their proapoptotic functions in activating the caspase, research suggests BH3-only proteins also have other roles such as mitochondrial dynamics regulation. Here, we find that EGL-1, the BH3-only protein in
Caenorhabditis elegans
, promotes the cell-autonomous production of exophers in adult neurons. Exophers are large, micron-scale vesicles that are ejected from the cell and contain cellular components such as mitochondria. EGL-1 facilitates exopher production potentially through regulation of mitochondrial dynamics. Moreover, an endogenous, low level of EGL-1 expression appears to benefit dendritic health. Our findings provide insights into the role of neuronal BH3-only protein in mitochondrial dynamics, downstream exopher production, and ultimately neuronal health.
Multiplicity of type 6 secretion system toxins limits the evolution of resistance
Authors: Smith William P. J. , Armstrong-Bond Ewan , Coyte Katharine Z. , Knight Christopher G. , Basler Marek , Brockhurst Michael A.
Published: January 2025

The bacterial type 6 secretion system (T6SS) is a toxin-injecting nanoweapon that mediates competition in plant- and animal-associated microbial communities. Bacteria can evolve de novo resistance against T6SS attacks, but resistance is far from universal in natural communities, suggesting key features of T6SS weaponry may act to limit its evolution. Here, we combine ecoevolutionary modeling and experimental evolution to examine how toxin type and multiplicity in
Acinetobacter baylyi
attackers shape resistance evolution in susceptible
Escherichia coli
competitors. In both our models and experiments, we find that combinations of multiple distinct toxins limit resistance evolution by creating genetic bottlenecks, driving resistant lineages extinct before they can reach high frequency. We also show that, paradoxically, single-toxin attackers can drive the evolution of cross-resistance, protecting bacteria against unfamiliar toxin combinations, even though such evolutionary pathways were inaccessible against multitoxin attackers. Our findings indicate that, comparable to antimicrobial and anticancer combination therapies, multitoxin T6SS arsenals function to limit resistance evolution in competing microbes. This helps us to understand why T6SSs remain widespread and effective weapons in microbial communities, and why many T6SS-armed bacteria encode functionally diverse anticompetitor toxins.
CryoEM structure of an MHC-I/TAPBPR peptide-bound intermediate reveals the mechanism of antigen proofreading
Authors: Sun Yi , Pumroy Ruth A. , Mallik Leena , Chaudhuri Apala , Wang Chloe , Hwang Daniel , Danon Julia N. , Dasteh Goli Kimia , Moiseenkova-Bell Vera Y. , Sgourakis Nikolaos G.
Published: January 2025

Class I major histocompatibility complex (MHC-I) proteins play a pivotal role in adaptive immunity by displaying epitopic peptides to CD8+ T cells. The chaperones tapasin and TAPBPR promote the selection of immunogenic antigens from a large pool of intracellular peptides. Interactions of chaperoned MHC-I molecules with incoming peptides are transient in nature, and as a result, the precise antigen proofreading mechanism remains elusive. Here, we leverage a high-fidelity TAPBPR variant and conformationally stabilized MHC-I, to determine the solution structure of the human antigen editing complex bound to a peptide decoy by cryogenic electron microscopy (cryo-EM) at an average resolution of 3.0 Å. Antigen proofreading is mediated by transient interactions formed between the nascent peptide binding groove with the P2/P3 peptide anchors, where conserved MHC-I residues stabilize incoming peptides through backbone-focused contacts. Finally, using our high-fidelity chaperone, we demonstrate robust peptide exchange on the cell surface across multiple clinically relevant human MHC-I allomorphs. Our work has important ramifications for understanding the selection of immunogenic epitopes for T cell screening and vaccine design applications.
Anti-CTLA-4 generates greater memory response than anti-PD-1 via TCF-1
Authors: Mok Stephen , Liu Huey , Ağaç Çobanoğlu Didem , Anang Nana-Ama A. S. , Mancuso James J. , Wherry E. John , Allison James P.
Published: January 2025

The effects of T cell differentiation arising from immune checkpoint inhibition targeting cytotoxic T lymphocyte–associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1) on the immunological memory response remain unclear. Our investigation into the effects of anti-CTLA-4 and anti-PD-1 on memory T cell formation in mice reveals that memory T cells generated by anti-CTLA-4 exhibit greater expansion, cytokine production, and antitumor activity than those from anti-PD-1. Notably, anti-CTLA-4 preserves more T cell factor-1 (TCF-1)+ T cells during priming, while anti-PD-1 leads to more thymocyte selection-associated high mobility group box (TOX)+ T cells. Experiments using conditional
Tcf7
- or
Tox
-knockout mice highlight that TCF-1 is essential for the memory response generated by anti-CTLA-4, whereas TOX deletion alone in T cells has no effect on the response to anti-PD-1. Deepening our understanding of how checkpoint inhibition affects memory response is crucial for advancing our understanding of the enduring impacts of these immunotherapies on the immune system.
Increasing pesticide diversity impairs soil microbial functions
Authors: Ni Bang , Xiao Lu , Lin Da , Zhang Tian-Lun , Zhang Qi , Liu Yanjie , Chen Quan , Zhu Dong , Qian Haifeng , Rillig Matthias C. , Zhu Yong-Guan
Published: January 2025

Pesticide application is essential for stabilizing agricultural production. However, the effects of increasing pesticide diversity on soil microbial functions remain unclear, particularly under varying nitrogen (N) fertilizer management practices. In this study, we investigated the stochasticity of soil microbes and multitrophic networks through amplicon sequencing, assessed soil community functions related to carbon (C), N, phosphorus (P), and sulfur (S) cycling, and characterized the dominant bacterial life history strategies via metagenomics along a gradient of increasing pesticide diversity under two N addition levels. Our findings show that higher pesticide diversity enriches the abundance of bacterial specialists and opportunists capable of degrading or resisting pesticides, reducing the proportion of bacterial generalists in the absence of N addition. These shifts can complicate multitrophic microbial networks. Under increased pesticide diversity, selective pressure may drive bacteria to streamline their average genome size to conserve energy while enhancing C, N, P, and S metabolic capacities, thus accelerating soil nutrient loss. In comparison, N addition was found to reduce bacterial niche differentiation at higher pesticide diversity, mitigating the impacts of network complexity and functional traits associated with pesticide diversity, ultimately alleviating soil nutrient loss. Our results reveal the contrasting impacts of pesticide diversity on microbial functions under different N input scenarios and emphasize that strategic N fertilizer management can mitigate the ecological effects of pesticide use in agricultural systems.
Loss of ATG7 in microglia impairs UPR, triggers ferroptosis, and weakens amyloid pathology control
Authors: Cai Zhangying , Wang Shoutang , Cao Siyan , Chen Yun , Penati Silvia , Peng Vincent , Yuede Carla M. , Beatty Wandy L. , Lin Kent , Zhu Yiyang , Zhou Yingyue , Colonna Marco
Published: January 2025

Microglia impact brain development, homeostasis, and pathology. One important microglial function in Alzheimer’s disease (AD) is to contain proteotoxic amyloid-β (Aβ) plaques. Recent studies reported the involvement of autophagy-related (ATG) proteins in this process. Here, we found that microglia-specific deletion of Atg7 in an AD mouse model impaired microglia coverage of Aβ plaques, increasing plaque diffusion and neurotoxicity. Single-cell RNA sequencing, biochemical, and immunofluorescence analyses revealed that Atg7 deficiency reduces unfolded protein response (UPR) while increasing oxidative stress. Cellular assays demonstrated that these changes lead to lipoperoxidation and ferroptosis of microglia. In aged mice without Aβ buildup, UPR reduction and increased oxidative damage induced by Atg7 deletion did not impact microglia numbers. We conclude that reduced UPR and increased oxidative stress in Atg7-deficient microglia lead to ferroptosis when exposed to proteotoxic stress from Aβ plaques. However, these microglia can still manage misfolded protein accumulation and oxidative stress as they age.
Experimental investigation of the effects of different DBD plasma actuators on the aerodynamic performance of the NACA0012 airfoil
Authors: Rezaei H , Kazemi M , Saeedi M , Jahangirian A , Mani M
Published: January 2025

This study aims to investigate the flow control performance of linear and serpentine dielectric barrier discharge (DBD) plasma actuators mounted on the leading edge of a NACA0012 airfoil to control flow separation and improve aerodynamic performance. Experiments were conducted in a subsonic wind tunnel at Reynolds numbers of 87 × 103, 131 × 103, and 175 × 103. Velocity profiles in the wake and static pressure distributions over airfoil were measured using hot-wire anemometry and pressure sensors, respectively. All experiments conducted in the different plasma actuation parameters, including wave voltages (6, 8, 10 kV) and wave frequencies (6, 10 kHz), and the optimal combination of V
pp = 10 kV and f
AC = 10 kHz was identified. The results of several wind tunnel experiments showed that, a substantial increase in the lift coefficient of up to 26.58% and a delay in stall angle of attack up to 4º due to the implementation of DBD plasma actuators. Additionally, the serpentine actuator demonstrated a more significant impact on separation control compared to the linear actuator where the serpentine actuator has controlled the stall phenomenon more effectively in post-stall angles compared to the linear actuator due to generation of 3D structures.
A first-principles investigation of the structural, mechanical, dynamic, electronic, magnetic, and optical properties of Ti2AC (A = Cd, S) MAX phase compounds
Authors: Rijal Om Shree , Neupane Hari Krishna , Oli Dipak , Neupane Ravi Kiran , Shrestha Pitamber , Sharma Shriram , Joshi Leela Pradhan , Parajuli Rajendra
Published: January 2025

MAX phase compounds have a unique blend of ceramic and metallic properties and have been established as promising materials for device applications. In the present work, we have investigated the structural, dynamical, mechanical, electronic, magnetic, and optical properties of Ti2CdC and Ti2SC MAX phase compounds using a density functional theory approach through the Vienna Ab initio Simulation Package. The structural, dynamical, and mechanical properties of both MAX phase compounds are studied and found to be stable. Additionally, the mechanical properties reveal that both compounds have hardness and a crystalline anisotropic, bond-stretching, and brittle nature. From the analysis of elastic anisotropy calculations, it is found that Ti2CdC has metallic-like bonding, while Ti2SC has covalent-like bonding. The electronic and magnetic properties of the Ti2CdC and Ti2SC compounds are determined, and it is found that they have metallic and non-magnetic properties for the generalized gradient approximation (GGA): Perdew–Burke–Ernzerh (PBE) functional, whereas they have metallic and magnetic properties for the GGA: PBE + U functional(U is Hubbard parameter). We have explored the optical properties of the considered materials and found that they have compelling interactions with electromagnetic radiation, notable values of absorption energy, that less transparency occurred at the lower energy region, and that they have high thermal properties. Based on the major findings of the above considered materials, Ti2CdC and Ti2SC can be used in the fields of optoelectronic devices, sensors, advanced photonic devices, infrared imaging, solar thermal collectors, thermal insulation, nuclear devices, and energy dissipation devices.
Raman signatures of inversion symmetry breaking structural transition in quasi-1D compound, (TaSe4)3I
Authors: Bera Arnab , Rana Partha Sarathi , Pradhan Suman Kalyan , Palit Mainak , Saha Surabhi , Kalimuddin Sk , Bera Satyabrata , Debnath Tuhin , Das Soham , Roy Deep Singha , Datta Subhadeep , Mondal Mintu
Published: January 2025

The breaking of inversion symmetry combined with spin-orbit coupling, can give rise to intrigu-ing quantum phases and collective excitations. Here, we report systematic temperature dependentRaman scattering and theoretical calculations of phonon modes across the inversion symmetry-breaking structural transitions in a quasi-one-dimensional compound (TaSe4)3I. Our investigationrevealed the emergence of three additional Raman-active modes in Raman spectra of the low-temperature (LT) non-centrosymmetric (NC) structure of the material. From polarization depen-dent Raman spectra and phonon mode symmetry analysis, we have identified the origin of thesethree newly appeared additional Raman-active modes. Notably, two of these modes become Ramanactive due to the loss of inversion symmetry, while the third mode is identified as a soft phonon mode,arising from the distinctive vibrational motion of tantalum (Ta) atoms along the -Ta-Ta- chains.Furthermore, the temperature evolution of self-energy parameters indicates significant changes inthe characteristics of the Raman modes across the transition. Latent heat measurements near thephase transition using Differential Scanning Calorimetry (DSC) confirm the first-order nature ofthe transition. Theoretical analysis, including group theory and modeling, reaffirms the displacivefirst-order nature of the structural transition. Our findings establish (TaSe4)3I as a model quasi-one-dimensional system with broken inversion symmetry facilitated through a displacive first-orderstructural transition.
A multi-dictionary fusion method based on improved dynamic time warping for moveable rail damage localization
Authors: Song Shuzhi , Zhang Xin , Chang Yongqi , Chen Yifei , Cui Jiazhong , Song Qinghua , Shen Yi
Published: January 2025

Traditional structural health monitoring (SHM) of rails relies on a fixed single sensor, limited by detection range and noise interference. Therefore, a multi-dictionary fusion method for movable rail damage localization is proposed based on improved dynamic time warping (DTW). The approach combines onboard acoustic emission sensors with peak detection frames to measure the moving distance of the inspection wheels and monitor a wide range of rails. Aiming to enhance the damage information, an innovative DTW-based multi-dictionary sparse representation algorithm is presented for data fusion. The second-order difference of the Mahalanobis distance is employed to optimize the fusion weights from the global property. A two-feature adaptive threshold is designed to precisely detect and localize damage on rails. The effectiveness of this method is verified at laboratory testing speeds less than 0.75 m s−1. The results demonstrate that it can accurately detect 2 mm deep strip and square damage, providing new inspiration for rail SHM.
Looseness detection system of bolted joints using a VMD-based nonlinear transformation approach with deep residual network
Authors: Kim Dong-Yoon , Kim Min-Je , Kim Chun-Il , Yoon Gil Ho
Published: January 2025

Bolted structures are subject to various vibrations, external forces and environmental factors, all of which can reduce their structural stability and compromise the integrity of bolted connections. Detecting bolt loosening in advance is crucial, as these effects often cause bolts to become loose, potentially leading to structural failure or collapse. However, identifying looseness in complex or large structures poses significant challenges, particularly when there is insufficient prior information about the loose-fit condition. To address this issue, the present study proposes a novel detection system for bolted joint looseness, employing a variational mode decomposition (VMD)-based nonlinear transformation (NT) approach integrated with a deep residual neural network, under several underlying assumptions. The proposed method utilizes VMD to decompose transverse vibrational modes into intrinsic mode functions (IMFs), selectively extracting signals with desired modes. The NT method is then applied to scale and shift the extracted signals, transforming them into a form that facilitates approximate classification. Image-based spectrograms are generated from the differences between transformed and reference signals, which are subsequently analyzed by the deep residual network. To validate the proposed method, several plates with bolted joints are considered.
Technology for detecting small-aperture leaks in natural gas pipelines utilizing transfer learning methodologies
Authors: Tang Jie , Wang Xiufang , Bi Hongbo , Dai Rui , Yue Youchen , Yang Jinghao , Feng Chao
Published: January 2025

To address the challenge of limited training samples in natural gas pipeline leakage detection, a novel transfer learning framework is proposed, which requires only a small amount of leakage data from a specific leak aperture as the source domain training set. Additionally, a new residual structure, named Dual-Pieces Net, is designed. This structure combines the cross-layer fusion of residual networks with a convolutional fragmenting mechanism. By processing feature maps in various ways through fragmentation, it enhances the model’s ability to refine and diversify signal processing. Unlike traditional detection models that rely on large amounts of diverse leakage data, the proposed transfer learning (TL) framework deepens the model’s extraction of invariant leakage features from the signal through stacked Dual-Pieces Net modules. This improves the TL performance and provides a solid technical foundation for more diverse TL scenarios. All transfer experiments were conducted on an experimental transport pipeline with a length of 169 m. The experimental results demonstrate that the proposed method offers significant advantages compared to other methods, proving its effectiveness and practical relevance in real-world applications.
Modelling diffusive phase transformations in multiphase systems using the Voronoi implicit interface method
Authors: Jacobsson E , Hallberg H , Hektor J , Ristinmaa M
Published: January 2025

This paper presents a general level set framework for modelling diffusive solid-state phase transformation processes in binary systems comprising several grains and phases. Notably, it is demonstrated how the Voronoi implicit interface method (VIIM) can be used to simulate microstructure evolution in polycrystals driven by diffusion. The key advantage of VIIM is that a single level set function can be utilized to describe the evolution of the microstructure. Thus, the computational cost is significantly reduced compared to more traditional approaches, in which each grain in the microstructure is assigned a separate level set function. Moreover, VIIM facilitates the reconstruction of a fully compatible grain boundary network without the presence of any void regions or grain overlaps. The reconstruction procedure does not require any special handling of junctions and can be easily extended to higher dimensions. Moreover, the reconstruction step enables an adaptive remeshing technique to be employed such that the mesh nodes conform with the location of the grain boundaries. Hence, the diffusion problem can be solved in distinct phase domains, demarcated by sharp interfaces. The kinetics of the phase interfaces are described by the amount of flux towards the interface in accordance with the theory of diffusion-controlled phase transformations. To examine the capabilities of the proposed model, both single-phase and multiphase problems are examined. For the single-phase problem, it is shown that the model behaves in accordance with analytical solutions and maintains mass conservation. For the multiphase problems, two model cases of polycrystals that share the same initial geometry but exhibit different phase distributions are investigated, as well as a representative microstructure example. The evolution of the microstructures towards an equilibrium state is in agreement with the expected phase fractions and demonstrates that the model produces reliable results. The error in mass conservation of the considered multiphase examples is within a margin of ±5%.
Modeling of β-Ga2O3 based double gate drain extended junction less FET and its parameter extraction methodology
Authors: Manukrishna Vanajakumari Raju , Nikhil KrishnanNadar Savithry
Published: January 2025

A static current model of β—Gallium Oxide (β-Ga2O3) based double gate drain extended junction less field effect transistor (DG-DeJLFET) is proposed. The model consists of two voltage-controlled current sources connected in series. One of the current sources accounts for the operation of the junction less field effect transistor whereas, the other takes care of the drift region current. The model is formulated by considering the mobility degradation caused by moderately elevated electric fields. A parameter extraction methodology for this model is also proposed by considering the dominance of certain parameters in the specific regions of operation. In general, the parameter extraction technique is based on the variation in the device current behavior at moderate electric fields. The proposed parameter extraction methodology is validated by comparing the results with the data obtained from the TCAD simulation.
Self-critical strategy adjustment based artificial intelligence method in generating diagnostic reports of respiratory diseases
Authors: Chen Binyue , Liu Guohua , Zhang Quan
Published: January 2025

Objective. Humanity faces many health challenges, among which respiratory diseases are one of the leading causes of human death. Existing AI-driven pre-diagnosis approaches can enhance the efficiency of diagnosis but still face challenges. For example, single-modal data suffer from information redundancy or loss, difficulty in learning relationships between features, and revealing the obscure characteristics of complex diseases. Therefore, it is critical to explore a method that can assist clinicians in detecting lesions early and in pre-diagnosing corresponding diseases. Approach. This paper introduces a novel network structure, SCSCS-Net, which can effectively extract image features from chest X-ray images and generate medical image descriptions, assist clinicians in analyzing patients' medical imaging information, deeply explore potential disease characteristics, and assist in making pre-diagnostic decisions. The SCSCS-Net consists of a reinforced cross-modal feature representation model (RCMFR) and a self-critical cross-modal alignment model (SCCMA), which are responsible for learning the features interdependence between images and reports by using a multi-subspace self-attention structure and guiding the model in learning report generation strategies to improve the professionalism and consistency of medical terms in generated reports, respectively. Main results. We further compare our model with some advanced models on the same dataset, and the results demonstrate that our method achieves better performance. Finally, the CE and NLG metrics further confirm that the proposed method acquires the ability to generate high-quality medical reports with higher clinical consistency in generating medical reports. Significance. Our novel method has the potential to improve the early detection and pre-diagnosis of respiratory diseases. The model proposed in this paper allows to narrow the gap between Artificial intelligence technology and clinical medical diagnosis and provides the possibility for in-depth integration.
Deep learning generalization for diabetic retinopathy staging from fundus images
Authors: Men Yevgeniy , Fhima Jonathan , Celi Leo Anthony , Ribeiro Lucas Zago , Nakayama Luis Filipe , Behar Joachim A
Published: January 2025

Objective. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-driven algorithms for DR staging from digital fundus images (DFIs) often struggle with generalization due to distribution shifts between training and target domains. Approach. To address this, DRStageNet, a deep learning model, was developed using six public and independent datasets with 91 984 DFIs from diverse demographics. Five pretrained self-supervised vision transformers (ViTs) were benchmarked, with the best further trained using a multi-source domain (MSD) fine-tuning strategy. Main results. DINOv2 showed a 27.4% improvement in L-Kappa versus other pretrained ViT. MSD fine-tuning improved performance in four of five target domains. The error analysis revealing 60% of errors due to incorrect labels, 77.5% of which were correctly classified by DRStageNet. Significance. We developed DRStageNet, a DL model for DR, designed to accurately stage the condition while addressing the challenge of generalizing performance across target domains. The model and explainability heatmaps are available at www.aimlab-technion.com/lirot-ai.
Mechanical behavior of HTS tapes in highly flexible REBCO cable under torsional and tensile loads
Authors: Tang Shengyi , Peng Enze , Li Yihao , Wei Liangyu , Shi Yunfan , Ge Zhendong , Yong Huadong , Zhou Youhe
Published: January 2025

Different cables composed of high-temperature superconducting (HTS) tapes (REBCO) have been developed for the generation of high magnetic fields and the transmission of large currents. Among the different HTS cables, the conductor on round core (CORC®) cable has become a promising candidate structure. In order to improve the flexibility of CORC® cable, a new kind of cable called HFRC (highly flexible REBCO cable) has been proposed, which has a similar configuration to CORC® cable. During the production of HFRC, REBCO tapes are wound on the surface of a spiral tube layer by layer. Compared to the solid core of traditional CORC® cable, the spiral tube is more prone to complex deformation under external loads, which results in special characteristics of REBCO tapes. In this paper, we analyze the deformation processes of spiral tubes under tension or torsion. Based on the geometrical relationship between the tapes and the cable core, the strain in REBCO tapes is predicted. Finite element models of HFRC/traditional CORC-like cable are built and verified by experiments. Through simulations, the variation of the strain and local creases of the tapes with the design parameters of the core is investigated. Relevant findings provide a reference for the structural optimization of HFRC.