bluuebunny/crossref_metadata_2025 huggingface dataset:
Discovery of a brain penetrant small molecule antagonist targeting LPA1 receptors to reduce neuroinflammation and promote remyelination in multiple sclerosis
Authors: Poon Michael M. , Lorrain Kym I. , Stebbins Karin J. , Edu Geraldine C. , Broadhead Alexander R. , Lorenzana Ariana O. , Paulson Bryanna E. , Baccei Christopher S. , Roppe Jeffrey R. , Schrader Thomas O. , Valdez Lino J. , Xiong Yifeng , Chen Austin C. , Lorrain Daniel S.
Published: May 2024

Multiple sclerosis (MS) is a chronic neurological disease characterized by inflammatory demyelination that disrupts neuronal transmission resulting in neurodegeneration progressive disability. While current treatments focus on immunosuppression to limit inflammation and further myelin loss, no approved therapies effectively promote remyelination to mitigate the progressive disability associated with chronic demyelination. Lysophosphatidic acid (LPA) is a pro-inflammatory lipid that is upregulated in MS patient plasma and cerebrospinal fluid (CSF). LPA activates the LPA1 receptor, resulting in elevated CNS cytokine and chemokine levels, infiltration of immune cells, and microglial/astrocyte activation. This results in a neuroinflammatory response leading to demyelination and suppressed remyelination. A medicinal chemistry effort identified PIPE-791, an oral, brain-penetrant, LPA1 antagonist. PIPE-791 was characterized in vitro and in vivo and was found to be a potent, selective LPA1 antagonist with slow receptor off-rate kinetics. In vitro, PIPE-791 induced OPC differentiation and promoted remyelination following a demyelinating insult. PIPE-791 further mitigated the macrophage-mediated inhibition of OPC differentiation and inhibited microglial and fibroblast activation. In vivo, the compound readily crossed the blood–brain barrier and blocked LPA1 in the CNS after oral dosing. Direct dosing of PIPE-791 in vivo increased oligodendrocyte number, and in the mouse experimental autoimmune encephalomyelitis (EAE) model of MS, we observed that PIPE-791 promoted myelination, reduced neuroinflammation, and restored visual evoked potential latencies (VEP). These findings support targeting LPA1 for remyelination and encourage development of PIPE-791 for treating MS patients with advantages not seen with current immunosuppressive disease modifying therapies.
Histological and immunohistochemical analyses of articular cartilage during onset and progression of pre- and early-stage osteoarthritis in a rodent model
Authors: Takahashi Ikufumi , Takeda Keisuke , Toyama Tadashi , Matsuzaki Taro , Kuroki Hiroshi , Hoso Masahiro
Published: May 2024

Early diagnosis and treatment of pre- and early-stage osteoarthritis (OA) is important. However, the cellular and cartilaginous changes occurring during these stages remain unclear. We investigated the histological and immunohistochemical changes over time between pre- and early-stage OA in a rat model of traumatic injury. Thirty-six male rats were divided into two groups, control and OA groups, based on destabilization of the medial meniscus. Histological and immunohistochemical analyses of articular cartilage were performed on days 1, 3, 7, 10, and 14 postoperatively. Cell density of proteins associated with cartilage degradation increased from postoperative day one. On postoperative day three, histological changes, including chondrocyte death, reduced matrix staining, and superficial fibrillation, were observed. Simultaneously, a compensatory increase in matrix staining was observed. The Osteoarthritis Research Society International score increased from postoperative day seven, indicating thinner cartilage. On postoperative day 10, the positive cell density decreased, whereas histological changes progressed with fissuring and matrix loss. The proteoglycan 4-positive cell density increased on postoperative day seven. These findings will help establish an experimental model and clarify the mechanism of the onset and progression of pre- and early-stage traumatic OA.
Neural-network quantum states for ultra-cold Fermi gases
Authors: Kim Jane , Pescia Gabriel , Fore Bryce , Nys Jannes , Carleo Giuseppe , Gandolfi Stefano , Hjorth-Jensen Morten , Lovato Alessandro
Published: May 2024

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases.
Predicting equilibrium distributions for molecular systems with deep learning
Authors: Zheng Shuxin , He Jiyan , Liu Chang , Shi Yu , Lu Ziheng , Feng Weitao , Ju Fusong , Wang Jiaxi , Zhu Jianwei , Min Yaosen , Zhang He , Tang Shidi , Hao Hongxia , Jin Peiran , Chen Chi , Noé Frank , Liu Haiguang , Liu Tie-Yan
Published: May 2024

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure but rather determined from the equilibrium distribution of structures. Conventional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. Here we introduce a deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG uses deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system such as a chemical graph or a protein sequence. This framework enables the efficient generation of diverse conformations and provides estimations of state densities, orders of magnitude faster than conventional methods. We demonstrate applications of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst–adsorbate sampling and property-guided structure generation. DiG presents a substantial advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in the molecular sciences.
Limited progress in global reduction of vulnerability to flood impacts over the past two decades
Authors: Sauer Inga J. , Mester Benedikt , Frieler Katja , Zimmermann Sandra , Schewe Jacob , Otto Christian
Published: May 2024

Global flood impacts have risen in recent decades. While increasing exposure was the dominant driver of surging impacts, counteracting vulnerability reductions have been detected, but were too weak to reverse this trend. To assess the ongoing progress on vulnerability reduction, we combine a recently available dataset of flooded areas derived from satellite imagery for 913 events with four global disaster databases and socio-economic data. Event-specific flood vulnerabilities for assets, fatalities and displacements reveal a lack of progress in reducing global flood vulnerability from 2000—2018. We examine the relationship between vulnerabilities and human development, inequality, flood exposure and local structural characteristics. We find that vulnerability levels are significantly lower in areas with good structural characteristics and significantly higher in low developed areas. However, socio-economic development was insufficient to reduce vulnerabilities over the study period. Nevertheless, the strong correlation between vulnerability and structural characteristics suggests further potential for adaptation through vulnerability reduction.
Release and re-enrichment of invisible gold in arsenian pyrite promoted by coupled dissolution-reprecipitation reactions
Authors: Yang Lin , Wang Qingfei , Fougerouse Denis , Xian Haiyang , Li Huajian , Wang Ruixue , Rickard William D. A. , Groves David I. , Deng Jun
Published: May 2024

The mechanisms responsible for invisible gold enrichment driven by coupled dissolution-reprecipitation reaction (CDR) are debated. Here we report the micro- to nano-scale textures of arsenian pyrite in a high-grade (>10 g/t) gold ore from the Chang’an deposit to trace the gold enrichment process. Our study records a CDR-driven evolution of mineral growth from an As-rich, Au-poor pyrite core, with numerous fine arsenopyrite inclusions, to an inclusion-free, As-Au-rich oscillatory pyrite rim. The reaction occurred at ~260 °C under 4.7 to 5.8 pH and –36.6 to –32.9 logfO2 conditions. The elevated As but depleted S contents in the pyrite core indicate a combined elevation of S fugacity and solubility of Au. The coprecipitation of arsenopyrite inclusions in the core caused a depletion of S fugacity to –13.8 ~ –11.7, triggering Au enrichment in the rim. This non-unique process has the potential to explain the upgrade of invisible Au in arsenian sulfides, worldwide.
Anti-hyperuricemia bioactive peptides: a review on obtaining, activity, and mechanism of action
Authors: Chen Ying , Li Hongyan , Cai Yunfei , Wang Ke , Wang Yousheng
Published: May 2024

The present work outlines the methods for preparing peptides from food proteins and elucidate their efficacy and mechanisms in combating hyperuricemia, supported byin vitroandin vivoevidence.
The future nickel metal supply for lithium-ion batteries
Authors: Sun Jiale , Zhou Haihui , Huang Zhongyuan
Published: May 2024

This article reviews the supply of nickel metal for lithium-ion batteries with regard to its resources, reserves, extraction and recycling, and application.
Rosavin improves insulin resistance and alleviates hepatic and kidney damage via modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals
Authors: Ali Hebatallah S. , Al-Amodi Hiba S. , Hamady Shaimaa , Roushdy Marian M. S. , Helmy Hasanin Amany , Ellithy Ghada , Elmansy Rasha A. , Ahmed Hagir H. T. , Ahmed Enshrah M. E. , Elzoghby Doaa M. A. , Kamel Hala F. M. , Hassan Ghida , ELsawi Hind A. , Farid Laila M. , Abouelkhair Mariam B. , Habib Eman K. , Elesawi Mohamed , Fikry Heba , Saleh Lobna A. , Matboli Marwa
Published: May 2024

Background: Inflammation-mediated insulin resistance in type 2 diabetes mellitus (T2DM) increases complications, necessitating investigation of its mechanism to find new safe therapies.
Cu2O-catalyzed cascade phosphinylation/cyclization of 2′-aminochalcones for the synthesis of hemi-indigo derivatives
Authors: Liu Xian , Hao Liqiang , Wang Yangyang , Yu Xiao , Yang Zhaoziyuan , Liu Yiping , Ji Yafei
Published: May 2024

An efficient Cu2O catalysis of 2′-aminochalcones and diphenylphosphine oxides leading to a series of hemi-indigo derivatives is presented.
Imidazolidin-2-iminato vanadium complexes for the synthesis of ethylene/propylene/5-ethylidene-2-norbornene (ENB) terpolymers with high ENB incorporation and ultra-high molecular weight
Authors: Zhang Xiao-Yu , Li Hao-Liang , Xiang Bo , Liu Xiang-Wei , Zhang Shu , Wu Yi-Xian
Published: May 2024

Ethylene/propylene/5-ethylidene-2-norbornene terpolymers with high ENB incorporation and ultra-high molecular weight were synthesized by using imidazolidin-2-iminato vanadium complexes.
Recent progress of poly(3-hexylthiophene)-based materials for thermoelectric applications
Authors: Zhu Maiyong , He Binqi , Zhang Kai , Hussain Shahid , Li Tie
Published: May 2024

Poly(3-hexylthiophene) (P3HT) shows great promising in thermoelectric field owing to its simple preparation, good stability and adjustable conductivity. Here the research progress of P3HT in thermoelectric field in recent years is reviewed.
DFT study of rhodium-catalyzed transformation of silacyclobutane with alkyne or H2O: Si–Cl bond reductive elimination vs. alkyne insertion
Authors: Li Jinxia , Chen Weichi , Qu Shuanglin
Published: May 2024

Density functional theory (DFT) calculations are employed to uncover the mechanisms of Rh/BINAP-catalyzed C(sp)–H/O–H silylation and unveil the ligand-dependent chemoselectivity.
Investigation of the cooperative-effects of Lewis- and Brønstedt acids in homogeneously catalyzed OME fuel synthesis by inline-NMR monitoring
Authors: Endres Patrick , Schuett Timo , Zechel Stefan , Hager Martin D. , Geitner Robert , Schubert Ulrich S.
Published: May 2024

The cooperative effect of Lewis and Brønsted acids within the OME fuel synthesis was investigated via inline NMR monitoring.
Application of static integrated skeletal reduction and tabulation of dynamic adaptive chemistry in the combustion simulation of ethylene-fueled scramjet combustor
Authors: Li Zhongwen , Liu Jianwen , Wang Jingbo
Published: May 2024

A compact and high-fidelity ethylene reduced mechanism is obtained by the integrated reduction, it presents good performance in scramjet simulation.
Effect of calcination temperature induced structural modifications on the photocatalytic efficacy of Fe2O3–ZrO2 nanostructures: mechanochemical synthesis
Authors: Zain Maqsoom , Yasin Khawaja Ansar , Haq Sirajul , Rehman Wajid , Din Salah Ud , Shujaat Shafia , Syed Asad , Hossain M. Khalid , Paray Bilal Ahamad , Razzokov Jamoliddin , Samad Abdus
Published: May 2024

Water contamination due to organic pollutants is a challenging issue around the globe, and several attempts have been made to deal with this issue.
The preparation of gallic acid-grafted cationic chitosan as effective salt-tolerant flocculants
Authors: Liu Jingxuan , Zhao Hongyu , Wang Mengyue , Ban Wenzheng , Lu Xi , Yan Bin
Published: May 2024

In this work, gallic acid was successfully grafted onto quaternary aminated chitosan to prepare a high efficiency cationic flocculant.
Developments and applications of α-bromonitrostyrenes in organic syntheses
Authors: Doraghi Fatemeh , Aghanour Ashtiani Mohammad Mahdi , Moradkhani Fatemeh , Larijani Bagher , Mahdavi Mohammad
Published: May 2024

In this work, we have described the use of α-bromonitrostyrenes in the synthesis of a wide variety of carbocyclic and heterocyclic compounds under organocatalysis, metal catalysis, and base-catalysis systems as well as catalyst-free reactions.
Tuning white light emission using single-component tetrachroic Dy3+ metallacrowns: the role of chromophoric building blocks
Authors: Salerno Elvin V. , Eliseeva Svetlana V. , Petoud Stéphane , Pecoraro Vincent L.
Published: May 2024

The fine tuning of CIE coordinates was demonstrated in a series of molecular tetrachroic chromophores, Dy3+/Ga3+ metallacrowns, through modifications of the symmetry around Dy3+ and the nature of the organic ligands.
Aromatization-driven deconstructive functionalization of spiro dihydroquinazolinones via dual photoredox/nickel catalysis
Authors: Miao Hong-Jie , Zhang Jin-Hua , Li Wenke , Yang Wenpeng , Xin Hong , Gao Pin , Duan Xin-Hua , Guo Li-Na
Published: May 2024

The aromatization-driven deconstructive coupling of spiro carbocycles with organic halides via dual photoredox/nickel catalysis is developed. An in-depth mechanism study revealed that this reaction proceeded via a radical-metal crossover pathway.
High-order layered self-assembled multicavity metal–-organic capsules and anti-cooperative host–multi-guest chemistry
Authors: Li Kaixiu , Li Zhengguang , Yuan Jie , Chen Mingzhao , Zhao He , Jiang Zhiyuan , Wang Jun , Jiang Zhilong , Li Yiming , Chan Yi-Tsu , Wang Pingshan , Liu Die
Published: May 2024

Complex metal–organic capsules with large cavities (1–4 pockets) have been prepared and used as host structures for multi-guest recognition, displaying intriguing negative cooperation behavior within separate rigid cavities in the binding of C60.
A comprehensive investigation of Ag7P3X11 (X = {O, S, and Se}) solid-state silver superionic conductors
Authors: Niksirat Amin , Soleimani Maryam , Zand Ali Lashani , Pourfath Mahdi
Published: May 2024

This investigation rigorously analyzes and introduces a new class of high-performance inorganic solid-state electrolytes, formulated as Ag7P3X11 (X = {O, S, Se}), through comprehensive ab initio calculations, highlighting their potential for advanced energy storage technologies.
A rigid–soft hybrid paper-based flexible pressure sensor with an ultrawide working range and frequency bandwidth
Authors: Wang Cong , Quan Jiamin , Liu Linpeng , Cao Peilin , Ding Kaiwen , Ding Yulong , Jia Xianshi , Yan Dejin , Lin Nai , Duan Ji'an
Published: May 2024

A rigid–soft hybrid design strategy to fabricate paper-based pressure sensor with ultrawide working range and frequency bandwidth.
Ultra‐fast finite element analysis of coreless axial flux permanent magnet synchronous machines
Authors: Chulaee Yaser , Ionel Dan M.
Published: May 2024

Large‐scale design optimisation techniques enable the design of high‐performance electric machines. Electromagnetic 3D finite element analysis (FEA) is typically employed in optimisation studies for accurate analysis of axial flux permanent magnet (AFPM) machines, which require extensive computational resources. To reduce the computational burden, a FEA‐based mathematical method relying on the geometric and magnetic symmetry of coreless AFPM machines is proposed to estimate the machine performance indicators using the least number of FEA solutions, thereby significantly lowering the running time. This method is generally applicable to AFPM machines with low saturation effects and cogging torque as exemplified for a printed circuit board (PCB) stator coreless AFPM machine. To further reduce the computation time, a systematically simplified equivalent 3D FEA model for planar PCB coils integrated with this machine is also proposed. The practical implementation of the introduced method is elaborated based on an example optimisation study, and an analytical method for fast design scaling is also discussed. The results of the proposed approach are compared with detailed transient FEA results, and a prototype 26‐pole PCB stator coreless AFPM machine was also used to validate the results experimentally.
A review of optic disc and optic cup segmentation based on fundus images
Authors: Ma Xiaoyue , Cao Guiqun , Chen Yuanyuan
Published: May 2024

Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area.
Detection of shading for short-term power forecasting of photovoltaic systems using machine learning techniques
Authors: Kappler Tim , Starosta Anna Sina , Munzke Nina , Schwarz Bernhard , Hiller Marc
Published: May 2024

This paper presents a machine learning based solar power forecast method that can take into account shading related fluctuations. The generated PV power is difficult to predict because there are various fluctuations. Such fluctuations can be weather related when a cloud passes over the array. But they can also occur due to shading caused by stationary obstacles, and this paper addresses this form of shading. In this work an approach is presented that improves the forecast under such fluctuations caused by shading. A correction of the prediction could successfully reduce error due to shading. The evaluation of the model is based on five sets of recorded shading data, where shading resulted from intentionally placed structures. The correction uses internal inverter data and irradiance values of the previous day to perform the correction and was able to reduce the RMSE of four 10 kWp systems with different orientation and tilt angle under shading and thus improve the prediction accuracy by up to 40%. The model can detect how intense the shading is and correct the forecast by itself.
A novel method for determining the resistivity of compressed superconducting materials
Authors: Sun Liling , Wu Qi , Cai Shu , Ding Yang , Mao Ho-kwang
Published: May 2024

The measurement of resistivity in a compressed material within a diamond anvil cell presents significant challenges. The high-pressure experimental setup makes it difficult to directly measure the size changes induced by pressure in the three crystallographic directions of the sample. In this study, we introduce a novel and effective method that addresses these technical challenges. This method is anticipated to offer a valuable foundation for high-pressure investigations on quantum materials, particularly those with anisotropic layered structures.
Pressure-Induced Distinct Self-Trapped Exciton Emission in Sb3+-Doped Cs2NaInCl6 Double Perovskite
Authors: Feng 冯 Youjia 友佳 , Chen 陈 Yaping 亚平 , Wang 王 Leyao 乐瑶 , Wang 王 Jiaxiang 家祥 , Chang 常 Duanhua 断华 , Yuan 袁 Yifang 亦方 , Wu 武 Min 敏 , Fu 付 Ruijing 瑞净 , Zhang 张 Lili 丽丽 , Wang 王 Qinglin 庆林 , Wang 王 Kai 凯 , Guo 郭 Haizhong 海中 , Wang 王 Lingrui 玲瑞
Published: May 2024

The Cs2NaInCl6 double perovskite is one of the most promising lead-free perovskites due to its exceptional stability and straightforward synthesis. However, it faces challenges related to inefficient photoluminescence. Doping and high pressure are employed to tailor the optical properties of Cs2NaInCl6. Herein, Sb3+ doped Cs2NaInCl6 (Sb3+:Cs2NaInCl6) was synthesized and it exhibits blue emission with a photoluminescence quantum yield of up to 37.3%. Further, by employing pressure tuning, a blue stable emission under a very wide range from 2.7 GPa to 9.8 GPa is realized in Sb3+:Cs2NaInCl6. Subsequently, the emission intensity of Sb3+:Cs2NaInCl6 experiences a significant increase (3.3 times) at 19.0 GPa. It is revealed that the pressure-induced distinct emissions can be attributed to the carrier self-trapping and detrapping between Cs2NaInCl6 and Sb3+. Notably, the lattice compression in the cubic phase inevitably modifies the band gap of Sb3+:Cs2NaInCl6. Our findings provide valuable insights into effects of the high pressure in further boosting unique emission characteristics but also offer promising opportunities for development of doped double perovskites with enhanced optical functionalities.
Enhancing automated inspection in metal industries: zero-shot segmentation of surface defects using bounding box prompts
Authors: Lema Darío G , Usamentiaga Rubén , García Daniel F
Published: May 2024

Surface defect detection poses a challenging problem that has been addressed for decades. Many of actual solutions are based on deep learning algorithms. However, these algorithms require a large amount of data to train accurate models. This becomes especially problematic for semantic segmentation algorithms, which need labeled datasets at the pixel level, a laborious and time-consuming task. Therefore, this paper proposes a semi-supervised method in which the predictions of an object detector are combined with the segmentation of a zero-shot model, eliminating the need to label a dataset for semantic segmentation. Results are compared with relevant supervised semantic segmentation models, such as UNet and DeepLabv3+. UNet achieves an F1 score of 0.824, while DeepLabv3+ achieves 0.847. The proposed method combining YOLOv8n and Segment Anything Model achieves an F1 score of 0.804. This slight loss of F1 is compensated by the significant time savings, unlike UNet and DeepLabv3+, where labeling of the dataset is necessary.
Thermally compensated common-path differential interferometer with reduced long-term zero-drifts
Authors: Rerucha Simon , Hola Miroslava , Oulehla Jindrich , Lazar Josef , Mikel Bretislav , Cip Ondrej
Published: May 2024

Laser interferometers have served as the workhorses in the metrology of length for several decades. Their broader application brings further challenges, especially for longer measurement time-frames or outside the laboratory environment with strictly controlled conditions. As a part of our team’s activities aiming at characterizing and eliminating the effects of unstable temperature on interferometric length measurements, we report on successfully remodelling a differential interferometer’s optical arrangement focused on increasing resilience against temperature changes. The experimental characterization under constant temperature and subsequently under thermal load proved a tenfold decrease in short-term fluctuations and reduced sensitivity to temperature changes by a factor of 100.
Erosion depth prediction of chloride ions under stray current using FEM based CNNs
Authors: Li Yu , Zhang Yishuang , Liu Gang , Li Zihao , Zhao Danyang , Xu Wenqiang , Qiang Sheng , Lai Jiayue
Published: May 2024

Stray currents can accelerate the transport of corrosive ions, especially Cl−, in concrete materials, which is very detrimental to structural safety. Effectively predicting the erosion depth of Cl− is crucial for evaluating structural safety. This article is based on a finite element model and verifies the erosion depth of Cl− under different voltages, Cl− concentrations, and corrosion time through experimental data. A polynomial was used to fit the quantitative relationship between erosion depth, Cl− concentrations, and corrosion time under single voltage condition. However, this formula only applies to a single voltage and has too many parameters. Therefore, this article also established a CNNs regression model to predict the depth of Cl−, and the results showed the multiple regression ability of CNNs. It has been proven that CNNs can accurately predict the erosion depth, which helps to accurately evaluate structural safety. After comparing experimental values, CNNs, ResNet, and ResNet-attention, it was found that residual networks and attention mechanisms did not significantly improve the prediction accuracy of deep networks, which may be related to insufficient data volume. After expanding the dataset, ResNet performed the best overall, and ResNet-attention had better testing performance, which is related to the powerful feature extraction ability of the attention mechanism.
Optimization of femtosecond laser processing parameters of SiC using ANN-NSGA-II
Authors: Li Chen , Ren Wanzhou , Wang Jing
Published: May 2024

In the field of femtosecond laser machining, it is essential to select the appropriate process parameters to obtain near thermal damage-free and high efficient machining of SiC wafer. In this work, a method of process parameter optimization for femtosecond laser machining of 4H–SiC was proposed by using the predictive ability of the Artificial Neural Network (ANN) and the optimization algorithm of the non-dominated sorting genetic algorithm (NSGA-II). Firstly, the femtosecond laser was used to fabricate microgrooves on SiC wafers, and the effects of process parameters (laser average power, scanning speed and repetition rate) on groove depth, width, heat affected zone and material removal rate were investigated. Secondly, the ANN model is established based on experimental data. Other experiments verify the accuracy of the model, and the average error in the model’s predictions is around 5%. Thirdly, Pareto optimal solutions are obtained by global optimization of the ANN model using the NSGA-II. The experimental results show that the Pareto optimal solutions are effective and reliable. This proposed method offers dependable guidance for the selecting and optimizing process parameters of high hardness and brittle materials in the field of femtosecond laser processing, and reduces the cost of selecting the appropriate processing parameters in the production process. The method can also be extended to other machining means, such as turning and milling.
Critical intermittency in rational maps
Authors: Jan Homburg Ale , Peters Han , Rabodonandrianandraina Vahatra
Published: May 2024

Intermittent dynamics is characterized by long periods of different types of dynamical characteristics, for instance almost periodic dynamics alternated by chaotic dynamics. Critical intermittency is intermittent dynamics that can occur in iterated function systems, and involves a superattracting periodic orbit. This paper will provide and study examples of iterated function systems by two rational maps on the Riemann sphere that give rise to critical intermittency. The main ingredient for this is a superattracting fixed point for one map that is mapped onto a common repelling fixed point by the other map. We include a study of topological properties such as topological transitivity.
Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy
Authors: Chen Mei , Pang Bo , Zeng Yiling , Xu Cheng , Chen Jiayi , Yang Kunyu , Chang Yu , Yang Zhiyong
Published: May 2024

Objective. To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model. Approach. Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds. Main results. Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively. Significance. Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.
Multiple skin transitions in two-band non-Hermitian systems with long-range nonreciprocal hopping
Authors: Li Ye-Wei-Yi , Nie Xiao-Feng , Cui Wen-Xue , Wang Hong-Fu
Published: May 2024

Non-Hermitian skin effect (NHSE) is a prominent feature in non-Hermitian physics, leading to novel topological properties and expanding the traditional energy band theories. In this paper, we investigate a two-band non-Hermitian system in which multiple skin transitions are induced by long-range nonreciprocal hopping. The spectral winding number under periodic boundary conditions reveals the localization directions of skin states. Further, we present the analytical solution of transition points by tracing the self-intersecting points on the complex plane. Interestingly, the current system exhibits the abundant NHSEs, including the normal, W-shaped, and bipolar localization properties, which the eigenstate distributions and the generalized Brillouin zone can clearly illustrate. We also provide a phase diagram to represent the skin transition properties of the system comprehensively. Further, we demonstrate that the multimer non-Hermitian lattices also present the anomalous skin effect and multiple transitions, which occur in the region of the bulk band touching, the same as the two-band lattice. Moreover, a feasible scheme is proposed to realize the current non-Hermitian system with long-range nonreciprocal hopping by a topoelectrical circuit. This work further supplies the content of skin transitions and may help us explore more plentiful localization features in the two-band non-Hermitian systems.
The magnetic coherent mode with shifted Alfvén gap frequency destabilized by the thermal trapped electron resonance in the pedestal of high-confinement tokamak plasmas
Authors: Wu (伍兴权) Xingquan , Xu (徐国盛) Guosheng , Chen (陈冉) Ran , Wan (万宝年) Baonian
Published: May 2024

The magnetic coherent modes (MCM) with toroidal mode number n about 1 (Chen R. et al 2018 Nucl. Fusion
58 112004) frequently appear in the edge pedestal of high-confinement tokamak plasmas on EAST in the absence of energetic particles. Although these modes are experimentally compatible with the steady-state operation of the pedestal, the driving mechanism without energetic particles of MCM is a long-standing mystery. To reveal the excitation mechanism, a fluid-drift kinetic hybrid local linear model has been developed. It is found that MCM is a new Alfvén eigenmode with a gap frequency much lower than the ideal Toroidal Alfvén Eigenmodes (TAEs) with two significant properties: (1) due to the unique steep pressure gradient in the pedestal region, the diamagnetic frequency becomes comparable to the ideal TAE frequency, which makes the Alfvén continuum in this region move significantly in the ion diamagnetic direction and form a gap of lower frequency; (2) due to the bounce frequencies of thermal electrons becoming also comparable to the ideal TAE frequency in the pedestal region, the free energy of the pressure gradient can be fed into the MCM through the thermal electron bounce resonance excitation, which is essentially the coupling between the shifted TAEs and low-

n

trapped electron modes. The low-

n

MCM is proved to be a shifted Alfvén gap mode in the pedestal region, which is anticipated to exist in low collisional plasmas of future fusion reactors. It is of great significance to carry out relevant physical model research to enhance the understanding of pedestal physics.
The impact of future UK offshore wind farm distribution and climate change on generation performance and variability
Authors: Giddings Josh , Bloomfield Hannah , James Rachel , Blair Michael
Published: May 2024

The UK plans to significantly increase offshore wind generation capacity as part of the effort to achieve net zero targets. Current installation is densely located in a few areas, particularly off the east coast of England, and although current siting proposals include new offshore regions, significant volumes of wind generation capacity are yet to be located to meet 2050 installation targets. This paper uses a recent dataset of multi-decadal offshore wind power capacity factor timeseries to assess how UK offshore wind generation is likely to be affected by both the spatial distribution of future wind farms, and by the impacts of near-future (2020–2050) climate change. We determine that a wider geographic spread of offshore capacity results in a much-improved and less-variable UK-aggregated power generation profile, with substantial reductions in periods of low generation and extreme wind power ramping events, without negatively impacting mean or peak generation outputs. The impact of near-term climate change appears to be minor, slightly reducing overall generation and possibly resulting in an underestimation of future installation requirements, but this climate signal is outweighed by the effects of spatial distribution, and even more so by inherent hourly to inter-annual wind speed variability. This study implies that the intermittency of wind generation can be partly mitigated through increasing the spatial diversity of the existing wind farm distribution. Alongside a more in depth investigation of future climate change, and a holistic assessment of relevant geospatial factors such as Levelised Cost of Energy, infrastructure, and environmental constraints, this study could be used for optimisation of future offshore wind siting.
Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study
Authors: Herth Jonas , Schmidt Felix , Basler Sarah , Sievi Noriane A , Kohler Malcolm
Published: May 2024

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient’s breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2 fold change ⩾1 and q-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z −242.18428 and m/z −117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.
Simple two-step synthesis of Ag/ZnO nanoparticles with enhanced photocatalytic response
Authors: Flores-Castañeda Mariela , Meza Lilibeth , Guevara-Martínez Santiago José , Pérez-Centeno A , Quiñones-Galván J G
Published: May 2024

This study presents a simple two-step synthesis method for the fabrication of Ag/ZnO nanocomposites to improve the photocatalytic response of ZnO. The synthesis involves ZnO nanoparticles that were fabricated from the thermal decomposition of commercial zinc acetate. In order to produce Ag/ZnO nanoparticles in a simple two-step process, ZnO nanoparticles were mixed with Ag nanoparticle suspensions previously obtained by the laser ablation of solids in liquids technique at three different fluences. Structural characterization of ZnO powders revealed the presence of single phase wurtzite ZnO nanoparticles with crystal sizes of 20 nm. On the other hand, XRD patterns for a composite sample revealed the presence of signals associated to both ZnO and Ag suggesting that silver nanoparticles were attached to the ZnO particles surface. Optical characterization of the ZnO powders, carried out by UV–vis spectroscopy, showed a strong absorption band centered at 380 nm, which is associated to excitonic transitions in ZnO nanoparticles, whilst absorption measurements of silver nanoparticles colloids revealed the presence of a strong band centered near 412 nm. This band shifts to shorter wavelengths with increasing fluence from 2.6 to 6.2 J cm−2, indicating changes in nanoparticles size. Photocatalytic degradation tests of methylene blue under UV irradiation were carried out using pure ZnO, Ag colloids and Ag/ZnO nanoparticles. After the first 30 min of irradiation, it was observed that the silver nanoparticles reached degradation percentages of 16, 22 and 29% for samples synthesized at 2.6, 4.2 and 6.2 J cm−2, respectively. Meanwhile the ZnO sample reached a value of 13% after 30 min. Regarding the Ag/ZnO composite sample, the percentage of degradation after 30 min was 36%, demonstrating a considerable enhanced photocatalytic activity as compared to ZnO. After 24 h irradiation, Ag/ZnO degraded 95% of the methylene blue solution. It was observed that decorating ZnO with laser produced silver nanoparticles accelerates the photocatalytic response of ZnO by enhancing the activity at short times.
Investigating the Z-scan technique for quantifying circulating cell-free DNA (ccfDNA) extracted from blood plasma as a potential biomarker for various cancers
Authors: Ara H , Tarek S A , Biswas M K , Al-Din S M S , Hoque E , Hasan K M E , Hossain A K M M , Faruque S B , Haque Y , Sharafuddin S M
Published: May 2024

The Z-scan technique is a nonlinear optical method that has found applications in characterizing various materials, particularly those exhibiting nonlinear optical response (NLOR). This study applies the continuous wave (CW) Z-scan technique to examine the NLOR in terms of the nonlinear optical phase shifts

(
Δ

Φ

0

) exhibited by the ccfDNA extracted from blood plasma samples collected from a group constituting 30 cancer-diagnosed patients and another group constituting 30 non-diagnosed individuals. The cancer group exhibited significantly higher

Δ

Φ

0

versus incident power slopes compared to the non-cancer group (0.34 versus 0.12) providing a clear distinction between the two groups. The receiver operating characteristic (ROC) curve analysis of the results indicates a clear separation between cancer and non-cancer groups, along with a 94% accuracy rate of the data. The Z-scan results are corroborated by spectrophotometric analysis, revealing a consistent trend in the concentration values of ccfDNA samples extracted from both cancerous and non-cancerous samples, measuring 3.24 and 1.41 respectively. Additionally, more sensitive fluorometric analyses of the respective samples demonstrate significantly higher concentrations of ccfDNA in the cancer group, further affirming the correlation with the Z-scan results. The study suggests that the Z-scan technique holds promise as an effective method for cancer detection, potentially contributing to improved oncology diagnosis and prognosis in the future.
An approach to calculate Franck-Condon factors involving anharmonic potentials using harmonic oscillator bases
Authors: Suárez E , Lemus R
Published: May 2024

In this contribution a discrete variable representation (DVR) approach to calculate Franck-Condon factors (FCFs) is presented. The method is illustrated using a harmonic oscillator basis. The advantage of this approach is that it is possible to calculate FCFs associated with general potentials based on the knowledge of the harmonic oscillator theory. First the coordinate and momentum representations are introduced. Based on this concept, the DVR is proposed, which allows the Franck-Condon factors to be calculated in a simple form. This method takes advantage of the momentum as generator of translations to include the displacement of the potentials. The calculation of the FCFs is exemplified for transitions involving the ground state

X

1

Σ

g

+

and the excited state a
1Π
g
of the diatomic molecule 14N2 both modelled with Morse potentials.
Deep learning-assisted structural health monitoring: acoustic emission analysis and domain adaptation with intelligent fiber optic signal processing
Authors: Huang Xuhui , Elshafiey Obaid , Mukherjee Subrata , Karim Farzia , Zhu Yupeng , Udpa Lalita , Han Ming , Deng Yiming
Published: May 2024

Structural health monitoring aims to detect damage progression in materials. This study focuses on categorizing crack stages, a critical aspect of monitoring structural integrity. By leveraging acoustic emission (AE) monitoring, cracks can be analyzed in a data-driven manner. However, applying AE analysis poses several challenges, including discrepancies between simulated AE data from models and experimental data from the field, as well as class imbalance in crack progression data, with a scarcity of late-stage data. To bridge the gap between theory and experiments, our approach employs domain adaptation to synchronize simulated and actual AE data. The model learns robust domain-invariant features through meticulous experimentation across training epochs. Quantitative analysis of the model’s performance provides key insights. F1 scores vary with feature counts, and domain adaptation outperforms by 20% on highly imbalanced datasets. This emphasizes the model’s adaptability for precise crack classification, even with underrepresented damage classes. In summary, this study advances structural health monitoring by offering a solid AE analysis approach. Core contributions include reconciling simulated and experimental data discrepancies, tackling class imbalance, optimizing feature extraction, and demonstrating robust crack stage categorization. The insights gained highlight the merits of domain adaptation and data-driven AE analysis for predicting crack progression.
Remote monitoring of sleep disorder using FBG sensors and FSO transmission system enabled smart vest
Authors: Kanwal Firdos , Atieh Ahmad , Ghafoor Salman , Haq Anwar-ul , Qureshi Khurram Karim , Aziz Imran , Mirza Jawad
Published: May 2024

Optical sensors, particularly fiber Bragg grating (FBG) sensors have achieved a fast ingress into the fields of medical diagnostic and vital signs monitoring. Wearable smart textiles equipped with FBG sensors are catching huge research attention in different applications for measurement and monitoring of physiological parameters. In this paper, we report a simple technique for remote monitoring of sleep disorder using a smart vest implemented with four FBG stress sensors located at different sides of the vest and free space optics (FSO) transmission system. The sleep disorder of the patient is monitored in real time through shifts in the original Bragg wavelengths of sensors by stress loading during random changes in patient’s sleeping postures. The reflected wavelength from a stress loaded sensor at a certain posture is transmitted over 0.5 km long FSO channel towards remote medical center, photodetected, and then can be processed in a PC to record the restlessness in a certain time interval in terms of total number of times sleeping postures are changed, total time spent at a certain posture etc. To correctly detect the stress loaded FBG sensor at the medical center, various parameters of FBG sensors and demultiplexer are carefully adjusted to minimize the power leakages from unloaded sensors that may result into errors in the detection. Maximum dynamic range around 45 dB has been achieved ensuring accurate detection. This study not only provides a cost-efficient and non-intrusive solution for monitoring the sleep disorder of patients but also can be used for real-time monitoring of various other ailments, such as lung, brain, and cardiac diseases in future.