bluuebunny/crossref_metadata_2025 huggingface dataset:
Comprehensive Guide to Transoral Endoscopic Thyroidectomy Vestibular Approach: Step-by-Step Technique
Authors: Anuwong Angkoon
Published: August 2024

Transoral endoscopic thyroidectomy vestibular approach (TOETVA) is a minimally invasive surgical technique that offers safety comparable to conventional open thyroidectomy, with the added benefit of superior cosmetic outcomes due to the absence of visible scars. This innovative approach is gaining popularity worldwide as an alternative for patients seeking thyroid surgery without postoperative scarring. TOETVA involves positioning the patient supine with slight neck extension under general anesthesia. The procedure uses three trocars placed via midline and lateral incisions, with a 10-mm 30-degree laparoscope for visualization. Preoperative care includes cleaning the surgical area and administering intravenous antibiotics to prevent infection. The technique mirrors conventional thyroidectomy but utilizes standard laparoscopic instruments. It emphasizes the precise identification and preservation of critical anatomical structures, such as the cricoid cartilage, thyroid isthmus, trachea, carotid artery, superior laryngeal nerve, and recurrent laryngeal nerve. The use of an energy device and careful dissection help minimize bleeding and reduce nerve injury risks. Superior pole management and meticulous handling of the thyroid lobe ensures optimal outcomes. The growing popularity of TOETVA indicates a shift toward minimally invasive thyroid surgery, with TOETVA emerging as a leading method. This paper provides a comprehensive overview of the procedure, highlighting its benefits and critical aspects of its successful implementation.
A Question of (Academic) Honour? Motivations for Member Participation in Advisory Boards in the German Science System
Authors: Beer Andreas , Hechler Daniel , Pasternack Peer
Published: August 2024

The paper analyses the motivations of academics to participate in advisory boards that are active within the German science system. Based on the results of an online survey and interviews, reasons for joining a board, staying active as well as reasons for disengagement are explored. The respondents of the survey and the interviewees include both members of advisory boards and their academic coordinators. In the final section of the paper, several possible changes in the work of boards that may influence members' motivation are discussed.
Implementation of the ARIMA model for prediction of economic variables: evidence from the health sector in Brazil
Authors: Pereira da Veiga Claudimar , Pereira da Veiga Cássia Rita , Girotto Felipe Mendes , Marconatto Diego Antonio Bittencourt , Su Zhaohui
Published: August 2024

In recent decades, quantitative models for forecasting economic crises have garnered significant interest from researchers, policymakers, and public and private institutions. Identifying the most appropriate models for predicting economic time series behaviors during crises is a pressing challenge. Effective techniques can be instrumental in forestalling financial irregularities, thus empowering institutions to deploy remedial actions and swiftly minimizing further economic setbacks. Contemporary literature introduces various forecasting models, such as the autoregressive integrated moving average (ARIMA) model. Recognized for its statistical alignment with numerous linear models, the ARIMA model has demonstrated its efficacy across various domains. This paper delves into applying the ARIMA model to predict five critical economic time series that substantially influenced Brazil’s public and private healthcare sectors throughout the economic crisis between 2000 and 2020. These time series encompassed the variables (i) the gross domestic product—GDP, (ii) the Extended National Consumer Price Index—IPCA, (iii) the unemployment rate, (iv) the total number of health plan beneficiaries, and (v) total number of individual health plan beneficiaries. Importantly, this study provides a comprehensive outline of the ARIMA implementation process, underscoring that precise forecasting is pivotal for managers aiming to curtail financial anomalies and avert resource shortages. The findings highlight the ARIMA model’s (1, 0, 2), (2, 2, 1), (0, 1, 2), (1, 1, 2), and (2, 2, 1) viability in accurately forecasting health-related time series, exceeding 95% accuracy for economic variables analyzed. These results have significant practical implications for healthcare managers and decision-makers. By offering accurate forecasts of critical economic metrics, such as the unemployment rate and the transition of beneficiaries between public and private health systems during economic downturns, this research provides valuable insights for strategic planning within the healthcare sector.
To share or not to share, that is the question: a qualitative study of Chinese astronomers’ perceptions, practices, and hesitations about open data sharing
Authors: Liu Jinya , Zhao Kunhua , Gu Liping , Xia Huichuan
Published: August 2024

Many astronomers in Western countries may have taken open data sharing (ODS) for granted to enhance astronomical discoveries and productivity. However, how strong such an assumption holds among Chinese astronomers has not been investigated or deliberated extensively. This may hinder international ODS with Chinese astronomers and lead to a misunderstanding of Chinese astronomers’ perceptions and practices of ODS. To fill this gap, we conducted a qualitative study comprising 14 semi-structured interviews and 136 open-ended survey responses with Chinese astronomers to understand their choices and concerns regarding ODS. We found that many Chinese astronomers conducted ODS to promote research outputs and respected it as a tradition. Some Chinese astronomers have advocated for data rights protection and data infrastructure’s further improvement in usability and availability to guarantee their ODS practices. Still, some Chinese astronomers agonized about ODS regarding the validity of oral commitment with international research groups and the choices between international traditions and domestic customs in ODS. We discovered two dimensions in Chinese astronomers’ action strategies and choices of ODS and discussed their descriptions and consequences. We also proposed the implications of our research for enhancing international ODS in future work.
Integrating industry 4.0 technologies for the administration of courts and justice dispensation—a systematic review
Authors: Bhatt Hitesh , Bahuguna Rajesh , Swami Siddharth , Singh Rajesh , Gehlot Anita , Akram Shaik Vaseem , Gupta Lovi Raj , Thakur Amit Kumar , Priyadarshi Neeraj , Twala Bhekisipho
Published: August 2024

The judiciary is a foundation of democracy, upholding the rule of law and protecting rights. Efficient court administration is vital for public trust, justice, and timely proceedings. Currently, courts face challenges such as inconsistencies, adjournments, and absence of transparency, undermining the justice system. Traditional manual processes and paper-based documentation cause significant backlogs, slow resolutions, and limited public access. As case volumes and complexities rise, modernizing court administration through digital transformation is progressively critical. Currently, many countries are integrating technologies in the courts for its administration and other activities. In recent years, courts and judges have been subjected to pressure to improve performance, uplifting judicial effectiveness to a top priority. Subsequently, several countries have integrated simplification and digitization strategies in judicial initiatives to enhance court efficiency. Switzerland’s Justitia 4.0 and Brazil’s PJE are notable initiatives that focused to strengthen court administration through digitalization. These aspects motivated this study to examine the detailed integration of industry 4.0 technologies such as the Internet of things, cloud computing, blockchain, big data analytics, robotics, drones, Metaverse, digital twins, and artificial intelligence for court administration with digitalized infrastructure. According to the study, integrating these technologies in less complex cases helps minimize expenditures and save time, making to resolve cases conveniently, efficiently, and superiorly. The study also identified the challenges and issues associated with industry 4.0 technologies such as evidence gathering, evidence preservation, robot judges for pre-judgment analysis, and judgment delivery, which future studies need to be explored.
Machine-learning model reveals critical features needed for high-throughput screening of candidates for carbon-dioxide adsorption
Authors: Lim Alane
Published: August 2024

Hundreds of features used in candidate metal-organic frameworks ranked and assessed.
Microfluidic mixing by magnetic particles: Progress and prospects
Authors: Misra I. , Kumaran V.
Published: August 2024

Microfluidic systems have enormous potential for enabling point-of-care diagnostics due to a number of advantages, such as low sample volumes, small footprint, low energy requirements, uncomplicated setup, high surface-to-volume ratios, cost-effectiveness, etc. However, fluid mixing operations are constrained by molecular diffusion since the flow is usually in the laminar regime. The slow nature of molecular diffusion is a technological barrier to implementing fluid transformations in a reasonable time. In this context, magnetically actuated micro-mixers of different sizes, shapes, materials, and actuation techniques provide a way to enhance fluid mixing in microfluidic devices. In this paper, we review the currently existing micro-mixing technologies. From a fundamental perspective, the different magnetization models for permanent and induced dipoles are discussed. The single-particle dynamics in steady and oscillating magnetic fields is studied in order to determine the flow generated and the torque exerted on the fluid due to the magnetic particles. The effect of particle interactions, both magnetic and hydrodynamic, is examined.
Nutritive value and multi-mycotoxin occurrence in feeds and milk of dairy cattle
Authors: Mannai Amal , Juan Cristina , Manes Jordi , Ben Salem Hichem
Published: August 2024

Context The contamination of feedstuffs with multi-mycotoxins and their transfer to milk is an important concern because it can affect the health of both animals and humans. Aims This study determined the nutrient composition and quantified the mycotoxins present in dairy feedstuffs and in cow’s milk. Methods Samples of feedstuffs and milk were collected from 11 dairy farms in northern Tunisia for physicochemical composition and mycotoxin analyses. The extraction of mycotoxins was performed by the QuEChERS procedure and the presence of 26 mycotoxins was checked using liquid or gas chromatography with tandem mass spectrometry (LC–MS/MS and GC–MS/MS). Key results Nutrient contents differ among feedstuffs (P < 0.05). The pH values of oat, ryegrass and pulp silages varied from 4.6 to 5.3. Dry-matter (DM) content was highest for concentrate feeds and hay (90% and 86% DM respectively). Crude protein (CP) content was highest in concentrate feeds (15.8% DM). The neutral detergent fibre, acid detergent fibre and acid detergent lignin contents were highest in green grass, hay, and silage. Eight Fusarium mycotoxins, enniatins (ENA1, ENB, ENB1), beauvericine (BEA), HT-2 toxin (HT-2), deoxynivalenol (DON), 15-acetyldeoxynivalenol (15-ADON), Fumonisin B2 (FB2) and one Alternaria mycotoxin tentoxin (TENT) were detected at low concentrations. There was a significant (P < 0.05) difference in the levels of contamination by ENB between the samples of forages and concentrate feeds. The ENs (ENA1, ENB, ENB1) were the most frequently detected combination of mycotoxins. Milk analysis for fat, CP, lactose and dehydrated dry extract averaged 3.49%, 3.10%, 4.71%, and 8.77% respectively; urea concentration was of 34.01 mg/dL, milk pH was 6.5, and a somatic cell count of 1168.63 103 cells/mL was found. Most milk samples (82%) were contaminated with ENB. Conclusions The levels of mycotoxin contamination in feedstuffs and milk were low and do not present a risk for dairy cattle and consumer health. Implications Mycotoxins in feedstuffs and milk should be regularly analysed to make sure that quality and safety standards are met.
Theory of topological exciton insulators and condensates in flat Chern bands
Authors: Xie Hong-Yi , Ghaemi Pouyan , Mitrano Matteo , Uchoa Bruno
Published: August 2024

Excitons are the neutral quasiparticles that form when Coulomb interactions create bound states between electrons and holes. Due to their bosonic nature, excitons are expected to condense and exhibit superfluidity at sufficiently low temperatures. In interacting Chern insulators, excitons may inherit the nontrivial topology and quantum geometry from the underlying electron wavefunctions. We theoretically investigate the excitonic bound states and superfluidity in flat-band insulators pumped with light. We find that the exciton wavefunctions exhibit vortex structures in momentum space, with the total vorticity being equal to the difference of Chern numbers between the conduction and valence bands. Moreover, both the exciton binding energy and the exciton superfluid density are proportional to the Brillouin-zone average of the quantum metric and the Coulomb potential energy per unit cell. Spontaneous emission of circularly polarized light from radiative decay is a detectable signature of the exciton vorticity. We propose that the vorticity can also be experimentally measured via the nonlinear anomalous Hall effect, whereas the exciton superfluidity can be detected by voltage-drop quantization through a combination of quantum geometry and Aharonov–Casher effect. Topological excitons and their superfluid phase could be realized in flat bands of twisted Van der Waals heterostructures.
LRG1 promotes atherosclerosis by inducing macrophage M1-like polarization
Authors: Wang Juan , Wang Jing , Zhong Jiuchang , Liu Hongbin , Li Weiming , Chen Mulei , Xu Li , Zhang Wenbin , Zhang Ze , Wei Zhizhong , Guo Jia , Wang Xinyu , Sui Jianhua , Liu Xingpeng , Zhang Sitao , Wang Xiaodong
Published: August 2024

Atherosclerosis is a chronic inflammatory disease of the arterial wall characterized by the accumulation of cholesterol-rich lipoproteins in macrophages. How macrophages commit to proinflammatory polarization under atherosclerosis conditions is not clear. Report here that the level of a circulating protein, leucine-rich alpha-2 glycoprotein 1 (LRG1), is elevated in the atherosclerotic tissue and serum samples from patients with coronary artery disease (CAD). LRG1 stimulated macrophages to proinflammatory M1-like polarization through the activation of extracellular signal-regulated kinase 1/2 (ERK1/2) and c-Jun N-terminal kinase (JNK) pathways. The
LRG1
knockout mice showed significantly delayed atherogenesis progression and reduced levels of macrophage-related proinflammatory cytokines in a high-fat diet–induced

Apoe
−/−

mouse atherosclerosis model. An anti-LRG1 neutralizing antibody also effectively blocked LRG1-induced macrophage M1-like polarization in vitro and conferred therapeutic benefits to animals with ApoE deficiency-induced atherosclerosis. LRG1 may therefore serve as an additional biomarker for CAD and targeting LRG1 could offer a potential therapeutic strategy for CAD patients by mitigating the proinflammatory response of macrophages.
Steel grades classification and heating defects detection of bearing steel bars via pulse eddy current testing
Authors: Qian Miao , Zhao Junjie , Yu Xiaoxiao , Xiang Zhong , Yu Pufan , Wang Feng , Zhang Jianxin
Published: August 2024

Nondestructive examination of bearing steel raw material is a crucial step in ensuring the production of bearing rings. Traditional nondestructive examination methods struggle with real-time sorting, leading to potential production risks. This study proposes a pulse eddy current testing (PECT) approach combined multidimensional features and classification algorithm, to realize the recognition of steel grades and heating defects of bearing steel bars. Firstly, the two-probes differential PECT system was established. Subsequently, pulse signals were collected, and 27 features in the time domain, and frequency domain were extracted and analyzed. Finally, two classification algorithms, BP neural networking and Rindom Forest, are used to classify bearing steel. Experimental results demonstrate the approach proposed can sort bearing steel, die steel, and high-speed steel bars. Furthermore, it is capable of distinguishing between various types (brand, heat number) of bearing steel bars and identifying their overheated or burnt conditions.
Research on the airflow field and interruption performance of 550 kV fast circuit breaker
Authors: Zou Huancheng , Lin Xin , Zhong Jianying , Wei Jianwei
Published: August 2024

The fast circuit breaker can achieve the rapid removal of short-circuit faults, which is of paramount significance for ensuring the safe and stable operation of the power system. This paper proposes a design scheme for the arc extinguishing chamber of the 550 kV fast circuit breaker, establishes a mathematical model of magneto-hydro-dynamics (MHD) arc, and calculates the trends of gas pressure, arc temperature, and nozzle airflow velocity inside the fast interruption arc extinguishing chamber under different arcing times. It compares and analyzes key interruption parameters such as airflow field characteristics, arc conductance, and post-arc current, researches the interruption performance of fast circuit breakers under different arcing times, and determines the prototype design scheme. It is found that the design scheme of the arc extinguishing chamber with the opening speed of 13.5 m s−1, inflation pressure of 0.6 MPa, overtravel of 33 mm, cylinder radius of 80 mm, and nozzle throat length of 27 mm can compress the short arcing time to 6 ms, while maintaining good interruption performance for medium and long arcing, meeting the requirements of fast interruption. The prototype successfully passed the T100(s) interruption test, achieving a short arcing time of 6.2 ms, as well as corresponding medium and long arcing interruptions, verifying the effectiveness of the simulation calculations. The 550 kV fast circuit breaker successfully reduced the total interruption time to within 25 ms, and the research results successfully guided the development of the fast circuit breaker prototype.
Prediction of thermo-hydraulic properties of flow in an innovative plate heat exchanger using machine learning algorithms
Authors: Aboul Khail Ahmad , Bakır Rezan , Bakır Halit
Published: August 2024

Reducing fuel consumption and toxic gas emissions is a major concern in modern energy research. This paper investigates the performance and heat transfer enhancement of an innovative plate heat exchanger (IPHE) using machine learning techniques. By optimizing the geometric parameters of the plate, we predict thermohydraulic characteristics—represented by the Nusselt number (Nu), coefficient of friction (f), and performance (P) within the Reynolds number range of 500–5000 based on numerical modeling data. This study addresses the need for improved efficiency in plate heat exchangers (PHEs) amid rising energy demands and environmental concerns. Traditional methods like numerical simulations or costly experiments have limitations, prompting interest in artificial intelligence (AI) and machine learning (ML) for thermal analysis and property prediction in PHEs. Various ML models, including Decision Trees, XGBoost, Gradient Boosting, and ensemble methods, are evaluated in predicting f, Nu, and overall performance (P). Our comprehensive experimentation and analysis identify top-performing models with robust predictive capabilities. For f, the highest R2 score was 0.98, indicating excellent prediction accuracy, with mean squared error (MSE) values consistently below 0.0016. Similarly, for Nu and P, top models achieved R2 scores of 0.979 and 0.9628, respectively, with MSE values below 0.0347 and 0.05. These results highlight the effectiveness of machine learning techniques in accurately predicting thermohydraulic properties and optimizing PHE performance.
Ultra-broadband terahertz metamaterial absorber based on a hollow vanadium dioxide circular-truncated cone resonator
Authors: Hu Dan , Jia Nan , Jiang Rongping , Shang Hongwei , Li Yaqin , Feng Mingchun , Yang Gui
Published: August 2024

Terahertz (THz) broadband metamaterial absorbers with high absorptivity have been of great interest due to their potential applications. However, their limited bandwidth severely hinders their further development and wide applications. To address this issue and achieve ultra-broadband and strong absorption properties at THz frequencies, we present an ultra-broadband THz perfect metamaterial absorber using a hollow vanadium dioxide (VO2) circular-truncated cone array and a VO2 film supported by an Au ground plane. Simulated results show that the absorptivity of the absorber reaches more than 90% in the frequency range of 0.31–10 THz at normal incidence with a relative absorption bandwidth of 188%. At the same time, the average absorptivity in the frequency range reaches 99.2%. The physical origin of broadband perfect absorption has been elucidated by impedance matching theory and wave interference theory, respectively. The electric field distribution is further discussed to explore the physical mechanism of this absorber. Additionally, it also has the characteristics of polarization insensitivity and wide incidence angle stability. The proposed absorber can have many promising applications in the THz region, such as thermal imaging, thermal detection, and cloaking.
Inverse problem of dynamics, Galiullin and Szebehely methods and curl force trajectories
Authors: Guha Partha
Published: August 2024

At first we study Galiullin’s construction of Bertrand problem and compare it with Szebehely’s method, latter is based on a first order partial differential equation for the unknown potential that produces a prescribed monoparametric family of planar trajectories. In the second part of the paper we study the inverse problem of the trajectories such that the corresponding force is a nonconservative position dependent one, satisfying the non-vanishing curl condition and not the gradient of a potential function. Recently this force has been introduced and popularized by Berry and Shukla (J. Phys. A 45 (2012) 305 201). We connect the inverse problem dynamics of these curl force trajectories with the generalized potentials obtained by Sarlet-Mestdag-Prince (Rep. Math. Phys. 72(2013) 65-84) from the inverse problem of ϕ(x, y) = xy

m
for m ≠ 0, m ≠ –1. Finally we show that the analog of these curly trajectories in momentum space can be manifested as kinetic energies of the pair of Calogero-Leyvraz Hamiltonians (J. Nonlinear Math. Phys. 26 (2019) 147-154) describing the motion of a particle in a magnetic field with friction.
Understanding energy-level structure using a quantum rubik’s cube
Authors: Wang Yu , Bo Maolin
Published: August 2024

This study combines the quantum Rubik’s Cube matrix with the Benalcazar–Bernevig–Hughes model, defines a matrix algorithm based on the reverse convolution process, and constructs an expression for the quantum Rubik’s Cube matrix and Hamiltonian. Furthermore, to make the operation of the quantum Rubik’s Cube matrix clearer, we use a Josephus ring to draw a topological graph of the Rubik’s Cube expansion. This study uses a quantum Rubik’s Cube to calculate energy-level transitions of electrons and shows that its operation corresponds to path integration. The band dispersion is obtained. This study provides new insights and methods for calculating Hamiltonians and studying energy-level structure.
On attempts to avoid the contradiction between the first law of thermodynamics and energy-momentum tensor conservation in cosmology
Authors: Yu Hao , Zhong Yi , Luo Zhi , Li Jin
Published: August 2024

For a general ideal fluid, the first law of thermodynamics (energy conservation in thermodynamics) and energy-momentum tensor conservation may be contradictory in the context of cosmology. In this work, it is found that the contradiction can be avoided by modifying the energy conservation equation of the ideal fluid in thermodynamics or redefining the equation of state of the ideal fluid in cosmology. We first study a special ideal fluid, i.e., ideal gas-like dust, whose thermodynamic properties are the same as the ones of the ideal gas. Employing these two schemes to the ideal gas-like dust respectively, we mathematically implement the consistency between energy conservation in thermodynamics and energy-momentum tensor conservation in cosmology. The entropy evolution of the ideal gas-like dust manifests that both schemes can be thermodynamically compliant under certain conditions. Finally, we extend the research to a general ideal fluid and obtain conditions for the ideal fluid to satisfy the second law of thermodynamics.
Enhanced the performance of lead-free material-based magnetoresistance random access memory
Authors: Kumari Seema , Yadav Rekha
Published: August 2024

The field of Spintronics uses a spin-dependent current to control the electron spin. Magnetic tunnel junctions (MTJs) are essential in spintronics because of their wide range of applications, especially their great scaling ability, fast write speeds, and high relative magnetoresistances. The performance of MTJ devices based on a single MgO barrier is poor compared to a double barrier layer. The lead free double perovskite materials can be used to enhance the performance and stability of MTJ. In this paper, perovskite materials (Cs2CuBiCl6, Cs2CuBiF6, and Cs2CuBiI6) are added to the MgO dielectric layer to form a Penta layer MTJ. The MTJ device performance parameters, such as tunneling magnetoresistance (TMR), differential resistance, antiparallel and parallel resistance, and differential TMR, with these materials as composite dielectrics, have been investigated using the non-equilibrium Green’s function (NEGF) simulator. The TMR (%) of Fe-MgO- Cs2CuBiF6 - Fe-MgO-based MTJ devices at room temperature is 1307%. The implementation of the magnetoresistance random access memory based on the suggested MTJ model has been demonstrated using the LTspice. The power dissipation of the Fe-MgO- Cs2CuBiF6- MgO-Fe-based MTJ model is low as compared to the other two double perovskite-based MTJ models. Hence, MTJ memory devices with composite dielectrics based on Cs2CuBiF6 perform better.
Phenomenology of Horndeski gravity under positivity bounds
Authors: de Boe Dani , Ye Gen , Renzi Fabrizio , Albuquerque Inês S. , Frusciante Noemi , Silvestri Alessandra
Published: August 2024

A set of conditions that any effective field theory needs to satisfy in order to allow for the existence of a viable UV completion, has recently gained attention in the cosmological context under the name of positivity bounds. In this paper we revisit the derivation of such bounds for Horndeski gravity, highlighting the limitations that come from applying the traditional methodology to a theory of gravity on a cosmological background. We then translate these bounds into a complete set of viability conditions in the language of effective field theory of dark energy. We implement the latter into EFTCAMB and explore the large scale structure phenomenology of Horndeski gravity under positivity bounds. We build a statistically significant sample of viable Horndeski models, and derive the corresponding predictions for the background evolution, in terms of w
DE, and the dynamics of linear perturbations, in terms of the phenomenological functions μ and Σ, associated to clustering and weak lensing, respectively. We find that the addition of positivity bounds to the traditional no-ghost and no-gradient conditions considerably tightens the theoretical constraints on all these functions. The most significant feature is a strengthening of the correlation μ ≃ Σ, and a related tight constraint on the luminal speed of gravitational waves c
2
T ≃ 1. In this work we demonstrate the strong potential of positivity bounds in shaping the viable parameter space of scalar-tensor theories. This is certainly promising, but it also highlights the importance of overcoming all issues that still plague a rigorous formulation of the positivity bounds in the cosmological context.
Kerr-MOG-(A)dS black hole and its shadow in scalar-tensor-vector gravity theory
Authors: Liu Wentao , Wu Di , Fang Xiongjun , Jing Jiliang , Wang Jieci
Published: August 2024

The scalar-tensor-vector gravity (STVG) theory has attracted significant interest due to its ability to effectively address the issue of galaxy rotation curves and clusters of galaxies without considering the influence of dark matter. In this paper, we construct rotating black hole solutions with a cosmological constant in the STVG theory (i.e., Kerr-MOG-(A)dS black hole solutions), where the import of a gravitational charge as a source modifies the gravitational constant, determined byGG =GN(1+α). For Kerr-MOG-dS spacetime, the observer is situated at a specific location within the domain of outer communication, rather than being located infinitely far away. Since black hole shadows are shaped by light propagation in spacetime, the interaction between the MOG parameter and the cosmological constant is expected to produce novel effects on these shadows. As the cosmological constant Λ increases, the apparent size of the black hole shadow decreases. Additionally, the shadow expands with an increase in the MOG parameterα, reaching a maximum at a certain value, and its shape becomes more rounded under an arbitrary rotation parameter, which leads to degeneracy between different black hole parameters. However, by employing numerical ray-tracing techniques, we have found that gravitational lensing and the frame-dragging effect effectively distinguish this degeneracy. Our work contributes to a deeper understanding of black holes in modified gravity, their observational signatures, and constraints.
A new perspective on the diffuse gamma-ray emission excess*
Authors: Chen 陈 En-Sheng 恩生 , Fang 方 Kun 堃 , Bi 毕 Xiao-Jun 效军
Published: August 2024

The Large High-Altitude Air Shower Observatory (LHAASO) recently published measurements of diffuse Galactic gamma-ray emission (DGE) in the 10−1000 TeV energy range. The measured DGE flux is significantly higher than the expectation from hadronic interactions between cosmic rays (CRs) and the interstellar medium. This excess has been proposed to originate from unknown extended sources produced by electron radiation, such as pulsar wind nebulae or pulsar halos (PWNe/halos). In this paper, we propose a new perspective to explain the DGE excess observed by LHAASO. The masking regions used in the LHAASO DGE measurement may not fully encompass the extended signals of

PWNe/halos. By employing a two-zone diffusion model for electrons around pulsars, we find that the DGE excess in most regions of the Galactic plane can be well explained by the signal leakage model under certain parameters. Our results indicate that this signal leakage from known sources and contributions from unresolved sources should be considered as complementary in explaining the DGE excess.
Advancing ambient water quality monitoring and management through citizen science in low- and middle-income countries
Authors: Castro Jose , Mateo-Sagasta Javier , Nowicki Saskia
Published: August 2024

In contexts where conventional environmental monitoring has historically been limited, citizen science (CS) for monitoring efforts can be an effective approach for decentralized data generation that also raises scientific literacy and environmental awareness. To that end, the United Nations Environmental Program is considering CS as a mechanism for producing ambient water quality data to track progress on sustainable development goal (SDG) indicator 6.3.2: ‘proportion of bodies of water with good ambient water quality’. However, the alignment of SDG 6.3.2 monitoring requirements with CS capacity and results in low- or middle-income countries has not been assessed. Through a systematic literature review of 49 journal publications, complemented by 15 key informant interviews, this article examines the methods and outputs of CS programs in resource-constrained settings. We explore the potential of these programs to contribute to tracking SDG 6.3.2. Using the CS impact assessment framework, we evaluate broader outcomes of CS programs across 5 domains: society, economy, environment, governance, and science and technology. Despite large variability in scope, CS programs were consistently found to generate useful data for national-level reporting on physicochemical and ecological parameters; however, data quality is a concern for CS measurement of microbiological parameters. The focus in literature to-date is predominantly on scientific data production which falls only within the ‘science and technology’ outcome domain. Societal, governance, economic, and environmental outcomes are infrequently evaluated. Of the studies reviewed in this article, 75% identified some form of pollution but only 22% of them reported follow-up actions such as reporting to authorities. While CS has important potential, work is still needed towards the ‘formalization’ of CS, particularly if intended for more vulnerable contexts.
Mitigating the effects of extreme weather on crop yields: insights from farm management strategies in the Netherlands
Authors: van der Veer S , Hamed R , Karabiyik H , Roskam J L
Published: August 2024

Weather extremes can drive substantial crop losses. Farm-level management strategies play a critical role in mitigating the impacts of and consequences for farmer livelihoods and food security. While the impacts of extreme weather on crop yields are well documented in recent studies, these predominantly focused on expansive geographical scales and commonly overlooked the critical role of management practices in modulating the dynamics of weather-crop sensitivities. We fill this gap in the literature by using a unique dataset that explores the timely relationship between extreme weather and crop yields at farm level in the Netherlands. We cover 10 types of crops and elucidate the role of soil types, irrigation and nutrient application in modulating the relationship between extreme weather and crops, by estimating fixed-effects regression models. We show substantial impacts from drought during the growing- and harvesting period and excessive precipitation during the planting- and growing period. Severe droughts show significant (

p
⩽
0.05

) reductions in yield for all crops, and lead to yield reductions up to 24 percent relative to average yields during the growing period. Meanwhile, eight crops show significant reductions in yield due to severe water excess during the planting period, with yield reductions up to 18 percent. Soils such as sand or loess amplify the negative impact of drought on crop yield, while softening the impact of excessive precipitation. Irrigation and to a lesser extent nutrient application are shown to moderately decrease the impact of extreme weather on crop yield. Our findings contribute valuable insights to guide local adaptation priorities which are critical given the projected increase in the intensity and frequency of extreme weather under climate change.
Behavioral plasticity of raccoon dogs (Nyctereutes procyonoides) provides new insights for urban wildlife management in metropolis Shanghai, China
Authors: Wang Yihan , Zhao Qianqian , Tang Lishan , Lin Weiming , Zhang Zhuojin , Diao Yixin , Weng Yue , Gu Bojian , Feng Yidi , Zhao Qing , Wang Fang
Published: August 2024

As urbanization exposes wildlife to new challenging conditions and environmental pressures, species that exhibit a high degree of behavioral plasticity are considered potentially capable of colonizing and adapting to urban environments. However, differences in the behavior of populations that inhabit urban and suburban landscapes pose unprecedented challenges to traditional methods in wildlife management which often fail to consider a species’ needs or mitigate human–wildlife conflict due to changes in species behavior in response to intensive human interference. Here, we investigate differences in the home range, diel activity, movement, and diet of raccoon dogs (Nyctereutes procyonoides) between residential districts and forest park habitats in Shanghai, China. Using GPS tracking data from 22 individuals, we find that the home ranges of raccoon dogs in residential districts (10.4 ± 8.8 ha) was 91.26% smaller than those in forest parks (119.6 ± 135.4 ha). We also find that raccoon dogs in residential districts exhibited significantly lower nocturnal movement speeds (134.55 ± 50.68 m h−1) compared to their forest park counterparts (263.22 ± 84.972 m h−1). An analysis of 528 fecal samples showed a significantly higher intake of ingredients from human food in residential districts (χ
2 = 4.691, P = 0.026), which indicates that urban raccoon dog foraging strategies differ from the forest park population due to the presence of discarded human food, cat food, and wet garbage in residential districts. Based on our findings, we propose a community-based wildlife management strategy and suggest modifying the current design of residential districts. Our results underscore the importance of mammal behavior studies in urban biodiversity management and provide a scientific basis for mitigating human–wildlife conflicts in urban environments in and beyond our study area.
Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit
Authors: Cao Shuxiang , Zhang Weixi , Tilly Jules , Agarwal Abhishek , Bakr Mustafa , Campanaro Giulio , D Fasciati Simone , Wills James , Shteynas Boris , Chidambaram Vivek , Leek Peter , Rungger Ivan
Published: August 2024

A qutrit represents a three-level quantum system, so that one qutrit can encode more information than a qubit, which corresponds to a two-level quantum system. This work investigates the potential of qutrit circuits in machine learning classification applications. We propose and evaluate different data-encoding schemes for qutrits, and find that the classification accuracy varies significantly depending on the used encoding. We therefore propose a training method for encoding optimization that allows to consistently achieve high classification accuracy, and show that it can also improve the performance within a data re-uploading approach. Our theoretical analysis and numerical simulations indicate that the qutrit classifier can achieve high classification accuracy using fewer components than a comparable qubit system. We showcase the qutrit classification using the encoding optimization method on a superconducting transmon qutrit, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit quantum circuits as a viable and efficient tool for quantum machine learning applications.
Experimental and modeling approaches for electric vehicle battery safety: a technical review
Authors: Long Teng , Wang Leyu , Kan Cing-Dao
Published: August 2024

Driven by the rising number of fire incidents involving Battery Electric Vehicles (BEVs), this work reviews the current state of knowledge in electric vehicle battery safety, focusing on simulation and experiment methodologies. The critical importance of battery safety is emphasized by the potential for thermal runaway and fires due to various factors. These factors include design and manufacturing flaws, excessive current loads, mechanical damage, improper charging practices (overcharging/overdischarging), extreme temperature exposure, and even as-yet unidentified causes. This study provides a comprehensive review of methodologies employed in lithium-ion battery safety modeling and experiment for BEVs. The review includes various aspects. It includes the high voltage battery system in BEVs, battery safety considerations in BEVs, geometry modeling of battery cells, material modeling of battery cells, simulation framework for batteries, cell-level experiment, testing of materials for cell components, and the application of machine learning. Physics-based simulations that accurately predict battery thermal runaway are crucial for guaranteeing the safety and optimizing the performance of BEVs. While Finite Element Analysis (FEA) is a well-established technique for evaluating the crashworthiness of conventional vehicles, its application to BEVs presents several significant challenges. However, limited literature exists on cell-level experiments involving spray and dropping scenarios. Furthermore, additional data on melting points, thermal properties, and porosity is necessary for component-level testing. This work also highlights the need for robust friction and fatigue models, which remain a critical knowledge gap in this field. Finally, the integration of machine learning approaches for constitutive laws and the development of more complex frameworks are essential advancements for future research. This review is expected to provide a guide in simulation and experiment in EV battery safety engineering.
A Modern Lyric View of Catullus
Authors: Oade Stephanie
Published: August 2024

Chapter 3 considers ideas surrounding the lyric genre and the categorization of lyric texts from the Romantic period onwards and relates these ideas to a contemporary understanding of the Catullan lyric. It identifies important lyric traits that characterize Catullus’s particular lyric voice, including addressivity, generic proximity, the lyric setting, subjectivity, and ‘universality’. One idea that has not been formally theorized but has often appeared as a subtext in discussions of Catullan style is developed further: that is the continuous yet almost imperceptible movement through Catullus’s poems, as the component parts shift to create an unstable atmosphere (a concept identified here as das Gleitende).
Paper as Artistic Medium at the Ottoman Court
Authors: Fetvacı Emine
Published: August 2024

The arts of cutting and joining paper (katʿı and vas·s·¯ale) were much prized at the early-modern Ottoman court in Istanbul. This essay takes a single folio of katʿı prepared by the Ottoman paper joinery master (vas·s·¯al) Kalender Efendi (d. 1616) as a starting point from which to explore the essential role of paper in the administration and unification of the Ottoman Empire, shaping the state’s relationships with its subjects, and making possible the environment in which Kalender was educated and rose to prominence. Through his skillful manipulation of paper, Kalender created intricate artworks that served as resting place for the eye and mind, astonishing viewers with his skills, and encouraging them to engage in insightful looking. Paper thus served as the locus of intellectual, artistic, and economic encounters, and gave shape to relationships both in the moment and over time
BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire
Authors: Zhang Min , Cheng Qi , Wei Zhenyu , Xu Jiayu , Wu Shiwei , Xu Nan , Zhao Chengkui , Yu Lei , Feng Weixing
Published: August 2024

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.
Advanced image generation for cancer using diffusion models
Authors: Kidder Benjamin L
Published: August 2024

Deep neural networks have significantly advanced the field of medical image analysis, yet their full potential is often limited by relatively small dataset sizes. Generative modeling, particularly through diffusion models, has unlocked remarkable capabilities in synthesizing photorealistic images, thereby broadening the scope of their application in medical imaging. This study specifically investigates the use of diffusion models to generate high-quality brain MRI scans, including those depicting low-grade gliomas, as well as contrast-enhanced spectral mammography (CESM) and chest and lung X-ray images. By leveraging the DreamBooth platform, we have successfully trained stable diffusion models utilizing text prompts alongside class and instance images to generate diverse medical images. This approach not only preserves patient anonymity but also substantially mitigates the risk of patient re-identification during data exchange for research purposes. To evaluate the quality of our synthesized images, we used the Fréchet inception distance metric, demonstrating high fidelity between the synthesized and real images. Our application of diffusion models effectively captures oncology-specific attributes across different imaging modalities, establishing a robust framework that integrates artificial intelligence in the generation of oncological medical imagery.
Trajectories of cardiovascular ageing—from molecular mechanisms to clinical implementation
Authors: Ministrini Stefano , Wenzl Florian A , Lüscher Thomas F , Camici Giovanni G
Published: August 2024

Due to its peculiar structure and function, the cardiovascular system is particularly vulnerable to the detrimental effects of ageing. Current knowledge about the molecular mechanisms of ageing revealed the processes actively promoting ageing, e.g. progressive telomere shortening, and the mechanisms opposing it, e.g. endogenous production of antioxidant substances. This knowledge can be used to measure biological age at cellular and molecular levels and to interfere with it by pharmacological or non-pharmacological interventions. Biological ageing is determined by the simultaneous occurrence of independent hallmarks, which encompass a wide range of biological processes, from genomic changes to systemic inflammation and dysbiosis. This narrative review will summarize the role of ageing hallmarks in the cardiovascular system, how they can be measured, and what are the possible interventions to counteract their effects.