bluuebunny/crossref_metadata_2025 huggingface dataset:
Acoustic echo cancellation based on two‐stage BLSTM
Authors: Niu Zhiwei , Ou Shifeng , Song Peng , Gao Ying
Published: April 2024

Acoustic echo cancellation (AEC) methods aim to suppress the acoustic coupling for hands‐free speech communication. Traditional AEC works by identifying the acoustic impulse response using adaptive algorithms. With recent research advances, deep learning has become an attractive choice for AEC. This paper introduces a two‐stage bidirectional long short term memory (TS‐BLSTM) framework, incorporating multi‐head self‐attention mechanisms after each BLSTM block. This is aimed at better capturing contextual information and further enhancing ability of the model to handle complex acoustic scenarios. The BLSTM blocks are utilized to aggregate magnitude spectrum information, modelling both time and frequency dependencies. Additionally, dilation convolution is introduced to broaden the range of information in each convolution output. The magnitude decoder estimates a mask for the input, resulting in the generation of an estimated magnitude spectrum for near‐end speech. Experimental results indicate that the proposed method achieves promising outcomes.
3D segmentation denoising technology of millimeter wave human body security imaging
Authors: An Deyue , Zhang Huakun , Tu Hao
Published: April 2024

To meet the demand of noise reduction in millimeter‐wave human body security imaging, this paper proposes a new method for 3D segmentation denoising in millimeter‐wave images. The test results indicate that with the incorporation of 3D segmentation denoising technology, the noise in the background area of millimeter wave images has decreased by approximately 20–40 dB, significantly improving image quality. The detection rate has increased from 90% to 95%, while the false positive rate has decreased from 13% to 5%. This has important practical significance for real‐world applications.
Reordered static layered schedule for 5G low‐density parity‐check codes
Authors: Zhou Yangcan , Wang Zhongfeng
Published: April 2024

The error performance of the 5G low‐density parity‐check codes is significantly impacted by the update order of layers. At present, the row degree (RD) and the number of punctured bits (PBs) are widely used to decide the update order of layers. Here, the authors propose a metric to assess the soft message recovery speed of coded bits and then suggest updating layers with the lowest RD first rather than those with the least PBs first. Besides, a new criterion is introduced to further determine the update order of layers with the same number of PBs and the same RD. The resulting reordered layered schedule yields better error performance and faster convergence speed than the prior arts.
Mixed reality guided root canal therapy
Authors: Li Fangjie , Gao Qingying , Wang Nengyu , Greene Nicholas , Song Tianyu , Dianat Omid , Azimi Ehsan
Published: April 2024

Root canal therapy (RCT) is a widely performed procedure in dentistry, with over 25 million individuals undergoing it annually. This procedure is carried out to address inflammation or infection within the root canal system of affected teeth. However, accurately aligning CT scan information with the patient's tooth has posed challenges, leading to errors in tool positioning and potential negative outcomes. To overcome these challenges, a mixed reality application is developed using an optical see‐through head‐mounted display (OST‐HMD). The application incorporates visual cues, an augmented mirror, and dynamically updated multi‐view CT slices to address depth perception issues and achieve accurate tooth localization, comprehensive canal exploration, and prevention of perforation during RCT. Through the preliminary experimental assessment, significant improvements in the accuracy of the procedure are observed. Specifically, with the system the accuracy in position was improved from 1.4 to 0.4 mm (more than a 70% gain) using an Optical Tracker (NDI) and from 2.8 to 2.4 mm using an HMD, thereby achieving submillimeter accuracy with NDI. 6 participants were enrolled in the user study. The result of the study suggests that the average displacement on the crown plane of 1.27 ± 0.83 cm, an average depth error of 0.90 ± 0.72 cm and an average angular deviation of 1.83 ± 0.83°. Our error analysis further highlights the impact of HMD spatial localization and head motion on the registration and calibration process. Through seamless integration of CT image information with the patient's tooth, our mixed reality application assists dentists in achieving precise tool placement. This advancement in technology has the potential to elevate the quality of root canal procedures, ensuring better accuracy and enhancing overall treatment outcomes.
Combining magneto-hydrostatic constraints with Stokes profile inversions
Authors: Borrero J. M. , Pastor Yabar A. , Ruiz Cobo B.
Published: April 2024

Context. Inferences of the magnetic field in the solar atmosphere by means of spectropolarimetric inversions (i.e., Stokes inversion codes) yield magnetic fields that are non-solenoidal (∇ ⋅ B ≠ 0). Because of this, results obtained by such methods are sometimes put into question.

Aims. We aim to develop and implement a new technique that, in conjunction with Stokes inversion codes, can retrieve magnetic fields that are simultaneously consistent with observed polarization signals and with the null divergence condition.

Methods. The method used in this work strictly imposes ∇ ⋅ B = 0 by determining the vertical component of the magnetic field (Bz) from the horizontal ones (Bx, By). We implement this technique, which we refer to as solenoidal inversion, into the FIRTEZ Stokes inversion code and apply it to spectropolarimetric observations of a sunspot observed with the Hinode/SP instrument.

Results. We show that the solenoidal inversion retrieves a vertical component of the magnetic field that is consistent in 80% of the analyzed three-dimensional (x, y, z) domain, with the vertical component of the magnetic field inferred from the non-solenoidal inversion. We demonstrate that the solenoidal inversion is capable of a better overall fitting to the observed Stokes vector than the non-solenoidal inversion. In fact, the solenoidal magnetic field fits Stokes V worse, but this is compensated by a better fit to Stokes I. We find a direct correlation between the worsening in the fit to the circular polarization profiles by the solenoidal inversion and the deviations in the inferred Bz with respect to the non-solenoidal inversion. Finally, we also show that the spatial distribution of the electric currents given by ∇ × B does not change significantly after imposing the null divergence condition.

Conclusions. In spite of being physically preferable, solenoidal magnetic fields are topologically very similar in 80% of the analyzed three-dimensional domain to the non-solenoidal fields obtained from spectropolarimetric inversions. These results support the idea that common Stokes inversion techniques fail to reproduce ∇ ⋅ B = 0 mainly as a consequence of the uncertainties in the determination of the individual components of the magnetic field. In the remaining 20% of the analyzed domain, where the Bz inferred by the solenoidal and non-solenoidal inversions disagree, it remains to be proven that the solenoidal inversion is to be preferred because even though the overall fit to the Stokes parameters improves, the fit to Stokes V worsens. It is in these regions where the application of the Stokes inversion constrained by the null divergence condition can yield new insights about the topology of the magnetic field in the solar photosphere.
SYREN-HALOFIT: A fast, interpretable, high-precision formula for the ΛCDM nonlinear matter power spectrum
Authors: Bartlett Deaglan J. , Wandelt Benjamin D. , Zennaro Matteo , Ferreira Pedro G. , Desmond Harry
Published: April 2024

Context.Rapid and accurate evaluation of the nonlinear matter power spectrum,P(k), as a function of cosmological parameters and redshift is of fundamental importance in cosmology. Analytic approximations provide an interpretable solution, yet current approximations are neither fast nor accurate relative to numerical emulators.

Aims.We aim to accelerate symbolic approximations toP(k) by removing the requirement to perform integrals, instead using short symbolic expressions to compute all variables of interest. We also wish to make such expressions more accurate by re-optimising the parameters of these models (using a larger number of cosmologies and focussing on cosmological parameters of more interest for present-day studies) and providing correction terms.

Methods.We use symbolic regression to obtain simple analytic approximations to the nonlinear scale,kσ, the effective spectral index,neff, and the curvature,C, which are required for theHALOFITmodel. We then re-optimise the coefficients ofHALOFITto fit a wide range of cosmologies and redshifts. We then again exploit symbolic regression to explore the space of analytic expressions to fit the residuals betweenP(k) and the optimised predictions ofHALOFIT. Our results are designed to match the predictions ofEUCLIDEMULATOR2, but we validate our methods againstN-body simulations.

Results.We find symbolic expressions forkσ,neffandCwhich have root mean squared fractional errors of 0.8%, 0.2% and 0.3%, respectively, for redshifts below 3 and a wide range of cosmologies. We provide re-optimisedHALOFITparameters, which reduce the root mean squared fractional error (compared toEUCLIDEMULATOR2) from 3% to below 2% for wavenumbersk = 9 × 10−3 − 9 h Mpc−1. We introduceSYREN-HALOFIT(symbolic-regression-enhancedHALOFIT), an extension toHALOFITcontaining a short symbolic correction which improves this error to 1%. Our method is 2350 and 3170 times faster than currentHALOFITandHMCODEimplementations, respectively, and 2680 and 64 times faster thanEUCLIDEMULATOR2 (which requires runningCLASS) and theBACCOemulator. We obtain comparable accuracy toEUCLIDEMULATOR2 and theBACCOemulator when tested onN-body simulations.

Conclusions.Our work greatly increases the speed and accuracy of symbolic approximations toP(k), making them significantly faster than their numerical counterparts without loss of accuracy.
Beneficial Effects of Echinacoside on Cognitive Impairment and
Diabetes in Type 2 Diabetic db/db Mice
Authors: Qin Fanglin , Yan Yiming , Yang Ningxi , Hao Yarong
Published: April 2024

Introduction Cognitive dysfunction is an important comorbidity of
diabetes. Insulin resistance may play a critical role in diabetes-related
cognitive impairment. Echinacoside (ECH), a natural phenylethanoid glycoside, is
the active component of anti-diabetes prescriptions in traditional Chinese
medicine. Its effect on modulating insulin resistance has been confirmed but
modulating neurodegenerative disease remains unclear.

Methods Db/db mice, a spontaneous type 2 diabetes mode, were
intragastrically administered ECH by 300 mg/kg or an equivalent volume of
saline. Weight, blood glucose, and insulin resistance index were measured.
Morris water maze test was performed to observe the compound effects on
cognition. Hippocampal lesions were observed by histochemical analysis.

Results In db/db mice, ECH alleviated diabetes symptoms, memory loss, and
hippocampal neuronal damage. Next, the expression of CD44 and phosphorylated tau
was upregulated in diabetic mice. In addition, the insulin receptor
substrate-1/phosphatidylinositol 3-kinase /protein kinase B signaling pathway
was dysregulated in diabetic mice. All these dysregulations could be reversed by
ECH.

Discussion This study provides theoretical support and experimental
evidence for the future application of ECH in diabetic cognition dysfunction
treatment, promoting the development of traditional medicines.
Blood Pressure Variability is Better Associated with Acute Relative
Hyperglycemia Than the Heart Rate Variability in Healthy Young
Adults
Authors: Prakash Kiran , Ranjan Navkiran , Malhotra Anita S.
Published: April 2024

Background Deranged cardiovascular autonomic functions are well-reported
complications of diabetes mellitus, where chronic hyperglycemia is an important
factor. The role of acute relative hyperglycemia on cardiovascular autonomic
functions, particularly on blood pressure variability in healthy subjects, has
been rarely explored. Therefore, this study aimed to examine the effect of acute
relative hyperglycemia on cardiovascular autonomic functions in healthy young
adults.

Methods Beat-to-beat blood pressure and electrocardiogram were recorded to
assess the heart rate variability and blood pressure variability in 42 young,
healthy subjects during fasting and relative hyperglycemic states. Recorded
cardiovascular parameters were analyzed in time and frequency domains.
Correlations among analyzed parameters of cardiovascular autonomic variabilities
were explored during fasting and relative hyperglycemic state.

Results A few of the systolic, mean, and diastolic
blood-pressure-variability parameters were significantly altered during acute
relative hyperglycemia when compared to the fasting state. However, no
significant changes were observed in any of the heart-rate-variability
parameters. Also, novel significant correlations were found among many of the
parameters of cardiovascular autonomic variabilities during fasting and relative
hyperglycemic states.

Conclusions The blood pressure variability is affected significantly
during acute relative hyperglycemia in healthy young adults; however, the heart
rate variability does not show such changes. Also, many blood pressure
variability parameters show significant correlations with heart rate variability
and baroreflex sensitivity. It may be hypothesized that although the
variabilities in heart rate and blood pressure assess cardiovascular autonomic
functions, blood pressure variability is a better indicator of cardiovascular
autonomic effects of acute relative hyperglycemia.
Effect of Perioperative Blood Transfusion on Postoperative Complications of Free-Flap Reconstruction for Oral Cancer: Analysis of Propensity Score-Matched Cohorts
Authors: Chen Yu , Lei Yinfu , Wu Hemei , Peng Xiaofeng , Gou Dengqun , Zhang Lu , Tao Ming
Published: April 2024

Background It has been confirmed in other fields that perioperative blood transfusion (PBT) will increase the incidence of complications after free-flap reconstruction and increase the risk of patients returning to the operating room within 48 hours after the initial operation. However, for head and neck tumors, whether PBT is related to postoperative complications is debatable. The aim of this study was to control the demographic and comorbidity characteristics of patients by propensity score matching (PSM) as well as to investigate the relationship between PBT and postoperative complications after oral cancer free-flap reconstruction.

Methods A total of 597 patients who underwent microvascular free tissue transfer in two top three hospitals in Southwest China from January 2015 to July 2023 were retrospectively reviewed. The study population was divided based on PBT within 24 hours of the start of the operation and to ensure homogeneity between groups by using the PSM. The primary outcomes were postoperative complications; secondary outcomes were to explore the intraoperative risk factors of PBT.

Results A total of 597 patients were included. Among them, 90 patients received a PBT. Those patients were successfully matched with 86 similar patients who did not receive a transfusion on a ratio of 1:1. This study did not find that patients receive a transfusion had a significantly higher risk of vascular pedicle thrombosis (p = 1.000), hematoma (p = 1.000), flap failure (p = 0.398), flap-related complications (p = 0.470), and other medical complications (p = 1.000). After controlling the preoperative confounding factors and adjusting the logistic regression model, it was concluded that the tumor location-mandible (odds ratio [OR] = 19.923, 95% confidence interval [CI]: 1.213–327.302, p = 0.036) and operation time (OR = 1.011, 95% CI: 1.008–1.014, p < 0.001) were the intraoperative risk factors for PBT.

Conclusion PBT is not associated with an increased probability of postoperative complications. Mandibular tumor may have a higher risk of PBT.
Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients
Authors: Vitt Jeffrey R. , Mainali Shraddha
Published: April 2024

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the “black box” nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.

This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.
Acousto-optic holography for pseudo-two-dimensional dynamic light patterning
Authors: Akemann Walther , Bourdieu Laurent
Published: April 2024

Optical systems use acousto-optic deflectors (AODs) mostly for fast angular scanning and spectral filtering of laser beams. However, AODs may transform laser light in much broader ways. When time-locked to the pulsing of low repetition rate laser amplifiers, AODs permit the holographic reconstruction of 1D and pseudo-two-dimensional (ps2D) intensity objects of rectangular shape by controlling the amplitude and phase of the light field at high (20–200 kHz) rates for microscopic light patterning. Using iterative Fourier transformations (IFTs), we searched for AOD-compatible holograms to reconstruct the given ps2D target patterns through either phase-only or complex light field modulation. We previously showed that phase-only holograms can adequately render grid-like patterns of diffraction-limited points with non-overlapping diffraction orders, while side lobes to the target pattern can be cured with an apodization mask. Dense target patterns, in contrast, are typically encumbered by apodization-resistant speckle noise. Here, we show the denoised rendering of dense ps2D objects by complex acousto-optic holograms deriving from simultaneous optimization of the amplitude and phase of the light field. Target patterns lacking ps2D symmetry, although not translatable into single holograms, were accessed by serial holography based on a segregation into ps2D-compatible components. The holograms retrieved under different regularizations were experimentally validated in an AOD random-access microscope. IFT regularizations characterized in this work extend the versatility of acousto-optic holography for fast dynamic light patterning.
Bi-reforming with a ratio of CH4/CO2/H2O = 3/1/2 by gliding arc plasma catalysis for power to fuels
Authors: Liu Jing-Lin , Zhu Ai-Min
Published: April 2024

With the aim of directly producing a high-quality syngas with a ratio of H2/CO = 2, bi-reforming of CH4 with the ideal stoichiometric ratio of CH4/CO2/H2O = 3/1/2 is carried out in a gliding arc-based warm plasma catalytic reactor. The gliding arc plasma is a typical warm plasma (WP), which provides favorable conditions for CO2 activation, and it is found that the highest conversions are obtained in the case of reaction using the WP alone. A comparison of reactions using the WP alone (the WP case), the conventional catalyst alone (the CC case), and the WP plus catalyst (the WPC case) reveals that the WPC case can overcome the disadvantages of both the WP and CC cases. In the WPC case, CH4, CO2, and H2O react at the ideal stoichiometric ratio of CH4/CO2/H2O = 3/1/2. In addition, higher reactant conversions and energy efficiencies are obtained in the WPC case than in the WP case. A high-quality syngas with H2/CO = 2 is obtained, with similar conversions of (89 ± 1)% for all of CH4, CO2, and H2O and an energy efficiency of 71%.
Harmonic characteristics of direct crossing locomotive connected into photovoltaic power grid
Authors: Jiang Xiaoyan , Zhao Yilin , Mou Hongfei
Published: April 2024

To investigate the harmonic characteristics of electric locomotives connected to a photovoltaic (PV) power grid based on the actual parameters of a region in Tibet, this paper establishes a simulation model of an electric power grid, including PV and electric locomotive. First, theoretical analysis is conducted to study the harmonic characteristics of electric locomotives and PV power stations. On this basis, we built the MATLAB model to analyze the influence of dynamic working conditions on the locomotive and PV sides on the harmonic characteristics of the power grid. Through the first, second, and third working conditions, a simulation analysis is performed to examine the harmonic characteristics of an electric locomotive when connected to the grid, which includes a PV power source. The results show that the grid side current, including PV and electric locomotive, has a three-phase equilibrium phenomenon, and at the same time, the grid side current harmonic characteristics, including PV and electric locomotive, are mainly reflected in the 17, 19, 21, 23, and 25 frequency odd harmonics.
A new car following model based on weighted average velocity field
Authors: Singh Nisha , Kumar Kranti
Published: April 2024

In real traffic, the motion status of forward moving vehicles play an important role in influencing the following vehicle’s motion behaviour. Nowadays, autonomous and electric vehicles have been incorporated with car-following mode when the control process is more complicated and more meaningful. As a result, researchers are studying car-following control strategies for vehicles to attain stability in traffic. Based on this, we propose a new car following model considering weighted average velocity field to describe the average effect of multi-vehicle interaction on the whole road. The weighted average velocity field can be regarded as the weighted average of velocities of finite number of vehicles preceding the objective vehicle with more weightage to more preceding vehicle. The stability condition is derived through stability analysis. Subsequently, the modified Korteweg–de Vries (mKdV) equation is formulated to unveil traffic congestion in the form of the kink-antikink density wave. Theoretical findings indicate that the weighted average velocity field yields larger stability regions compared to the average velocity field. Numerical simulations were conducted, and the results obtained demonstrate that the weighted average effect of multi-vehicle interactions play a crucial role in suppressing traffic jams faster than a simple average.
Thermalization and annihilation of dark matter in neutron stars
Authors: Bell Nicole F. , Busoni Giorgio , Robles Sandra , Virgato Michael
Published: April 2024

The capture of dark matter, and its subsequent annihilation, can heat old, isolated
neutron stars. In order for kinetic heating to be achieved, the captured dark matter must undergo
sufficient scattering to deposit its kinetic energy in the star. We find that this energy deposit
typically occurs quickly, for most of the relevant parameter space. In order for appreciable
annihilation heating to also be achieved, the dark matter must reach a state of
capture-annihilation equilibrium in the star. We show that this can be fulfilled for all types of
dark matter-baryon interactions. This includes cases where the scattering or annihilation cross
sections are momentum or velocity suppressed in the non-relativistic limit. Importantly, we find
that capture-annihilation equilibrium, and hence maximal annihilation heating, can be achieved
without complete thermalization of the captured dark matter. For scattering cross sections that
saturate the capture rate, we find that capture-annihilation equilibrium is typically reached on a
timescale of less than 1 year for vector interactions and 104 years for scalar interactions.
Generating a maximally entangled state via a pure global noise environment
Authors: Kong Fan-Zhen , Zhao Jun-Long
Published: April 2024

We studied the entangling power of two pure global noises, i.e. amplitude damping noise and classic noise. The entangling power of the two-qubit amplitude damping global noise periodically oscillates with time. Additionally, the entangling power of two-qubit global classical noise increases exponentially with time. The maximum entangling power of both of them exceeds that of the perfect entanglers. Based on this, we propose the conditions for generating a maximally entangled state with global noise acting on a two-qubit separable state. Only if the two-qubit composite system, which is initially in one of those product states:

1

2

(

|

0
⟩
±

|

1
⟩
)
⊗

1

2

(

|

0
⟩
±

|

1
⟩
)

,

|
10
⟩

and

|
01
⟩

suffers an amplitude damping global noise, can we prepare this system in the maximally entangled state by appropriately controlling the evolution time of amplitude damping. Finally, we investigate the disentanglement of the maximum entangled Bell state using these two types of global noise. The two global noises cannot completely disentangle the Bell states

Φ

±

. Under the influence of amplitude damping global noise, the entanglement of Bell state

Ψ
+

undergoes a cyclical variation, alternating between disappearance and recovery to reach maximum entanglement within one period. The entanglement of either Bell state

Ψ
+

or

Ψ
−

is completely independent of global classical noise. The entanglement of Bell state

Ψ
−

is also robust against amplitude damping global noise.
Metasurface CGH optimization for CTIS based on interior point method
Authors: Zhou Pengwei , Zhou Jiamin , Lv Yangfan
Published: April 2024

In the design of the Computed Tomographic Imaging Spectrometer (CTIS), in order to optimize the holographic grating and achieve better design performance, this paper proposes a novel optimization algorithm based on the Gerchberg–Saxton (GS) iterative algorithm. This algorithm combines the weighted GS algorithm with the interior point method (IPM). By introducing weight factors for phase and amplitude in the optimization process of the GS algorithm, and incorporating the actual diffraction characteristics of the holographic grating obtained from the Computer Simulation Technology Studio Suite into the IPM optimization process, a more optimized design performance is achieved. Using this algorithm, a metasurface holographic grating is designed, which can transform the input parallel light into a dispersion image of 25 diffraction orders on a focal plane array. The transmission efficiency exceeds 72%, and the root mean square error between different diffraction orders is less than 0.1. Among them, the optimization time is shortened by approximately 70% due to a significant reduction in the number of independent variables through symmetry. Through comparison, this method can further improve the uniformity of energy distribution based on the original algorithm, avoid being trapped in local extreme values, and thus enhance the overall design quality of the CTIS.
Evolutionary accessibility of random and structured fitness landscapes
Authors: Krug Joachim , Oros Daniel
Published: April 2024

Biological evolution can be conceptualized as a search process in the space of gene sequences guided by the fitness landscape, a mapping that assigns a measure of reproductive value to each genotype. Here, we discuss probabilistic models of fitness landscapes with a focus on their evolutionary accessibility, where a path in a fitness landscape is said to be accessible if the fitness values encountered along the path increase monotonically. For uncorrelated (random) landscapes with independent and identically distributed fitness values, the probability of existence of accessible paths between genotypes at a distance linear in the sequence length L becomes nonzero at a nontrivial threshold value of the fitness difference between the initial and final genotypes, which can be explicitly computed for large classes of genotype graphs. The behaviour of uncorrelated random landscapes is contrasted with landscape models that display additional, biologically motivated structural features. In particular, landscapes defined by a tradeoff between adaptation to environmental extremes have been found to display a combinatorially large number of accessible paths to all local fitness maxima. We show that this property is characteristic of a broad class of models that satisfy a certain global constraint, and provide further examples from this class.
3D bioprinting approaches for spinal cord injury repair
Authors: Jiu Jingwei , Liu Haifeng , Li Dijun , Li Jiarong , Liu Lu , Yang Wenjie , Yan Lei , Li Songyan , Zhang Jing , Li Xiaoke , Li Jiao Jiao , Wang Bin
Published: April 2024

Regenerative healing of spinal cord injury (SCI) poses an ongoing medical challenge by causing persistent neurological impairment and a significant socioeconomic burden. The complexity of spinal cord tissue presents hurdles to successful regeneration following injury, due to the difficulty of forming a biomimetic structure that faithfully replicates native tissue using conventional tissue engineering scaffolds. 3D bioprinting is a rapidly evolving technology with unmatched potential to create 3D biological tissues with complicated and hierarchical structure and composition. With the addition of biological additives such as cells and biomolecules, 3D bioprinting can fabricate preclinical implants, tissue or organ-like constructs, and in vitro models through precise control over the deposition of biomaterials and other building blocks. This review highlights the characteristics and advantages of 3D bioprinting for scaffold fabrication to enable SCI repair, including bottom–up manufacturing, mechanical customization, and spatial heterogeneity. This review also critically discusses the impact of various fabrication parameters on the efficacy of spinal cord repair using 3D bioprinted scaffolds, including the choice of printing method, scaffold shape, biomaterials, and biological supplements such as cells and growth factors. High-quality preclinical studies are required to accelerate the translation of 3D bioprinting into clinical practice for spinal cord repair. Meanwhile, other technological advances will continue to improve the regenerative capability of bioprinted scaffolds, such as the incorporation of nanoscale biological particles and the development of 4D printing.
3D printing processes in precise drug delivery for personalized medicine
Authors: Peng Haisheng , Han Bo , Tong Tianjian , Jin Xin , Peng Yanbo , Guo Meitong , Li Bian , Ding Jiaxin , Kong Qingfei , Wang Qun
Published: April 2024

With the advent of personalized medicine, the drug delivery system will be changed significantly. The development of personalized medicine needs the support of many technologies, among which three-dimensional printing (3DP) technology is a novel formulation-preparing process that creates 3D objects by depositing printing materials layer-by-layer based on the computer-aided design method. Compared with traditional pharmaceutical processes, 3DP produces complex drug combinations, personalized dosage, and flexible shape and structure of dosage forms (DFs) on demand. In the future, personalized 3DP drugs may supplement and even replace their traditional counterpart. We systematically introduce the applications of 3DP technologies in the pharmaceutical industry and summarize the virtues and shortcomings of each technique. The release behaviors and control mechanisms of the pharmaceutical DFs with desired structures are also analyzed. Finally, the benefits, challenges, and prospects of 3DP technology to the pharmaceutical industry are discussed.
Phosphorus adsorbed by HCl desorption–activated red mud adsorbents: a molecular dynamics studys
Authors: Li Longjiang , Yang Zhiwen , Qiu Yueqin , Wang Yalan
Published: April 2024

In this study, modified red mud after phosphorus adsorption was used as the adsorbent, and HCl and deionized water were used as desorbents to desorb phosphorus. The components in the adsorbent were optimized based on density functional theory, and adsorbent and desorbent models were established. Molecular dynamics simulation was performed to determine the phosphorus concentration before and after desorption, interaction energies, radial distribution function(RDF), mean-square displacement(MSD), and diffusion coefficient. The Monte Carlo method was used to simulate the desorption isotherm, desorption site, heat of desorption, and desorption energy. Simulation results showed that deionized water could only desorb phosphorus on the adsorbent surface, and the stability of the system deteriorated upon adding HCl. HCl destroyed the ionic and hydrogen bonding between the O atoms in H2PO4
− and reactive metal and oxygen atoms in the activated red mud particles. Moreover, the van der Waals force decreased considerably. The ionic and hydrogen bonds between H2PO4
− and the surface of activated red mud particles were broken by HCl, which accelerated the desorption of phosphorus from the adsorbent surface. The interaction between HCl and phosphorus accelerated the diffusion, which decreased the adsorption capacity. Moreover, the desorption capacity increased with increasing temperature.
Free vibration analysis of jute/kenaf/banana hybrid biocomposites: effects of various stacking sequences
Authors: Jeyaguru Sangilimuthukumar , Thiagamani Senthil Muthu Kumar , Mahmoud Mohamed H , Fouad Hassan
Published: April 2024

This work provides an insight of the free vibration behaviour based on experimental modal analysis of hybrid natural fibre reinforced polymer composites fabricated via compression moulding method. This study aims to study the impact of different polymer matrices (vinyl ester and epoxy) and stacking order of different natural fibres (Jute, Kenaf and Banana) on the composite’s free vibration characteristics. Jute-Kenaf-Banana-Epoxy (JKBE) matrix composites exhibited enhanced free vibration properties compared to single fiber reinforcements, except the banana/epoxy composites. Additionally, Jute-Kenaf-Banana-Vinyl ester (JKBV) composites demonstrated improved free vibration properties in comparison to single fiber reinforcements, with the exception of kenaf/vinyl ester composites. Kenaf-Banana-Jute-Jute-Banana-Kenaf-Epoxy (KBJJBKE) and Kenaf-Banana-Jute-Jute-Banana-Kenaf-Vinyl ester (KBJJBKV) hybrid composites showed the highest natural frequencies of 68.36 Hz and 97.66 Hz, respectively. However, there was no significant improvements in the natural frequencies of Jute-Kenaf-Banana-Kenaf-Banana-Jute-Epoxy (JKBKBJE) and Jute-Kenaf-Banana-Kenaf-Banana-Jute-Vinyl ester (JKBKBJV) hybrid composites. The highest damping factor was observed for JKBKBJE (0.153) and JKBKBJV (0.224) hybrid composites. These hybrid composites with better free vibration properties shall be a potential candidate in the automobile interior applications.
Investigation of the thermodynamic, structural, electronic, mechanical and phonon properties of D0c Ru-based intermetallic alloys: an ab-initio study
Authors: Mnisi B O , Benecha E M , Tibane M M
Published: April 2024

We present the structural, elastic, electronic, magnetic, and phonon properties of D0c X3Ru (X = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn) alloys in their respective ground-states at zero pressure using first-principles density functional theory (DFT). The calculated heat of formation for Sc3Ru, Ti3Ru, V3Ru, Mn3Ru, and Zn3Ru are negative, signifying their thermodynamic stability. Meanwhile, we find that Sc3Ru, V3Ru, Mn3Ru, Co3Ru, Ni3Ru, Cu3Ru and Zn3Ru alloys are mechanically stable. The electronic properties indicate a metallic nature in all the X3Ru alloys due to valence-conduction band overlap at the Fermi energy. Additionally, the phonon dispersion curves suggest that Cr3Ru, Fe3Ru, Ni3Ru, Cu3Ru, and Zn3Ru are dynamically stable. These results provide a comprehensive overview of the stability, electronic, and mechanical properties of D0c Zn3Ru structures, suggesting their suitability for engineering novel alloys in high-temperature structural applications.
Spatially modulated ablation driven by chaotic attractors in human lung epithelial cancer cells
Authors: Martines-Arano Hilario , Valdivia-Flores Alejandra , Castillo-Cruz Juan , García-Pérez Blanca Estela , Torres-Torres Carlos
Published: April 2024

A significant modification in photoinduced energy transfer in cancer cells is reported by the assistance of a dynamic modulation of the beam size of laser irradiation. Human lung epithelial cancer cells in monolayer form were studied. In contrast to the quantum and thermal ablation effect promoted by a standard focused Gaussian beam, a spatially modulated beam can caused around 15% of decrease in the ablation threshold and formation of a ring-shaped distribution of the photothermal transfer effect. Optical irradiation was conducted in A549 cells by a 532 nm single-beam emerging from a Nd:YVO4 system. Ablation effects derived from spatially modulated convergent waves were controlled by an electrically focus-tunable lens. The proposed chaotic behavior of the spatial modulation followed an Arneodo chaotic oscillator. Fractional dynamic thermal transport was analyzed in order to describe photoenergy in propagation through the samples. Immediate applications of chaos theory for developing phototechnology devices driving biological functions or phototherapy treatments can be considered.
Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review
Authors: Sriraam Natarajan , Chinta Babu , Suresh Seshadhri , Sudharshan Suresh
Published: April 2024

For prenatal screening, ultrasound (US) imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as artificial intelligence (AI)-based US image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deep learning is suggested to construct charts depicting anomaly screening results from US video feeds. AI modelling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increase imaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.
Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations
Authors: Villarreal Joshua , Winklehner Daniel , Koser Daniel , Conrad Janet M
Published: April 2024

Radio-frequency quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations. Several recent papers have demonstrated that machine learning can be used to build surrogate models (fast-executing replacements of computationally costly beam simulations) for order-of-magnitude computing time speedups. However, while these pilot studies are encouraging, there is room to improve their predictive accuracy. Particularly, beam summary statistics such as emittances (an important figure of merit in particle accelerator physics) have historically been challenging to predict. For the first time, we present a surrogate model trained on 200 000 samples that yields

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6% mean average percent error for the predictions of all relevant beam output parameters from defining RFQ design parameters, solving the problem of poor emittance predictions by identifying and including hidden variables which were not accounted for previously. These surrogate models were made possible by using the Julia language and GPU computing; we briefly discuss both. We demonstrate the utility of surrogate modeling by performing a multi-objective optimization using our best model as a callback in the objective function to select an optimal RFQ design. We consider trade-offs in RFQ performance for various choices of Pareto-optimal design variables—common issues for any multi-objective optimization scheme. Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.
GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders
Authors: Raikman Ryan , Moreno Eric A , Govorkova Ekaterina , Marx Ethan J , Gunny Alec , Benoit William , Chatterjee Deep , Omer Rafia , Saleem Muhammed , Rankin Dylan S , Coughlin Michael W , Harris Philip C , Katsavounidis Erik
Published: April 2024

Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name ‘Gravitational Wave Anomalous Knowledge’ (GWAK). While the semi-supervised approach to this problem entails a potential reduction in accuracy compared to fully supervised methods, it offers a generalizability advantage by enhancing the reach of experimental sensitivity beyond the constraints of pre-defined signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.
GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity
Authors: Auddy Sayantan , Dey Ramit , Turner Neal J , Basu Shantanu
Published: April 2024

Modeling self-gravitating gas flows is essential to answering many fundamental questions in astrophysics. This spans many topics including planet-forming disks, star-forming clouds, galaxy formation, and the development of large-scale structures in the Universe. However, the nonlinear interaction between gravity and fluid dynamics offers a formidable challenge to solving the resulting time-dependent partial differential equations (PDEs) in three dimensions (3D). By leveraging the universal approximation capabilities of a neural network within a mesh-free framework, physics informed neural networks (PINNs) offer a new way of addressing this challenge. We introduce the gravity-informed neural network (GRINN), a PINN-based code, to simulate 3D self-gravitating hydrodynamic systems. Here, we specifically study gravitational instability and wave propagation in an isothermal gas. Our results match a linear analytic solution to within 1% in the linear regime and a conventional grid code solution to within 5% as the disturbance grows into the nonlinear regime. We find that the computation time of the GRINN does not scale with the number of dimensions. This is in contrast to the scaling of the grid-based code for the hydrodynamic and self-gravity calculations as the number of dimensions is increased. Our results show that the GRINN computation time is longer than the grid code in one- and two- dimensional calculations but is an order of magnitude lesser than the grid code in 3D with similar accuracy. Physics-informed neural networks like GRINN thus show promise for advancing our ability to model 3D astrophysical flows.