bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of quantum corrections to Lorentzian vacuum transitions in the presence of gravity
Authors: García-Compeán H , Hernández-Aguilar J , Mata-Pacheco D , Ramírez C
Published: December 2024

We present a study of the vacuum transition probabilities taking into account quantum corrections. We first introduce a general method that expands previous works employing the Lorentzian formalism of the Wheeler–De Witt equation by considering higher order terms in the semiclassical expansion. The method presented is applicable in principle to any model in the minisuperspace and up to any desired order in the quantum correction terms. Then, we apply this method to obtain analytical solutions for the probabilities up to second quantum corrections for homogeneous isotropic and anisotropic universes. We use the Friedmann–Lemaitre–Robertson–Walker metric with positive and zero curvature for the isotropic case and the Bianchi III and Kantowski–Sachs metrics for the anisotropic case. Interpreting the results as distribution probabilities of creating universes by vacuum decay with a given size, we found that the general behaviour is that considering up to the second quantum correction leads to an avoidance of the initial singularity. However, we show that this result can only be achieved for the isotropic Universe. Furthermore, we also study the effect of anisotropy on the transition probabilities.
Fictitious null spaces for improving the solution of injective inverse problems
Authors: Elvetun Ole Løseth , Knudsen Kim , Nielsen Bjørn Fredrik
Published: December 2024

For linear ill-posed problems with nontrivial null spaces, Tikhonov regularization and truncated singular value decomposition typically yield solutions that are close to the minimum norm solution. Such a bias is not always desirable, and we have therefore in a series of papers developed a weighting procedure which produces solutions with a different and controlled bias. This methodology can also conveniently be invoked when sparsity regularization is employed. The purpose of the present work is to study the potential use of this weighting applied to injective operators. The image under a compact operator of the singular vectors/functions associated with very small singular values will be almost zero. Consequently, one may regard these singular vectors/functions to constitute a basis for a fictitious null space that allows us to mimic the previous weighting procedure. It turns out that this regularization by weighting can improve the solution of injective inverse problems compared with more traditional approaches. We present some analysis of this methodology and exemplify it numerically, using sparsity regularization, for three PDE-driven inverse problems: the inverse heat conduction problem, the Cauchy problem for Laplace’s equation, and the (linearized) Electrical Impedance Tomography problem with experimental data.
Modelling of microarcs in copper metal vapour dominated air
Authors: Baeva Margarita , Uhrlandt Dirk
Published: December 2024

A fluid model of direct current microarcs in copper metal vapour dominated air is developed to resemble the microarcs that occur at low-voltage and low-current conditions in switching devices during a contact separation. The model is capable of predicting the basic plasma parameters, including the electric potential, the temperatures of electrons and heavy particles, the number densities of charged and neutral particles in the spatially resolved inter-electrode region, the heating of the electrodes as well as the release of copper metal vapour from the electrodes. The model predicts a positive anode fall and an increase of the electron temperature in the proximity of the anode.
Global model of low-frequency-driven cold atmospheric He + air plasmas
Authors: Sun Bowen , Zhang Hao , Niu Zhe , Lu Baofeng , Zhang Mingyan , He Yujie , Liu Dingxin , Wang Xiaohua
Published: December 2024

He + air mixture is a commonly used background gas for cold atmospheric-pressure plasmas (CAPs) because of its good discharge stability and chemical reactivity. However, the internal physical and chemical characteristics, especially the mechanism of the reactive species in low-frequency (LF)-driven He + air CAPs, are not fully understood. Hence, in this study, a global model incorporating 59 species and 866 volume reactions is developed to investigate the species densities and chemical pathways in 50 kHz-driven He + air CAPs with air contents ranging from 200 to 5000 ppm. In the entire air content range, O4
+ is the most abundant cation, and NO3
− is the most abundant anion. HNO2 is the dominant reactive nitrogen species (RNS), and the dominant reactive oxygen species (ROS) changes from H2O2 to O3 when the air concentration increases above 1000 ppm. Moreover, since He + air CAPs are typically driven by low frequency to radio frequency (RF) power sources, in this work, the species densities in LF (f = 50 kHz)- and RF (f = 13.56 MHz)-driven He + air plasmas are compared.
Disorder-induced phase transitions in a two-dimensional magnetic topological insulator system
Authors: Song Yong-Liang , Shang Shu-xiu , Chen Xin-lian , Zhang Chang-wen , Zhang Shu-feng
Published: December 2024

We investigate the phase diagram of a two-dimensional magnetic topological system in the parameter space of uncorrelated Anderson disorder and Zeeman splitting energy. In the absence of disorder, the system undergoes the phases of higher-order topological insulators (HOTIs), Chern insulators (CIs) with Chern numbers C = 2 and C = 1, and band insulators successively with enhancing Zeeman energy. The phase boundary separating these phases is found to be strongly deformed by the disorder, which leads to several topological Anderson insulators. Specifically, there exist phase transitions between CI with C = 2 and HOTI, and between CIs with C = 1 and C = 2. For the former one, it is in fact a phase transition between first-order and second-order topological phases. Besides, these disorder induced phase transitions are well explained by self-consistent Born approximation.
A novel cross domain diagnosis method based on physical feature weighting and deep residual shrinkage network
Authors: ChaoYong Ma , Nan Si , Kun Zhang , XiangFeng Zhang , Jia Chen , YongGang Xu
Published: December 2024

Traditional intelligent diagnostic methods often exhibit poor generalization ability when faced with data from different experimental platforms due to their differing distributions. Current transfer learning methods typically require fine-tuning of the model with some target domain data to adapt to different data distributions. However, in practical applications, it is often difficult to obtain sufficient target domain data. To address this issue, this paper proposes an innovative cross-domain diagnostic method that can diagnose faults on a target platform without relying on its data. The method introduces fault information of bearings into the attention weights of a deep residual shrinkage network (DRSN) through a novel physical feature weighting layer. This effectively extracts the hidden fault information from the signals, which is further refined and classified by DRSN to obtain an accurate fault diagnosis result. Experiments conducted on three bearing datasets validate the effectiveness of the proposed method, demonstrating its promising application prospects for bearing fault diagnosis under different operating conditions.
Modified kernel global-local marginal fisher analysis for rolling bearing feature extraction
Authors: Tian Yu , Guo Shunsheng , Jiang Li , Li Yibing , Zhang Xin
Published: December 2024

The vibration signals of rolling bearings usually exhibit high-dimensional, nonlinear, and non-Gaussian distribution characteristics due to long-term operation under complex working conditions. Therefore, we proposed a novel algorithm named modified kernel global-local marginal fisher analysis (MKGLMFA) for bearing feature extraction and dimensionality reduction. The proposed MKGLMFA algorithm introduces the kernel function to map data into a high-dimensional space to represent data nonlinearly first. It enhances the within-class compactness and between-class dispersibility by considering spatial relationships and label information when constructing adjacency graphs and simultaneously exploits the local and global geometry of data. Furthermore, a bearing fault diagnosis approach is presented based on MKGLMFA. It first processes the original vibration signals through MKGLMFA to obtain low-dimensional manifold features. Then these characteristics were input into the K-nearest neighbor (KNN) classifier to achieve fault pattern recognition. The superiority of the proposed MKGLMFA algorithm in feature extraction is verified in comparison with some existing state-of-the-art machine learning methods on three rolling bearings datasets. And the subsequent classification diagnosis experiments indicate the effectiveness and high efficiency of the newly raised MKGLMFA algorithm. In comparison with the representative diagnosis methods, the proposed method can extract more sensitive discriminant features, and the classification accuracy of diagnosis is significantly improved in consequence.
Simultaneous analysis of swirl spray dynamics using a telecentric shadowgraphy system
Authors: Machado Danilo A , Costa Fernando S , Dias Gabriel S , Mota Fábio A S
Published: December 2024

Pressure swirl injectors generate conical hollow sprays with a liquid film adjacent to the exit nozzle. This paper describes macroscopic and microscopic parameters of sprays and disturbances of the liquid films formed by dual pressure swirl injectors, utilizing a telecentric shadowgraphy optical system. High-speed images with kHz frequencies were used for the simultaneous measurement of spray cone angles, breakup lengths, mean droplet velocities and wave amplitude ratios. Two injectors were tested with inner nozzles having Abramovich geometric constants K
int = 2 and 2.125 and external nozzles with geometric constants K
ext = 2.9 and 1.9792, respectively. Water was used as test fluid and the injection pressures were varied from 0.05 to 0.5 MPa. Simultaneous data from the same set of images allowed the calculation of the maximum surface wave growth rate. The results indicated that the maximum surface wave growth rate varies approximately linearly with the injection pressure. Measured maximum amplitude ratios of the conical liquid film disturbances varied in the range 2.32 <

ln
⁡
(

η

bu

/

η
0

)

< 4.61, relatively close to recent experimental data on conical liquid films, but significantly lower than the calculated values for planar liquid films. The maximum amplitude ratio is a crucial parameter to determine breakup lengths. A new semi-empirical equation is proposed to describe the disturbance ratios of conical liquid films of pressure swirl injectors.
Persistence of hyperbolic-type degenerate invariant tori with prescribed frequencies in reversible systems
Authors: Qu Ru , Xu Junxiang
Published: December 2024

In this paper, we focus on the persistence of hyperbolic-type degenerate lower-dimensional invariant tori with prescribed frequencies in reversible systems. The proof is based on the stability analysis of minimal points of real analytic functions and the non-degeneracy brought by higher-order terms.
High fidelity simulations of the multi-species Vlasov equation in the electro-static, collisional-less limit
Authors: Wilhelm Rostislav-Paul , Eifert Jan , Torrilhon Manuel , Orland Fabian
Published: December 2024

The accurate prediction of occurrence and strength of kinetic instabilities in plasmas remains a significant challenge in nuclear fusion research. To accurately capture the plasma’s dynamics one is required to solve the Vlasov equation for several species which, however, comes with a number of challenges as high dimensionality of the model as well as turbulence and development of fine but relevant structures in the distribution function. The predominantly employed Particle-in-Cell (PIC) method often lacks the accuracy to resolve the dynamics correctly, which can only be remedied by going to higher resolutions but at a prohibitorily high cost due to the high-dimensionality. Thus in this work we discuss the usage of the numerical flow iteration (NuFI) as high fidelity approach, in contrast to e.g. PIC or grid-based approaches, to solve the multi-species Vlasov equation in modes leading to kinetic instabilities.
Micro-transfer printing of InGaAs/InP avalanche photodiode on Si substrate
Authors: Alimi Yasaman , Guilhabert Benoit , Jevtics Dimitars , Sala Elisa M , Strain Michael J , Groom Kristian M , Heffernan Jon
Published: December 2024

We report on the fabrication and micro-transfer printing (µ-TP) of InGaAs/InP avalanche photodiodes (APDs) onto silicon substrates. A process flow was developed to suspend the devices using semiconductor tethers. The developed process reduces the number of fabrication steps required compared to methods based on the use of photoresist tethers. Furthermore, our process is compatible with devices that may be susceptible to damage induced by the photoresist removal process. APDs were characterised in linear mode operation both before suspension and after printing. Despite the additional fabrication steps required to suspend the APD membranes and the physical nature of the µ-TP process, the electrical characteristics of the devices were preserved. No degradation in the optical performance of the devices was measured. Our work represents the first demonstration of µ-TP of InGaAs/InP APDs onto silicon substrates. The results highlight the viability of µ-TP for effective heterogeneous integration of InGaAs/InP APDs with silicon photonic integrated circuits for optical and quantum communication and other light detection applications.
Influence of the AlN-sapphire template on the optical polarization and efficiency of AlGaN-based far-UVC micro LED arrays
Authors: Rass J , Hagedorn S , Cho H K , Kolbe T , Guttmann M , Breuer S , Ruschel J , Einfeldt S
Published: December 2024

Arrays of far-UVC micro light emitting diodes (LEDs) based on AlGaN and emitting at 233–235 nm have been fabricated on different types of AlN-sapphire templates and the optical polarization, output power, and efficiencies have been studied in dependence of the template technology and the mesa diameter of the micro-pixels. While LEDs fabricated on metal organic vapor phase epitaxy (MOVPE) AlN-sapphire templates show dominant TM polarized emission with a degree of polarization (DoP) of −0.2, LEDs on high temperature annealed AlN-sapphire templates show dominant TE polarized emission with a DoP of 0.2–0.3. The output power and external quantum efficiency increases with decreasing diameter of the slanted and reflective micro LED mesa. Peak output powers of 18 mW at 200 mA and peak external quantum efficiencies of up to 2.7% for mesa diameters of 1.5 µm on annealed templates were measured, corresponding to peak wall plug efficiencies of 1.7%, while conventional LEDs with large mesa areas on the same template showed maximum EQEs of 1.1%. The relative increase in output power by using the micro LED approach as compared to a conventional large area emitter is stronger for LEDs on MOVPE AlN templates than on annealed templates (about a factor of 3.7 vs. 2.3, respectively, at 50 mA) which is attributed to the polarization dependence of the light extraction.
Role of microstructure on flux expulsion of superconducting radio frequency cavities
Authors: Khanal B D , Balachandran S , Chetri S , Barron M , Mullinix R , Williams A , Xu P , Ingrole A , Lee P J , Ciovati G , Dhakal P
Published: December 2024

The trapped residual magnetic flux during the cool-down due to the incomplete Meissner state is a significant source of radio frequency losses in superconducting radio frequency cavities. Here, we clearly correlate the niobium microstructure in elliptical cavity geometry and flux expulsion behavior. In particular, a traditionally fabricated Nb cavity half-cell from an annealed poly-crystalline Nb sheet after an 800 ∘C heat treatment leads to a bi-modal microstructure that ties in with flux trapping and inefficient flux expulsion. This non-uniform microstructure is related to varying strain profiles along the cavity shape. A novel approach to prevent this non-uniform microstructure is presented by fabricating a 1.3 GHz single cell Nb cavity with a cold-worked sheet and subsequent heat treatment leading to better flux expulsion after 800 ∘C/3 h. Microstructural evolution by electron backscattered diffraction-orientation imaging microscopy on cavity cutouts, and flux pinning behavior by dc-magnetization on coupon samples confirms a reduction in flux pinning centers with increased heat treatment temperature. The heat treatment temperature-dependent mechanical properties and thermal conductivity are reported. The significant impact of cold work in this study demonstrates clear evidence for the importance of the microstructure required for high-performance superconducting cavities with reduced losses caused by magnetic flux trapping.
Extended new Painlevé integrable KdV–CBS equation: multiple shocks, lump, breathers, and other physical wave solutions
Authors: Alhejaili Weaam , Wazwaz Abdul-Majid , El-Tantawy Samir A
Published: December 2024

In this work, we construct a new evolutionary equation with multiple applications in fluids and engineering. We call it the extended (3+1)-dimensional KdV-CBS equation, an extension of the (3+1)-dimensional KdV-Calogero-Bogoyavlenskii-Schiff (KdV-CBS) equation. We apply the Painlevé integrability test to examine the compatibility conditions of this new extended model before analyzing and solving it. Subsequently, we implement the simplified Hirota's method (SHM) to analyze this model, deriving multiple soliton/shock and lump solutions, as well as breather wave solutions, based on the derived dispersion relation, with the assistance of advanced computational programs like Maple and Mathematica. Furthermore, many other techniques, such as the Tanh method and different exponential formulas, will be used to derive different physical solutions that may simulate many nonlinear phenomena that arise in fluid or plasma physics.
Incorporation of chromium nanostructures into PVC interlayer to improve electrical features of Au/n-Si schottky diodes
Authors: Yükseltürk E , Tanrıkulu E
Published: December 2024

This work comprehensively examined the effects of polyvinyl chloride (PVC) polymer and polyvinyl chloride-chromium (PVC:Cr) thin layers on the electronic characteristics of Au/n-Si (D0) sample. To achieve this, the configurations Au/PVC/n-Si (D1) and Au/PVC:Cr/n-Si (D2) were created. A detailed description of the PVC:Cr nanocomposite synthesis process was given. The Cr nanoparticles and PVC:Cr nanocomposite were analyzed using energy-dispersive x-ray (EDX) spectroscopy and field emission scanning electron microscopy (FE-SEM) to determine the purity and surface morphology. Following the structural analysis, current–voltage (I-V) measurements were taken at a wide voltage range (±3 V), and several methodologies were applied to obtain and compare the major electronic variables of the created Schottky diodes. Experimental results show that PVC:Cr nanocomposite reduced ideality factor (n), surface states density (Nss), and series resistance (Rs) while increasing barrier height (BH) of the electric potential, shunt resistance (Rsh), and rectification rate (RR). It was found that the D2 sample’s RR was 89 times greater than the D0 sample’s. Furthermore, the surface state density (Nss) depending on the energy was determined using the n(V) and ΦB0(V) functions. Based on the ln(IR)-VR
0.5 profile in the reverse bias region, a Schottky emission (SE) transport mechanism was found to be effective for the D0 structure. On the other hand, the indicates that D1 and D2 structures exhibited the Poole–Frenkel emission (PFE) type.
The Companion Mass Distribution of Post Common Envelope Hot Subdwarf Binaries: Evidence for Boosted and Disrupted Magnetic Braking?
Authors: Blomberg Lisa , El-Badry Kareem , Breivik Katelyn , Caiazzo Ilaria , Nagarajan Pranav , Rodriguez Antonio , van Roestel Jan , Vanderbosch Zachary P. , Yamaguchi Natsuko
Published: December 2024

We measure the mass distribution of main-sequence (MS) companions to hot subdwarf B stars (sdBs) in post-common envelope binaries (PCEBs). We carried out a spectroscopic survey of 14 eclipsing systems (“HW Vir binaries”) with orbital periods of 3.8 < P
orb < 12 hr, resulting in a well-understood selection function and a near-complete sample of HW Vir binaries with G < 16. We constrain companion masses from the radial velocity curves of the sdB stars. The companion mass distribution peaks at M
MS ≈ 0.15 M
⊙ and drops off at M
MS > 0.2 M
⊙, with only two systems hosting companions above the fully convective limit. There is no correlation between P
orb and M
MS within the sample. A similar drop-off in the companion mass distribution of white dwarf (WD) + MS PCEBs has been attributed to disrupted magnetic braking (MB) below the fully convective limit. We compare the sdB companion mass distribution to predictions of binary evolution simulations with a range of MB laws. Because sdBs have short lifetimes compared to WDs, explaining the lack of higher-mass MS companions to sdBs with disrupted MB requires MB to be boosted by a factor of 20–100 relative to MB laws inferred from the rotation evolution of single stars. We speculate that such boosting may be a result of irradiation-driven enhancement of the MS stars’ winds. An alternative possibility is that common envelope evolution favors low-mass companions in short-period orbits, but the existence of massive WD companions to sdBs with similar periods disfavors this scenario.
Schottky anomaly of Reissner-Nordström-de Sitter spacetime*
Authors: Zhen 甄 Hai-Long 海龙 , Ma 马 Yu-Bo 宇波 , Li 李 Huai-Fan 怀繁 , Zhang 张 Li-Chun 丽春 , Du 杜 Yun-Zhi 云芝
Published: December 2024

In the extended thermodynamics of black holes, there exists a thermodynamical pressure whose dual thermodynamical quantity is volume. Extensive studies have been conducted on the phase structure of numerous black holes, demonstrating striking similarities to the phase structures of various ordinary matter systems. From a comparison between the thermodynamic properties of spherically symmetric AdS black holes and ordinary thermodynamic systems, we know that the isovolumetric heat capacity of the former is zero, whereas that of the latter is non-zero. The intrinsic reason for this discrepancy is a topic of interest. For Reissner-Nördstrom-de Sitter (RN-dS) spacetime with the coexistence of the black hole and cosmological horizons, the effective thermodynamic quantities are presented alongside the interaction between two horizons. The heat capacity in RN-dS spacetime is then investigated, and it is demonstrated that the behavior of the heat capacity in RN-dS spacetime is analogous to that of Schottky specific heat. Treating two horizons in RN-dS spacetime as two distinct energy levels in a two-energy-level system, we investigate the thermodynamic properties in RN-dS spacetime by studying the thermodynamic properties in an ordinary two-energy system, thereby elucidating the intrinsic reasons for the occurrence of Schottky specific heat in RN-dS spacetime. The heat capacity observed in RN-dS spacetime is not only consistent with that of Schottky specific heat described by the effective thermodynamic quantities in RN-dS spacetime but also with that of an ordinary two-energy-level system. These results not only reveal the quantum properties of RN-dS spacetime but also provide a new avenue for further in-depth study of the quantum properties of black holes and dS spacetime.
Enhanced empirical formulas for α-decay of heavy and superheavy nuclei: Incorporating deformation effects of daughter nuclei
Authors: Ismail M. , Adel A. , Ibrahim Asmaa
Published: December 2024

The latest experimental data of α-decay half-lives for 573 nuclei within the range of

are utilized to enhance empirical formulas with updated coefficients. These formulas are enhanced by analyzing the contributions of orbital angular momentum and isospin asymmetry. The effect of deformation of daughter nuclei on the α-decay half-life is modeled by incorporating two additional terms, dependent on the quadrupole and hexadecapole deformation parameters, into the empirical formulas for α-decay half-lives. Incorporating these deformation-dependent terms, along with angular momentum and isospin asymmetry, we improve the standard deviation by approximately 17%. The revised empirical formulas for α-decay half-lives demonstrate better agreement with experimental data when deformation factors are included. The modified formulas are validated through comparisons with recent experimental results and further theoretical predictions. This paper presents and compares α-decay half-life predictions for several isotopes of superheavy nuclei with

, which are yet to be experimentally synthesized. For various isotopes of each element, the variation in

with changes in the neutron number is also explored.
Nanograin-enhanced surface-layer strengthening of 3D printed intervertebral cage induced by sandblasting
Authors: Yu Dongmei , Wu Suhua , Zhao Taotao , Zhang Yongquan , Yao Li , Zheng Zeyang , Li Tao , Wu Hao , Wang Ning , Li Xiaokang , Liu Chaozong , Guo Zheng
Published: December 2024

3D-printed customized titanium alloy (Ti6Al4V, TC4) as load-bearing prostheses and implants, such as intervertebral cages, have been widely used in clinical practice. Native biological inertia and inadequate bone in-growth of porous titanium alloy scaffolds hampered their clinical application efficiency and then extended the healing period. To improve the osseointegration capacity of 3D-printed intervertebral cages, sandblasting was selected to execute their surface treatment. On the one hand, sandblasting treatment can efficiently eliminate incomplete unmelted powder that adheres to struts in intervertebral cages during the manufacture of 3D printing, resulting in high surface area and low surface flatness induced by the rough surface could favor osseointegration. On the other hand, sandblasting can also induce ultrafine grains and nanograins in the near-surface layer that are conductive to mechanical strength enhancement. This can be verified by both microhardness and residual compressive stress reaching peak values (404.2 HV, 539.1 MPa) in the transverse section of its near-surface layer along the depth from the surface. This is attributed to the fact that more grain boundaries can impede dislocation movement. Sandblasting surfaces in intervertebral cages could favor osseointegration and in-growth, providing a foundation for sandblasting treatment of 3D-printed intervertebral cages in clinical applications.
Assessment of Wear-Resistance in Hydro Turbine Steel: The Impact of Sediment Erosion on a Metal-Ceramic Coating
Authors: Pandey Ashwin , Kumar Deepak , Goyal Rahul
Published: December 2024

Wear of the components of the hydropower plant is mainly because of the striking of rigid silt particles which is carried out with the water. The present study focuses on the development of a ceramic and metal-ceramic protective coating using flame spray and high-velocity oxy-fuel (HVOF) coating technique. A slurry erosion testing equipment has been utilized to examine the wear behavior of specimens with and without coating. Experiments are performed on the coating compositions developed by varying the weight percentage of Co-NiCrAlY (metal) and Al2O3 (ceramic). The erosive behavior of all the specimens has been investigated at impact angles 30°, 45°, 60°, and 90°, impact velocities varying between 6 to 12 m/s. The metallurgical characterization of the developed coatings has been performed using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) analysis. Vickers hardness measurement and surface wettability analysis have been conducted for the mechanical characterization of the coatings. The metal-ceramic coating shows overall better erosion-resistive behavior due to the combined shielding effect and reduction in impact energy of the ceramic and metal respectively. The acquired findings pertain to the refinement of metallurgical, microstructural, and mechanical attributes of the investigated specimen.
Numerical study on the optimal design of straight-through labyrinth seals for minimum leakage loss of Francis turbine
Authors: Rijal Mamata , Gunnar Dahlhaug Ole , Prasad Neopane Hari , Chitrakar Sailesh
Published: December 2024

Labyrinth seals are important non-contact seals in Francis turbine. They help to reduce leakage flow from high to low-pressure regions. Therefore, this study focuses on the design of labyrinth seals providing more difference in pressure and minimum leakage flow. The 3-dimensional numerical models were built in one of the CAD software. The meshing and the simulation of the different designs of unilateral and bilateral labyrinth seals were done in ANSYS Workbench. ANSYS CFX has been used as a flow solver for steady-state flow analysis with water as a working fluid. The geometries of seals were modified by varying cavity numbers, cavity length, cavity depth, clearance gap and shape of the cavity. The numerical results showed less leakage loss when the cavity number increased for different seal designs. Also, the results verified that leakage losses increase when the depth of the cavity increases and leakage losses decrease when the length of the cavity increases. Similarly, the leakage losses decrease as seal clearance spaces decrease. However, seals with modified cavity shapes with higher cavity numbers had similar pressure differences and leakage losses compared to the original cavity shape of straight-through seals.
Data-driven approaches to improving waste management systems
Authors: Vista U F , Assobry M H , Rakhman F F , Pusparani A , Azaria N
Published: December 2024

An application called Pilah Sampah is designed to handle garbage. However, data is not stored and the service delivery process is sluggish in the absence of a computerized and recorded waste management system. Therefore, for its management, an appropriate management information system is needed. In this study, an information system for waste bank management is designed and constructed using the waterfall technique. System requirements analysis, design, implementation, testing, and maintenance are the first steps in the research process. The waste sorting application is made easier for management and business by the research’s findings.
An immunocompetent human kidney on-a-chip model to study renal inflammation and immune-mediated injury
Authors: Gijzen Linda , Bokkers Marleen , Hanamsagar Richa , Olivier Thomas , Burton Todd P , Tool Laura M , Rahman Mouly F , Lowman John , Savova Virginia , Means Terry K , Lanz Henriette L
Published: December 2024

Kidney damage and dysfunction is an emerging health issue worldwide resulting in high morbidity and mortality rates. Numerous renal diseases are recognized to be driven by the immune system. Despite this recognition, the development of targeted therapies has been challenging as knowledge of the underlying mechanism and complex interactions remains insufficient. Recent advancements in the field offer promising avenues for exploring the interplay between renal cells and immune cells and their role in the development of renal inflammation and diseases. This study describes the establishment of a human immunocompetent 3D in vitro co-culture model of the proximal tubule (PT) in a novel high-throughput microfluidic platform that can be used to study renal functionality and inflammatory processes. The model incorporated renal PT epithelial cell in the top compartment and human umbilical vein endothelial cells in the bottom compartment cultured under flow and in direct contact with a collagen-I extracellular matrix (ECM) gel resulting in the formation of polarized tubular structures. As an immune component, human primary monocytes of different donors were added to the lumen of the endothelium. Renal inflammation was successfully induced using complement activated serum (CAS) as evident by epithelial morphological changes, increased expression of adhesion molecules, release of pro-inflammatory cytokines, and reduced epithelial viability. Realtime migratory behavior of monocytes showed increased extravasation and migration towards the ECM and Renal compartment upon exposure to CAS with donor-to-donor differences observed. Finally, immune modulatory compounds showed efficacious inhibition of monocyte migration under inflammatory conditions in the microfluidic co-culture model. A successful co-culture model was established and can be applied to study renal functionality in health and disease but also for drug screening due to the compatibility of the platform with automation and relatively high throughput. Overall, the described PT model has high potential to fill the gap that currently exists to study renal inflammation preclinically.
Embedded 3D printing of engineered lung cancer model for assisting fine-needle aspiration biopsy
Authors: Hua Weijian , Zhang Cheng , Raymond Lily , Mitchell Kellen , Xiao Kuo , Coulter Ryan , Bandala Erick , Bishwokarma Manish , Yang Ying , Zhao Danyang , Xiao Na , Jin Yifei
Published: December 2024

Lung cancer is a serious global health issue that requires the development of patient-specific, lung cancer model for surgical planning to train interventionalists and improve the accuracy of biopsies. Although the emergence of three-dimensional (3D) printing provides a promising solution to create customized models with complicated architectures, current 3D printing methods cannot accurately duplicate anatomical-level lung constructs with tumor(s) which are applicable for hands-on training and procedure planning. To address this issue, an embedded printing strategy is proposed to create respiratory bronchioles, blood vessels, and tumors in a photocurable yield-stress matrix bath. After crosslinking, a patient-specific lung cancer analogous model is produced, which has tunable transparency and mechanical properties to mimic lung parenchyma. This engineered model not only enables the practical training of fine-needle aspiration biopsy but also provides the necessary information, such as coordinates of aspiration, wound depth, and interference with surrounding tissues, for procedure optimization.
Influence of forging on the microstructure and tensile properties of Al–based vortex plate
Authors: Yang Yang , Zhang Zhixia
Published: December 2024

An AHS-2 alloy billet (∼80 mm in diameter), with the composition of Al-10.13 wt% Si-4.08 wt% Cu-0.7 wt% Mg-0.24 wt% Fe, was hot forged to a vortex plate (∼120 mm in diameter) product by hot die forging process (HDFP), through the back-pressure processing technology. The microstructure of both the billet and the product was investigated by optical microscope, x-ray diffraction, field emission scanning electron microscope, and electron backscatter diffraction. The main second phases in the alloy include Si, Al2Cu, Mg2Si and Al5FeSi. The phase particles were broken into smaller ones during the HDFP process, while the averaged grain size of α-Al matrix was slightly increased from 3.8 to 4.5 μm. The α-Al matrix of the billet has the texture close to 〈100〉 // ND (normal direction) and 〈111〉 // ND, while the product keeps the texture close to 〈100〉 // TeD (tensile direction) and 〈111〉 // TeD. The yield strength is increased from 142 ± 2 MPa for the billet to 430 ± 2 MPa for the product, while the ultimate tensile strength is simultaneously enhanced from 222 ± 3 MPa to 477 ± 2 MPa. The enhancement of yield strengthening is regarded mainly related to the texture difference of α-Al matrix, while the ultimate tensile strength difference has relationship with the variance of both matrix texture and size change of second phase particles. This study may be referred for material choosing and forging parameter designing in new energy automobile industries.
Development of a low-dose strategy for propagation-based imaging helical computed tomography (PBI-HCT): high image quality and reduced radiation dose
Authors: Duan Xiaoman , Ding Xiao Fan , Khoz Samira , Chen Xiongbiao , Zhu Ning
Published: December 2024

Background. Propagation-based imaging computed tomography (PBI-CT) has been recently emerging for visualizing low-density materials due to its excellent image contrast and high resolution. Based on this, PBI-CT with a helical acquisition mode (PBI-HCT) offers superior imaging quality (e.g., fewer ring artifacts) and dose uniformity, making it ideal for biomedical imaging applications. However, the excessive radiation dose associated with high-resolution PBI-HCT may potentially harm objects or hosts being imaged, especially in live animal imaging, raising a great need to reduce radiation dose.Methods. In this study, we strategically integrated Sparse2Noise (a deep learning approach) with PBI-HCT imaging to reduce radiation dose without compromising image quality. Sparse2Noise uses paired low-dose noisy images with different photon fluxes and projection numbers for high-quality reconstruction via a convolutional neural network (CNN). Then, we examined the imaging quality and radiation dose of PBI-HCT imaging using Sparse2Noise, as compared to when Sparse2Noise was used in low-dose PBI-CT imaging (circular scanning mode). Furthermore, we conducted a comparison study on the use of Sparse2Noise versus two other state-of-the-art low-dose imaging algorithms (i.e., Noise2Noise and Noise2Inverse) for imaging low-density materials using PBI-HCT at equivalent dose levels. Results. Sparse2Noise allowed for a 90% dose reduction in PBI-HCT imaging while maintaining high image quality. As compared to PBI-CT imaging, the use of Sparse2Noise in PBI-HCT imaging shows more effective by reducing additional radiation dose (30%–36%). Furthermore, helical scanning mode also enhances the performance of existing low-dose algorithms (Noise2Noise and Noise2Inverse); nevertheless, Sparse2Noise shows significantly higher signal-to-noise ratio (SNR) value compared to Noise2Noise and Noise2Inverse at the same radiation dose level. Conclusions and significance. Our proposed low-dose imaging strategy Sparse2Noise can be effectively applied to PBI-HCT imaging technique and requires lower dose for acceptable quality imaging. This would represent a significant advance imaging for low-density materials imaging and for future live animals imaging applications.
Validation of a rapid algorithm for repeated intensity modulated radiation therapy dose calculations
Authors: Shaffer Nathan , Snyder Jeffrey , St-Aubin Joel
Published: December 2024

As adaptive radiotherapy workflows and deep learning model training rise in popularity, the need for repeated applications of a rapid dose calculation algorithm increases. In this work we evaluate the feasibility of a simple algorithm that can calculate dose directly from MLC positions in near real-time. Given the necessary machine parameters, the intensity modulated radiation therapy (IMRT) doses are calculated and can be used in optimization, deep learning model training, or other cases where fast repeated segment dose calculations are needed. The algorithm uses normalized beamlets to modify a pre-calculated patient specific open field into any MLC segment shape. This algorithm was validated on 91 prostate IMRT plans as well as 20 lung IMRT plans generated for the Elekta Unity MR-Linac. IMRT plans calculated using the proposed method were found to match reference Monte Carlo calculated dose within

98.02
±
0.84
%

and

96.57
±
2.41
%

for prostate and lung patients respectively with a 3%/2 mm gamma criterion. After the patient-specific open field calculation, the algorithm can calculate the dose of a 9-field IMRT plan in 1.016 ± 0.284 s for a single patient or 0.264 ms per patient for a parallelized batch of 24 patients relevant for deep learning training. The presented algorithm demonstrates an alternative rapid IMRT dose calculator that does not rely on training a deep learning model while still being competitive in terms of speed and accuracy making it a compelling choice in cases where repetitive dose calculation is desired.
Expressive quantum perceptrons for quantum neuromorphic computing
Authors: Bravo Rodrigo Araiza , Patti Taylor L , Najafi Khadijeh , Gao Xun , Yelin Susanne F
Published: December 2024

Quantum neuromorphic computing (QNC) is a sub-field of quantum machine learning (QML) that capitalizes on inherent system dynamics. As a result, QNC can run on contemporary, noisy quantum hardware and is poised to realize challenging algorithms in the near term. One key issue in QNC is the characterization of the requisite dynamics for ensuring expressive quantum neuromorphic computation. We address this issue by adapting previous proposals of quantum perceptrons (QPs), a quantum version of a simplistic model for neural computation, to the QNC setting. Our QPs compute based on the analog dynamics of interacting qubits with tunable coupling constants. We show that QPs are, with restricted resources, a quantum equivalent to the classical perceptron, a simple mathematical model for a neuron that is the building block of various machine learning architectures. Moreover, we show that QPs are theoretically capable of producing any unitary operation. Thus, QPs are computationally more expressive than their classical counterparts. As a result, QNC architectures built using our QPs are, theoretically, universal. We introduce a technique for mitigating barren plateaus in QPs called entanglement thinning. We demonstrate QPs’ effectiveness by applying them to numerous QML problems, including calculating the inner products between quantum states, energy measurements, and time reversal. Finally, we discuss potential implementations of QPs and how they can be used to build more complex QNC architectures such as quantum reservoir computers.
Quantum distance approximation for persistence diagrams
Authors: Ameneyro Bernardo , Herrman Rebekah , Siopsis George , Maroulas Vasileios
Published: December 2024

Topological data analysis (TDA) methods can be useful for classification and clustering tasks in many different fields as they can provide two dimensional persistence diagrams that summarize important information about the shape of potentially complex and high dimensional data sets. The space of persistence diagrams can be endowed with various metrics, which admit a statistical structure and allow to use these summaries for machine learning algorithms, e.g. the Wasserstein distance. However, computing the distance between two persistence diagrams involves finding an optimal way to match the points of the two diagrams and may not always be an easy task for classical computers. Recently, quantum algorithms have shown the potential to speedup the process of obtaining the persistence information displayed on persistence diagrams by estimating the spectra of persistent Dirac operators. So, in this work we explore the potential of quantum computers to estimate the distance between persistence diagrams as the next step in the design of a fully quantum framework for TDA. In particular we propose variational quantum algorithms for the Wasserstein distance as well as the

d

p

c

distance. Our implementation is a weighted version of the quantum approximate optimization Algorithm that relies on control clauses to encode the constraints of the optimization problem.
Parameter uncertainties for imperfect surrogate models in the low-noise regime
Authors: Swinburne Thomas D , Perez Danny
Published: December 2024

Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly underestimated and vanish in the large data limit. As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. We analyze the true generalization error of misspecified, near-deterministic surrogate models, a regime of broad relevance in science and engineering. We show that posterior parameter distributions must cover every training point to avoid a divergence in the generalization error and design a compatible ansatz which incurs minimal overhead for linear models. The approach is demonstrated on model problems before application to thousand-dimensional datasets in atomistic machine learning. Our efficient misspecification-aware scheme gives accurate prediction and bounding of test errors in terms of parameter uncertainties, allowing this important source of uncertainty to be incorporated in multi-scale computational workflows.
Amplifying exploration of regional climate risks: clustering future projections on regionally relevant impact drivers instead of emission scenarios
Authors: Buskop Franciscus Eduard , Sperna Weiland Frederiek , van den Hurk Bart
Published: December 2024

Climate impacts will continue to evolve over the coming decades, requiring regions worldwide to obtain actionable climate information. Global climate models (GCMs) are often used to explore future conditions, but the variability of projections among GCMs complicates regional climate risk assessments. This variability in future projections is only partly explained by the often-used emission scenarios. Model uncertainty and internal variability play a major role in the outcomes of projected meteorological conditions, especially for local precipitation patterns. As precipitation is a key driver for hazards such as floods, droughts, and wildfires, local assessment of resulting risks using emission-based multi-model means probably leads to limited impact exploration. This study proposes a method to select more impact-relevant scenarios by determining regionally relevant climatic impact drivers and clustering GCMs on their projected changes in these drivers. We quantify the effectiveness of our approach by comparing future impacts covered by multi-model means per emission scenario with our approach, expressed as an ‘exploratory amplification’ factor. We illustrate the method for flood risk in the Latvian Lielupe basin and find the novel method has an exploratory amplification up to a factor of eight for the mid-century. We conclude that our method results in locally relevant climate scenarios that significantly improve regional exploration of future climate impacts. Such scenarios provide targeted risk information that can be used in adaptation planning.