bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ab initio investigations on the effect of pressure on the structural, electronic, optical and vibrational properties of SnS
Authors: Afzal Basha M I , Meher S R 
Published: February 2025

Abstract
SnS is a promising material for optoelectronic devices due to its earth abundance, low cost, and narrow band gap. In this study, we employed ab-initio density functional theory to investigate the structural, electronic, optical, and vibrational properties of SnS under varying hydrostatic pressures ranging from 0 to 16 GPa. Our results indicate that pressure significantly affects the lattice parameters, bond lengths, and bond angles. The onset of phase transition from α-SnS to β-SnS begins at ∼ 4 GPa which involves the continuous movement of atoms along the z-direction till 12 GPa. Orbital projected electronic density of states and band structure reveal that hybridization between S-3p, Sn-5p, and Sn-5s orbitals forms the valence band and conduction band edges. At 0 GPa, the indirect band gap is observed to be 0.69 eV, while the direct band gap is found to be 0.81 eV using generalized gradient approximation based exchange-correlation. However, the Hubbard U correction yield more accurate results of 1.07 eV and 1.20 eV for indirect and direct band gap respectively. The applied hydrostatic pressure reduces the band gap and induces a transition from a semiconducting to a metallic state at 10 GPa, as the valence band maxima and conduction band minima cross the Fermi level. Optical properties demonstrate a red-shift in the absorption coefficients with increasing pressure, reflecting changes in the electronic structure. Phonon calculations reveal a gap between acoustic and optical phonon branches, attributed to the mass difference between Sn and S atoms. The absence of imaginary modes across the Brillouin zone indicates the dynamical stability of α-SnS, at lower pressures (0 GPa, 3 GPa and 6 GPa). At higher pressures, the appearance of imaginary modes suggests the onset of material instability.
A graphene based multi-band antenna array for next generation IoT networks in THz spectrum
Authors: D Kaleeswaran , Kavitha R , Anchitaalagammai J V 
Published: February 2025

Abstract
A novel two-element MIMO (Multiple-Input-Multiple-Output) antenna with THz radiation for next-generation communication networks is designed and analysed in this paper. The antenna is constructed using graphene-thin films developed on a polyimide substrate. Each element in the MIMO scheme consists of a series-fed radiating element that is structurally modified to yield quad-band radiation centred around 0.18, 0.36, 0.54, and 0.68 THz, with fractional bandwidths (at a −10 dB reflection coefficient) of 13.2%, 10.3%, 8.62%, and 7.4%, respectively. The conventional square patch-based series-fed antenna array is loaded with bow-tie slots, and the transmission lines connecting the patch radiators are optimised to realize the four frequency bands with |S11| ≤ −10 dB. The electrical size of the designed antenna is 0.383λeff × 0.173λeff, and the inherent inter-port isolation exceeds 30 dB in all realised frequency bands. The antenna exhibits directional radiation characteristics, offering precision communication with directivity and gain greater than 8 dBi and 4 dBi, respectively. The realized MIMO antenna array is subjected to performance evaluation under different channel conditions. The analysis suggests that the proposed array is a promising space-saving solution with miniaturized configuration for next-generation wireless sensor networks with nano-sensors designed for Internet of Things (IoT) communications that demand high data rate, good throughput and improved coverage through enhanced antenna radiation properties such as gain and efficiency.
Predicting chaos in permanent magnet synchronous motor using dual reservoir interaction with high-order state reservoir computing
Authors: Ye Zuo Wei , Wei Du Qu 
Published: February 2025

Abstract
Permanent Magnet Synchronous Motor (PMSM), as core driving components of new energy vehicles, face significant challenges in performance and stability due to their complex chaotic behavior. Accurate prediction of chaotic behavior is crucial for the modeling and control of PMSM. This paper proposes a novel approach based on Reservoir Computing—the Dual Reservoir Interacting with High-Order State Reservoir Computing (DRI-HOSRC)—for predicting chaos in PMSM. By introducing high-order state expansion and the dual reservoir interaction mechanism, this method significantly enhances the ability to capture the nonlinear dynamic characteristics of PMSM. Experimental results show that the proposed DRI-HOSRC model outperforms traditional methods in terms of both prediction accuracy and time range, enabling a more precise capture of the system’s complex chaotic features. These results validate the model’s potential for application in PMSM, providing an innovative solution for accurate forecasting. The research also demonstrates the advantages of the dual reservoir interacting with high-order state reservoir model in nonlinear dynamics modeling and offers an effective pathway for long-term predictions of complex systems.
Probing the Cosmological Principle with weak lensing shear
Authors: Adam James , Maartens Roy , Larena Julien , Clarkson Chris 
Published: February 2025

Abstract
The Cosmological Principle is a cornerstone of the standard model of cosmology and shapes how we view the Universe and our place within it. It is imperative, then, to devise multiple observational tests which can identify and quantify possible violations of this foundational principle. One possible method of probing large-scale anisotropies involves the use of weak gravitational lensing. We revisit this approach in order to analyse the imprint of late-time anisotropic expansion on cosmic shear. We show that the cross-correlation of shear E- and B-modes on large scales can be used to constrain the magnitude (and possibly direction) of anisotropic expansion. We estimate the signal to noise for multipoles 10 ≲ ℓ ≲ 100 that is achievable by a Euclid-like survey. Our findings suggest that such a survey could detect the E-B signal for reasonable values of the late-time anisotropy parameter.
On the rework and development of new Geant4 Cherenkov models
Authors: Đurnić B. , Potylitsyn A. , Bogdanov A. , Gogolev S. 
Published: February 2025

Abstract
A recent experiment showed that a Cherenkov radiation
spectrum from thin radiators with a wavelength-dependent refractive
index could transform into quasi-monochromatic spectral
lines. Simulations based on the standard Geant4 toolkit could not
correctly describe the experimental results because Geant4 uses the
traditional Frank-Tamm theory developed for an ideal (infinitely
thick) radiator. Because of that, we endeavor to present a new way
of utilizing Cherenkov radiation using newly developed Geant4 models
and comparing simulation results with the experimental ones. Now,
one can analyze quasi-monochromatic Cherenkov spectral lines using
Geant4, which is a significant milestone for investigating the
possibilities of this technique. Also, this paper should be used as
a text to help people understand the Cherenkov process in Geant4 and
what one can expect from various models.
A cell-based drug screening assay on a centrifugal platform
Authors: Shih Chia-Tse , Guo Huan-Jun , Shih Chih-Hsin , Li Yi-Chen Ethan 
Published: February 2025

Abstract
Drug screening is an indispensable procedure in drug development and pharmaceutical research. For cell-based drug testing, cells were treated with compounds at different concentrations, and their responses were measured to assess the compounds’ effects on cellular behavior. A concentration gradient test creates a growth environment with different compound concentrations for cultured cells, facilitating faster determination of the compound concentration’s effect on cellular responses. However, most concentration gradient tests on cell cultures were carried out manually, which is labor-intensive and time-consuming. Microfluidic technology enables drug screening to be conducted in microstructures, which not only improves efficiency and sensitivity but also reduces reagent usage and operating time. Centrifugal microfluidics utilizes the rotation of a disk platform to perform complex fluid functions such as pumping, metering, and mixing. The complete process can be carried out with a low-cost motor without the need for an expensive pumping system. In this work, a centrifugal platform for drug screening is presented. The microfluidic platform can be divided into two parts. The inner disk features branch structures designed to establish a concentration gradient for cell growth. The outer ring contains fluidics for cell culturing, which can discharge the waste fluid when the nutrient is exhausted and replenish the new culture medium by spinning the platform. In conclusion, the proposed centrifugal platform can provide a rapid generation of the concentration gradients and automate the operation of cell culturing. It provides an efficient and low-cost platform for drug screening.
Electronic and structural properties of atomically thin metallenes
Authors: Abidi Kameyab Raza , Koskinen Pekka 
Published: February 2025

Abstract
Although metallic elements favor three-dimensional (3D) geometries due to their isotropic, metallic bonding, experiments have reported metals also with two-dimensional allotropes, the so-called metallenes. And while bulk metals’ electronic and structural properties are well known, the corresponding knowledge for atomically thin metallenes remains scattered. Therefore, in this work, we use density-functional theory to investigate the electronic and structural properties of 45 elemental metals with honeycomb, square, and hexagonal lattices, along with their buckled counterparts, resulting in a comprehensive catalog of 270 metallenes with their properties. We systematically present their structural, energetic, and electronic structure properties and discuss similarities and differences compared to their 3D counterparts. As a result, simple and noble metals exhibit similar characteristics and lack buckled hexagonal lattice. Apart from scattered exceptions, the trends in several properties, such as bond lengths, cohesion energies, and projected densities of states, are governed by coordination numbers and exhibit systematic patterns. This systematic reporting provides a necessary reference for the selection and categorization of metallenes for further experimental efforts to develop them for catalytic, sensing, plasmonic, and nanoelectronics applications.
Validating large-scale quantum machine learning: efficient simulation of quantum support vector machines using tensor networks
Authors: Chen Kuan-Cheng , Li Tai-Yue , Wang Yun-Yuan , See Simon , Wang Chun-Chieh , Wille Robert , Chen Nan-Yow , Yang An-Cheng , Lin Chun-Yu 
Published: February 2025

Abstract
We present an efficient tensor-network-based approach for simulating large-scale quantum circuits exemplified by quantum support vector machines (QSVMs). Experimentally, leveraging the cuTensorNet library on multiple GPUs, our method effectively reduces the exponential runtime growth to near-quadratic scaling with respect to the number of qubits in practical scenarios. Traditional state-vector simulations become computationally infeasible beyond approximately 50 qubits; in contrast, our simulator successfully handles QSVMs with up to 784 qubits, executing simulations within seconds on a single high-performance GPU. Furthermore, utilizing the message passing interface for multi-GPU environments, our method demonstrates strong linear scalability, effectively decreasing computation time as dataset sizes increase. We validate our framework using the MNIST and Fashion MNIST datasets, achieving successful multiclass classification and highlighting the potential of QSVMs for high-dimensional data analysis. By integrating tensor-network techniques with advanced high-performance computing resources, this work demonstrates both the feasibility and scalability of simulating large-qubit quantum machine learning models, providing a valuable validation tool within the emerging Quantum-HPC ecosystem.
Learning group invariant Calabi–Yau metrics by fundamental domain projections
Authors: Hendi Yacoub , Larfors Magdalena , Walden Moritz 
Published: February 2025

Abstract
We present new invariant machine learning models that approximate the Ricci-flat metric on Calabi–Yau (CY) manifolds with discrete symmetries. We accomplish this by combining state of the art models for predicting such metrics, based on the so-called φ-model of the cymetric package, with non-trainable, G-invariant, canonicalization layers that project the φ-model’s input data (i.e. points sampled from the CY geometry) to the fundamental domain of a given symmetry group G. These G-invariant layers are easy to concatenate, provided one compatibility condition is fulfilled, and combine well with both standard and spectral versions of the φ-model. Through experiments on different CY geometries, we find that, for fixed point sample size and training time, canonicalized models give slightly more accurate metric approximations than the standard φ-model. On highly symmetric spaces, we also observe significantly faster convergence upon training. The method may also be used to compute the Ricci-flat metric on smooth CY quotients. We demonstrate this aspect by experiments on a smooth

Z

5
2

quotient of a 5-parameter quintic CY manifold.
Enhanced feature encoding and classification on distributed quantum hardware
Authors: Moretti R , Giachero A , Radescu V , Grossi M 
Published: February 2025

Abstract
The steady progress of quantum hardware is motivating the search for novel quantum algorithm optimization strategies for near-term, real-world applications. In this study, we propose a novel feature map optimization strategy for quantum support vector machines (QSVMs), designed to enhance binary classification while taking into account backend-specific parameters, including qubit connectivity, native gate sets, and circuit depth, which are critical factors in noisy intermediate scale quantum devices. The dataset we utilised belongs to the neutrino physics domain, with applications in the search for neutrinoless double beta decay. A key contribution of this work is the parallelization of the classification task to commercially available superconducting quantum hardware to speed up the genetic search processes. The study was carried out by partitioning each quantum processing unit (QPU) into several sub-units with the same topology to implement individual QSVM instances. We conducted parallelization experiments with three IBM backends with more than 100 qubits, ranking the sub-units based on their susceptibility to noise. Data-driven simulations show how, under certain restrictions, parallelized genetic optimization can occur with the tested devices when retaining the top 20% ranked sub-units in the QPU.
Performance enhancement of passive and low-consumption cooling strategies in tropical climates by integrating control systems: a case study in Panama
Authors: Chen Austin Miguel , Salado Rodrigo 
Published: February 2025

Abstract
Countries in tropical climate systems adhere to higher cooling needs than other latitudes; thus, climate change and population growth urge sustainable cooling strategies. Bioclimatic principles provide a range of passive solutions at the buildings’ envelope and surroundings, where increasing air velocity near occupants could provide thermal comfort even at high temperatures. Such passive strategies combined with control strategies may enhance performance and reduce energy consumption. Thus, this work reviews passive and low-consumption strategies implemented in tropical countries to highlight research interests and trends and presents a case study in a humid tropical developing country such as Panama. Nine cases are studied with different low-consumption solutions to find optimal combinations through simple control strategies to reduce overall energy consumption. The performance of such cases is assessed via energy intensity, relative annual indoor temperature to comfort limits, and the predictive mean vote.
Contextualising a multi-dimensional perspective on energy poverty for South Africa’s just energy transition: a systematic literature review
Authors: Tshabalala Thandeka , Davies Megan , Mohlakoana Nthabiseng 
Published: February 2025

Abstract
Energy poverty is a multifaceted and urgent issue in the Global South, especially in South Africa, where, despite an electrification rate exceeding 90%, a substantial 40% of the population still experiences energy poverty. This paper presents a systematic review of literature on energy poverty in South Africa, covering the period from 1994 to 2023. The review identifies eleven key themes that offer a multi-dimensional perspective on such energy poverty. It offers insights into addressing critical issues for advancing an equitable and just energy system. Understanding the extent and nature of energy poverty could facilitate a deeper understanding of (in)justices entrenched in South Africa’s socio-technical energy system, for policymakers, practitioners and experts alike. Energy justice is employed as an analytical framework to examine the implications of energy poverty through the lenses of restorative, distributive, procedural, and recognitional justice. The analysis seeks to contribute to South Africa’s just transition (JT) Framework, which currently addresses energy poverty primarily as a matter of restorative justice. South Africa’s just energy transition cannot be achieved in an inclusive and transformative manner without accounting for multifaceted dynamics at the household level; here, energy poverty serves to bring about a more intersectional focus on the justice dimensions inherent in energy transitions. The findings underscore the need to address energy poverty at the household level, where it intersects with broader socio-technical dynamics. Such a multi-dimensional perspective on energy poverty in South Africa could help to inform targeted policies and initiatives to meet the specific needs of energy-poor households, while broader socio-technical changes are accelerated as part of the energy transition, thus more strongly meeting the goals spelled out in the JT Framework.
Duplex sequencing identifies unique characteristics of ENU-induced mutations in male mouse germ cells
Authors: LeBlanc Danielle P M , Zhou Gu , Williams Andrew , Meier Matthew J , Valentine Charles C , Salk Jesse J , Yauk Carole L , Marchetti Francesco 
Published: February 2025

Abstract
Germ cell mutagenicity testing is increasingly required for chemical risk assessment. Duplex Sequencing (DS) is rapidly gaining acceptance as a method to assess in vivo mutagenesis, and as a valid alternative to transgenic rodent (TGR) mutation models such as the MutaMouse. We used a DS panel of 20 genomic targets and the TGR assay to measure mutations in the germ cells of MutaMouse males exposed to 0, 1, 2, or 5 mg/kg N-ethyl-N-nitrosourea (ENU) for 28 days. Germ cells from the seminiferous tubules were collected 28 days post-exposure. The TGR assay showed a significant increase in mutant frequencies at the high (p &lt; 0.001) and medium (p = 0.01) ENU doses relative to controls, while DS revealed a significant increase (p &lt; 0.001) in ENU-induced mutations only at the high-dose. DS mutation frequencies were lower in genic than in intergenic targets, suggesting a protective role for transcription-coupled repair. Interestingly, we observed several unique germ cell characteristics with respect to DS data from rodent somatic tissues: 1) larger inter-animal variability in clonally expanded mutations that affects the ability to detect significant increases in mutation frequency; 2) a target on chromosome 2 showing much higher susceptibility to spontaneous and chemical-induced mutagenesis than other targets; and 3) a mutation spectrum consistent with that observed in the offspring of ENU-treated males but not with the spectrum in bone marrow of directly-exposed males. These results suggest that DS is a promising approach for characterizing germ cell mutagenesis and that mutagenic mechanisms operating in germ cells differ from those in somatic tissues.
Testable implications of outcome-independent missingness not at random in covariates
Authors: Sjölander A , Hägg S 
Published: February 2025

Abstract
A common aim of empirical research is to regress an outcome on a set of covariates, when some covariates are subject to missingness. If the probability of missingness is conditionally independent of the outcome, given the covariates, then a complete-case analysis is unbiased for parameters conditional on covariates. We derive all testable constraints that such outcome-independent missingness not at random implies on the observed data distribution, for settings where both the outcome and covariates are categorical. By assessing if these constraints are violated for a particular observed data distribution, the analyst can infer whether the assumption of outcome-independent missingness not at random is violated for that distribution. The constraints are formulated implicitly, in terms of consistency requirements on certain linear equation systems. We also derive explicit inequality constraints that are more easily assessable, but also more permissive than the implicit constraints.
18F-FDG PET or PET/CT in detecting high-grade transformation of chronic lymphocytic leukaemia and indolent lymphomas: A systematic review and meta-analysis
Authors: Lee Osher N Y , Kuruvilla John , Hodgson David C , Veit-Haibach Patrick , Metser Ur 
Published: February 2025

Abstract

Objectives
To evaluate the diagnostic accuracy of 18F-FDG PET or PET/CT in detecting histological transformation (HT) of indolent lymphomas.

Methods
A systematic search of articles up to July 2024 was performed in Embase and Medline. Eligible studies included adults with histologically proven indolent lymphoma, 18F-FDG PET or PET/CT as the index test, and sufficient data to assess diagnostic performance. Summary receiver operating characteristic curves were plotted using a bivariate model to estimate diagnostic accuracy with area under the curve (AUC).

Results
Fifteen studies with 1,307 participants were included. Ten studies assessed PET ability to detect Richter’s transformation, and 5 studies focussed on HT in follicular lymphoma and other subtypes. A meta-analysis of the former showed pooled sensitivity of 0.90 (95% CI, 0.84–0.93) and specificity of 0.54 (95% CI, 0.28–0.77) when using a SUVmax threshold of around 5. AUC was 0.89. Pooled sensitivity was 0.74 (95% CI, 0.54–0.87) and specificity was 0.84 (95% CI, 0.67–0.93) when using a SUVmax threshold of around 10. AUC was 0.84. For detecting HT in follicular lymphoma, thresholds were found higher than those for Richter’s transformation.

Conclusions
18F-FDG PET or PET/CT demonstrates good diagnostic accuracy to detect Richter’s transformation, best when employing SUVmax ≥ 5. SUVmax thresholds may be limited in discriminating follicular lymphoma from HT, and alternatives should be sought.

Advances in knowledge
If biopsy is feasible, SUVmax ≥ 5 can guide biopsy in patients with clinically suspicious Richter’s transformation. If biopsy is infeasible, SUVmax ≥ 10 can better identify HT and guide patient management.