bluuebunny/crossref_metadata_2025 huggingface dataset:
Thickness-dependence of the in-plane thermal conductivity and the interfacial thermal conductance of supported MoS2
Authors: Melo Henrique Felipe , Brant Juliana , Guimaraes Paulo
Published: January 2025

Nowadays, experimental research advances in condensed matter physics are deep-rooted in the development and manipulation of nanomaterials, making it essential to explore the fundamental properties of materials that are candidates for nanotechnology. In this work, we study the dependence of the molybdenum disulfide (MoS2) Raman modes on the sample temperature and on the excitation laser power. From the correlation between these two sets of measurements, we determine the planar thermal conductivity of MoS2 monolayers, bilayers, trilayers, four layers, seven layers, and eight layers. We find a nonmonotonic behavior, with the thermal conductivity decreasing from 38 Wm−1K−1 to 24 Wm−1K−1, going from monolayer to trilayers, and then increasing from 24 Wm−1K−1 to 50 Wm−1K−1 when the thickness increases from three to eight layers. We associate this behavior with a convolution of two different phonon scattering processes: boundary scattering and interlayer scattering. We also report a monotonic thickness dependence of the interfacial thermal conductance of n-layers of MoS2 on SiO2/Si, which ranges from 0.9 MWm−2K−1 for a monolayer to 3.2 MWm−2K−1 for eight layers films.
Anomalous pumping in the non-Hermitian Rice-Mele model
Authors: Kumar Abhishek , mazumdar sarbajit , Mahanti S D , Saha Kush
Published: January 2025

We study topological charge pumping (TCP) in the Rice-Mele (RM) model with irreciprocal hopping. The non-Hermiticity gives rise to interesting pumping physics, owing to the presence of skin effect and exceptional points. In the static one-dimensional (1D) RM model, we find two independent tuning knobs that can drive the topological transition, namely, non-Hermitian parameter $\gamma$ and system size $N$. To elucidate the system-size dependency, we use a finite-size generalized Brillouin zone (GBZ) scheme to show that the edge modes can be distinguished from the non-hermiticity induced skin modes. Moreover, this scheme can capture the state pumping of topological edge modes as a function of $\gamma$ in the static 1D RM model and it further provides insight into engineering novel gapless exceptional edge modes with the help of adiabatic drive. Furthermore, we show that the standard topological pumping due to the adiabatic and periodic variation of the model parameters survives even with finite $\gamma$. However, we observe that it depends upon the driving protocols and strength of the non-Hermiticity ($\gamma$). With increasing $\gamma$, the adiabatic pumping for non-trivial protocols is destroyed first and then re-emerges as an anomalous pumping which does not have any Hermitian counterpart. Additionally, we observe that even a trivial adiabatic protocol can give rise to pumping as opposed to the Hermitian system. This is attributed to the inherent point gap physics of non-Hermitian system which we explain by reformulating a non-Bloch topological invariant for the 1+1D RM model. This invariant explains the number of pumped charges (in each period) for all the driving protocols.
Research on Composite Strain and Stress Sensors Based on Piezoresistive and Triboelectric Effects
Authors: Li Hu , Yang Li , wang zhihua
Published: January 2025

In view of the problems that robotic arms find it difficult to effectively identify and grasp workpieces with different textures and hardness in complex industrial environments, and the low path planning accuracy of robotic arms in practical application scenarios, this paper proposes a composite sensor based on piezoresistive effect and triboelectric effect. The piezoresistive sensor and the triboelectric sensor simultaneously generate output signals, and the identification of different achieves high-precision dynamic monitoring of the robotic arm's gripping objects and joint movements. The base material of the piezoresistive sensor and the negative electrode material of the triboelectric sensor are both porous MXene/PDMS structures. The piezoresistive sensor enhances its conductivity by spin-coating CNT slurry on the surface of the base material and uses PVA hydrogel as the electrode. The triboelectric sensor uses copper as the positive electrode material. Experiments show that the developed sensor has a measurement range (0.015 kPa-70 kPa) and good repeatability. The experiment verifies that the composite sensor can be applied to the high-precision detection of robotic arm's flexible gripping and joint movements.
Infrared photoresponse of GeSiSn p-i-n photodiodes based on quantum dots, quantum wells, pseudomorphic and relaxed layers
Authors: Timofeev Vyacheslav Alexeyevich , Skvortsov Ilya , Mashanov Vladimir Ivanovich , Bloshkin Aleksei A , Loshkarev Ivan Dmitrievich , Kirienko Victor V , Zalyalov Timur Maratovich , Lozovoy Kirill
Published: January 2025

Structural and photoelectric properties of p-i-n photodiodes based on GeSiSn/Si multiple quantum dots both on Si and silicon-on-insulator (SOI) substrates were investigated. Elastic strained state of grown films was demonstrated by x-ray diffractometry. Annealing of p-i-n structures before the mesa fabrication can improve the ideality factor of current-voltage characteristics. The lowest dark current density of p-i-n photodiodes based on quantum dots at the reverse bias of 1 V reaches the value of 0.8 mA/cm2. The cutoff wavelength shifts to the long-wavelength region with the Sn content increase. Maximum cutoff wavelength value is found to be 2.6 μm. Moreover, multilayer periodic structures with GeSiSn/Ge quantum wells and GeSiSn relaxed layers on Ge substrates were obtained. Reciprocal space maps were used to study the strained state of GeSiSn layers. The optimal growth parameters were determined to obtain slightly relaxed GeSiSn layers. Designed p-i-n photodiodes based on these structures demonstrated the minimal dark current density of 0.7 mA/cm2 and the cutoff wavelength of about 2 μm.
Research on hybrid processing of silicon carbide based on laser cutting
Authors: Wang jinhu , Jiang Kai , Yue Xiuhui , Zhang Qianyong , Wang Cuishan , Jing Xueting , Wang Jing , Tang Wenjing , Xia Wei
Published: January 2025

With the rapid development of semiconductor technology, silicon carbide has been widely used in various power electronic device. However, the material has an extremely high hardness ranking second only to diamond, which makes processing difficult. In this work, the two methods of laser cutting and dicing saw are utilized to test the cutting process of silicon carbide respectively, then the appropriate through-cutting process parameters are obtained. The two methods are combined to compare the cutting quality of the three approaches. As a result, the laser cutting is affected by heat with through-cutting 0.5 mm-thick silicon carbide, which results in the rough front-surface edge and much residue, and the cutting groove of the back surface is irregular. The dicing saw having more mechanical stress, resulting in serious tool wear, prone to chipping and micro-cracks and other phenomena, the back surface chipping is as high as 171.6 µm. The hybrid processing method not only reduces the degree of thermal damage, but also reduces tool wear, and the front surface is flat and clean after cutting, without a chipping phenomenon, and the back surface chipping is only 7.9 µm, which is 20 times lower than the dicing saw method.
Islands on codim-2 branes in Gauss-Bonnet Gravity
Authors: Li Zhengjiang , Hong Zekai
Published: January 2025

We study the black hole information problem on codim-2 branes in Gauss-Bonnet gravity.Thanks to the island surface ending on the brane, the Page curve of eternal black holes canbe recovered for all of the GB couplings within the causal constraints. Our results stronglysupport the universality of the island mechanism. Similar to Einstein’s gravity, the HM surfacecan exist only in a finite time in GB gravity. Remarkably, for various parameters, the maximumtimes of HM surface are always larger than the Page times. As a result, the strange behavior ofHM surfaces does not affect the Page curves for general GB gravity. Finally, we establish thecorrelation between the Page time, GB couplings, and brane tension, revealing that the Pagetime increases with these factors.
Lightweight Noise Robust Spoofing Attack Detection using Cochleagram and ResNet Amalgamated Features
Authors: Chakravarty Nidhi , Dua Mohit
Published: January 2025

Speaker verification, like other biometric technologies, is vulnerable to spoofing attacks. An attacker impersonates a specific target speaker using impersonation, replay, Text-to-Speech (TTS), or Voice conversion (VC) techniques to gain unauthorized access to the system. The work in this paper, proposes a solution that uses an amalgamation of Cochleagram and Residual Network (ResNet) to implement the frontend feature extraction phase of an Audio Spoof Detection (ASD) system. Cochleagram generation, feature extraction-dimensionality reduction and classification are the three main phases of the proposed ASD system. In the first phase, the recorded audios have been converted into Cochleagrams by using Equivalent Rectangular Bandwidth (ERB) based gammatone filters. In the next phase, three variants of Residual Network (ResNet), ResNet50, ResNet41 and ResNet27, one by one, have been used for extracting dynamic features and Resnet50, ResNet41 that are fed to Linear Decrement Analysis (LDA) for dimensionality reduction. At last, in the classification phase, the LDA reduced features have been used for training four different machine learning classifiers such as Random Forest, Naïve Bayes, K-Nearest Neighbour (KNN), and eXtreme Gradient Boosting (XGBoost), individually. The proposed work in this paper concentrates on synthetic, replay, and deepfake attacks. The state-of-the-art ASVspoof 2019 Logical Access (LA), Physical Access (PA), Voice Spoofing Detection Corpus (VSDC) and DEepfake CROss-lingual (DECRO) datasets are utilised for training and testing the proposed ASD system. Additionally, we have assessed the performance of our proposed system under the influence of additive noise. Airplane noise at different SNR rate (0, dB 5dB, 10dB and -5dB) has been added to training and testing audios for the same. From the obtained results, it can be concluded that combination of Cochleagram and ResNet50 with XGBoost classifier outperforms all other implemented systems for detecting fake audios under noisy environment.
Dunkl-Schrödinger equation in higher dimensions
Authors: Hamil Bilel , Lütfüoğlu Bekir Can , Merad Mahmoud
Published: January 2025

This paper presents analytical solutions for eigenvalues and eigenfunctions of the Schrödinger equation in higher dimensions, incorporating the Dunkl operator. Two fundamental quantum mechanical problems are examined in their exact forms: the d-dimensional harmonic oscillator and the Coulomb potential. In order to obtain analytical solutions to these problems, both Cartesian and polar coordinate systems were employed. Firstly, the Dunkl-Schrödinger equation is derived in d-dimensional Cartesian coordinates, and then for the isotropic harmonic potential interaction, its solutions are given. Subsequently, using polar coordinates the angular and radial parts of the Dunkl-Schrödinger equation are obtained. It is demonstrated that the system permits the separation of variables in both coordinate systems, with the resulting separated solutions expressed through Laguerre and Jacobi polynomials. Then, the radial Dunkl-Schrödinger equation is solved using the isotropic harmonic, pseudoharmonic, and Coulomb potentials. The eigenstates and eigenvalues are obtained for each case and the behavior of the energy eigenvalue functions are illustrated graphically with the reduced probability densities.
Modeling and simulation of the effect of surface roughness on the Fowler-Nordheim conduction in MOS structures
Authors: sabrina meguellati , Zitouni Messai , Sarra Mustapha
Published: January 2025

A compact Fowler-Nordheim (FN) tunneling current model is derived to describe the non-ideal, surface roughness-related relationship between current and voltage in a metal-oxide-semiconductor (MOS). This relationship is achieved by applying a fractal continuum Laplacian operator in the 1D Schrödinger equation, resulting in a model that extends the standard FN to account for surface inhomogeneities through a fractional power α. Our error analysis predicts significant inaccuracies in the extracted FN parameters when using the standard FN model to interpret rough surface data.A methodology for extracting FN fractal parameters and the flat band voltage via a fractal FN plot is proposed. Simulated datasets and real measurements of Al/SiO₂/4H-SiC capacitors are used to validate the model. Comparison of the parameters extracted using the fractal FN model and the standard FN model confirms the predictions from the error analysis and the suitability of the fractal FN model. The fractal FN model offers a potential explanation for the large discrepancies observed in FN parameters reported in the literature.
New Approach to Modeling of Thermal Electrons Heating Rate in Ionosphere F-layer
Authors: Lu Zhijian , Yao Jingfeng , Aktaev Nurken , Kudryavtsev Anatoly , Yerzhanov Koblandy , Zhou Zhongxiang , Wang Ying , Li Hui , Wu Jian , Yuan Chengxun
Published: January 2025

Heating rate of thermal electrons by superthermal electrons in ionospheric F-layer at altitudes of 150-350 km was studied over a wide range of zenith angles 45-90 degrees. As a result, a simple and universal formula for calculating the heating rate was obtained. The generalized Hoegy theory was used to obtain the formula. Within the framework of the Hoegy theory, the heating rate is determined by superthermal electrons flux and energy loss function. The energy dependence of the superthermal electron flux was obtained using the bi-exponential function. The kinetic approach was used to receive an analytical expression for the energy loss function. It has been shown that the obtained expression in good agreement with the widely used Swartz approximation. The applicability limits of the obtained expression for energy losses are discussed. It was demonstrated that the heating rate calculations using the formula obtained in the work has a good agreement with the experiments.
Electron and positron impact ionization cross sections of SiF4
Authors: Song Yunliang , Ma Yuwei , Li Bowen , Chen Ximeng
Published: January 2025

Based on the Binary-Encounter-Bethe (BEB) model, we present comprehensive cross-sectional data on total ionization cross sections, partial cross sections, single differential cross sections, average secondary electron energy and stopping cross sections for the SiF4 molecule in the case of electron and positron impact. For the partial cross sections, we employ the mass spectrum data (MSD) method and Huber’s model. By comparing theoretical calculations with experimental measurements, we determine the fragmentation pattern of SiF4. For positron scattering, we utilize three methods known as BEB0, BEBA, and BEBW. There is limited data of electron impact available on SiF4, and there is a notable lack of relevant investigations regarding positron impact with SiF4. Our research continues the exploration of electron and positron impact cross sections for SiF4 and serves as a reference for future studies.
Measurement-Device-Independent High-Capacity Quantum Secure Direct Communication Utilizing Hyperentangled Bell States
Authors: Meng Hai , Zhou Ri-Gui , Du Lin-Tao
Published: January 2025

Quantum secure direct communication (QSDC) enables the direct transmission of secret messages without prior sharing of keys among the communicating parties. However, in practical situations, an eavesdropper can compromise the measurement device and potentially steal secret messages. To enhance the reliability of QSDC systems, measurement-device-independent (MDI) QSDC protocols have been developed. In this paper, we propose a high-capacity MDI-QSDC protocol with authentication by utilizing hyperentangled Bell states. The protocol allows the sender to transmit six classical bit messages to the receiver using one quantum state. Our protocol has a higher coding capacity compared to other MDI-QSDC protocols. Additionally, we achieve mutual authentication of the identities of the communicating parties, and our scheme has significant advantages over the traditional QSDC protocols, where the legitimacy of the communicating parties has to be assumed. Theoretical analysis proves that our protocol can effectively defend against common attacks.
Effect of combined Kappa-Cairns distributed electrons on modulational instability and envelope soliton of ion-acoustic waves in electron-ion dusty plasma
Authors: Pain Rittika , Dalui Sandip , Sardar Sankirtan , Bandyopadhyay Anup
Published: January 2025

Theoretical and numerical investigations are performed to study the amplitude modulation of ion-acoustic waves in an unmagnetized collisionless dusty plasma system whose components are warm ions (adiabatic), charged (negative) dust particles and Combined Kappa-Cairns distributed electrons. Modulational instability of ion-acoustic waves is analyzed through derivation of the nonlinear Schr{"o}dinger equation using the reductive perturbation technique by implementing hydrodynamic equations for this system. The conditions for modulational instability for this electron-ion dusty plasma system have been developed, and the impacts of the relevant physical parameters on the instability region along with the maximum growth rate of instability are demonstrated. Bright and dark envelope solitons are presented, and it is observed that the profiles of envelope solitons are significantly influenced by the existence of high-energy Combined Kappa-Cairns distributed electrons.
Re-evaluation of the impact of wall retention on Tritium-self-sufficiency in fusion reactors
Authors: Schmid K.
Published: January 2025

In recent years several publications have claimed that the retention of tritium (T) in the first wall armor material poses a limitation on when a fusion reactor becomes T-self-sufficient. While the prediction on wall retention therein are correct, the model applied to assess the T-self-sufficiency is based on wrong assumptions and thus, the importance of T-retention in the first wall is greatly overestimated. In this publication the predictions on T-retention in the first wall armor components are included in state of the art residence time models of the entire T-cycle. These calculations show that T-wall-retention only affects T-self-sufficiency when the T-breeding ratio (TBR) is so low that the power plant is only marginally T-self-sufficient. For TBR values that allow for a finite T-doubling time and maintain a T-reserve inventory, the T-production must be so high, that the T-wall-retention is insignificant. The paper will explain the error in the previous T-self-sufficiency estimates, how to correct them and what this means for the conclusions drawn by previous publications. Then an augmented residence time model will be presented, that can include diffusion/trapping model based, time dependent wall loss data. This augmented model results in orders of magnitude higher T-retention in the first wall compared to current assumptions in residence time models. Still, it is negligible compared to the T-content in the entire T-cycle. Thus it can be concluded that based on current material data T-retention in the first wall armor will not affect T-self-sufficiency.
A continental approach to estimate the area required for proposed wind-power parks and their overlap with protected areas in Africa
Authors: Mayer Jana , Peters Rebecca , Bange Jens , Mauz Moritz , Platis Andreas , Zarfl Christiane
Published: January 2025

Wind energy is one of the key renewable energy technologies required to transform the energy sector to reduce climate change. In the global expansion of wind energy, one main concern is that wind-power parks (WPPs) take up large areas of land, causing conflicts with other uses such as nature conservation. Existing impact analyses for WPPs are mostly restricted to case studies, and it lacks studies that investigate potential impacts at a larger scale because no scientific consensus on the area associated to a WPP exists. This study proposes a continental, GIS-based approach to estimate the area required for proposed onshore WPPs and to estimate their potential overlap with protected areas on the African continent. The results of the spatial analysis show that, in total, the currently proposed 149 WPPs would require 852 km2 of land on the African continent, thereof 11 would overlap with protected areas. The overlaps sum up to an area of 42 km2, which corresponds to an affected nominal power of 834 MW (5% of the total projected wind power capacity). These findings reveal the need for further purposefully local in-depth analyses to investigate if the WPP can be operated in accordance with the conservation of the protected area. This work provides new data and a transferable methodological approach on the expansion of wind energy and its potential space requirement and contributes to the investigation of potential land-use conflicts of proposed onshore WPPs on a broader scale.
Understanding the influence of hydrogen on BCC iron grain boundaries using the kinetic activation relaxation technique (k-ART)
Authors: Khosravi Aynour , Song Jun , Mousseau Normand
Published: January 2025

Hydrogen embrittlement (HE) poses a significant challenge to the mechanical integrity of iron and its alloys. This study explores the influence of hydrogen atoms on two distinct grain boundaries (GBs), $\Sigma37$ and $\Sigma3$, in body-centered-cubic (BCC) iron. Using the kinetic activation relaxation technique (k-ART), an off-lattice kinetic Monte Carlo approach with an EAM-based potential, extensive catalogs of activated events for atoms in both H-free and H-saturated GBs were generated.Studying the diffusion of H, we find that, for these systems, while GB is energetically favorable for H, this element diffuses more slowly at the GBs than in the bulk. The results further indicate that the $\Sigma 3$ GB exhibits higher stability in its pure form compared to the $\Sigma 37$ GB, with notable differences in energy barriers and diffusion behaviors. Moreover, with detailed information about the evolution landscape around the GB, we find that the saturation of a GB with hydrogen both stabilizes the GB by shifting barriers associated with Fe diffusion to higher energies and reducing the number of diffusion events. For the $\Sigma 37$ GB, the presence of hydrogen causes elastic deformation, affecting the diffusion of Fe atoms both at the GB and in adjacent positions. This results in new diffusion pathways but with higher diffusion barriers, unlike for the $\Sigma 3$ GB. These results indicate that the presence of hydrogen rigidifies the direct GB interface layers while allowing more atoms to be active for the $\Sigma 37$ GB. This provides a microscopic basis to support the existence of competing mechanisms compatible with either plasticity (such as hydrogen enhanced localized plasticity --- HELP) or energy-dominated (hydrogen enhanced decohesion mechanism ---HEDE) embrittlement, with the relative importance of these mechanisms determined by the local geometry of the GBs.
A design method to optimize GaN-based class E resonant inductive Wireless Power Transfer systems – application to non-mating connectors
Authors: CÔTE Nathis , GARRAUD Nicolas , Sterna Léo , Périchon Pierre , Frassati François , Boisseau Sebastien
Published: January 2025

Wireless Power Transfer (WPT) systems have demonstrated their efficiency over a broad frequency spectrum. However, the design and the optimization of multi-MHz frequency WPT systems remain complex due to parasitic components specifically. This paper outlines a methodology for designing and optimizing a compact 13.56 MHz resonant inductive coupling WPT systems based on a GaN Class E inverter. The proposed design approach combines analytical modeling and advanced numerical simulations taking into account parasitic elements. The effectiveness of this approach is experimentally confirmed through a prototype, which shows an efficiency of 78% for a transmitted power of 43 W to a 5.54 cm³ coupler. The advanced modeling results perfectly match with the experimental ones (mismatch of only 2% for the evaluation of efficiency). The WPT system developed thanks to this method shows a power density among the highest ones in the non-mating connectors’ state of the art.
Underwater target detection algorithm based on feature enhancement and feature fusion
Authors: Liu Qinxiao , Ji Longlong , Zhao Fen
Published: January 2025

In the field of target detection, identifying underwater targets remains a formidable challenge due to the complexity of the underwater environment and the substantial variations in target scale. To tackle the difficulties of extracting complex features in such conditions, this study introduces a novel feature extraction module built upon the YOLOv7 model. The module incorporates a custom-designed information attention mechanism and deformable convolution, enhancing the network’s ability to capture and process intricate underwater features. Additionally, a feature enhancement and interaction module was developed further to strengthen the extraction and utilization of key features. An adaptive feature fusion module was also introduced, allowing for dynamic adjustment of fusion weights based on the characteristics of the input features, thereby enabling more efficient and accurate multi-scale feature fusion. Experimental results reveal that, compared to the original YOLOv7 model on the URPC2020 dataset, the proposed method achieves improvements of 3.3% and 2.7% in mAP50 and mAP50:95 metrics, respectively. These significant performance gains highlight the effectiveness of the proposed approach in underwater target detection and provide valuable new directions for research and applications in this domain.
Advancing haptic realism: modelling grasp contact vibrations for enhanced virtual environment interaction
Authors: Najdovski Zoran , Pedrammehr Siamak , Qazani Mohammad Reza Chalak , Abdi Hamid , Asadi Houshyar
Published: January 2025

In haptic technology, achieving realistic tactile feedback is crucial for enhancing the user experience in virtual environments. Previous studies lack effective methods for transmitting high-frequency vibrations crucial for realistic tactile feedback in haptic interfaces, highlighting the need for our research to address this gap. This paper explores the application of a tactile gripping interface to transmit the high-frequency vibrations produced when contacting a hard object's surface. These short vibrations improve the tactile sensation of hard virtual surfaces when overlaid on traditional position-based force feedback within a haptic environment. The enhanced realism of virtual objects is achieved by effectively estimating the vibration composition from user-induced parameters. This work presents a prototype grasping interface and empirically demonstrates this device's utility. We examine empirical grasp contact data, recorded and interpreted, to recognise the relationship between dynamic user-controlled parameters and the resulting vibration transients. This relationship effectively incorporates these changing dynamics to model the grasp impact and estimate the essential system parameters to understand the influence of the user's grasp force. Through our multi-point grasping interface design, this work demonstrates a mathematical relationship between the user's grasp force and the high-frequency vibrations from contact with hard surfaces. The study found that the proposed haptic interface had an RMSE of 0.05, demonstrating the system's high level of accuracy. This low RMSE value indicates that the predicted vibrations closely matched the actual measured vibrations, proving the effectiveness of our methods through objective validation.
Interface characterization of additively manufactured Inconel 718 heterogeneous products: Microstructural and surface characteristics
Authors: Shrivastava Abhishek , S Anand Kumar , Rao Samrat
Published: January 2025

The present work analyses the effect of laser powder bed fusion (LPBF) remanufacturing of wrought Inconel 718 on the metallurgical and surface characteristics of the part. The remanufactured part experienced geometrical mismatch due to computer-aided design (CAD) misalignment with the substrate. As a result, a ~200 μm shift in a material deposition is observed, leading to a material deposition without support from the substrate on one edge. The microstructural analysis of the remanufactured part showed an interface between the wrought substrate and LPBF processed layers. The substrate showed an equiaxed grain structure. However, a strongly textured columnar grain structure was observed in the remanufactured region. The remanufactured Inconel 718 showed lower micro-hardness, inhibiting the precipitation of strengthening precipitates during the LPBF process. The surface characterization showed a higher surface roughness and anisotropy in material distribution for overhanging edges due to layer deformation and lack of support from the substrate. It was observed that due to geometry mismatch, the overhanging side experienced layer deformation, thus leading to the formation of large undulations on the surface. A higher area fraction of fused powder particles on the overhanging side shows ineffective heat transfer due to the region's lack of efficient support.
A Method for Mitigating NLOS Error for UWB Based on CNN-Stacked-LSTM Model
Authors: Zhang Lieping , Liu Jinming , Zheng Xinpeng , Zou Jianchu
Published: January 2025

To address the shortcomings of traditional Long Short-Term Memory (LSTM) network in Non-Line-of-Sight (NLOS) mitigation, such as the large amount of training data required and the lengthy training times, in order to enhance the model's ability to process spatial features and multi-level features, this paper proposes an NLOS mitigation method based on Stacked Long Short-Term Memory (Stacked-LSTM) network and Convolutional Neural Network (CNN). This method combines CNN and Stacked-LSTM models to efficiently extract spatial and higher-level temporal features from the Channel Impulse Response (CIR) signal, reducing the input dimension and improving the performance of the model. The constructed CNN-Stacked-LSTM model is used to mitigate NLOS errors and reduce the impact of NLOS in the original ranging data. In the model performance validation experiment, the accuracy of the CNN-Stacked-LSTM model was improved by 4%-14% compared with the CNN-LSTM, Transformer, Attention-LSTM and LSTM models, and the training time was reduced by 0.07h compared with the traditional LSTM model. The experimental results in the two actual Ultra-WideBand (UWB) environments show that compared with the other four models, the RMSE value of the CNN-Stacked-LSTM model proposed in this paper is reduced by 19.55%-58.96% and 8.64%-45.52%, respectively. It has the best mitigation effect on NLOS and the highest positioning accuracy.
Slax: a composable JAX library for rapid and flexible prototyping of spiking neural networks
Authors: Summe Thomas Manuel , Joshi Siddharth
Published: January 2025

Spiking Neural Networks (SNNs) offer rich temporal dynamics and unique capabilities, but their training presents challenges. While backpropagation through time (BPTT) with surrogate gradients is the defacto standard for training SNNs, it scales poorly with long time sequences. Alternative learning rules and algorithms could help further develop models and systems across the spectrum of performance, bio-plausibility, and complexity. However, implementing and evaluating these alternatives at scale is cumbersome and error-prone, requiring repeated reimplementations. To address this, we introduce Slax, a JAX-based library designed to accelerate SNN algorithm design and evaluation. Slax is compatible with the broader JAX and Flax ecosystem and provides optimized implementations of diverse training algorithms, enabling direct performance comparisons. Its toolkit includes methods to visualize and debug algorithms through loss landscapes, gradient similarities, and other metrics of model behavior during training. By streamlining the implementation and evaluation of novel SNN learning algorithms, Slax aims to facilitate research and development in this promising field.
Evaluating the effectiveness of cost-minimal planning of decarbonized electricity systems in reducing life cycle greenhouse gas emissions
Authors: Tarroja Brian , Mulvaney Dustin , Peer Rebecca A M , Grubert Emily
Published: January 2025

The decarbonization of regional electricity systems is a critical enabler of broader economy-wide decarbonization strategies and has motivated robust research on how to decarbonize electricity systems at minimum monetary cost. Since these efforts often focus on the operation of electricity systems, however, they do not account for emissions of the full life cycle associated with different resources. Different resource mixes can achieve a similar level of decarbonization apparent decarbonization through operational emissions, while achieving very different levels of life cycle greenhouse gas (GHG) emissions reductions. To explore these differences, we model the expansion of the California electricity system from 2030 to 2045 using a simplified electricity dispatch model to investigate how the planning of future electricity systems may differ between prioritizing minimum cost versus minimum life cycle GHG emissions under a common target for operational GHG reductions. We find that explicitly planning for minimum life cycle GHG emissions yields an additional 1.6-2.0% reduction in annual life cycle GHG emissions at a cost penalty of 3.2–9.6% and that electricity resource mixes that minimize life cycle GHG emissions tend to favor high capacity factor zero-carbon resources and long-duration storage compared to a minimum cost approach. Comparatively, we find that aggressive supply chain decarbonization of generation and storage technologies reduces life cycle GHG emissions of electricity supplies by 3.0% to 14% and combining both increases these reductions to 5.0% to 16%. Further, we find that applying a carbon tax to minimum cost-based capacity expansion can incentivize planning to account for life cycle GHG emissions. Our results indicate that a planning approach focused on minimizing life cycle GHG emissions may not be palatable, but significant reductions in life cycle emissions can be realized through conventional mechanisms like carbon taxes, import standards, and targeted supply chain decarbonization.
An Individualist Bias
Authors: Zahavi Dan
Published: January 2025

Standard accounts of collective intentionality typically see the main challenge to be that of explaining how collective intentionality can grow out of individual intentionality. But why assume that the first-person singular is more self-explanatory than the first-person plural? What is ultimately the relation between self-identity and self-experience on the one hand and collective identities and we-experiences on the other? Might the former ultimately be enabled by the latter? Engaging with discussions in the philosophical, sociological, and anthropological literature, this chapter considers a variety of attempts to assign primacy to the we and argues that they all fall short by operating with too undifferentiated conceptions of selfhood.
Local Elections and Ranked Choice
Authors: Endersby James W , Towle Michael J
Published: January 2025

This chapter provides historical context and electoral experience with preferential voting in nonpartisan elections for local governments throughout North America. Early adoptions of ranked choice voting (RCV) and single transferable vote (STV) occurred during the Progressive Era in the early twentieth century in locations ranging from across the US to provinces in western Canada. Emphasis was often placed on proportional representation and the STV. Retrenchment occurred as most communities abandoned preferential voting. A subsequent wave occurred in the late twentieth century and early twenty-first century as local communities adopted, and sometimes repealed, instant runoff voting. A renewal in election policy adoption followed as the system was retitled RCV and the number of municipal governments adopting the system expanded from the Bay Area in California to communities across the United States. Other aligned systems used for local elections include Bucklin voting, the limited vote, Nanson’s rule, cumulative voting, and approval voting.
Model selection and model robustness for population size estimation in two-sample capture-recapture studies
Authors: Dong Gracia Y , McNichol Jennifer , Cowen Laura L E
Published: January 2025

Two-sample capture-recapture studies are commonly used in the epidemiological and ecological literature. Most of these studies have been limited to analysis using the Lincoln-Petersen estimator, especially in epidemiological studies. We examine the use of the Lincoln-Petersen estimator and two alternative closed-population methods: Huggins’ conditional likelihood method and Pledger’s likelihood method with mixtures. We assessed the ability of aic to perform model selection when time, behavioural effects, or heterogeneity are present in capture probabilities. In addition, we examined the effects of model misspecification on estimates of population size. An extensive simulation study found that these models are not robust to misspecification and that aic was not capable of selecting the correct model with two capture occasions.
Decoding Monogenic Hypertension: A Review of Rare Hypertension Disorders
Authors: Gallegos Flora R , Delahunty Meaghan P , Hu Jieji  , Yerigeri Shivani B , Dev Vishnu , Bhatt Girish , Raina Rupesh 
Published: January 2025

BACKGROUND

Hypertension is a growing concern worldwide, with increasing prevalence rates in both children and adults. Most cases of hypertension are multifactorial, with various genetic, environmental, socioeconomic, and lifestyle influences. However, monogenic hypertension, a blanket term for a group of rare hypertensive disorders, is caused by single-gene mutations that are typically inherited in an autosomal dominant fashion, and ultimately disrupt normal blood pressure regulation in the kidney or adrenal gland. Being able to recognize and understand the pathophysiology of these rare disorders is critical for properly diagnosing hypertension, particularly in children and young adults, as treating each form of monogenic hypertension requires specific and targeted treatment approaches.

METHODS

A scoping literature review was conducted on the available knowledge regarding each of the disorders currently categorized as forms of monogenic hypertension.

RESULTS

This narrative review serves to highlight the epidemiology, pathophysiology, clinical presentation, recent case reports, and most current methods of evaluation and treatment for familial hyperaldosteronism types 1–4, Gordon syndrome. Liddle syndrome, syndrome of apparent mineralocorticoid excess, congenital adrenal hyperplasia, Geller syndrome, hereditary syndromes related to pheochromocytomas and paragangliomas, and brachydactyly type E.

CONCLUSIONS

Recent and future advances in genetic analysis techniques will further enhance the diagnosis and early management of these disorders, preventing the consequences of uncontrolled hypertension.
Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks
Authors: Cai Lingsheng , Ma Xiuli , Ma Jianzhu
Published: January 2025

Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships. To address these limitations, we introduce single-cell Multi-omics Integration (scMI), a heterogeneous graph embedding method that encodes both cells and modality features from single-cell RNA-seq and ATAC-seq data into a shared latent space by learning cross-modality relationships. By modeling cells and modality features as distinct node types, we design an inter-type attention mechanism to effectively capture long-range cross-modality interactions between genes and peaks. Benchmark results demonstrate that embeddings learned by scMI preserve more biological information and achieve comparable or superior performance in downstream tasks including modality prediction, cell clustering, and gene regulatory network inference compared to methods that rely on databases. Furthermore, scMI significantly improves the alignment and integration of unmatched multi-omics data, enabling more accurate embedding and improved outcomes in downstream tasks.
DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations
Authors: Jin Iljung , Lee Songyeon , Schmuhalek Martin , Nam Hojung
Published: January 2025

Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy. DD-PRiSM consists of two predictive models. The first is the Monotherapy model, which predicts parameters of the drug response curve based on drug structure and cell line gene expression. This reconstructed curve is then used to predict cell viability at the given drug dosage. The second is the Combination therapy model, which predicts the efficacy of drug combinations by analyzing individual drug effects and their synergistic interactions with a specific dosage level of individual drugs. The efficacy of DD-PRiSM is demonstrated through its performance metrics, achieving a root mean square error of 0.0854, a Pearson correlation coefficient of 0.9063, and an R2 of 0.8209 for unseen pairs. Furthermore, DD-PRiSM distinguishes itself by its capability to decompose combination therapy efficacy, successfully identifying synergistic drug pairs. We demonstrated synergistic responses vary across cancer types and identified hub drugs that trigger synergistic effects. Finally, we suggested a promising drug pair through our case study.
Deep learning in integrating spatial transcriptomics with other modalities
Authors: Luo Jiajian , Fu Jiye , Lu Zuhong , Tu Jing
Published: January 2025

Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells. Integrating such additional data from other modalities can effectively enhance spatial transcriptomics data, and, conversely, spatial transcriptomics data can supplement scRNA-seq with spatial information. Moreover, the rapid development of spatial multi-omics technology has spurred the demand for the integration of spatial multi-omics data to present a more detailed molecular landscape within tissues. Numerous deep learning (DL) methods have been developed for integrating spatial transcriptomics with other modalities. However, a comprehensive review of DL approaches for integrating spatial transcriptomics data with other modalities remains absent. In this study, we systematically review the applications of DL in integrating spatial transcriptomics data with other modalities. We first delineate the DL techniques applied in this integration and the key tasks involved. Next, we detail these methods and categorize them based on integrated modality and key task. Furthermore, we summarize the integration strategies of these integration methods. Finally, we discuss the challenges and future directions in integrating spatial transcriptomics with other modalities, aiming to facilitate the development of robust computational methods that more comprehensively exploit multimodal information.
TRIAGE: an R package for regulatory gene analysis
Authors: Zhao Qiongyi , Shim Woo Jun , Sun Yuliangzi , Sinniah Enakshi , Shen Sophie , Boden Mikael , Palpant Nathan J
Published: January 2025

Regulatory genes are critical determinants of cellular responses in development and disease, but standard RNA sequencing (RNA-seq) analysis workflows, such as differential expression analysis, have significant limitations in revealing the regulatory basis of cell identity and function. To address this challenge, we present the TRIAGE R package, a toolkit specifically designed to analyze regulatory elements in both bulk and single-cell RNA-seq datasets. The package is built upon TRIAGE methods, which leverage consortium-level H3K27me3 data to enrich for cell-type-specific regulatory regions. It facilitates the construction of efficient and adaptable pipelines for transcriptomic data analysis and visualization, with a focus on revealing regulatory gene networks. We demonstrate the utility of the TRIAGE R package using three independent transcriptomic datasets, showcasing its integration into standard analysis workflows for examining regulatory mechanisms across diverse biological contexts. The TRIAGE R package is available on GitHub at https://github.com/palpant-comp/TRIAGE_R_Package.
FlexLMM: a Nextflow linear mixed model framework for GWAS
Authors: Pierotti Saul , Fitzgerald Tomas , Birney Ewan
Published: January 2025

Linear mixed models (LMMs) are a commonly used statistical approach in genome-wide association studies when population structure is present. However, naive permutations of the phenotype to empirically estimate the null distribution of a statistic of interest are not appropriate in the presence of population structure or covariates. This is because the samples are not exchangeable with each other under the null hypothesis, and because permuting the phenotypes breaks the relationship among those and eventual covariates. For this reason, we developed FlexLMM, a Nextflow pipeline that can perform appropriate permutations in LMMs while allowing for flexibility in the definition of the exact statistical model to be used. FlexLMM can set a significance threshold via permutations, thanks to a two-step process where the population structure is first regressed out, and only then are the permutations performed on the uncorrelated residuals. We envision this pipeline will be particularly useful for researchers working on multi-parental crosses among inbred lines of model organisms or farm animals and plants.

Availability and implementation

The source code and documentation for the FlexLMM is available at https://github.com/birneylab/flexlmm.