bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ultra-sensitive molecular residual disease detection through whole genome sequencing with single-read error correction
Authors: Li Xinxing , Liu Tao , Bacchiocchi Antonella , Li Mengxing , Cheng Wen , Wittkop Tobias , Mendez Fernando L , Wang Yingyu , Tang Paul , Yao Qianqian , Bosenberg Marcus W , Sznol Mario , Yan Qin , Faham Malek , Weng Li , Halaban Ruth , Jin Hai , Hu Zhiqian 
Published: August 2024

AbstractWhile whole genome sequencing (WGS) of cell-free DNA (cfDNA) holds enormous promise for detection of molecular residual disease (MRD), its performance is limited by WGS error rate. Here we introduce AccuScan, an efficient cfDNA WGS technology that enables genome-wide error correction at single read-level, achieving an error rate of 4.2 × 10−7, which is about two orders of magnitude lower than a read-centric de-noising method. The application of AccuScan to MRD demonstrated analytical sensitivity down to 10−6 circulating variant allele frequency at 99% sample-level specificity. AccuScan showed 90% landmark sensitivity (within 6 weeks after surgery) and 100% specificity for predicting relapse in colorectal cancer. It also showed 67% sensitivity and 100% specificity in esophageal cancer using samples collected within one week after surgery. When AccuScan was applied to monitor immunotherapy in melanoma patients, the circulating tumor DNA (ctDNA) levels and dynamic profiles were consistent with clinical outcomes. Overall, AccuScan provides a highly accurate WGS solution for MRD detection, empowering ctDNA detection at parts per million range without requiring high sample input or personalized reagents.
BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation
Authors: Khan Muhammad Abrar Ahmad , Khan Muhammad Attique , Rehman Ateeq Ur , Alzahrani Ahmed Ibrahim , Alalwan Nasser , Gupta Deepak , Rahin Saima Ahmed , Zhang Yudong 
Published: August 2024

AbstractBiometric characteristics are playing a vital role in security for the last few years. Human gait classification in video sequences is an important biometrics attribute and is used for security purposes. A new framework for human gait classification in video sequences using deep learning (DL) fusion assisted and posterior probability‐based moth flames optimization (MFO) is proposed. In the first step, the video frames are resized and fine‐tuned by two pre‐trained lightweight DL models, EfficientNetB0 and MobileNetV2. Both models are selected based on the top‐5 accuracy and less number of parameters. Later, both models are trained through deep transfer learning and extracted deep features fused using a voting scheme. In the last step, the authors develop a posterior probability‐based MFO feature selection algorithm to select the best features. The selected features are classified using several supervised learning methods. The CASIA‐B publicly available dataset has been employed for the experimental process. On this dataset, the authors selected six angles such as 0°, 18°, 90°, 108°, 162°, and 180° and obtained an average accuracy of 96.9%, 95.7%, 86.8%, 90.0%, 95.1%, and 99.7%. Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state‐of‐the‐art techniques.
A multi‐objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources
Authors: Zhang Yuying , Liang Chen , Wang Han , Zhang Jiayi , Zeng Bo , Liu Wenxia 
Published: August 2024

AbstractWith the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach.
A survey on deep learning based reenactment methods for deepfake applications
Authors: Dhanyalakshmi Ramamurthy , Popirlan Claudiu‐Ionut , Hemanth Duraisamy Jude 
Published: August 2024

AbstractAmong the sectors that deep learning has transformed, deepfake, a novel method of manipulating multimedia, deserves particular attention. The long‐term objective of many researchers is to seamlessly mimic human facial movement or whole‐body activity, referred to as reenactment. Deepfake progress has made this goal much more feasible in recent years. Yet, achieving more realistic facial and body reenactment remains a challenging task. The primary focus of this study is to explore the current capability of the reenactment techniques and expand them further to attain greater results. The analysis offers a thorough overview of the various techniques involved, the challenges addressed, the datasets utilized, and the metrics employed by the underlying methods of reenactment technologies. The study also addresses the potential risks and their mitigating strategies to ensure responsible reenactment techniques. To the best of the authors' knowledge, this is the first survey paper that delves deeper into the topic of deepfake reenactment.
Majorisation‐minimisation algorithm for optimal state discrimination in quantum communications
Authors: Kundu Neel Kanth , Babu Prabhu , Stoica Petre 
Published: August 2024

AbstractDesigning optimal measurement operators for quantum state discrimination (QSD) is an important problem in quantum communications and cryptography applications. Prior works have demonstrated that optimal quantum measurement operators can be obtained by solving a convex semidefinite program (SDP). However, solving the SDP can represent a high computational burden for many real‐time quantum communication systems. To address this issue, a majorisation‐minimisation (MM)‐based algorithm, called Quantum Majorisation‐Minimisation (QMM) is proposed for solving the QSD problem. In QMM, the authors reparametrise the original objective, then tightly upper‐bound it at any given iterate, and obtain the next iterate as a closed‐form solution to the upper‐bound minimisation problem. Our numerical simulations demonstrate that the proposed QMM algorithm significantly outperforms the state‐of‐the‐art SDP algorithm in terms of speed, while maintaining comparable performance for solving QSD problems in quantum communication applications.
A multi‐power quality problems management strategy based on VSCs and switches in AC/DC hybrid LVDN with large PVs
Authors: Fu Yu , Yang Weichen , Li Yue , Bai Hao , Cai Yongxiang , Li Wei 
Published: August 2024

AbstractA large scale of distributed photovoltaics is accessed through a low voltage distribution network in a single‐phase or two‐phase, which causes three‐phase unbalance and over‐voltage problems. These problems can be solved by using a voltage source converter to realize AC/DC interconnection and flexible power transfer between lines. How to utilize the characteristics of voltage source converters to optimize the power qualities, and fully promote distributed PV systems consumption in low voltage distribution network is an important research point. This paper proposes a power regulation model of VSC. The goal of this model is to adjust and optimize over‐voltage and three‐phase unbalance problems. By constructing a voltage‐power sensitivity calculation model for a three‐phase four‐wire system and integrating different control modes and adjustment capabilities of voltage source converters, a control strategy based on voltage source converters is proposed to address three‐phase unbalance and over‐voltage issues. Finally, the simulation results demonstrate the effectiveness of the proposed strategy.
Low‐carbon scheduling model of multi‐virtual power plants based on cooperative game considering failure risks
Authors: Wu Chen , Wei Zhinong , Cao Yang , Xu Yiteng , Wei Tiantian , Han Haiteng , Chen Sheng , Zang Haixiang 
Published: August 2024

AbstractThe increasing amount of distributed renewable energy (DRE) is participating in grid‐connected operation as an important unit of the virtual power plant (VPP) aggregation. VPP also contains a variety of flexible resources such as demand response (DR), energy storage (ES), and fuel cell (FC). How to achieve efficient energy utilization while reducing carbon emissions and resisting the risk of failure caused by extreme weather has attracted widespread attention. In this article, a cooperative game‐based low‐carbon scheduling model for multi‐VPPs under the consideration of typhoon‐induced grid outage risks is proposed. First, a cooperative game mechanism for multi‐VPPs is constructed. And a bi‐level model of multi‐VPPs low‐carbon scheduling is built under the framework of electricity‐carbon trading markets. Second, the bi‐level scheduling model is linearized based on the Strong Duality Theorem and Karush‐Kuhn‐Tucker (KKT) condition. Then, the dispatch scheme of each VPP under the cooperative game form is obtained. Finally, simulations are performed to verify the validity of the proposed model. The results show that the economic and low‐carbon performance of multi‐VPPs can be improved by applying the cooperative game, which can also enhance the power system ability of resisting line faults.
Symmetrical components‐based robust stabilizing control of a grid‐connected inverter under unbalanced voltage sag
Authors: Choeung Chivon , Danayiyen Yahya , Srang Sarot , Lee Young‐Il 
Published: August 2024

AbstractThis study presents an approach for robust current and power control for a three‐phase grid‐connected inverter set up with an L‐filter that operates under an unbalanced AC grid‐voltage source. This control method consists of two paralleled current control for the positive and negative sequences to accomplish one of three selective control targets: a) supplying balanced three‐phase current; b) supplying non‐oscillated real power; and c) supplying non‐oscillated imaginary power to the AC grid for a given reference value utilizing the identical control gains. The real and imaginary power references, together with the measured grid voltages, are used in the computation of the reference current for each control target. In addition, the proposed dual‐current control system has a control law that comprises state feedback to offer stability and an integral effect to get rid of the offset error in the dq‐frame. To reduce settling time under uncertain system conditions, an optimization problem based on linear matrix inequalities is solved to determine a set of robust stabilizing gains. Simulation and experimental results using a TMS320F28377D digital signal processor validate the proposed control performance under three control targets.
Supplements in Rare Bone Diseases
Authors: Hadzimuratovic Benjamin , Lechner Lisa , Resch Heinrich 
Published: August 2024

AbstractDespite having different aetiologies, different rare bone diseases (RBDs) such as
hypophosphatasia (HPP), autosomal dominant hypophosphatemic rickets (ADHR),
X-linked hypophosphatemia (XLH) and osteogenesis imperfecta (OI) share common
clinical features such as growth disturbances, pathological fractures,
pseudo-fractures and chronic musculoskeletal pain. The role of micronutrients
including minerals, trace elements and vitamin D in the physiological bone
metabolism are well established. A significant share of RBD patients suffer from
nutritional deficiencies due to the underlying disease or do not achieve the
recommended daily intake (RDI) for micronutrients. The supplementation of
micronutrients in RBDs should have the goal of achieving the RDI and promoting
bone metabolism without increasing the burden of disease. Specific diets and an
increased intake of specific micronutrients could potentially improve some of
the disease symptoms, however special caution should be taken to avoid
over-supplementation and to avoid adverse effects such as hypercalciuria,
ectopic calcifications, GI-upset and nephrocalcinosis in case of calcium
over-supplementation.
Enhancing magnetocaloric material discovery: A machine learning approach using an autogenerated database by large language models
Authors: Yuan Jiaoyue , Yang Runqing , Patra Lokanath , Liao Bolin 
Published: August 2024

Magnetic cooling based on the magnetocaloric effect is a promising solid-state refrigeration technology for a wide range of applications in different temperature ranges. Previous studies have mostly focused on near room temperature (300 K) and cryogenic temperature (&lt;10 K) ranges, while important applications such as hydrogen liquefaction call for efficient magnetic refrigerants for the intermediate temperature range of 10–100 K. For efficient use in this range, new magnetocaloric materials with matching Curie temperatures need to be discovered, while conventional experimental approaches are typically time-consuming and expensive. Here, we report a computational material discovery pipeline based on a materials database containing more than 6000 entries auto-generated by extracting reported material properties from the literature using a large language model. We then use this database to train a machine learning model that can efficiently predict the magnetocaloric properties of materials based on their chemical composition. We further verify the magnetocaloric properties of the predicted compounds using ab initio atomistic spin dynamics simulations to complete the computational material discovery. Using this approach, we identify 11 new promising magnetocaloric materials for the target temperature range. Our work demonstrates the potential of combining large language models, machine learning, and ab initio simulations to efficiently discover new functional materials.
MXene enhanced reduced graphene oxide aerogel for high-performance supercapacitors
Authors: Wang Zhenjiang , Yang Xinli , Wang Gang , Yang Xiping , Qiao Longhao , Lu Mingxia 
Published: August 2024

Three-dimensional (3D) reduced graphene oxide (rGO)/Ti2CTx MXene hybrid aerogels were effectively prepared by a two-step method involving hydrothermal reaction and freeze-drying. The intimately coupled rGO/Ti2CTx hybrid aerogel combined high electrical conductivity, large interlayer spacing, and excellent mechanical stability of Ti2CTx, which not only effectively prevents the self-restacking of Ti2CTx nanosheets, exposes more active sites exposed, and improves the volume change during the charge/discharge process but also increases the accessibility of ions and promotes the rapid transfer of ions/electrons. As a result, rGO/Ti2CTx 17.5–2.5 as the working electrode of electric double layer capacitors delivers a large specific capacity (107.05 F g−1 at 0.5 A g−1 in a 1M Na2SO4 electrolyte), a high rate capability (maintains 30% of its initial capacitance at 10 A g−1, which is much better than rGO and Ti2CTx), and excellent long-term large-current cycle stability (the initial capacitance remains above 71.1% after 10 000 cycles at 1 A g−1). In addition to providing a high-performance electrode for supercapacitors, this study proposes an efficient and time-saving strategy for constructing 3D structures from 2D materials.
Moving towards peace, compassion and empathy through semiotic enquiry
Authors: Etaywe Awni , Thomson Elizabeth A. , Wijeyewardene Ingrid 
Published: August 2024

Abstract
In our interconnected world, exposure to hate, fear, violence and adversarial communication is commonplace. Yet,
this exposure underscores the urgent need for a more just, peaceful, and inclusive society. While research stresses the importance
of nurturing peace, compassion and empathy for enhanced well-being and social justice, these three terms are rarely discussed at
length in linguistics and communication studies. Little systematic work explores how language and semiotic resources actualise
these ideals across contexts. This special issue, grounded in systemic functional linguistics, addresses this gap. It aims to
contribute to a more just world, promote non-violent efforts for social justice, and lay the foundation for future research on the
semiotics of peace, compassion and empathy. This paper outlines key definitions and contributions.
Compassion as appraisal, performative identity and moral affiliation
Authors: Etaywe Awni 
Published: August 2024

Abstract
This article addresses the underexplored topic of compassion in digital activism through linguistic research.
Utilising corpus linguistics and the SFL appraisal framework, the study initially analyses the lemma “compassion” and its
appraisal nature within The Coronavirus Corpus. It then models compassion development as a social discursive process, taking
Boycott, Divestment and Sanctions (BDS-Australia) campaigns as a case study, drawing on “communion” and “tendering” strategies,
and positioning theory-based morality by linking structure to agency. The analysis provides evidence that the concept of
“compassion” extends beyond triggered emotions, expressing institutionalised feelings. Compassion also develops through a moral
affiliation process: aligning identities, positioning others within shared moral and sociocultural frameworks, and exhorting
people towards purposeful social actions as commodities. This research underscores compassion’s normative (i.e. moral orders) core
and performative essence.
Asymptotic stability of a finite sum of solitary waves for the Zakharov–Kuznetsov equation
Authors: Pilod Didier , Valet Frédéric 
Published: August 2024

Abstract
We prove the asymptotic stability of a finite sum of well-ordered solitary waves for the Zakharov–Kuznetsov equation in dimensions two and three. We also derive a qualitative version of the orbital stability result, which will be useful for studying the collision of two solitary waves in a forthcoming paper.
The proof extends the ideas of Martel, Merle and Tsai for the sub-critical gKdV equation in dimension one to the higher-dimensional case. It relies on monotonicity properties on oblique half-spaces and rigidity properties around one solitary wave introduced by Côte, Muñoz, Pilod and Simpson in dimension two, and by Farah, Holmer, Roudenko and Yang in dimension three.
An Automated Catalog of Long Period Variables using Infrared Lightcurves from Palomar Gattini-IR
Authors: Suresh Aswin , Karambelkar Viraj , Kasliwal Mansi M. , Ashley Michael C. B. , De Kishalay , Hankins Matthew J. , Moore Anna M. , Soon Jamie , Soria Roberto , Travouillon Tony , Truong Kayton K. 
Published: August 2024

Abstract
Long Period Variables (LPVs) are stars with periods of several hundred days, representing the late, dust-enshrouded phase of stellar evolution in low to intermediate mass stars. In this paper, we present a catalog of 154,755 LPVs using near-IR lightcurves from the Palomar Gattini-IR (PGIR) survey. PGIR has been surveying the entire accessible northern sky (δ > −28°) in the J-band at a cadence of 2–3 days since 2018 September, and has produced J-band lightcurves for more than 60 million sources. We used a gradient-boosted decision tree classifier trained on a comprehensive feature set extracted from PGIR lightcurves to search for LPVs in this data set. We developed a parallelized and optimized code to extract features at a rate of ∼0.1 s per lightcurve. Our model can successfully distinguish LPVs from other stars with a true positive rate of 95%. Cross-matching with known LPVs, we find 70,369 (∼46%) new LPVs in our catalog.
Coupling and characterization of a Si/SiGe triple quantum dot array with a microwave resonator
Authors: Jiang 江 Shun-Li 顺利 , Jiang 蒋 Tian-Yi 天翼 , Xu 徐 Yong-Qiang 永强 , Wu 吴 Rui 睿 , Hao 郝 Tian-Yue 天岳 , Ye 叶 Shu-Kun 澍坤 , Cai 蔡 Ran-Ran 冉冉 , Wang 王 Bao-Chuan 保传 , Li 李 Hai-Ou 海欧 , Cao 曹 Gang 刚 , Guo 郭 Guo-Ping 国平 
Published: August 2024

Abstract
Scaling up spin qubits in silicon-based quantum dots is one of the pivotal challenges in achieving large-scale semiconductor quantum computation. To satisfy the connectivity requirements and reduce the lithographic complexity, utilizing the qubit array structure and the circuit quantum electrodynamics (cQED) architecture together is expected to be a feasible scaling scheme. A triple-quantum dot (TQD) coupled with a superconducting resonator is regarded as a basic cell to demonstrate this extension scheme. In this article, we investigate a system consisting of a silicon TQD and a high-impedance TiN coplanar waveguide (CPW) resonator. The TQD can couple to the resonator via the right double-quantum dot (RDQD), which reaches the strong coupling regime with a charge–photon coupling strength of g
0/(2π) = 175 MHz. Moreover, we illustrate the high tunability of the TQD through the characterization of stability diagrams, quadruple points (QPs), and the quantum cellular automata (QCA) process. Our results contribute to fostering the exploration of silicon-based qubit integration.
Design and parametrization of TPMS lattice using computational and experimental approach
Authors: Kumar Raj , Ramkumar Janakarajan , Balani Kantesh 
Published: August 2024

Abstract
Triply periodic minimal surface (TPMS) based lattices are extensively explored as a scaffold design for bone regeneration. TPMS maintains zero mean curvature at each point and offers a large surface area comparable to a trabecular bone. The best four TPMS minimal surfaces (IWP. Neovius, primitive, and F-RD) were selected, designed, and fabricated using acrylonitrile butadiene styrene (ABS) resin through the stereolithography (SLA) technique. The results indicate that small changes in unit cell dimensions do not significantly alter the structure topology, which ensures stress distribution within the lattice remains relatively uniform across different unit cell sizes when the porosity level is constant. The optimal unit cell size (2 to 5 mm) and porosity (70 to 80%) significantly affect the compressive strength and surface area to volume (SA/V) ratio due to a unique arrangement of the internal architecture of each TPMS unit cell. The lattice structure (formed by stacking unit cell) of unit cell size 2.11 mm with 70% porosity exhibited a maximum compressive strength of 39.8 MPa in IWP, followed by Neovius, primitive, and F-RD-based lattice structures. Moreover, the lattice showed more stability under compression force, minimized stress concentration compared to a unit cell, and exhibited distinct deformation patterns at different strain levels during compression.