bluuebunny/crossref_metadata_2025_split huggingface dataset:
Economic evaluation of battery energy storage system on the generation side for frequency and peak regulation considering the benefits of unit loss reduction
Authors: Liu Gengming , Liu Wenxia , Shi Qingxin 
Published: December 2023

AbstractThe indirect benefits of battery energy storage system (BESS) on the generation side participating in auxiliary service are hardly quantified in prior works. Nevertheless, the configuration of BESS could be affected by its indirect benefits. In this paper, the authors purpose a quantitative economic evaluation method of BESS considering the indirect benefits from the reduction in unit loss and the delay in investment. First, the authors complete further the cost model of BESS for frequency and peak regulation based on the whole life cycle theory. Second, the authors quantify the indirect benefits of BESS in thermal power plants based on the theory of rotor fatigue life loss and establish a benefits model that considers the unit loss reduction during frequency regulation and the delay in investment during peak regulation. Finally, the authors propose a set of indexes for economic evaluation of the thermal power plant with BESS. The simulation results show that the total benefits of BESS can be improved effectively by considering the indirect benefits from unit loss reduction and the delay in investment, proving the effectiveness of the proposed approach which can be meaningful for the future investment in BESS on the generation side.
First‐in‐human real‐time AI‐assisted instrument deocclusion during augmented reality robotic surgery
Authors: Hofman Jasper , De Backer Pieter , Manghi Ilaria , Simoens Jente , De Groote Ruben , Van Den Bossche Hannes , D'Hondt Mathieu , Oosterlinck Tim , Lippens Julie , Van Praet Charles , Ferraguti Federica , Debbaut Charlotte , Li Zhijin , Kutter Oliver , Mottrie Alexandre , Decaestecker Karel 
Published: December 2023

AbstractThe integration of Augmented Reality (AR) into daily surgical practice is withheld by the correct registration of pre‐operative data. This includes intelligent 3D model superposition whilst simultaneously handling real and virtual occlusions caused by the AR overlay. Occlusions can negatively impact surgical safety and as such deteriorate rather than improve surgical care. Robotic surgery is particularly suited to tackle these integration challenges in a stepwise approach as the robotic console allows for different inputs to be displayed in parallel to the surgeon. Nevertheless, real‐time de‐occlusion requires extensive computational resources which further complicates clinical integration. This work tackles the problem of instrument occlusion and presents, to the authors’ best knowledge, the first‐in‐human on edge deployment of a real‐time binary segmentation pipeline during three robot‐assisted surgeries: partial nephrectomy, migrated endovascular stent removal, and liver metastasectomy. To this end, a state‐of‐the‐art real‐time segmentation and 3D model pipeline was implemented and presented to the surgeon during live surgery. The pipeline allows real‐time binary segmentation of 37 non‐organic surgical items, which are never occluded during AR. The application features real‐time manual 3D model manipulation for correct soft tissue alignment. The proposed pipeline can contribute towards surgical safety, ergonomics, and acceptance of AR in minimally invasive surgery.
An image inpainting method based on generative adversarial networks inversion and autoencoder
Authors: Wang Yechen , Song Bin , Zhang Zhiyong 
Published: December 2023

AbstractImage inpainting aims to repair the damaged region according to the known content in the damaged image. Recently, image inpainting methods have poor effects on high‐resolution damaged images, and the research on the inpainting of large‐area damaged images is limited. Therefore, this paper proposes an image inpainting method based on Generative Adversarial Networks (GAN) inversion and autoencoder. This work consists of two phases: first, the authors design an autoencoder‐based GAN, which learns the mapping from noise to low‐dimensional feature maps by training a generator, and then converts the generated feature maps into high‐resolution images. Thus, the difficulty of learning the mapping relationship is reduced. Second, the authors adopt the learning‐based GAN inversion to infer the closest latent code. The trained GAN is then used to reconstruct the complete image. Finally, the authors compare their method with other classical methods on the CelebAMask‐HQ, Flickr‐Faces‐HQ, and ImageNet datasets. According to the quantitative comparison, when the mask range is large, in other words, when the image has a large area of damage, the authors’ method is superior to the comparison methods. According to the qualitative comparison, the structure of the high‐resolution image inpainted by the authors’ method is more reasonable and the texture details are more realistic.
Kein Sex ist auch keine Lösung
Authors: Dathan-Stumpf Anne , Stark Sylvia 
Published: December 2023

ZUSAMMENFASSUNGDie Verhütungsberatung von Jugendlichen stellt für Kinder- und Frauenärzte eine besondere Herausforderung dar. Entscheidend für die Zufriedenheit und letztlich auch zuverlässige Anwendung sind die selbstbestimmte Wahl des Kontrazeptivums nach einer bedürfnisorientierten Beratung, bei der auf die Ängste und Sorgen der Mädchen eingegangen wird. Aufgrund der Vielzahl der heute zur Verfügung stehenden Verhütungsmethoden, sollten die Vor- und Nachteile gründlich diskutiert werden. Zu den Kontrazeptiva der ersten Wahl in der Adoleszenz zählen die kombinierten oralen Kontrazeptiva in Form der Pille oder des Vaginalringes, bei Kontraindikation für eine Östrogengabe die Gestagenpille. Als weitere Optionen gelten intrauterine Kontrazeptiva (hormonhaltig, hormonfrei) und das Verhütungsstäbchen. Außerdem müssen Jugendliche explizit auf die additive Verwendung eines Kondoms in Hinblick auf die Vermeidung sexuell übertragbarer Erkrankungen hingewiesen werden.
Quality by Design Approach for Development and Characterization of Granisetron Hydrochloride-Loaded Orodispersible Films
Authors: Li Ming-Yan , Wang Bing , Zhang Jun-Qi , Yang Liu-Liu , He Jun-Tao , Chen Fang 
Published: December 2023

AbstractGranisetron hydrochloride can be used to prevent and treat nausea and vomiting induced by chemotherapy. Its prolonged half-life and reduced dose requirement improve patient acceptance. However, patients undergoing chemotherapy often suffer from dysphagia and drug spitting due to emesis. Hence, the development of a patient-centered formulation of granisetron hydrochloride with simple medication and high compliance is crucial. The current study employed a polymer combination of polyvinylpyrrolidone/polyvinyl alcohol (PVP/PVA) as film-forming materials and Lycoat® RS 780 as a disintegrant to formulate orodispersible films (ODFs) loaded with granisetron hydrochloride. Guided by the concept of quality by design, the quality target product profile and critical quality attributes (CQAs) for the ODF were defined. Through the quality risk assessment, essential factors that have a significant impact on CQAs were identified. The formulation was screened using the Box–Behnken statistical design with three factors and three levels. Our data suggested that all ODF formulations exhibited a disintegration time of less than 60 seconds and complete dissolution within 5 minutes. Furthermore, the formulation displayed appropriate mechanical properties, water residue, and pH values. Thus, the granisetron hydrochloride-loaded ODF is regarded as a patient-friendly formulation that enhances compliance and consequently aids in therapeutic effectiveness.
Diet and Exercise in the Management of Polycystic Ovary Syndrome: Practical Considerations for Person-Centered Care
Authors: Colombo Giorgia E. , Pirotta Stephanie , Sabag Angelo 
Published: December 2023

AbstractPolycystic ovary syndrome (PCOS) is a complex multisystem condition associated with life-long reproductive, metabolic, and psychological symptoms. Individuals with PCOS are at an increased risk of cardiovascular disease and type 2 diabetes, with approximately 70% of all PCOS cases presenting with insulin resistance. Lifestyle interventions have historically been recommended as first-line therapies for the management of PCOS-related cardiometabolic disorders. The term “lifestyle management” incorporates a multifaceted approach to dietary, exercise, and behavioral strategies, aiming to promote a healthy lifestyle. This approach has been commonly employed in practice, in particular through exercise and dietary modulation, due to its effect on cardiometabolic outcomes as well as its tolerability. Furthermore, there is evidence to suggest that combining dietary change with exercise may yield the greatest improvements in clinical outcomes. However, such practices require careful consideration and coordination, as there are instances where certain exercise and/or dietary prescriptions may compromise the effectiveness of the respective interventions. Thus, this review aims to provide practical guidance on diet and exercise planning in the routine care of PCOS. Such recommendations include emphasizing realistic and achievable goals, as well as minimizing barriers to lifestyle changes in order to increase the long-term sustainability of this treatment strategy.
Agri-food crises and news framing of media: an application to the Spanish greenhouse sector
Authors: Pérez-Mesa Juan Carlos , García Barranco Mª Carmen , Serrano Arcos Mª Mar , Sánchez Fernández Raquel 
Published: December 2023

AbstractThe contemporary media narratives frequently exhibit significant contradictions due to the influence of diverse interests. In this context, the framing of information assumes critical importance in shaping consumer opinions, necessitating a comprehensive examination of its management. This article investigates the portrayal of crises in the agri-food sector within the mass media when not anchored in objective and verifiable facts, thereby exerting a consequential impact on the sector’s reputation and public image. Specifically, a detailed analysis is conducted on the greenhouse horticulture sector in southeast Spain, recognized as the primary European supplier. Examination of these news items uncovers a discernible bias in the disseminated information, resulting in an information asymmetry between farmers and consumers. As a remedy for the affected sector, the current study advocates the implementation of a proactive crisis detection and management model grounded in the development and dissemination of verifiable information.
Fighting wildfires: predicting initial attack success across Victoria, Australia
Authors: Plucinski M. P. , Dunstall S. , McCarthy N. F. , Deutsch S. , Tartaglia E. , Huston C. , Stephenson A. G. 
Published: December 2023

Background The small portion of fires that escape initial attack (IA) have the greatest impacts on communities and incur most suppression costs. Early identification of fires with potential for escaping IA can prompt fire managers to order additional suppression resources, issue timely public warnings and plan longer-term containment strategies when they have the greatest potential for reducing a fire’s impact. Aims To develop IA models from a state-wide incident dataset containing novel variables that can be used to estimate the probability of IA when a new fire has been reported. Methods A large dataset was compiled from bushfire incident records, geographical data and weather observations across the state of Victoria (n = 35 154) and was used to develop logistic regression models predicting the probability of initial attack success in grassland-, forest- and shrubland-dominated vegetation types. Key results Models including input variables describing weather conditions, travel delay, slope and distance from roads were able to reasonably discriminate fires contained to 5 ha. Conclusions and implications The models can be used to estimate IA success – using information available when the location of a new fire can be estimated – and they can be used to prompt planning for larger fires.
A note on null distance and causality encoding
*
Authors: Galloway Gregory J 
Published: December 2023

Abstract
Under natural conditions, the null distance introduced by Sormani and Vega (2016 Class. Quantum Grav.
33 085001) is a metric space distance function on spacetime, which, in a certain precise sense, can encode the causality of spacetime. The null distance function requires the choice of a time function. The purpose of this note is to observe that the causality assumptions related to such a choice in results used to establish global encoding of causality, due to Sakovich and Sormani (2023 J. Math. Phys.
64 012502) and to Burtscher and García-Heveling (2022 arXiv:2209.15610 [math.DG]), can be weakened.
3D tomographic phase retrieval and unwrapping
Authors: Fannjiang Albert 
Published: December 2023

Abstract
This paper develops uniqueness theory for 3D phase retrieval with finite, discrete measurement data for strong phase objects and weak phase objects, including: (i) Unique determination of (phase) projections from diffraction patterns—General measurement schemes with coded and uncoded apertures are proposed and shown to ensure unique reduction of diffraction patterns to the phase projection for a strong phase object (respectively, the projection for a weak phase object) in each direction separately without the knowledge of relative orientations and locations. (ii) Uniqueness for 3D phase unwrapping—General conditions for unique determination of a 3D strong phase object from its phase projection data are established, including, but not limited to, random tilt schemes densely sampled from a spherical triangle of vertexes in three orthogonal directions and other deterministic tilt schemes. (iii) Uniqueness for projection tomography—Unique determination of an object of n
3 voxels from generic n projections or n + 1 coded diffraction patterns is proved. This approach of reducing 3D phase retrieval to the problem of (phase) projection tomography has the practical implication of enabling classification and alignment, when relative orientations are unknown, to be carried out in terms of (phase) projections, instead of diffraction patterns. The applications with the measurement schemes such as single-axis tilt, conical tilt, dual-axis tilt, random conical tilt and general random tilt are discussed.
Flexible SnO2–MoS2 based memristive device exhibiting stable and enhanced memory phenomenon
Authors: Komal Km , Singh Mukhtiyar , Singh Bharti 
Published: December 2023

Abstract
Flexible non-volatile memory devices have been gaining interest in expanding the digital data storage world. Due to the burgeoning advancement in the healthcare industry, the Internet of Things, and wearable electronics, the demand for ultra-thin, low-power, and flexible memory is increasing. Further, the advancement of synthesis procedures for two-dimensional nanomaterials having better optical, electrical, and mechanical strength with flexibility has fuelled the flexible memory device area, as commonly used flash memory is approaching its physical limit. In this context, the present work reports the flexible resistive switching memory device based on pure molybdenum disulfide (MoS2) and tin oxide (SnO2) based nanocomposite powder synthesized using the simple hydrothermal process. The nanocomposite formation was characterized using x-ray diffraction and Raman spectroscopic techniques. The memory device was fabricated by spin-coating the pure MoS2, pure SnO2, and MoS2–SnO2 nanocomposite film over the ITO-PET flexible substrate. The device was completed by thermally evaporating the thin Al layer through a shadow mask. It was found that MoS2–SnO2 based memory devices exhibited improved switching performance having a higher I
ON/I
OFF ratio, and lower switching voltage in comparison to pure MoS2 and pure SnO2-based devices (I
ON/I
OFF ratio ∼100, V = 0.5 V). Furthermore, to check the stability and cyclic performance of the fabricated device, the retention and endurance test was also performed, and the MoS2–SnO2 device retained the HRS and LRS states up to 2 × 103s and showed stable performance up to 100 switching cycles without much degradation, respectively. It should be mentioned that the presently proposed ReRAM device based on SnO2 and MoS2 with flexible and low-power features had excellent potential for use in the wearable device industry.
Thermal monitoring and deep learning approach for early warning prediction of rock burst in underground structures
Authors: Jaiswal Mrityunjay , Sebastian Resmi , Mulaveesala Ravibabu 
Published: December 2023

Abstract
The occurrence of rockburst has the potential to result in significant economic and human losses in underground mining and excavation operations. The accuracy of traditional methods for early prediction is considerably affected by factors such as site conditions, noise levels, accessibility, and other variables. This study proposes a methodology for identifying the most defected region in a hard rock sample by integrating motion thermogram data obtained from the laboratory monitoring of rock burst phenomena with a cutting-edge deep neural network approach based on a regional convolutional network (i.e. Mask RCNN). The efficacy of the suggested approach was evaluated by determining the F1 score and average precision matrices based on a specific intersection over union value. The findings demonstrate that the proposed approach possesses satisfactory precision with respect to detection, localization, and segmentation, thereby establishing its potential utility as an autonomous predictor of rock bursts.
Compact simply-connected SOI spot size converters for TE and TM polarizations
Authors: Yang Shanglin , Fu Xin , Niu Jiaqi , Yang Lin 
Published: December 2023

Abstract
Spot size converters (SSCs) are pivotal building blocks of silicon on insulator (SOI) on-chip optical systems, which enable efficient coupling of modes between waveguides of different widths. In this paper, we propose two SSCs for TE and TM polarizations based on level-set and adjoint methods, which can convert modes between 0.5 µm and 10 µm wide waveguides. The devices’ lengths are 22.4 µm and 23.7 µm, respectively, with simply-connected structures and the 200 nm minimum feature size. The simulation results demonstrate minimal insertion losses of 0.04 dB and 0.12 dB for the two SSCs, with respective 1 dB bandwidths exceeding 170 nm and 250 nm. Within the wavelength range of 1520–1590 nm, the insertion losses are below 0.42 dB and 0.24 dB. Experimental results, normalized with a referenced 250 µm length adiabatic linear taper, indicates insertion losses of less than

0.26
±
0.35

dB and

0.43
±
0.18

dB within the 1520–1590 nm wavelength range for the proposed SSCs, demonstrating a ten-fold reduction in length while maintaining comparable performance to the reference taper. Our devices hold the potential to facilitate the utilization of grating couplers in high-efficiency and high integration applications.
Electrochemical lift-off of GaN films for GaN-on-GaN technology
Authors: Liu Yuzhen , Feng Meixin , Yang Shanshan , Li Chuanjie , Dai Yayu , Zhang Shuming , Liu Jianxun , Jin Jing , Sun Qian , Yang Hui 
Published: December 2023

Abstract
Lifting off the native GaN substrate is an essential step in the fabrication of high-performance devices. In this study, we report a method to separate GaN thin films from GaN substrate through electrochemical (EC) lateral etching. By employing tetramethylammonium hydroxide to treat the dry-etched sidewalls, we addressed the issue of non-uniformity at the EC etching front. Meantime, we investigated the effect of Si doping concentration on the roughness of the lift-off GaN films. It is found that as increasing the doping concentration, the roughness decreases together with a reduced applied bias. Finally, we achieved an epitaxial-level smooth surface with a small roughness of only 0.3 nm.
Sensor self-diagnosis method based on a graph neural network
Authors: Jiang Dongnian , Luo Xiaomin 
Published: December 2023

Abstract
Many types of sensors are used in industrial processes, and their reliability is high. However, the traditional method of regularly detecting and evaluating their health status is time-consuming and laborious, and is not suitable for the development of intelligent sensors. In this work, the relative entropy method is first used to quantitatively evaluate the redundancy relationship between sensors, and a sensor graph network is established based on this relationship. Secondly, an unsupervised multi-sensor self-diagnosis model, called attention-based pruning graph convolutional network, is proposed. In order to capture the strong redundancy among sensors by the attention mechanism, multi-sensor timing prediction is realised using a graph convolutional neural network, and the health status of each sensor can be independently judged by the changes in redundancy among the sensors. Finally, a temperature measurement system in a nickel flash furnace is considered as a case study to verify the feasibility and effectiveness of the proposed method.
Rolling bearing fault diagnosis method based on MTF-MFACNN
Authors: Lei Chunli , Miao Chengxiang , Wan Huiyuan , Zhou Jiyang , Hao Dongfeng , Feng Ruicheng 
Published: December 2023

Abstract
A rolling bearing fault diagnosis method based on the Markov transition field (MTF) and multi-scale feature aggregation convolutional neural network (MFACNN) is proposed to address the problems of excessive parameter number, slow training speed, and insufficient generalization of traditional CNNs. Firstly, the original vibration signal is input into the MTF and converted into two-dimensional images with time correlation. Then, in order to effectively aggregate feature information at different scales and levels, a MFA module is presented to capture rich information from feature maps at different scales and assign different weights to these features for fusion. Secondly, while ensuring the lightweight of the model, utilizing feature information of different resolutions, a lightweight feature fusion module is put forward to fuse multiple feature maps together to improve the performance and efficiency of the model. On this basis, an MFACNN model is constructed. Finally, the two-dimensional images are input into MTF-MFACNN and experimentally validated using two different datasets. The results show that the proposed method has faster calculation speed, higher fault recognition accuracy, and stronger generalization performance compared to other methods.
Weak fault feature extraction of rolling bearing based on multi-system coupled cascaded stochastic resonance system
Authors: Li Jimeng , Peng Junling , Zhang Shi , Zhang Jinfeng , Meng Zong 
Published: December 2023

Abstract
The accurate extraction of weak signal features under strong noise background plays a crucial role in the fault detection of rolling bearings. In order to promote the ability of stochastic resonance (SR) system to detect weak signals and improve the output performance of the system, a multi-system coupled cascaded SR (MCCSR) system is investigated and applied to the fault detection of rolling bearings. Firstly, a MCCSR system is constructed by exploiting the positive synergistic effect between multiple systems, which consists of a triangular-topology coupled system composed of three SR subsystems and a cascaded SR system with topology output as input. This system makes full use of the advantages of coupled system and cascaded system in weak signal detection. In terms of parameter optimization, a stepwise multi-parameter optimization strategy is proposed, which adopts different optimization methods for different parameters, and avoids the inconsistency between error and step factor by improving the least mean square algorithm. Finally, through the comparative analysis of numerical simulation and experimental signals, it is verified that the proposed method can effectively enhance the weak signal features and improve the system output signal-to-noise ratio, which can better serve for rolling bearing fault detection.