bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mobile battery energy storage system control with knowledge‐assisted deep reinforcement learning
Authors: Zhao Huan , Liu Zifan , Mai Xuan , Zhao Junhua , Qiu Jing , Liu Guolong , Dong Zhao Yang , Ghias Amer M. Y. M. 
Published: December 2022

AbstractMost mobile battery energy storage systems (MBESSs) are designed to enhance power system resilience and provide ancillary service for the system operator using energy storage. As the penetration of renewable energy and fluctuation of the electricity price increase in the power system, the demand‐side commercial entities can be more profitable utilizing the mobility and flexibility of MBESSs compared to the stational energy storage system. The profit is closely related to the spatiotemporal decision model and is influenced by environmental uncertainties, such as electricity price and traffic conditions. However, solving the real‐time control problem considering long‐term profit and uncertainties is time‐consuming. To address this problem, this paper proposes a deep reinforcement learning framework for MBESSs to maximize profit through market arbitrage. A knowledge‐assisted double deep Q network (KA‐DDQN) algorithm is proposed based on such framework to learn the optimal policy and increase the learning efficiency. Moreover, two criteria action generation methods of knowledge‐assisted learning are proposed for integer actions utilizing scheduling and short‐term programming results. Simulation results show that the proposed framework and method can achieve the optimal result, and KA‐DDQN can accelerate the learning process compared to the original method by approximately 30%.
Operation strategies of battery energy storage systems for preventive and curative congestion management in transmission grids
Authors: Lindner Martin , Peper Jan , Offermann Nils , Biele Charlotte , Teodosic Milijana , Pohl Oliver , Menne Julian , Häger Ulf 
Published: December 2022

AbstractAnticipating and relieving congestions is an ongoing challenge for transmission system operators. Distributed grid‐scale battery energy storage systems enable operators to shift power flows and remedy congestion through virtual power lines and grid boosters. This paper includes battery energy storage systems in a combined preventive and curative congestion management optimization. First, it analyzes the impact of the two operational strategies in a case study of the German transmission grid. Furthermore, it outlines curative ad‐hoc measures to overcome uncertainties during operational planning and real‐time operation. The simulation results indicate that battery energy storage systems further increase the use of curative measures and reduce congestion management costs.
Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation
Authors: Peng Siyao , Ding Lijian , Li Weitao , Sun Wei , Li Qiyue 
Published: December 2022

AbstractAutomatically and accurately detecting the self‐blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open‐loop cognitive models to detect the self‐blast state of glass insulators, this study explores a mixed data augmentation‐based intelligent recognition method to detect the self‐blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high‐quality generative self‐blast samples of the glass insulator, and the non‐generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi‐scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi‐deformable convolutional network. Then, the extracted feature maps are transmitted to a two‐dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self‐blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self‐blast state of the glass insulator. Then, based on transfer learning and the established self‐optimising feedback mechanism for feature pyramid network, the self‐optimising adjustment and reconstruction of the feature map space with strong semantics and multi‐scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi‐channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.
Effect of radical scavenger on electrical tree in cross‐linked polyethylene with large harmonic superimposed DC voltage
Authors: Zhu Lewei , Li Zhonglei , Hou Kai 
Published: December 2022

AbstractIn this study, three kinds of radical scavenger Chimassorb 944, Tinuvin 622, and Tinuvin 770 are used to suppress the growth of electrical trees in cross‐linked polyethylene (XLPE) with transient superimposed voltage under the temperature gradient. The tree morphology, tree length, accumulated damage, and time to breakdown are used to investigate the effect of radical scavenger on the electrical treeing process. It is found that under the temperature gradient caused by the temperature rise on the high voltage side, only Tinuvin 622 can always suppress the electrical tree as the temperature gradient rises. Under the temperature gradient caused by the temperature rise on the ground side, the three radical scavengers can all suppress the electrical tree. The breakdown of electrical tree exhibits the strong DC polarity dependence. Meanwhile, energy levels of these three radical scavengers are calculated through quantum chemistry, and the results indicate that radical scavengers have greater electron affinity, smaller ionisation energy, and smaller energy gap than XLPE. According to the surface potential decay test results at 60°C, it is found that all three radical scavengers can introduce deep traps. The different performances of radical scavengers under different temperature gradients and voltages are determined by the trap distribution characteristics, the molecular structure and chemical reaction of the additives themselves. It is concluded that Tinuvin 622 has potential for use in high voltage direct current XLPE cable application.
Dual‐ and triple‐stream RESUNET/UNET architectures for multi‐modal liver segmentation
Authors: Elghazy Hagar Louye , Fakhr Mohamed Waleed 
Published: December 2022

AbstractDeep learning image segmentation has become an important field of interest in recent years, especially when it comes to medical images. Segmentation of medical image modalities such as magnetic resonance imaging (MRI) and computed tomography (CT) can benefit diagnosis accuracy, speed up diagnosis process, and decrease workload. The most famously used deep learning models in the medical image segmentation are the UNET‐based models, which have been repeatedly proven to provide a high percentage of accuracy in medical image segmentation. But, most of the available datasets contain a single modality and thus are not big enough to train complex architectures. Lately, it has been shown that using multiple modalities with multiple streams architectures can provide higher accuracy more than single modality with a single stream architecture. In this paper, the benefits of dual‐stream and triple‐stream architectures are demonstrated when processing multiple modalities. This work shows that dual stream can achieve dice of 0.97 on CT images and 0.89 on MRI images, while in triple stream architectures can achieve dice of 0.97 on CT images and 0.96 on MRI images. To the best of our knowledge, these are the best results to date.
Synthetic aperture radar image despeckling using convolutional neural networks in wavelet domain
Authors: Liu Jing , Liu Runchuan 
Published: December 2022

AbstractIt is difficult for a convolutional neural network (CNN) to capture the detailed features of synthetic aperture radar (SAR) images when increasing the network depth. To capture sufficient information for reconstructing image details, the authors propose a multidirectional and multiscale convolutional neural network (MMCNN) in which the wavelet subband is input into each independent subnetwork to be trained. Each subnetwork has few convolution layers and a loss function. When the loss function reaches its optimal value, all subbands are integrated to produce the despeckled SAR image through the inverse Wavelet transform. The proposed MMCNN consisting of multiple subnetworks extracts the detailed features and suppresses speckle noise from different directions and scales; thus, its performance is improved by broadening the network width rather than increasing the depth. Experimental results on synthetic and real SAR images show that the proposed method shows superior performance over the state‐of‐the‐art methods in terms of both quantitative assessments and subjective visual quality, especially for strong speckle noise.
A CNN with noise inclined module and denoise framework for hyperspectral image classification
Authors: Gong Zhiqiang , Zhong Ping , Yao Wen , Zhou Weien , Qi Jiahao , Hu Panhe 
Published: December 2022

AbstractDeep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical noise generation. This would make these deep models unable to generate discriminative features and provide impressive classification performance. To leverage such intrinsic information, this work develops a novel deep learning framework with the noise inclined module and denoise framework for hyperspectral image classification. First, the spectral signature of hyperspectral image is modeled with the physical noise model to describe the high intra‐class variance of each class and great overlapping between different classes in the image. Then, a noise inclined module is developed to capture the physical noise within each object and a denoise framework is then followed to remove such noise from the object. Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image. Experiments are conducted over two commonly used real‐world datasets and the experimental results show the effectiveness of the proposed method. The implementation of the proposed method and other compared methods could be accessed at https://github.com/shendu‐sw/noise‐physical‐framework.
Parking space inventory from above: Detection on aerial images and estimation for unobserved regions
Authors: Hellekes Jens , Kehlbacher Ariane , Díaz María López , Merkle Nina , Henry Corentin , Kurz Franz , Heinrichs Matthias 
Published: December 2022

AbstractParking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. A systematic relationship is found between the road length and type and the parking area obtained. It is suggested that these results are informative to those needing information on parking in structurally similar regions.
Die Analyse von Schnarchen bei Patienten mit obstruktiver Schlafapnoe (OSA) anhand von Polysomnographie und LEOSound
Authors: Moaeri S. , Hildebrandt Olaf , Cassel W. , Viniol C. , Schäfer A. , Kesper K. , Sohrabi K. , Gross V. , Koehler Ulrich 
Published: December 2022

Zusammenfassung
Einleitung In dieser Studie wird bei Patienten mit OSA parallel zur Erfassung des Biosignals Schnarchen aus der Polysomnografie auch eine akustische Aufzeichnung mit dem LEOSound (akustische Langzeitregistrierung der Atem- und Atemnebengeräusche) durchgeführt. Es stellt sich die Frage, wie viele der obstruktiven Apnoen bei apnoeterminierender Wiederaufnahme der Atmung mit Schnarchen einhergehen.
Methode Parallel zur polysomnografischen Messung wurde bei 40 Patienten mit OSA (AHI > 15/h) eine Atem- und Atemnebengeräuschaufzeichnung mit dem LEOSound-Rekorder durchgeführt. Das durchschnittliche Alter der Patienten lag bei 57 ± 11 Jahren. Das durchschnittliche Körpergewicht betrug 100 ± 19 kg bei einem mittleren Body-Mass-Index (BMI) von 33 ± 7 kg/m2. 12 der 40 Aufzeichnungen mussten aufgrund qualitativ unzureichender Messqualität von der Bewertung ausgeschlossen werden. Die visuell detektierten Schnarchereignisse in der Polysomnografie wurden mit den audiovisuell erfassten im LEOSound verglichen.
Ergebnisse Insgesamt konnten 3778 obstruktive Apnoephasen registriert werden. In der Polysomnografie gingen 2229 (58,8%) Apnoen mit Schnarchen einher, in der akustischen Messung 1921 (51,0%). Bei einem der Patienten war eine ausgeprägtere Abweichung der Frequenz der Schnarchereignisse von PSG und LEOSound nachweisbar.
Diskussion Schnarchen wird in beiden Verfahren bei etwa 60% der obstruktiven Apnoephasen gefunden. Eine audiovisuelle Beurteilung von Schnarchen ist mittels Geräuschaufzeichnung sehr zuverlässig durchführbar. Es bedarf der weiteren Klärung, warum nur 60% der apnoeterminierenden Hyperpnoen mit Schnarchen einhergehen. Pathophysiologisch wäre zu erwarten, dass das Öffnen des obstruierten Atemwegs in einem sehr viel höheren Prozentsatz mit einer turbulenten Strömung, Weichteilschwingung und damit auch Schnarchen kombiniert ist.
Effektivität der Novafon Local Vibration Voice Therapy & Water Resistance Therapy: eine Meta-Analyse
Authors: Latoszek Ben Barsties v. , Hetjens Svetlana 
Published: December 2022

Zusammenfassung
Hintergrund Die Behandlungsansätze für die Stimmtherapie sind
vielfältig. Zwei neuere Behandlungsansätze in der Stimmtherapie mit Hilfsgeräten sollen auf
ihre Effektivität untersucht und verglichen werden. Die Auswertungen der Behandlungseffekte
verschiedener Ansätze sind wichtig für die klinische Beratung und die evidenzbasierte
Praxis.
Ziel Die Wirksamkeit der Novafon Local Vibration Voice Therapy
(NLVVT) und der Water Resistance Therapy (WRT) hinsichtlich der Bewertung mittels Voice
Handicap Index (VHI) und seinen Aspekten soll überprüft werden.
Design Systematische Literatursuche und Meta-Analyse mit
Random-Effects-Modell.
Datenquellen Die Auswertung der 2 Datenbanken (MEDLINE und
CENTRAL) und einer manuellen Suche von Beginn bis zum 19. September 2021.
Studienauswahl Jede klinische Gruppenstudie in einem
Prä-post-Design, die in englischer oder deutscher Sprache verfasst wurde und die Wirksamkeit
von NLVVT oder WRT mittels VHI (funktionelle [F], physische [P] und emotionale [E] Aspekte und
der Gesamtwert [T]) bei erwachsenen Stimmpatienten mit einer organischen oder funktionellen
Stimmstörung überprüft hat, wurde berücksichtigt.
Ergebnisse Fünf geeignete Studien wurden identifiziert (NLVVT n=
22; WRT n= 42). Beide Behandlungsansätze zeigten hochsignifikante Verbesserungen bei allen
Aspekten des VHI (p<0,01). Im direkten Vergleich erreichte NLVVT signifikant stärkere
Verbesserungen bei VHI-T, VHI-F und VHI-E als WRT (p<0,01). Vergleichbar war die
Verbesserung bei VHI-P (p=0,28).
Fazit Die subjektive Bewertung nach dem Voice Handicap Index
ergab, dass NLVVT und WRT effektive Behandlungsmethoden bei Stimmstörungen sind. Im direkten
Vergleich erreichte NLVVT in den meisten VHI-Aspekten stärkere Verbesserungen als WRT.
Systematic Review of Kangaroo Care Duration's Impact in Neonatal Intensive Care Units on Infant–Maternal Health
Authors: Clarke-Sather Abigail R. , Compton Crystal , Roberts Kari , Brearley Ann , Wang Sonya G. 
Published: December 2022

Kangaroo care (KC), skin-to-skin contact between infants and caregivers, is encouraged in neonatal intensive care units (NICUs) to support health through improved weight, growth, and infant–maternal attachment while reducing the incidence of sepsis and infant pain. However, the optimal duration and frequency of KC to maximize health outcomes is unknown. Given parents' time stressors, identifying optimal KC time is critical. A literature review was undertaken on May 28, 2021 via querying the PubMed database from January 1, 1995, to May 28, 2021, regarding KC and NICUs with 442 results. Eleven studies met the eligibility criteria of (1) comparative KC between infants and adult caregivers in NICUs as a randomized controlled trial, (2) peer-reviewed articles in English, (3) study subjects ≥5, (4) health outcomes, and (5) KC sessions >1. Infant physical growth parameters, infant neurodevelopment, infant stress via salivary cortisol levels, and breastfeeding outcomes appear to increase with KC as compared with standard care (SC) without KC. Improvements were observed with longer KC duration, 2 h/d as compared with 1 h/d, for neurodevelopment and breastfeeding outcomes, but no greater improvement with longer KC duration was shown for reducing infant stress through salivary cortisol levels. Regarding maternal stress, the influence of KC duration showed mixed Parental Stressor Score: NICU scores. Further study on the impact of KC duration and frequency on health outcomes and dose–response relationship would help determine how much and how frequent KC is needed to improve specific health outcomes for infants and their mothers.
Key Points
Outcomes of Free Muscle Flaps versus Free Fasciocutaneous Flaps for Lower Limb Reconstruction following Trauma: A Systematic Review and Meta-Analysis
Authors: Dow Todd , ElAbd Rawan , McGuire Connor , Corkum Joseph , Youha Sarah Al , Samargandi Osama , Williams Jason 
Published: December 2022

Abstract
Background Free flap reconstruction of the lower limb following trauma often suffers higher complication rates than other areas of the body. The choice of muscle or fasciocutaneous free flap is an area of active debate.
Methods A systematic review of EMBASE, MEDLINE, PubMed, and Cochrane Register from inception to April 1, 2022 was performed. Articles were assessed using the methodological index for non-randomized studies instrument. The primary outcome was to assess and compare the major surgical outcomes of partial or total flap failure, reoperation, and amputation rates.
Results Seventeen studies were included. All studies were retrospective in nature, of level three evidence, and published between 1986 and 2021. The most common muscle and fasciocutaneous free flaps used were latissimus dorsi flap (38.1%) and anterolateral thigh (ALT) flap (64.8%), respectively. Meta-analysis found no significance difference in rates of total flap failure, takeback operations, or limb salvage, whereas partial flap failure rate was significantly lower for fasciocutaneous flaps. The majority of studies found no significant difference in complication rates, osteomyelitis, time to fracture union, or time to functional recovery. Most, 82.4% (14/17), of the included studies were of high methodological quality.
Conclusion The rate of total flap failure, reoperation, or limb salvage is not significantly different between muscle and fasciocutaneous free flaps after lower limb reconstruction following trauma. Partial flap failure rates appear to be lower with fasciocutaneous free flaps. Outcomes traditionally thought to be managed better with muscle free flaps, such as osteomyelitis and rates of fracture union, were comparable.
Immediate Intraoperative Microsurgical Repair of the Recurrent Laryngeal Nerve: A Fifteen-Year Institutional Experience
Authors: McGraw J Reed , Bascone Corey M. , Jaimez Ivan A. , Barrero Carlos E. , Fosnot Joshua , Fraker Douglas L. , Kovach Stephen J. 
Published: December 2022

Abstract
Background Recurrent laryngeal nerve (RLN) injury carries significant morbidity. Microsurgical repair of the RLN has proven promising for enhancing patient recovery of vocal function; however, data remains limited.
Methods This retrospective cohort study included patients who underwent RLN repair from 2007 to 2022. Demographics and medical history were collected. The location and etiology of RLN injury, as well as the repair technique, were collected. Follow-up data were collected at the initial postoperative visit, at 6 months and at 1 year. Hoarseness was classified as mild, moderate, or severe. Of patients who underwent nasopharyngolaryngoscopy (NPL) following repair, the glottic gap was measured. Vocal interventions performed were also recorded. This study utilized descriptive statistical methods.
Results Eleven patients underwent RLN repair. All patients underwent immediate repair. Fifty-four percent (n = 6) of RLN injuries resulted from tumor inflammation or nerve encasement. Eighty-two percent (n = 9) underwent direct RLN coaptation, 9% (n = 1) underwent vagus-RLN anastomosis, and 9% (n = 1) underwent an interposition nerve graft. Technical success was 100%. Seventy-three percent (n = 8) required otolaryngology referral, and of those, 50% (n = 4) required intervention. At initial evaluation, 91% (n = 10) suffered from mild to severe hoarseness, and of patients who underwent NPL, all had a glottic gap. At 1 year, 82% of patients (n = 9) improved to having mild to no appreciable hoarseness. Of the patients who underwent NPL, 62% (n = 5) had closure of the glottic gap.
Conclusion Patients undergoing repair of the RLN following injury showed excellent recovery of vocal function and resolution of glottic gap at 1 year.
Accuracy of a Clinical Applicable Method for Prediction of
VO2max Using Seismocardiography
Authors: Hansen Mikkel Thunestvedt , Husted Karina Louise Skov , Fogelstrøm Mathilde , Rømer Tue , Schmidt Samuel Emil , Sørensen Kasper , Helge Jørn 
Published: December 2022

AbstractCardiorespiratory fitness measured as ˙VO2max is considered an
important variable in the risk prediction of cardiovascular disease and
all-cause mortality. Non-exercise ˙VO2max prediction models
are applicable, but lack accuracy. Here a model for the prediction of
˙VO2max using seismocardiography (SCG) was investigated.
97 healthy participants (18–65 yrs., 51 females) underwent
measurement of SCG at rest in the supine position combined with demographic data
to predict ˙VO2max before performing a graded exercise test
(GET) on a cycle ergometer for determination of ˙VO2max using
pulmonary gas exchange measurements for comparison. Accuracy assessment revealed
no significant difference between SCG and GET ˙VO2max
(mean±95% CI; 38.3±1.6 and
39.3±1.6 ml·min−1·kg−1,
respectively. P=0.075). Further, a Pearson correlation of
r=0.73, a standard error of estimate (SEE) of
5.9 ml·min−1·kg−1,
and a coefficient of variation (CV) of 8±1% were found. The SCG
˙VO2max showed higher accuracy, than the non-exercise
model based on the FRIENDS study, when this was applied to the present
population
(bias=−3.7±1.3 ml·min−1·kg−1,
p<0.0001. r=0.70.
SEE=7.4 ml·min−1·kg−1,
and CV=12±2%). The SCG ˙VO2max
prediction model is an accurate method for the determination of
˙VO2max in a healthy adult population. However, further
investigation on the validity and reliability of the SCG
˙VO2max prediction model in different populations is needed
for consideration of clinical applicability.