bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introducing SNAP: a novel pedal-assisted electric ultralight vehicle
Authors: Passarella Francesco , Mantriota Giacomo , Reina Giulio 
Published: December 2022

AbstractInnovation in transportation and mobility is central to sustainable development. There is a widespread awareness that society would benefit if transportation became more sustainable, promoting economic growth while respecting the environment. This paper introduces SNAP, a four-wheel pedal-assisted electric vehicle that represents a new concept in sustainable mobility towards filling the gap between bicycle and automobile. The choice of the architecture for the hybrid powertrain where the driver pedals are assisted with an electric motor is discussed along with the experimental analysis of the power loss associated with the single components of the transmission. In turn, this knowledge allows the overall quadricycle performance to be evaluated in terms of maximum speed, efficiency, and travel range at varying operating conditions that include vehicle load, road slope, and gear change. It is shown that SNAP can be a promising answer to address the problem of sustainable and safe micro mobility of persons and goods in urban settings.
The Giant Ionospheric Depletion on 15 January 2022 Around the Hunga Tonga‐Hunga Ha'apai Volcanic Eruption
Authors: He Jianhui , Astafyeva Elvira , Yue Xinan , Ding Feng , Maletckii Boris 
Published: December 2022

AbstractThe 15 January 2022 Tonga eruption seemed to have caused a strong depletion in the ionospheric electron density. However, the eruption occurred during a moderate geomagnetic storm, so that the depletion could be a local negative effect of the storm. In this work, for the first time, we analyze this depletion and discuss relative contributions of the eruption and the storm through measurements of GNSS‐derived vertical total electron content (VTEC), O/N2 ratio by TIMED/GUVI, ion density and temperature by ICON/IVM, and simulations by Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). We show that shortly after the eruption onset the VTEC in the vicinity of the volcano dropped by ∼80%–95% below the quiet‐time values. The depletion extended up to 4,000 km away from the volcano and lasted for ∼16 hr, that is, until local morning hours. Our results suggest that the depletion was initially caused by the eruption (60%–75% negative deviation) and was further reinforced by the storm, by at least 20%. Spatially, only ∼1,500 km could be attributed to the eruption. This study provides a good illustration of understanding the contributions of forcings from above and below to the ionosphere.
A flexible neural implant with ultrathin substrate for low-invasive brain–computer interface applications
Authors: Guo Zhejun , Wang Fang , Wang Longchun , Tu Kejun , Jiang Chunpeng , Xi Ye , Hong Wen , Xu Qingda , Wang Xiaolin , Yang Bin , Sun Bomin , Lin Zude , Liu Jingquan 
Published: December 2022

AbstractImplantable brain–computer interface (BCI) devices are an effective tool to decipher fundamental brain mechanisms and treat neural diseases. However, traditional neural implants with rigid or bulky cross-sections cause trauma and decrease the quality of the neuronal signal. Here, we propose a MEMS-fabricated flexible interface device for BCI applications. The microdevice with a thin film substrate can be readily reduced to submicron scale for low-invasive implantation. An elaborate silicon shuttle with an improved structure is designed to reliably implant the flexible device into brain tissue. The flexible substrate is temporarily bonded to the silicon shuttle by polyethylene glycol. On the flexible substrate, eight electrodes with different diameters are distributed evenly for local field potential and neural spike recording, both of which are modified by Pt-black to enhance the charge storage capacity and reduce the impedance. The mechanical and electrochemical characteristics of this interface were investigated in vitro. In vivo, the small cross-section of the device promises reduced trauma, and the neuronal signals can still be recorded one month after implantation, demonstrating the promise of this kind of flexible BCI device as a low-invasive tool for brain–computer communication.
Transparent planar solar absorber for winter thermal management
Authors: Asad Muhammad Saad , Alam M. Z. 
Published: December 2022

AbstractIndoor heating during winters accounts for a significant portion of energy consumed by buildings in regions of cold climate. Development of transparent coatings for windows that efficiently harvest solar energy can play a major role in reducing energy consumption and fuel costs incurred for winter heating. In recent years, there has been a great research effort towards designing transparent solar absorber coatings using nanophotonic structures. The potential of coatings based on planar multilayer structures, however, has received very little attention. In this work we investigate the performance of planar multilayer thin films using low-cost materials for design of transparent solar absorber window coatings. Our study led to the proposal of two planar multilayer designs. Simulation results predict that an increase in surface temperature by 27 K and 25 K, while maintaining mean visible transmittance of over 50% is possible using these designs. These results illustrate the great promise planar multilayer structures hold for winter thermal management of buildings.
Humoral and cellular immune responses to Lassa fever virus in Lassa fever survivors and their exposed contacts in Southern Nigeria
Authors: Ugwu Chinedu , Olumade Testimony , Nwakpakpa Ebenezer , Onyia Venatius , Odeh Elizabeth , Duruiheoma Rosemary Ogonna , Ojide Chiedozie K. , Eke Matthew Afam , Nwafor Ifeanyi Emmanuel , Chika-Igwenyi Nneka , Abu Augustine M. , Azuogu Benedict , Ajayi Nnennaya , Ogah Emeka , Ayodeji Oluwafemi , Abejegah Chukwuyem , Adedosu Nelson , Oyejide Nicholas , Abah Sylvester , Omidele Abiola , Ingbian Winifred , Osoba Emmanuel , Eromon Philomena , Oluniyi Paul , Ogunsanya Olusola , Happi Anise , Otuh Patricia , Nadesalingam Angalee , Carnell George , Krause Nina , Aguinam Ernest , Kinsley Rebecca , Storisteanu Daniel Matthew L. , Tonks Paul , Nelson Diana , McAlister Carley , Boisen Matthew , Garry Robert , Wright Edward , Temperton Nigel , Frost Simon , Heeney Jonathan Luke , Happi Christian 
Published: December 2022

AbstractElucidating the adaptive immune characteristics of natural protection to Lassa fever (LF) is vital in designing and selecting optimal vaccine candidates. With rejuvenated interest in LF and a call for accelerated research on the Lassa virus (LASV) vaccine, there is a need to define the correlates of natural protective immune responses to LF. Here, we describe cellular and antibody immune responses present in survivors of LF (N = 370) and their exposed contacts (N = 170) in a LASV endemic region in Nigeria. Interestingly, our data showed comparable T cell and binding antibody responses from both survivors and their contacts, while neutralizing antibody responses were primarily seen in the LF survivors and not their contacts. Neutralizing antibody responses were found to be cross-reactive against all five lineages of LASV with a strong bias to Lineage II, the prevalent strain in southern Nigeria. We demonstrated that both T cell and antibody responses were not detectable in peripheral blood after a decade in LF survivors. Notably LF survivors maintained high levels of detectable binding antibody response for six months while their contacts did not. Lastly, as potential vaccine targets, we identified the regions of the LASV Glycoprotein (GP) and Nucleoprotein (NP) that induced the broadest peptide-specific T cell responses. Taken together this data informs immunological readouts and potential benchmarks for clinical trials evaluating LASV vaccine candidates.
Three-dimensional muon imaging of cavities inside the Temperino mine (Italy)
Authors: Borselli Diletta , Beni Tommaso , Bonechi Lorenzo , Bongi Massimo , Brocchini Debora , Casagli Nicola , Ciaranfi Roberto , Cimmino Luigi , Ciulli Vitaliano , D’Alessandro Raffaello , Dini Andrea , Frosin Catalin , Gigli Giovanni , Gonzi Sandro , Guideri Silvia , Lombardi Luca , Nocentini Massimiliano , Saracino Giulio 
Published: December 2022

AbstractMuon radiography (muography) is an imaging technique based on atmospheric muon absorption in matter that allows to obtain two and three-dimensional images of internal details of hidden objects or structures. The technique relies on atmospheric muon flux measurements performed around and underneath the object under examination. It is a non-invasive and passive technique and thus can be thought of as a valid alternative to common prospecting techniques used in archaeological, geological and civil security fields. This paper describes muon radiography measurements, in the context of archaeological and geological studies carried out at the Temperino mine (LI, Tuscany, Italy), for the search and three-dimensional visualisation of cavities. This mine has been exploited since Etruscan times until recently (1973), and is now an active tourist attraction with public access to the tunnels. Apart from the archaeological interest, the importance of mapping the cavities within this mine lies in identifying the areas where the extraction ores were found and also in the safety issues arising from the tourist presence inside the mine. The three-dimensional imaging is achieved with two different algorithms: one involving a triangulation of two or more measurements at different locations; the other, an innovative technique used here for the first time, is based on the back-projections of reconstructed muon tracks. The latter requires only a single muographic data tacking and is to be preferred in applications where more than one site location can be difficult to access. Finally the quality of the three-dimensional muographic imaging was evaluated by comparing the results with the laser scan profiles obtained for some known cavities within the Temperino mine.
Identifying cause-and-effect relationships of manufacturing errors using sequence-to-sequence learning
Authors: Reimer Jeff , Wang Yandong , Laridi Sofiane , Urdich Juergen , Wilmsmeier Sören , Palmer Gregory 
Published: December 2022

AbstractIn car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines. The body passes through multiple stations in succession, and is processed according to the order requirements. The timely completion of orders depends on the individual station-based operations concluding within their scheduled cycle times. If an error occurs in one station, it can have a knock-on effect, resulting in delays on the downstream stations. To the best of our knowledge, there exist no methods for automatically distinguishing between source and knock-on errors in this setting, as well as establishing a causal relation between them. Utilizing real-time information about conditions collected by a production data acquisition system, we propose a novel vehicle manufacturing analysis system, which uses deep learning to establish a link between source and knock-on errors. We benchmark three sequence-to-sequence models, and introduce a novel composite time-weighted action metric for evaluating models in this context. We evaluate our framework on a real-world car production dataset recorded by Volkswagen Commercial Vehicles. Surprisingly we find that 71.68% of sequences contain either a source or knock-on error. With respect to seq2seq model training, we find that the Transformer demonstrates a better performance compared to LSTM and GRU in this domain, in particular when the prediction range with respect to the durations of future actions is increased.
An observability and detectability analysis for non-linear uncertain CSTR model of biochemical processes
Authors: Czyżniewski Mateusz , Łangowski Rafał 
Published: December 2022

AbstractThe problem of proving observability/detectability properties for selected non-linear uncertain model of biochemical processes has been addressed in this paper. In particular, the analysis of observability/detectability in the face of parametric and unstructured uncertainty in system dynamics transformed into unknown inputs, and unknown initial conditions has been performed. Various sets of system measured outputs were taken into account during the research. The considered biochemical processes were modelled as a continuous stirred tank reactor with the microbial growth reaction and microbial mortality with the aggregated substrate and biomass concentrations in aerobic phase. Classical tools based on differential geometry and the method of indistinguishable state trajectories (indistinguishable dynamics) were used to verify the properties of the system. The observability/detectability analysis was performed for nine cases covering a wide range of possible combinations of system measured outputs and unknown inputs. The obtained results of are crucial meaning for system state reconstruction (estimation), which involves the synthesis of state observers.
Hierarchical multi‐agent reinforcement learning for multi‐aircraft close‐range air combat
Authors: Kong Wei‐ren , Zhou De‐yun , Du Yong‐jie , Zhou Ying , Zhao Yi‐yang 
Published: December 2022

AbstractThe close‐range autonomous air combat has gained significant attention from researchers involved in applications related to artificial intelligence (AI). A majority of the previous studies on autonomous air combat were focused on one‐on‐one air combat scenarios, however, the modern air combat is mostly conducted in formations. With regard to the aforementioned factors, a novel hierarchical maneuvering control architecture is introduced that is applied to the multi‐aircraft close‐range air combat scenario, which can handle air combat scenarios with variable‐size formation. Subsequently, three air combat sub‐tasks are designed, and recurrent soft actor‐critic (RSAC) algorithm combined with competitive self‐play (SP) is incorporated to learn the sub‐strategies. A novel hierarchical multi‐agent reinforcement learning (HMARL) algorithm is proposed to obtain the high‐level strategy for target and sub‐strategy selection. The training performance of the training algorithm of sub‐strategies and high‐level strategy in different air combat scenarios is evaluated. The obtained strategies are analyzed and it is found that the formations exhibit effective cooperative behavior in symmetric and asymmetric scenarios. Finally, the ideas of engineering implementation of the maneuvering control architecture are given. The study provides a solution for future multi‐aircraft autonomous air combat.
Performance evaluation of a new modular split‐tooth permanent magnet‐assisted switched reluctance motor
Authors: Radmanesh Hamid , Farmahini Farahani Ehsan 
Published: December 2022

AbstractThis study elaborates upon a new modular split‐tooth permanent magnet‐assisted switched reluctance motor (MSTPM‐SRM). In the proposed topology, the stator comprises six modular E‐cores in which the middle poles are split into two teeth. Two permanent magnets (PMs) are placed between each module's middle and side poles. Primarily, the proposed topology is introduced. Next, the principle of operation of the proposed topology are elucidated, and the magnetic equivalent circuit analysis is adopted in order to validate the operational basics. It is proved that the embedded PMs significantly increase the air‐gap's flux density and adjusts the poles' flux density. The flux density distributions, static, and steady‐state characteristics of the proposed MSTPM‐SRM motor and its PMless counterpart are extracted by utilising 2‐D finite element analysis. It is illustrated that the average torque of the proposed MSTPM‐SRM is dramatically increased compared to its PMless counterpart, especially at high excitation currents. Furthermore, the cogging torque analysis is done and it is shown that the proposed structure has approximately zero cogging torque. Finally, a prototyping version of the proposed motor is fabricated, and the experimental results are elicited. It is shown that the experimental results endorse the simulation results.
Overview of collaborative response between the power distribution network and urban transportation network coupled by electric vehicle cluster under unconventional events
Authors: Wang Ying , Xu Yin , He Jinghan , Lee Seung Jae 
Published: December 2022

AbstractWith the rapid development of electric vehicles, they have become an important part of urban distribution and transportation networks. The power distribution network and transportation network are coupled by electric vehicle clusters and integrated through strong interactions, creating a coupled system. This paper presents the study on their collaborative responses is essential to reduce losses and improve urban resilience during unconventional events. First, the multidimensional and deep‐level time‐varying closed‐loop coupling effects of the power distribution network and urban transportation network coupled by electric vehicle clusters are analysed under unconventional events. Second, based on the different scales of unconventional events, a summary of relevant studies is made on the collaborative response strategies of the coupled system to urban local power outages and large‐scale blackouts following unconventional events. Finally, future research directions are discussed.
Independency‐enhancing adversarial active learning
Authors: Guo Jifeng , Pang Zhiqi , Bai Miaoyuan , Xiao Yanbang , Zhang Jian 
Published: December 2022

AbstractThe core idea of active learning is to obtain higher model performance with less annotation cost. This paper proposes an independency‐enhancing adversarial active learning method. Independency‐enhancing adversarial active learning is different from the previous methods and pays more attention to sample independence. Specifically, it is believed that the informativeness of a group of samples is related to sample independence rather than the simple sum of the informativeness of each sample in the group. Therefore, an independent sample selection module based on hierarchical clustering is designed to ensure sample independence. An adversarial approach is used to learn the feature representation of a sample and use the predicted loss value to label the state of the sample. Finally, samples are selected according to the uncertainty of the samples, the diversity of the samples and the independence of the samples. The experimental results on four datasets (CIFAR‐100, Caltech‐101, Cityscapes and BDD100K) demonstrate the effectiveness and superiority of independency‐enhancing adversarial active learning.
Two‐stage learning framework for single image deraining
Authors: Jiang Rui , Li Yaoshun , Chen Cheng , Liu Wei 
Published: December 2022

AbstractSingle image deraining methods have been extensively studied for its ability to remarkably improve the performance of computer vision tasks in rainy environments. However, most existing rain removal methods still have two major drawbacks which are hindering the technology development. First, the rain streaks are seriously coupled with the background information in a single rainy image, which leads to incorrect identification of rain streaks by many methods and further makes the loss of texture details in the rain removal results. Second, they spend excessive computational cost, which is not conducive to practical applications. To address these issues, a progressive separation network (PSN) is proposed by decomposing the rain removal task into two stages, the bilateral grid learning stage and the joint feature refinement stage, from a novel perspective. The bilateral grid learning stage is designed to expand the distance between the rain streaks and the background information while preserving the image edge details to guide the subsequent refinement. For the joint feature refinement stage, a dual‐path interaction module is constructed to dynamically and gradually decouple the rain streak content and the intermediate features of the clear image details. In addition, an activation‐free feature refinement block is designed to further improve the computational efficiency by removing or replacing the activation function without loss of accuracy. Extensive experiments on synthetic and real datasets show that PSN outperforms state‐of‐the‐art rain removal methods in terms of quantitative accuracy and subjective visual quality. Furthermore, competitive results are derived by extending PSN to the defogging task.
The Significance of Oral Inflammation in Elite Sports: A Narrative
Review
Authors: Merle Cordula Leonie , Wuestenfeld Jan C. , Fenkse Fabian , Wolfarth Bernd , Haak Rainer , Schmalz Gerhard , Ziebolz Dirk 
Published: December 2022

AbstractRecently, there has been intense discussion about sports dentistry and potential
interactions between oral health and athletes’ performance. This
narrative review aims to provide a comprehensive overview of the available
literature about oral inflammation in sports. For this purpose, it presents the
most common types of oral inflammation (gingivitis, periodontitis,
pericoronitis, apical periodontitis), and their prevalence in athletes. Both the
impact of oral inflammation on performance and causes for oral inflammation in
athletes are discussed by presenting current literature. Finally, international
recommendations for dental care in sports are presented. Several studies stated
a high prevalence of oral inflammation in athletes, especially of gingivitis
(58–97%) and periodontitis (41%). Also, many athletes
report oral pain (17–30%) and a negative impact of oral health
on training (3–9%). Besides this, a systemic impact of oral
inflammation is discussed: In periodontitis patients, blood parameters and
physical fitness are changed. In athletes, associations between muscle injuries
and poor oral health are reported. There are deficits in oral health behavior.
Furthermore, systemic changes due to physical stress could influence oral
tissues. Overall, complex bidirectional interactions between competitive sports
and oral inflammation are possible. Regular dental examinations and prevention
strategies should be implemented in sports.