bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks
Authors: Ebrahimi Hasan , Rezaeian‐Marjani Saeed , Farhadi‐Kangarlu Mohammad , Galvani Sadjad 
Published: October 2022

AbstractHarmonics originating from non‐linear loads in distribution systems will cause undesired effects and malfunctions of equipment. It is important to use efficient techniques for eliminating the voltage and current harmonic components. However, the uncertain nature of the active distribution networks (ADN) makes it difficult to apply these techniques, efficiently. Renewable energy sources (RESs), such as wind turbines (WTs) besides load fluctuations are the most important uncertainty sources. This study employs energy storage systems (ESS) to reduce total harmonic distortion (THD) as well as the ADN operating costs. In addition to the ESS, network structure reconfiguration and reactive power scheduling is presented for a more efficient operation of ADN. Also, the uncertainty of the network is modelled by the K‐means and LHS methods. This methodology is conducted under a multi‐objective optimization problem, which is handled by a multi‐objective particle swarm optimization (MOPSO) algorithm. Furthermore, a technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to obtain the best compromise between two objectives. The efficacy of the proposed methodology is investigated in the IEEE 33 bus test system. Also, for validating the results, a large‐scale network, the IEEE 118 bus test system is surveyed, too.
An image layered scrambling encryption algorithm based on a novel discrete chaotic map
Authors: Tang Jianeng , Zhang Zezong , Chen Peiyang , Zhang Feng , Ni Hui , Huang Zhongming 
Published: October 2022

AbstractAt present, the image encryption algorithm based on the bit‐plane has the problem that the encryption speed and the encryption effect cannot be well balanced. To address the problem, an image layered scrambling encryption algorithm is proposed in this paper. First, a new one‐dimensional Cosine Logistic compound chaotic map (1DCLC) is proposed. Several characteristics of the new chaotic map are analyzed by using various measures, such as bifurcation diagram, Lyapunov exponent, sample entropy etc. And the results show that the novel chaotic map has a better chaotic performance. Second, based on the chaotic map, an image encryption algorithm is proposed. The algorithm first performs an XOR encryption operation on the image. Second, the layered scrambling is applied to the XOR encrypted image. Then the layered and scrambled image is recombined for the second XOR encryption operation. In particular, in the layered scrambling, not only can the position of the pixel be changed but also the value of the pixel can be modified, which improves the security of the algorithm. In addition, in order to defend against various plaintext attacks and increase the security of the overall algorithm, the Hash function is introduced to update the key of the proposed algorithm. Finally, the original algorithm is improved and the improved algorithm's ability to resist differential attacks is analyzed. The experimental simulation and security test results show that the proposed encryption algorithm has a comparable encryption effect and a faster encryption speed.
A transfer learning‐based system for grading breast invasive ductal carcinoma
Authors: Sujatha Radhakrishnan , Chatterjee Jyotir Moy , Angelopoulou Anastassia , Kapetanios Epaminondas , Srinivasu Parvathaneni Naga , Hemanth Duraisamy Jude 
Published: October 2022

AbstractBreast carcinoma is a sort of malignancy that begins in the breast. Breast malignancy cells generally structure a tumour that can routinely be seen on an x‐ray or felt like a lump. Despite advances in screening, treatment, and observation that have improved patient endurance rates, breast carcinoma is the most regularly analyzed malignant growth and the subsequent driving reason for malignancy mortality among ladies. Invasive ductal carcinoma is the most boundless breast malignant growth with about 80% of all analyzed cases. It has been found from numerous types of research that artificial intelligence has tremendous capabilities, which is why it is used in various sectors, especially in the healthcare domain. In the initial phase of the medical field, mammography is used for diagnosis, and finding cancer in the case of a dense breast is challenging. The evolution of deep learning and applying the same in the findings are helpful for earlier tracking and medication. The authors have tried to utilize the deep learning concepts for grading breast invasive ductal carcinoma using Transfer Learning in the present work. The authors have used five transfer learning approaches here, namely VGG16, VGG19, InceptionReNetV2, DenseNet121, and DenseNet201 with 50 epochs in the Google Colab platform which has a single 12GB NVIDIA Tesla K80 graphical processing unit (GPU) support that can be used up to 12 h continuously. The dataset used for this work can be openly accessed from http://databiox.com. The experimental results that the authors have received regarding the algorithm's accuracy are as follows: VGG16 with 92.5%, VGG19 with 89.77%, InceptionReNetV2 with 84.46%, DenseNet121 with 92.64%, DenseNet201 with 85.22%. From the experimental results, it is clear that the DenseNet121 gives the maximum accuracy in terms of cancer grading, whereas the InceptionReNetV2 has minimal accuracy.
Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway
Authors: Yao Zhihong , Liu Meng , Jiang Yangsheng , Tang Youhua , Ran Bin 
Published: October 2022

AbstractVehicular trajectory data collected by connected automated vehicles (CAVs) is minimal due to the low penetration rates (PRs) of CAVs, and fail to capture the characteristics of traffic flow. This study proposes a fully sampled trajectory reconstruction method for mixed traffic flow with regular vehicles (RVs), connected vehicles (CVs), and CAVs based on car‐following behaviour. Firstly, considering the minimum safety distance constraints between vehicles, an optimization model for minimizing the impact on the acceleration of the known vehicles is developed to obtain the number of inserted RVs. Secondly, the speed of the inserted RVs is estimated based on the traffic flow model. Then, an optimization model is proposed to determine the position of each inserted RV. Finally, numerical simulation is designed to investigate the influence of traffic density and PRs of CAVs and CVs. Results show that the proposed method can better reconstruct the vehicle trajectory on the freeway under the different traffic densities in a congested state. The MAPE of the number and position of inserted RVs is less than 10.7% and 0.37%, respectively. In addition, the proposed method performs well even if the PRs of CAVs and CVs are extremely low.
Chirurgische Umwandlung von Arealpunktion in Strickleiterpunktion
Authors: Krönung Gerhard 
Published: October 2022

ZUSAMMENFASSUNGPlastisch-gefäßchirurgische Techniken und die Vielfachpunktion können einzeln oder kombiniert zur plastischen Verbesserung und Korrektur des Punktionssegments der Shuntvene eingesetzt werden. Die Vielfachpunktion braucht dabei ein langfristiges Konzept, das substratbezogen relativ einfach beschreibbar ist, in der Umsetzung aber eher komplexe gruppendynamische Prozesse erfordert.
Interprofessionelle Zusammenarbeit in der Dialyse
Authors: Venzin Christina 
Published: October 2022

ZUSAMMENFASSUNGDie Zentrumshämodialyse ist ein Paradebeispiel für etablierte Interprofessionalität zwischen Fachpflegenden und Fachärzt*innen. Diese Studie quantifiziert die interprofessionelle Zusammenarbeit (IPZ) in diesem Setting und vergleicht sie mit anderen Fachbereichen der Inneren Medizin. Zudem identifiziert sie Faktoren, welche die Interprofessionalität im Bereich Hämodialyse begünstigen: Die hohe Komplexität medizinischer und psychosozialer Faktoren auf Seite der Patient*innen kann besser durch die Zusammenarbeit von 2 Berufsgruppen bewältigt werden. Auf Organisationsseite führt der intensivere Kontakt der Pflegenden mit den Patient*innen zu einem Wissensvorsprung gegenüber der Ärzteschaft, was das übliche Gefälle zwischen den beiden Berufsgruppen abschwächt. Die Leistungen beider Berufsgruppen werden dann pauschal pro Dialyse vergütet, was einer Anerkennung von IPZ auf Systemebene entspricht. Begünstigend wirken sich auch die Erfüllung psychologischer Grundbedürfnisse am Arbeitsplatz in diesem Setting aus.
Lebensqualität von geriatrischen Patienten*innen mit Peritonealdialyse
Authors: Horvath Oliver , Obereder Sylvia , Ruppert Sabine 
Published: October 2022

ZUSAMMENFASSUNGDer vorliegende Beitrag befasst sich mit der Auswirkung der Peritonealdialyse bei geriatrischen Patienten*innen auf deren Lebensqualität. Studienergebnisse zeigen signifikante Unterschiede in Bezug auf die allgemeine Zufriedenheit der Patient*innen, den kognitiven Status und der sexuellen Funktion. Die Patient*innen erleiden weit weniger Symptome und weisen eine höhere Zufriedenheit mit der Behandlung auf. Weiterhin stellt die Peritonealdialyse für die Betroffenen eine weit weniger aggressive Behandlungsform dar. Die Art der jeweiligen Nierenersatztherapie sollte immer unter Berücksichtigung der Lebensqualität der Patient*innen gewählt werden.
Towards understanding the characteristics of successful and unsuccessful collaborations: a case-based team science study
Authors: Love Hannah B. , Fosdick Bailey K. , Cross Jennifer E. , Suter Meghan , Egan Dinaida , Tofany Elizabeth , Fisher Ellen R. 
Published: October 2022

AbstractScientific breakthroughs for complex, large-scale problems require a combination of contributory expertize, disciplinary expertize, and interactional expertize, or socialized knowledge. There is, however, little formal recognition of what expertize is important for team success, and how to evaluate different types of contributions. This is problematic for the field of the Science of Team Sciences (SciTS). Funding is increasing for team science globally, but how do we know if teams are collaborating in meaningful ways to meet their goals? Many studies use bibliometric and citation data to understand team development and success; nevertheless, this type of data does not provide timely metrics about collaboration. This study asks: Can we determine if a team is collaborating and working together in meaningful ways in a process evaluation to achieve their goals and be successful in an outcome evaluation, and if so, how? This exploratory longitudinal, mixed-methods, case-based study, reports on eight interdisciplinary scientific teams that were studied from 2015–2017. The study used six different methods of data collection: a social network analysis at three-time points, participant observation, interviews, focus groups, turn-taking data during team meetings, and outcome metrics (publications, award dollars, etc.). After collecting and analyzing the data, a Kendall Rank Correlation was used to examine which development and process metrics correlated with traditional outcome metrics: publications, proposals submitted, and awards received. Five major implications, practical applications, and outputs arise from this case-based study: (1) Practicing even turn-taking is essential to team success. (2) The proportion of women on the team impacts the outcomes of the team. (3) Further evidence that successful team science is not about picking the right people, but on how to build the right team for success. (4) This article presents process metrics to increase understanding of successful and unsuccessful teams. (5) Teams need to engage in practices that build relationships for knowledge integration. This case-based study represents an early step to more effectively communicate how teams form and produce successful outcomes and increase their capacity for knowledge integration. The results contribute to the knowledge bank of integration and implementation by providing additional evidence about evaluation for scientific teams, including the know-how related to everyday interactions that lead to goal attainment. This study provides further evidence that to create new knowledge, scientific teams need both contributory and interactional expertize.
Empirical evidence of the impact of mobility on property crimes during the first two waves of the COVID-19 pandemic
Authors: Paramasivan Kandaswamy , Subburaj Rahul , Jaiswal Saish , Sudarsanam Nandan 
Published: October 2022

AbstractThis paper seeks to evaluate the impact of the removal of restrictions (partial and complete) imposed during COVID-19-induced lockdowns on property offences such as robbery, burglary, and theft during the milder wave one and the more severe wave two of the pandemic in 2020 and 2021, respectively. Using 10-year data of the daily counts of crimes, the authors adopt an auto-regressive neural networks method to make counterfactual predictions of crimes, representing a scenario without the pandemic-induced lockdowns. The difference between the actual and forecast is the causal impact of the lockdown in all phases. Further, the research uses Google Mobility Community Reports to measure mobility. The analysis has been done at two levels: first, for the state of Tamil Nadu, which has a sizeable rural landscape, and second for Chennai, the largest metropolitan city with an urban populace. During the pandemic-induced lockdown in wave one, there was a steep decline in the incidence of property offences. On removing restrictions, the cases soared above the counterfactual predicted counts. In wave two, despite the higher severity and fatality in the COVID-19 pandemic, a similar trend of fall and rise in property cases was observed. However, the drop in mobility was less substantial, and the increase in the magnitude of property offences was more significant in wave two than in wave one. The overall trend of fluctuations is related to mobility during various phases of restrictions in the pandemic. When most curbs were removed, there was a surge in robberies in Tamil Nadu and Chennai after adjusting for mobility. This trend highlights the effective increase in crime due to pandemic-related economic and social consequences. Further, the research enables law enforcement to strengthen preventive crime work in similar situations, when most curbs are removed after a pandemic or other unanticipated scenarios.
Rare earth Y doping induced lattice strain of mesoporous PtPd nanospheres for alkaline oxygen reduction electrocatalysis
Authors: Wang Hongjing , Ren Hang , Liu Songliang , Deng Kai , Yu Hongjie , Wang Xin , Xu You , Wang Ziqiang , Wang Liang 
Published: October 2022

Abstract
The synthesis of catalysts with controllable morphology and composition is important to enhance the catalytic performance for oxygen reduction reaction (ORR). Herein, trimetallic PtPdY mesoporous nanospheres (PtPdY MNs) are produced via a one-step chemical reduction method applying F127 as soft temple under acidic condition. The mesoporous structure provides a large contact area and also stimulates the diffusion and mass transfer of reactants and products. Besides, synergistic effect among Pt, Pd and Y elements effectively alters their electronic structure, enhancing the catalytic activity. Therefore, the PtPdY MNs show excellent ORR permanence to Pt/C under the alkaline solution. This study offers an effective channel for the preparation of mesoporous metals with rare earth metal doping towards promising electrocatalytic applications.
Characterization of a miniaturized scintillator detector for time-resolved treatment monitoring in HDR-brachytherapy
Authors: Gonod Mathieu , Suarez Miguel Angel , Chacon Avila Carlos , Karakhanyan Vage , Eustache Clément , Crouzilles Julien , Laskri Samir , Vinchant Jean-François , Aubignac Léone , Grosjean Thierry 
Published: October 2022

Abstract

Purpose. HDR brachytherapy combines steep dose gradients in space and time, thereby requiring detectors of high spatial and temporal resolution to perform accurate treatment monitoring. We demonstrate a miniaturized fiber-integrated scintillator detector (MSD) of unmatched compactness which fulfills these conditions. Methods. The MSD consists of a 0.28 mm large and 0.43 mm long detection cell (Gd2O2S:Tb) coupled to a 110 micron outer diameter silica optical fiber. The fiber probe is tested in a phantom using a MicroSelectron 9.1 Ci Ir-192 HDR afterloader. The detection signal is acquired at a rate of 0.08 s with a standard sCMOS camera coupled to a chromatic filter (to cancel spurious Cerenkov signal). The dwell position and time monitoring are analyzed over prostate treatment sequences with dwell times spanning from 0.1 to 11 s. The dose rate at the probe position is both evaluated from a direct measurement and by reconstruction from the measured dwell position using the AAPM TG-43 formalism. Results. A total number of 1384 dwell positions are analyzed. In average, the measured dwell positions differ by 0.023 ± 0.077 mm from planned values over a 6–54 mm source-probe distance range. The standard deviation of the measured dwell positions is below 0.8 mm. 94% of the 966 dwell positions occurring at a source-probe inter-catheter spacing below 20 mm are successfully identified, with a 100% detection rate for dwell times exceeding 0.5 s. The average deviation to the planned dwell times is of 0.005 ± 0.060 s. The instant dose retrieval from dwell position monitoring leads to a relative mismatch to planned values of 0.14% ± 0.7%. Conclusion. A miniaturized Gd2O2S:Tb detector coupled to a standard sCMOS camera can be used for time-resolved treatment monitoring in HDR Brachytherapy.
Dynamic guidance performance of HTS bulk under varying external magnetic field
Authors: Huang Zhichuan , Hong Ye , Ke Zhihao , Liu Jianxin , Deng Zigang 
Published: October 2022

Abstract
With the unique flux pinning characteristics, high temperature superconducting (HTS) bulk can stably levitate above the permanent magnet guideway (PMG). Moreover, owing to its advantages of environmental protection, frictionless and pollution-free, the HTS maglev has shown significant potential in high-speed rail transit. At present, HTS maglev technology is at a critical moment from laboratory research to engineering application. Among the numerous parameters related to its engineering application, the curve negotiation ability determined by the guidance performance of the HTS bulk superconductors is crucial to the safety and stability of its operation. Thus, in this paper, we experimentally investigated the dynamic guidance force of the HTS bulks under different operational conditions including different rotational speeds, maximum lateral displacement (MLD), field cooling height (FCH), and working height (WH) based on a customized HTS maglev dynamic measurement system (SCML-03). In addition, the dynamic guidance force of HTS bulk under long-time operation was studied. It is concluded that FCH, WH, and MLD are intimately associated with the dynamic guidance performance of the HTS bulk, while the rotational speed of the circular PMG has little influence on it. Namely, to improve the dynamic guidance performance of the HTS bulk, reducing the FCH and increasing the WH properly can be regarded as an effective method. Eventually, this research also verifies the long-term lateral stability of the HTS maglev system during dynamic operation.
Simple design of metamaterial sensor for biomedical sensing
Authors: Anwar Shahzad , Khan Maaz 
Published: October 2022

Abstract
Design a highly sensitive refractive index (RI) sensor based on perfect metamaterial absorber (PMA) in the terahertz region is reported. The design structure is formed of metal dielectric metal layered. The results of simulation show that the absorber has two resonance peaks with absorption rates 99.6% and 99.7%. The physical origin of the two band absorber is investigated by near-field distribution. The proposed design sensing performance determined by two important parameters, sensitivity (S) and figure of merit (FOM). In this work, the sensitivity and figure of merit (FOM) can reach 0.86 THz/RIU and 61.8 which is much higher than the previous work in terahertz region. The design RI sensor can be fitted for biomedical sensing applications such cancerous cell detection as the RI varied in the range of 1.35 to 1.402.
Monte Carlo simulations of Upsilon meson production in p + p at

s

= 500 GeV
Authors: Bielcik Jaroslav , Ceska Jakub , Das Ayanabha , Kosarzewski Leszek , Myska Miroslav 
Published: October 2022

Abstract
Quarkonium production in proton-proton collisions serves as an essential baseline for studying the effect of quarkonium suppression in proton-nucleus and nucleus-nucleus collisions. This paper presents the main characteristics of Upsilon mesons production in proton-proton collisions at

s

=
500

GeV

studied with two Monte Carlo event generators: Pythia and Herwig and the results compared. Main aim of these simulations is to explore the dependence of normalised Upsilon meson yield on normalised event multiplicity and the influence of the feed-down effect on Upsilon meson production. The simulations show that the normalised Upsilon yield rises stronger than linear with regards to the normalised charged particle multiplicity. A mass ordering is found for Upsilons originating from feed-down and it suggests that the observed strong increase depends on the scale of the original hard process producing the quarkonium state.
Hyperfine structure studies of neutral praseodymium with Fourier transform spectroscopy
Authors: Wang Ting , Zheng Hongfeng , Geng Yidan , Yu Ziqing , Dai Zhenwen 
Published: October 2022

Abstract
The hyperfine structure patterns of some neutral praseodymium (Pr I) lines in the range of 2969 − 34998 cm−1 were investigated based on the archival data from the Fourier transform spectrometer at the US National Solar Observatory. The hyperfine magnetic dipole interaction constants A are presented by fitting the spectra with our spectral analysis program. The HFS constants A for 56 levels of Pr I from 0.0 to 27602.42 cm−1 were determined.
Revisiting the Affleck-Dine mechanism for primordial black hole formation
Authors: Kasai Kentaro , Kawasaki Masahiro , Murai Kai 
Published: October 2022

Abstract
We study a primordial black hole (PBH) formation scenario based on the Affleck-Dine (AD)
mechanism and investigate two PBH mass regions: M ∼ 30 M
⊙ motivated by the LIGO-Virgo
observations of the binary black hole mergers and M ≳ 104
M
⊙ motivated by the
observations of supermassive black holes at the center of galaxies. In the previous studies, it
has been considered that the inhomogeneous AD baryogenesis generates regions with a large baryon
asymmetry, some of which collapse into PBHs. In this paper, we show that this scenario is
severely constrained due to the baryon asymmetry remaining outside PBHs, which would spoil the
success of the big bang nucleosynthesis. Then, we propose an alternative scenario where the AD
leptogenesis results in the inhomogeneous formation of Q-balls with lepton charges, which collapse
into PBHs. As a result, we find that our scenario can explain the favorable PBH abundance without
conflicting with the observational constraints.