bluuebunny/crossref_metadata_2025_split huggingface dataset:
Queries allocation in WSNs with fuzzy control system
Authors: Abbassi Kamel , Ezzedine Tahar 
Published: March 2023

AbstractProcessing queries in wireless sensor networks causes quality of service challenges such as data latency and validity. In fact, this paper is going to shed light on identifying the right trade‐off between latency and the freshness of the data. For this purpose, a fuzzy logic‐based decision‐making system that assigns incoming queries either to the sensor network, where the queries are waiting to be processed into a queue, or a central database which provides stored and potentially stale data or to a cache server which in turn provides acceptable latency and recent data, is provided. Thus, a new rule‐based system decision is introduced, that minimizes the request latency and returns the most recent data. In addition, a comparison between the approach and other work that is restricted to the database server and sensor network with no focus on the caching server is provided. This paper is going to carry on with the theory of Markov Chain which is more complicated compared to the fuzzy logic system. This approach is confirmed in the basis of an experience is developed with python.
An analytical optimum method for simultaneous integration of PV, wind turbine and BESS to maximize technical benefits
Authors: Khoubseresht Omid , Rajabinezhad Mohamadamin , Mousazadeh Mousavi Seyyed Yousef 
Published: March 2023

AbstractThe present paper introduces an analytical approach for integrating distributed energy resources (DERs) and battery energy storage systems (BESSs) into power grids. The given method aims to simultaneously identify optimal buses and capacities of DERs and BESSs, with regard to the responsive load demand along with the stochastic nature of wind turbine (WT) and photovoltaic (PV) units, and then to minimize energy losses and improve voltage profiles. For this purpose, the responsive load model, the uncertainties in the WT and PV units, and the BESSs are firstly modeled. Secondly, the output curve of BESSs is obtained so that demand and supply can be balanced effectively, so as to utilize the full‐potential output of DERs. Thirdly, a set of formulation is developed for simultaneous integration of the DERs and the BESSs. This formulation reflects on different levels of loads and DERs for each period as well as each segment with their corresponding probability density. Finally, the proposed method is tested on the standard IEEE 33‐bus network. These results verify that sizing and sitting of DERs and BESSs can significantly shape the planning results of DERs and increase their penetration in the power grids at the same time.
An improved generative adversarial network for remote sensing image super‐resolution
Authors: Guo Jifeng , Lv Feicai , Shen Jiayou , Liu Jing , Wang Mingzhi 
Published: March 2023

AbstractSpatial resolution is an important indicator that measures the quality of remote sensing images. Image texture has been successfully recovered by generative adversarial networks in deep learning super‐resolution (SR) methods. However, the existing methods are prone to image texture distortion. To solve the above problems, this paper proposes an improved generative adversarial network to enhance the super‐resolution reconstruction effect of medium‐ and low‐resolution (LR) remote sensing images. This network is based on the Super Resolution Generative Adversarial Network (SRGAN), which makes great improvements in the structure of the connection between the inside and outside of the residual block and the design of the model loss function. At the same time, the G1–G2–G3 structure between residuals effectively combines the image information of the three scales of small, medium and large. The model loss function can be designed based on the Charbonnier loss function to narrow the pixel distance between the reconstructed remote sensing image and the original image. Furthermore, targeted perceptual loss can direct the network to restore the texture details of the image according to the semantic category. The subjective and objective evaluation of the generated images and the ablation experiments prove that compared with SRGAN and other networks, our method can generate more realistic and reliable textures. Additionally, the indicators [peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), multiscale structural similarity (MS‐SSIM)] used to measure the quality of the reconstructed image obtain improved objective quantitative evaluation.
Non‐negative low‐rank adaptive preserving sparse matrix regression model for supervised image feature selection and classification
Authors: Chen Xiuhong , Zhu Xingyu , Lu Yun , Pu Zhifang 
Published: March 2023

AbstractThe sparse matrix regression (SMR) model for the feature selection method has attracted much attention. However, most existing models do not consider the globality and adaptively preserve the local structure of the image data in projection space. To settle such issues, an adaptive non‐negative low‐rank preserving SMR model for supervised image feature selection is proposed. It first uses the low‐rank representation with non‐negative constraint to capture the globality and more discriminative information of image data and makes the error matrix in self‐representation of training data sparse. Next, the non‐negative low‐rank representation coefficients are used to establish a graph matrix learning model to reveal the local manifold structure of the image data. Thus, the proposed model enhances the discriminative ability as well as performs feature selection by the obtained transformation matrix. Finally, an alternating iterative algorithm for solving this model is developed and its convergence and complexity are also analyzed. Experimental results on some image data sets show that the proposed algorithm is effective for images and its recognition ability is obviously superior to other existing methods. In addition, the proposed method is also applied to two scene image classifications to further verify its effectiveness.
A differentiated decision‐making algorithm for automated vehicles based on pedestrian feature estimation
Authors: Wang Yuning , Huang Heye , Zhang Bo , Wang Jianqiang 
Published: March 2023

AbstractOne critical difficulty to high‐level automated driving is the decision‐making process of automated vehicles in complicated traffic environments, especially in situations mixed of pedestrians and vehicles. This paper proposes a differentiated decision‐making algorithm to promote passing capability and efficiency in mixed traffic conditions. First, the behavioural characteristic of pedestrians, denoted as the pedestrian feature index, is estimated by a multi‐layer perception module input with quantitative analysis of pedestrian action. Based on estimation results, the decision algorithm merges pedestrian feature index into intelligent driver model and adjusts corresponding parameters, which used to be unchangeable so that the ego‐vehicle can make differential decisions according to various pedestrian features. Validation on the PIE dataset shows that the accuracy of pedestrian feature estimation is ensured. A simulation scenario is established utilizing cellular automata, and the results indicate that the proposed decision‐making algorithm can greatly improve passing efficiency under safety and manoeuvrability prerequisite.
Whether and how congested is a road? Indices, updating strategy and a vision‐based detection framework
Authors: Gao Fei , You Shengzhe , Wang Jinchao , Zhang Shifeng , Xie Di 
Published: March 2023

AbstractTraffic congestion detection method based on surveillance video is gradually widely used in intelligent transportation systems (ITS). Due to complex challenges such as weather change, vehicle occlusion, camera jitter, camera installation location, and so on, current methods are difficult to balance in real time and accuracy. Here, a new real‐time and robust traffic congestion detection framework and a vision‐based multi‐dimensional congestion detection model are proposed. Firstly, the framework introduces an object detector based on the lightweight convolutional neural network (CNN) and an efficient multi‐object IoU‐like tracker to obtain traffic dynamic information in real time. Then, traffic density, traffic velocity, and duration of instantaneous congestion are defined and a multi‐dimensional congestion detection model is established. Furthermore, an adaptive updating strategy of dynamic parameters is investigated. Finally, in multiple groups of comparative experiments, the framework is verified to be applicable to a variety of lightweight CNN detectors and IoU‐like trackers. The precision and recall of the multi‐dimensional congestion model can reach 95.1% and 92.1% respectively, with 43FPS. The comparative experimental results show that the proposed method is real‐time, more robust and accurate, and can be employed for online traffic congestion detection based on surveillance video.
Effizienz im OP – korrelieren Zahlen und Benchmarks tatsächlich mit einer Kapazitätsoptimierung?
Authors: Schüpfer Guido , Scherzinger Bernhard , Mauch Jacqueline 
Published: March 2023

ZusammenfassungDie Effizienz eines OP-Betriebes trägt wesentlich zur Performance einer chirurgischen Klinik bei. Durch das Erreichen einer vordefinierten OP-Auslastung mittels optimierter Prozesse wird
der größtmögliche Deckungsbeitrag pro OP-Stunde für eine Disziplin angestrebt. Ineffizienzen wie Unpünktlichkeit, lange Wechselzeiten, Fallabsagen, fehlende Standardisierungen in Prozessen
und Materialeinsatz werden konsequent monitorisiert und verbessert. Zur zweckdienlichen Steuerung des OP-Betriebes ist ein mehrdimensionales Kennzahlensystem Voraussetzung. Eine enge
Verzahnung des OP-Prozesses mit dem perioperativen Geschehen ermöglicht eine optimierte präoperative Verweildauer und steuert die mittlere Aufenthaltsdauer im Krankenhaus mit. Überlastungen
des Personals sind zu vermeiden und genügend Kapazitäten für Notfälle so vorzuhalten, dass diese zeitgerecht versorgt werden können. In einem ersten Schritt sind für die erhobenen Kennzahlen
Sollwerte zu definieren, damit Verbesserungen ausgelöst werden können. Durch Managementmaßnahmen, die auch auf mathematischen Ansätzen wie der Queuing-Theorie beruhen können, ist die
Variabilität der Nachfrage zu senken und für die richtige Bereitstellung der Notfallkapazitäten zu sorgen.
Vertical and Leg Stiffness Modeling During Running: Effect of Speed
and Incline
Authors: Meyer Frédéric , Falbriard Mathieu , Aminian Kamiar , Millet Gregoire P 
Published: March 2023

AbstractA spring mass model is often used to describe human running, allowing to
understand the concept of elastic energy storage and restitution. The stiffness
of the spring is a key parameter and different methods have been developed to
estimate both the vertical and the leg stiffness components. Nevertheless, the
validity and the range of application of these models are still debated. The aim
of the present study was to compare three methods (i. e., Temporal,
Kinetic and Kinematic-Kinetic) of stiffness determination. Twenty-nine healthy
participants equipped with reflective markers performed 5-min running bouts at
four running speeds and eight inclines on an instrumented treadmill surrounded
by a tri-dimensional motion camera system. The three methods provided valid
results among the different speeds, but the reference method (i. e.,
Kinematic-Kinetic) provided higher vertical stiffness and lower leg stiffness
than the two other methods (both p<0.001). On inclined terrain, the
method using temporal parameters provided non valid outcomes and should not be
used. Finally, this study highlights that both the assumption of symmetry
between compression and decompression phases or the estimation of the vertical
displacement and changes in leg length are the major sources of errors when
comparing different speeds or different slopes.
A Novel Technique Restores Function while Eliminating Intractable Neuropathic Pain in a 71-Year-Old Diabetic Patient under Challenging Injury Conditions
Authors: Micheo William F. , Foy Christian A. , Kuffler Damien P. 
Published: March 2023

Abstract
Background The extent of functional recovery induced in healthy patients by sensory nerve grafts, the clinical “gold standard” technique for repairing peripheral nerves with a gap, is significantly limited by increasing gap length, time between trauma and repair, and patient age. When the values of any two, or all three, variables increase simultaneously, there is little to no recovery. For diabetic patients, even under the best of conditions and without any large variables, the extent of axon regeneration and functional recovery is significantly less, but generally none. Therefore, novel techniques are required that enhance recovery in diabetic patients.
Methods A 12-cm long median nerve gap in the wrist/palm of a 71-year-old male long-term diabetic patient was bridged 1.3 years post nerve injury with a sural nerve graft within a platelet-rich plasma-filled collagen tube.
Results By 2 months post-repair, the patient's level 6 chronic neuropathic pain was permanently eliminated. By 6.75 months, the palm had recovered good sensitivity to stimuli of all sensory modalities, including 4.56 g pressure and less than 15 mm two-point discrimination. Each finger had good motor function of M3–5, with partial to complete sensitivity to stimuli of all sensory modalities and an overall recovery of S3.
Conclusion This technique permanently eliminates severe chronic neuropathic pain while simultaneously inducing good motor and sensory recovery in a long-term diabetic patient, under conditions where recovery is rarely, if ever, seen, even in non-diabetic patients. This technique holds great promise of restoring function to diabetic patients, for whom it is otherwise not possible.
BMP2 and GDF5 for Compartmentalized Regeneration of the Scapholunate Ligament
Authors: Lui Hayman , Vaquette Cedryck , Denbeigh Janet M. , Bindra Randy , van Wijnen Andre J. , Kakar Sanjeev 
Published: March 2023

Abstract
Background Chronic injuries to the scapholunate ligament (SLIL) alter carpal kinematics and may progress to early degenerative osteoarthritis. To date, there is no consensus for the best method for SLIL reconstruction. This study aims to assess the use of growth factors (bone morphogenetic protein [BMP]2 and growth and differentiation factor 5 [GDF5]) for compartmentalized regeneration of bone and ligament in this multiphasic scaffold in a rabbit knee model.
Case Description A total of 100 µg of BMP2 and 30 µg of GDF5 were encapsulated into a heparinized gelatin–hyaluronic acid hydrogel and loaded into the appropriate compartment of the multiphasic scaffold. The multiphasic scaffold was implanted to replace the native rabbit medial collateral ligament (n = 16). The rabbits were randomly assigned to two different treatment groups. The first group was immobilized postoperatively with the knee pinned in flexion with K-wires for 4 weeks (n = 8) prior to sacrifice. The second group was immobilized for 4 weeks, had the K-wires removed followed by a further 4 weeks of mobilization prior to sample harvesting.
Literature Review Heterotopic ossification as early as 4 weeks was noted on gross dissection and confirmed by microcomputed tomography and histological staining. This analysis revealed formation of a bony bridge located within and over the ligament compartment in the intra-articular region. Biomechanical testing showed increased ultimate force of the ligament compartment at 4 weeks postimplantation consistent with the presence of bone formation and higher numbers of scaffold failures at the bone–tendon junction. This study has demonstrated that the addition of BMP2 and GDF5 in the bone–ligament–bone (BLB) scaffold resulted in heterotopic bone formation and failure of the ligament compartment.
Clinical Relevance The implantation of a three-dimensional-printed BLB scaffold alone demonstrated superior biomechanical and histological results, and further investigation is needed as a possible clinical reconstruction for the SLIL.
Application of electroplated Ni–Fe thick film to magnetostrictive material in vibration power generator
Authors: Aketa Shunsuke , Ohtake Mitsuru , Ishikawa Eishi , Nakamura Yuta , Kawai Tetsuroh , Futamoto Masaaki 
Published: March 2023

Ni100−xFex (at. %) films of about 37 μm thickness are electroplated on Cu base plates of 50 mm length, 10 mm width, and 300 μm thickness by varying the composition from x = 0 to 99. They are applied to cantilevers in magnetostrictive vibration power generators. The influences of magnetic and magnetostrictive properties of electroplated film on the vibration power generation property are systematically investigated. The films with x = 41–65 show low coercivities of about 4 Oe reflecting the low magnetocrystalline anisotropy constants. Large negative magnetostriction is recognized for the films with x = 0–14, whereas large positive magnetostriction is observed for the films with x = 57–80. The cantilevers are vibrated at the respective resonance frequencies of about 105–115 Hz and with the acceleration of 1.5 G while applying the bias magnetic field in a range of 0–300 Oe along the length direction. The output voltages are detected by using a coil with 8000 turns. Peak voltages higher than 1 V are obtained for the films with x = 61–65 whose coercivities are low and magnetostrictions are large. The present study has shown that an Ni–Fe film not only with large magnetostriction but also with low magnetic anisotropy constant is useful as the magnetostrictive material in vibration power generator.
Spatial ecology of invasive Bennett’s wallaby in South Island, New Zealand
Authors: Latham A. David M. , Latham M. Cecilia , Warburton Bruce 
Published: March 2023

Context Bennett’s wallaby (Notamacropus rufogriseus) was introduced into New Zealand in the late-1800s, and has subsequently become a significant pest to agriculture, silviculture, and native vegetation. Effective management of wallabies requires robust spatial and temporal ecological information, which can be used operationally to increase detection and kill rates. Aims To determine home range size and shape, resource (i.e. habitat and topography) selection, and daily movement patterns of three populations of Bennett’s wallaby in the South Island. Methods We attached GPS-collars to 38 adult wallabies (17 females, 21 males) between May 2018 and May 2019, and obtained usable location data from 30 individuals. We used kernel density estimators and resource selection functions to quantify seasonal home ranges and resource selection, respectively. Key results The mean seasonal home range sizes of males and females were similar (26.8 ha and 24.8 ha, respectively; combined range = 1.2–101.9 ha), although the largest home ranges belonged to large males. Resource selection was strongly influenced by distance to concealment cover, ridgelines and streams, i.e. wallabies selected to be closer to these features, though not necessarily at them. Wallabies selected areas close to concealment cover throughout 24-h periods, but most strongly during the day, which is when they bed in dense cover. Wallabies sampled outside of peak breeding moved most during dusk and dawn (median = ~50 m/h), whereas those sampled during peak breeding moved widely during day, dusk, and night (110–280 m/h), but not dawn (median = ~50 m/h). Conclusions The home range sizes of wallabies in New Zealand were highly variable, but on average were similar for males and females. Wallabies selected pasture close to areas with some cover, suggesting a trade-off between access to high-quality forage and concealment cover. Implications Our results provide robust data for modelling management strategies for wallabies, and contribute directly to operational planning by identifying habitats where wallabies are most likely to be found temporally. This information can be used to determine the appropriate survey methods and control tools to maximise detection rates and kill rates of wallabies, based on habitat.
Income and emotional well-being: A conflict resolved
Authors: Killingsworth Matthew A. , Kahneman Daniel , Mellers Barbara 
Published: March 2023

Do larger incomes make people happier? Two authors of the present paper have published contradictory answers. Using dichotomous questions about the preceding day, [Kahneman and Deaton, Proc. Natl. Acad. Sci. U.S.A. 107, 16489–16493 (2010)] reported a flattening pattern: happiness increased steadily with log(income) up to a threshold and then plateaued. Using experience sampling with a continuous scale, [Killingsworth, Proc. Natl. Acad. Sci. U.S.A. 118, e2016976118 (2021)] reported a linear-log pattern in which average happiness rose consistently with log(income). We engaged in an adversarial collaboration to search for a coherent interpretation of both studies. A reanalysis of Killingsworth’s experienced sampling data confirmed the flattening pattern only for the least happy people. Happiness increases steadily with log(income) among happier people, and even accelerates in the happiest group. Complementary nonlinearities contribute to the overall linear-log relationship. We then explain why Kahneman and Deaton overstated the flattening pattern and why Killingsworth failed to find it. We suggest that Kahneman and Deaton might have reached the correct conclusion if they had described their results in terms of unhappiness rather than happiness; their measures could not discriminate among degrees of happiness because of a ceiling effect. The authors of both studies failed to anticipate that increased income is associated with systematic changes in the shape of the happiness distribution. The mislabeling of the dependent variable and the incorrect assumption of homogeneity were consequences of practices that are standard in social science but should be questioned more often. We flag the benefits of adversarial collaboration.
Near single-photon imaging in the shortwave infrared using homodyne detection
Authors: Wolley O. , Mekhail S. , Moreau P.-A. , Gregory T. , Gibson G. , Leuchs G. , Padgett M. J. 
Published: March 2023

Low-light imaging is challenging in regimes where low-noise detectors are not yet available. One such regime is the shortwave infrared where even the best multipixel detector arrays typically have a noise floor in excess of
100
photons per pixel per frame. We present a homodyne imaging system capable of recovering both intensity and phase images of an object from a single frame despite an illumination intensity of
≈
​
1
photon per pixel. We interfere this weak signal which is below the noise floor of the detector with a reference beam that is
∼
​
300
,

000
times brighter, record the resulting interference pattern in the spatial domain on a detector array, and use Fourier techniques to extract the intensity and phase images. We believe our approach could vastly extend the range of applications for low-light imaging by accessing domains where low-noise cameras are not currently available and for which low-intensity illumination is required.