bluuebunny/crossref_metadata_2025 huggingface dataset:
Orchard Energy Management to Improve Fruit Quality Based on the Internet of Things
Authors: Zhang Pingchuan , Wang Sijie , Li Xiaowen , Chen Zhao , Chen Xu , Hu Yanjun , Zhang Hangsen , Zhang Jianming , Li Mingjing , Huang Zhenzhen , Li Yan , Li Liutong , Xu Xiaoman , Yang Yiwen , Song Huaping , Huo Huanhuan , Shi Yiran , Hu Xueqian , Wu Yabin , Wang Chenguang , Chen Feilong , Yang Bo , Zhang Bo , Zhang Yusen
Published: July 2022

The crop growth is an energy conversion process, and energy management has an important impact on the quality and yield of crop products. As IoT (the Internet of Things) is widely used in agriculture, for example, orchard IoT is often used to realize water-saving irrigation, this paper innovatively proposes a scheme to improve fruit quality by using IoT to realize orchard energy management. The designed Internet of things, in addition to the usual orchard environmental parameters and water-saving irrigation, can further adjust the temperature difference between day and night according to the local temperature, that is, by spraying low-temperature water mist at 16 ℃ to reduce the ambient temperature of the orchard at night, creating an environment conducive to the conversion of carbohydrate into sugar. The experiment in peach orchard shows that the orchard energy management method based on Internet of Things works effectively, which can reduce the peach orchard temperature to 20° at night in summer, which is beneficial to improve the peach fruit sweetness.
Development of Deep Learning Algorithms, Frameworks and Hardwares
Authors: Ji Jinbao , Hu Zongxiang , Zhang Weiqi , Yang Sen
Published: July 2022

As the core algorithm of artificial intelligence, deep learning has brought new breakthroughs and opportunities to all walks of life. This paper summarizes the principles of deep learning algorithms such as Autoencoder (AE), Boltzmann Machine (BM), Deep Belief Network (DBM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Recursive Neural Network (RNN). The characteristics and differences of deep learning frameworks such as Tensorflow, Caffe, Theano and PyTorch are compared and analyzed. Finally, the application and performance of hardware platforms such as CPU and GPU in deep learning acceleration are introduced. In this paper, the development and application of deep learning algorithm, framework and hardware technology can provide reference and basis for the selection of deep learning technology.
A Fully-Nested Encoder-Decoder Framework for Anomaly Detection
Authors: Gong Yansheng , Jing Wenfeng
Published: July 2022

Anomaly detection is an important branch of computer vision. At present, a variety of deep learning models are applied to anomaly detection. However, the lack of abnormal samples makes supervised learning difficult to implement. In this paper, we mainly study abnormal detection tasks based on unsupervised learning and propose a Fully-Nested Encoder-decoder Framework. The main part of the proposed generating model consists of a generator and a discriminator, which are adversarially trained based on normal data samples. In order to improve the image reconstruction capability of the generator, we design a Fully-Nested Residual Encoder-decoder Network, which is used to encode and decode the images. In addition, we add residual structure into both encoder and decoder, which reduces the risk of overfitting and enhances the feature expression ability. In the test phase, a distance measurement model is used to determine whether the test sample is abnormal. The experimental results on the CIFAR-10 dataset demonstrate the excellent performance of our method. Compared with the existing models, our method achieves the state-of-the-art result.
The Method for Micro Expression Recognition Based on Improved Light-Weight CNN
Authors: Luo Li , He Jianjun , Cai Huapeng
Published: July 2022

In view of the particularity of micro expression, there are some problems, such as resource waste or parameter redundancy in micro expression training and recognition by using large convolutional neural network model alone. Therefore, a method of using lightweight model to recognize micro expression is proposed, which aims to reduce the size of model space and the number of parameters, and improve the accuracy at the same time. This method uses mini-Xception as the framework and Non-Local Net and SeNet as parallel auxiliary feature extractors to enhance feature extraction. Finally, the simulation experiments are carried out on the two public data sets of fer2013 and CK+. After a certain training cycle, the accuracy can reach 74.5% and 97.8% respectively, which slightly exceeds the commonly used classical models. It is proved that the improved lightweight model has higher accuracy, lower parameters and model size than the large convolution network model.
A Lightweight Verification Scheme Based on Dynamic Convolution
Authors: Tang Lihe , Yang Weidong , Gao Qiang , Xu Rui , Ye Rongzhi
Published: July 2022

Since Electricity Grid Engineering involves a large number of personnel in the construction process, face recognition algorithms can be used to solve the personnel management problem. The recognition devices used in Electricity Grid Engineering are often mobile, embedded, and other lightweight devices with limited hardware performance. Although a large number of existing face recognition algorithms based on deep convolutional neural networks have high recognition accuracy, they are difficult to run in mobile devices or offline environments due to high computational complexity. In order to maintain the accuracy of face recognition while reducing the complexity of face recognition networks, a lightweight face recognition network based on Dynamic Convolution is proposed. Based on MobileNetV2, this paper introduces the Dynamic Convolution operation. It proposes a Dynamic Inverted Residuals Block, which enables the lightweight neural network to combine the feature extraction and learning ability of large neural networks to improve the recognition accuracy of the model. The experiments prove that the proposed model maintains high recognition accuracy while ensuring lightweight.
Cross-Knowledge Graph Entity Alignment via Neural Tensor Network
Authors: Wang Jingchu , Liu Jianyi , Chen Feiyu , Lu Teng , Huang Hua , Zhao Jinmeng
Published: July 2022

With the expansion of the current knowledge graph scale and the increase of the number of entities, a large number of knowledge graphs express the same entity in different ways, so the importance of knowledge graph fusion is increasingly manifested. Traditional entity alignment algorithms have limited application scope and low efficiency. This paper proposes an entity alignment method based on neural tensor network (NtnEA), which can obtain the inherent semantic information of text without being restricted by linguistic features and structural information, and without relying on string information. In the three cross-lingual language data sets DBPFR−EN, DBPZH−EN and DBPJP−EN of the DBP15K data set, Mean Reciprocal Rank and Hits@k are used as the alignment effect evaluation indicators for entity alignment tasks. Compared with the existing entity alignment methods of MTransE, IPTransE, AlignE and AVR-GCN, the Hit@10 values of the NtnEA method are 85.67, 79.20, and 78.93, and the MRR is 0.558, 0.511, and 0.499, which are better than traditional methods and improved 10.7% on average.
A Method for Obtaining Highly Robust Memristor Based Binarized Convolutional Neural Network
Authors: Huang Lixing , Diao Jietao , Teng Shuhua , Li Zhiwei , Wang Wei , Liu Sen , Li Minghou , Liu Haijun
Published: July 2022

Recently, memristor based binarized convolutional neural network has been widely investigated owing to its strong processing capability, low power consumption and high computing efficiency.However, it has not been widely applied in the field of embedded neuromorphic computing for manufacturing technology of the memristor being not mature. With respect to this, we propose a method for obtaining highly robust memristor based binarized convolutional neural network. To demonstrate the performance of the method, a convolutional neural network architecture with two layers is used for simulation, and the simulation results show that binarized convolutional neural network can still achieve more than 96.75% recognition rate on MNIST dataset under the condition of 80% yield of the memristor array, and the recognition rate is 94.53% when the variation of memristance is 26%, and it is 94.66% when the variation of the neuron output is 0.8.
Real-Time Estimation of GPS Satellite Clock Errors and Its Precise Point Positioning Performance
Authors: Zou Junping , Wang Jiexian
Published: July 2022

The current stochastic model in GNSS processing is constructed based on the prior experience, for example the ratio of the weight of the pseudorange and phase observations is generally determined as 1:10000. These methods ignore the precision differences of the different GNSS receivers and observation space. In this paper, the standard deviation of differenced ionosphere-free pseudorange and phase observations is computed with dual-frequency observations and then the weight ratio of the pseudorange and phase observations is obtained using the computed standard deviation. This method is introduced in satellite clock estimating and the data is processed. The results show that the presented method is feasible, with which the accuracy of the estimated satellite clock results is improved. The estimated satellite clock results are further adopted in PPP and the positioning results of the 10 users validate that the estimated satellite clock, which uses the presented method, can accelerate the convergence of PPP compared with the traditional method.
Optimal Decision Threshold-Moving Strategy for Skewed Gaussian Naive Bayes Classifier
Authors: He Qinyuan , Yu Hualong
Published: July 2022

Gaussian Naive Bayes (GNB) is a popular supervised learning algorithm to address various classification issues. GNB has strong theoretical basis, however, its performance tends to be hurt by skewed data distribution. In this study, we present an optimal decision threshold-moving strategy for helping GNB to adapt imbalanced classification data. Specifically, a PSO-based optimal procedure is conducted to tune the posterior probabilities produced by GNB, further repairing the bias on classification boundary. The proposed GNB-ODTM algorithm presents excellent adaptation to skewed data distribution. Experimental results on eight class imbalance data sets also indicate the effectiveness and superiority of the proposed algorithm.
Open-Set Recognition of Shortwave Signal Based on Dual-Input Regression Neural Network
Authors: Zhang Jian , Wu Di , Hu Tao , Wang Shu , Wang Shiju , Li Tingli
Published: July 2022

Open-set recognition in blind shortwave signal processing is an important issue in modern communication signal processing. This paper presents a novel method for this problem. By preprocessing, the signal data matrix and vector diagram are obtained as network input. Then, the network is trained and tested with the known signal, and the upper and lower quintile algorithm is used to obtain the interval threshold for judging the known signal and the distance threshold for intercepting the length range of the unknown signal. Finally, the network is used for numerical regression in open-set range, the threshold combined with kernel density clustering algorithm is used to identify different signals. Simulation results show that the proposed method overcomes the defects of traditional algorithm, which cannot distinguish different types of unknown signals and only applicable for few signal types.
Deep Person Re-identification with the Combination of Physical Biometric Information and Appearance Features
Authors: Hua Chunsheng , Zhao Xiaoheng , Meng Wei , Pan Yingjie
Published: July 2022

In this paper, we propose a novel Person Re-identification model that combines physical biometric information and traditional appearance features. After manually obtaining a target human ROI from human detection results, the skeleton points of target person will be automatically extracted by OpenPose algorithm. Combining the skeleton points with the biometric information (height, shoulder width.) calculated by the vision-based geometric estimation, the further physical biometric information (stride length, swinging arm.) of target person could be estimated. In order to improve the person re-identification performance, an improved triplet loss function has been applied in the framework of [1] where both the human appearance feature and the calculated human biometric information are utilized by a full connection layer (FCL). Through the experiments carried out on public datasets and the real school surveillance video, the effectiveness and efficiency of proposed algorithm have been confirmed.
Automatic Modulation Classification Based on One-Dimensional Convolution Feature Fusion Network
Authors: Ma Ruipeng , Wu Di , Hu Tao , Yi Dong , Zhang Yuqiao , Chen Jianxia
Published: July 2022

Deep learning method has been gradually applied to Automatic Modulation Classification (AMC) because of its excellent performance. In this paper, a lightweight one-dimensional convolutional neural network module (OnedimCNN) is proposed. We explore the recognition effects of this module and other different neural networks on IQ features and AP features. We conclude that the two features are complementary under high and low SNR. Therefore, we use this module and probabilistic principal component analysis (PPCA) to fuse the two features, and propose a one-dimensional convolution feature fusion network (FF-Onedimcnn). Simulation results show that the overall recognition rate of this model is improved by about 10%, and compared with other automatic modulation classification (AMC) network models, our model has the lowest complexity and the highest accuracy.
Machine Learning in Medical Image Processing
Authors: Elmahalawy Ahmed , Abdel-Aziz Ghada
Published: July 2022

Medical images provide information that can be used to detect and diagnose a variety of diseases and abnormalities. Because cardiovascular disorders are the primary cause of death and cancer is the second, good early identification can aid in the reduction of cancer mortality rates. There are different medical imaging modalities that the radiologists use in order to study the organ or tissue structure. The significance of each imaging modality is changing depending on the medical field. The goal of this research is to give a review that shows new machine learning applications for medical image processing and gives a review of the field’s progress. The classification of medical photographs of various sections of the human body is the focus of this review. Additional information on methodology developed using various machine learning algorithms to aid in the classification of tumors, non-tumors, and other dense masses is available. It begins with an introduction of several medical imaging modalities, followed by a discussion of various machine learning algorithms to segmentation and feature extraction.
A New Prefetching Unit for Digital Signal Processor
Authors: Ji Rongju , Ren Haoqi
Published: July 2022

In this paper, a new structure of instruction prefetching unit is proposed. The prefetching is achieved by building the relationship between the branch source and its branch target and the relationship between the branch target and the first branch in its following instruction sequence. With the help of the proposed structure, it is easy to know whether the instruction block of branch target blocks exist in the instruction cache based on the recorded branch information. The two-level depth target prefetching can be performed to eliminate or reduce the instruction cache miss penalty. Experimental results demonstrate that the proposed instruction prefetching scheme can achieve lower cache miss rate and miss penalty than the traditional next-line prefetching technique.
Building Machine Learning Models for Classification of Text and Non-text Elements in Natural Scene Images
Authors: Soni Rituraj , Sharma Deepak
Published: July 2022

Computer vision aims to build autonomous systems that can perform some of the human visual system’s tasks (and even surpass it in many cases)among the several applications of Computer Vision, extracting the information from the natural scene images is famous and influential. The information gained from an image can vary from identification, space measurements for navigation, or augmented reality applications. These scene images contain relevant text elements as well as many non-text elements. Prior to extracting meaningful information from the text, the foremost task is to classify the text & non-text elements correctly in the given images. The present paper aims to build machine learning models for accurately organizing the text and non-text elements in the benchmark dataset ICDAR 2013. The result is obtained in terms of the confusion matrix to determine the overall accuracy of the different machine learning models.
Gravitational waves from first-order electroweak phase transition in a model with light sgoldstinos
Authors: Demidov S. , Gorbunov D. , Kriukova E.
Published: July 2022

Abstract

We study previously unexplored possibility of triggering the first order electroweak phase transition (EWPT) by interactions of the Standard Model (SM) particles with the sector responsible for low scale supersymmetry breaking. The low-energy theory apart from the SM particles contains additional scalar degrees of freedom — sgoldstinos — which contribute to the effective scalar potential and thus can trigger the first order EWPT. Remarkably, the latter requires only moderate couplings in the scalar sector. The perturbative description in terms of the effective theory seems formally to be applicable upto the scale of supersymmetry breaking: the Landau pole in the scalar sector is above 108-109 GeV. We calculate the gravitational wave signal generated at this transition (it can be tested, e.g. by LISA, BBO and DECIGO) and briefly discuss the collider phenomenology of this scenario.
Power suppressed corrections show new features of infrared cancellations
Authors: Ciafaloni Paolo , Comelli Denis , Urbano Alfredo
Published: July 2022

Abstract

The cancellation of infrared (IR) divergences is an old topic in quantum field theory whose main results are condensed into the celebrated Kinoshita-Lee-Nauenberg (KLN) theorem. In this paper we consider mass-suppressed corrections to the leading (i.e. double-logarithmic) IR divergences in the context of spontaneously broken gauge theories. We work in a simplified theoretical set-up based on the spontaneously broken U′(1) ⨂ U(1) gauge group. We analyze, at the one-loop level and including mass-suppressed terms, the double-logarithmic corrections to the decay channels of an hypothetical heavy Z′ gauge boson coupled to light chiral fermions and mixed with a light massive Z gauge boson. Limited to this theoretical framework, only final state IR corrections are relevant. We find that full exploitation of the KLN theorem requires non-trivial combinations of various decay channels in order to get rid of the mass-suppressed IR corrections. Based on this observation we show that, starting from any two-body decay of the heavy Z′ gauge boson, the cancellation of the mass-suppressed double-logarithmic corrections requires the sum over the full decay width (thus enforcing the inclusion of final states which are naïvely unrelated to the starting one). En route, we prove a number of technical results that are relevant for the computation of mass-suppressed double-logarithms of IR origin. Our results are relevant for models that enlarge the Standard Model by adding a heavy Z′.
Gluon fusion production at NLO: merging the transverse momentum and the high-energy expansions
Authors: Bellafronte Luigi , Degrassi Giuseppe , Giardino Pier Paolo , Gröber Ramona , Vitti Marco
Published: July 2022

Abstract

The virtual corrections to gg → HH and gg → ZH are analytically evaluated combining an expansion in the small transverse momentum of the final particles with an expansion valid at high energies. The two expansion methods describe complementary regions of the phase space and we merge their results, extending the range of validity of both expansions using Padé approximants. We show that this approach can reproduce the available numerical results retaining the exact top quark mass dependence with an accuracy below the 1% level. Our results allow a fast and flexible evaluation of the virtual corrections of the considered processes. Furthermore, they are available in different renormalisation schemes of the top quark mass.
SIRT1 pharmacological activation rescues vascular dysfunction and prevents thrombosis in MTHFR deficiency
Authors: Carrizzo Albino , Iside Concetta , Nebbioso Angela , Carafa Vincenzo , Damato Antonio , Sciarretta Sebastiano , Frati Giacomo , Di Nonno Flavio , Valenti Valentina , Ciccarelli Michele , Venturini Eleonora , Scioli Mariarosaria , Di Pietro Paola , Bucci Tommaso , Giudice Valentina , Storto Marianna , Serio Bianca , Puca Annibale Alessandro , Giugliano Giuseppe , Trimarco Valentina , Izzo Raffaele , Trimarco Bruno , Selleri Carmine , Altucci Lucia , Vecchione Carmine
Published: July 2022

Beyond well-assessed risk factors, cardiovascular events could be also associated with the presence of epigenetic and genetic alterations, such as the methylenetetrahydrofolate-reductase (MTHFR) C677T polymorphism. This gene variant is related to increased circulating levels of homocysteine (Hcy) and cardiovascular risk. However, heterozygous carriers have an augmented risk of cardiovascular accidents independently from normal Hcy levels, suggesting the presence of additional deregulated processes in MTHFR C677T carriers. Here, we hypothesize that targeting Sirtuin 1 (SIRT1) could be an alternative mechanism to control the cardiovascular risk associated to MTHFR deficiency condition. Flow Mediated Dilatation (FMD) and light transmission aggregometry assay were performed in subjects carrying MTHFR C677T allele after administration of resveratrol, the most powerful natural clinical usable compound that owns SIRT1 activating properties. MTHFR C677T carriers with normal Hcy levels revealed endothelial dysfunction and enhanced platelet aggregation associated with SIRT1 downregulation. SIRT1 activity stimulation by resveratrol intake was able to override these abnormalities without affecting Hcy levels. Impaired endothelial function, bleeding time, and wire-induced thrombus formation were rescued in a heterozygous Mthfr-deficient (Mthfr+/–) mouse model after resveratrol treatment. Using a cell-based high-throughput multiplexed screening (HTS) assay, a novel selective synthetic SIRT1 activator, namely ISIDE11, was identified. Ex vivo and in vivo treatment of Mthfr+/– mice with ISIDE11 rescues endothelial vasorelaxation and reduces wire-induced thrombus formation, effects that were abolished by SIRT1 inhibitor. Moreover, platelets from MTHFR C677T allele carriers treated with ISIDE11 showed normalization of their typical hyper-reactivity. These results candidate SIRT1 activation as a new therapeutic strategy to contain cardio and cerebrovascular events in MTHFR carriers.
Occult hypoperfusion and changes of systemic lipid levels after severe trauma: an analysis in a standardized porcine polytrauma model
Authors: Kumabe Yohei , Kalbas Yannik , Halvachizadeh Sascha , Teuben Michel , Cesarovic Nikola , Weisskopf Miriam , Hülsmeier Andreas , Hornemann Thorsten , Cinelli Paolo , Pape Hans-Christoph , Pfeifer Roman
Published: July 2022

Background

Occult hypoperfusion describes the absence of sufficient microcirculation despite normal vital signs. It is known to be associated with prolonged elevation of serum lactate and later complications in severely injured patients. We hypothesized that changes in circulating lipids are related to responsiveness to resuscitation. The purpose of this study is investigating the relation between responsiveness to resuscitation and lipidomic course after poly trauma.

Methods

Twenty-five male pigs were exposed a combined injury of blunt chest trauma, liver laceration, controlled haemorrhagic shock, and femoral shaft fracture. After 1 h, animals received resuscitation and fracture stabilization. Venous blood was taken regularly and 233 specific lipids were analysed. Animals were divided into two groups based on serum lactate level at the end point as an indicator of responsiveness to resuscitation (<2 mmol/L: responder group (R group), ≧2 mmol/L: occult hypoperfusion group (OH group)).

Results

Eighteen animals met criteria for the R group, four animals for the OH group, and three animals died. Acylcarnitines showed a significant increase at 1 h compared to baseline in both groups. Six lipid subgroups showed a significant increase only in R group at 2 h. There was no significant change at other time points.

Conclusions

Six lipid groups increased significantly only in the R group at 2 h, which may support the idea that they could serve as potential biomarkers to help us to detect the presence of occult hypoperfusion and insufficient resuscitation. We feel that further study is required to confirm the role and mechanism of lipid changes after trauma.
Reduced performance after return to competition in ACL injuries: an analysis on return to competition in the ‘ACL registry in German Football’
Authors: Szymski Dominik , Achenbach Leonard , Weber Johannes , Huber Lorenz , Memmel Clemens , Kerschbaum Maximilian , Alt Volker , Krutsch Werner
Published: July 2022

Purpose

ACL injuries are one of the most severe injuries in football, but medical consequences and performance outcomes after return to competition are only rarely investigated. Aim of this study was to analyse the time of return to competition (RTC) in German professional, semi-professional and amateur football. Also, this investigation highlights the rate of career ending and performance outcome after RTC in different playing levels by the measurement of playing level, performed matches and played minutes.

Methods

Database of this investigation is the ‘ACL registry in German Football’ with prospectively collected injury data. Between 2014 and 2018, four seasons in professional (1st–3rd league), semi-professional (4th–6th league) and amateur leagues (7th league) were analysed regarding the return to competition period and performance parameters. Data were collected for three subsequent seasons after injury and compared with the pre-injury and injury season. Data collection was performed using standardized methods.

Results

A total of 607 ACL injuries were registered during the 4-year period with a mean RTC time of 337.1 day (SD: 183). After primary ACL ruptures, the fastest RTC was found in professional football (247.3 days), while in semi-professional (333.5 d; p < 0.0001) and amateur football (376.2 d; p < 0.0001) a prolonged absence was detected. Re-ruptures occurred in 17.8% (n = 108) and showed similar trend with fastest RTC in professionals (289.9 days; p = 0.002). Within the first three seasons after injury, 92 players (36.7%) in semi-professional and 24 (20%) in professionals had to end their career. Keeping the level of play was only possible for 48 (47.5%) of professionals, while only 47 (29.6%) of semi-professionals and 43 (28.1%) of amateurs were able to. Only in professional football, no significant difference could be seen in the played minutes and games after 2 years compared to the pre-injury season.

Conclusion

Lower playing levels and re-ruptures are the main factors for a prolonged return to competition after ACL rupture in German football. Significant reduction in playing level and a high rate of career endings were found for all levels of play. However, only professional players were able to regain their playing minutes and games 2 years after injury, while lower classed athletes did not reach the same amount within 3 years.

Level of evidence

Level III.
Hip replacement improves lumbar flexibility and intervertebral disc height — a prospective observational investigation with standing and sitting assessment of patients undergoing total hip arthroplasty
Authors: Muellner Maximilian , Wang Zhen , Hu Zhouyang , Hardt Sebastian , Pumberger Matthias , Becker Luis , Haffer Henryk
Published: July 2022

Purpose

The pathogenic mechanism of the hip–spine syndrome is still poorly elucidated. Some studies have reported a reduction in low back pain after total hip arthroplasty (THA). However, the biomechanical mechanisms of THA acting on the lumbar spine are not well understood. The aim of the study is to evaluate the influence of THA on (1) the lumbar lordosis and the lumbar flexibility and (2) the lumbar intervertebral disc height.

Methods

A total of 197 primary THA patients were prospectively enrolled. Pre- and post-operative biplanar stereoradiography was performed in standing and sitting positions. Spinopelvic parameters (lumbar lordosis (LL), pelvic tilt, sacral slope, pelvic incidence), sagittal spinal alignment (sagittal vertical axis, PI-LL mismatch (PI-LL)) and lumbar disc height index (DHI) for each segment (L1/2 to L5/S1) were evaluated. The difference between standing and sitting LL (∆LL = LLstanding − LLsitting) was determined as lumbar flexibility. Osteochondrosis intervertebralis was graded according to Kellgren and Lawrence (0–4), and patients were assigned to subgroups (mild: 0–2; severe: 3–4).

Results

Lumbar flexibility increased significantly after THA (pre: 22.04 ± 12.26°; post: 25.87 ± 12.26°; p < 0.001), due to significant alterations in LL in standing (pre: 51.3 ± 14.3°; post: 52.4 ± 13.8°; p < 0.001) and sitting (pre: 29.4 ± 15.4°; post: 26.7 ± 15.4°; p = 0.01). ∆LL increased significantly in both subgroups stratified by osteochondrosis (pre/post: ΔLLmild: 25.4 (± 11.8)/29.4 ± 12.0°; p < 0.001; ΔLLsevere: 17.5 (± 11.4)/21.0 ± 10.9°; p = 0.003). The DHI increased significantly from pre-operatively to post-operatively in each lumbar segment. PI-LL mismatch decreased significantly after THA (pre: 3.5°; post: 1.4°; p < 0.001).

Conclusion

The impact of THA on the spinopelvic complex was demonstrated by significantly improved lumbar flexibility and a gain in post-operative disc height. These results illustrate the close interaction between the pelvis and the vertebral column. The investigation provides new insights into the biomechanical patterns influencing the hip–spine syndrome.
Familiarity effects on fish behaviour are disrupted in shoals that contain also unfamiliar individuals
Authors: Lucon-Xiccato Tyrone , Cattelan Silvia , Griggio Matteo
Published: July 2022

Research on several social fishes has revealed that shoals constituted by familiar individuals behave remarkably differently compared to shoals formed by unfamiliar individuals. However, whether these behavioural changes may arise also in shoals composed by a mixture of familiar and unfamiliar individuals, a situation that may commonly occur in nature, is not clear. Here, we observed the behaviour of Mediterranean killifish (Aphanius fasciatus) shoals that were composed by both familiar and unfamiliar individuals (i.e. individuals were familiar to each other in pairs) and compared it with shoals entirely made by either unfamiliar or familiar individuals. Shoals formed by familiar individuals took longer to emerge from a refuge and swam more cohesively compared to shoals formed by unfamiliar fish. Shoals formed by a mixture of familiar and unfamiliar individuals behaved as shoals formed by unfamiliar individuals. Moreover, mixed shoals did not segregate in pairs according to their familiarity. This study suggests that mixed shoals do not show the behavioural effects of familiarity.

Significance statement

Laboratory studies have compared the behaviour of shoals formed by familiar fish versus shoals formed by unfamiliar fish, finding notable advantages in the former ones, such as improved antipredator and foraging behaviour. However, comparing these two opposite shoal types may not provide information on the natural situation, because in nature, shoals often change composition. We investigated how shoals formed by a mixture of familiar and unfamiliar fish behaved. We analysed shoals’ preference for open environment versus covers and shoals’ swimming cohesion. Results showed that shoals formed by both familiar and unfamiliar individuals mostly behave like shoals entirely formed by unfamiliar individuals. This suggests that the advantages of social groups formed by familiar fish might be hardly seen in nature for species in which shoal composition changes frequently.
Ultimate mechanical properties of enstatite
Authors: Gouriet Karine , Roussel Pascal , Carrez Philippe , Cordier Patrick
Published: July 2022

The ultimate mechanical properties of MgSiO3 orthoenstatite (OEN), as characterized here by the ideal strengths, have been calculated under tensile and shear loadings using first-principles calculations. Both ideal tensile strength (ITS) and shear strength (ISS) are computed by applying homogeneous strain increments along high-symmetry directions ([100], [010], and [001]) and low index shear planes ((100), (010), and (001)) of the orthorhombic lattice. We show that the ultimate mechanical properties of OEN are highly anisotropic during tensile loading, with ITS ranging from 4.5 GPa along [001] to 8.7 GPa along [100], and quite isotropic during the shear loading with ISS ranging from 7.4 to 8.9 GPa. During tensile test along [100] and [001], a modified structure close to OEN has been found. This modified structure is more stable than OEN under stress (or strain). We have characterized its elastic and ultimate properties under tensile loading. With ITS ranging from 7.6 GPa along [010] to 25.6 GPa along [001], this modified structure appears to be very anisotropic with exceptional strength along [001].
Long-term outcome after chronic anastomotic leakage following surgery for low rectal cancer
Authors: Ponholzer Florian , Klingler Clemens Paul , Gasser Elisabeth , Gehwolf Philipp , Ninkovic Marijana , Bellotti Ruben , Kafka-Ritsch Reinhold , Öfner Dietmar
Published: July 2022

Purpose

This study analyzed the prevalence and factors influencing the history of chronic anastomotic leakage following low anterior resection for rectal cancer. Furthermore, the treatment of a persisting presacral sinus and the impact of stoma reversal on outcome were evaluated.

Methods

The institutional database was scanned for all patients with anastomotic leakage, who were primarily treated for low rectal cancer between January 1995 and December 2019. Patients with rectovaginal and rectovesical fistula or an inadequate follow-up were excluded (n = 5). After applying the exclusion criteria, 71 patients remained for analysis.

Results

A total of 39 patients out of 71 patients with anastomotic leakage (54.9%) developed a persisting presacral sinus. Neoadjuvant radiochemotherapy or chemotherapy showed a significant impact on the formation of a chronic anastomotic leakage (radiochemotherapy: p = 0.034; chemotherapy: p = 0.050), while initial surgical treatment showed no difference for anastomotic healing (p = 0.502), but a significantly better overall survival (p = 0.042). Multiple therapies and surgical revision had a negative impact on patients’ rate of natural bowel continuity (p = 0.006/ < 0.001). In addition, the stoma reversal cohort showed improved overall 10-year survival (p = 0.004) and functional results (bowel continuity: p = 0.026; pain: p = 0.031).

Conclusion

Primary surgical therapy for chronic anastomotic leakage should consist of surgical treatment. Furthermore, the reversal of a protective stoma should be considered a viable option in treating chronic presacral sinus to improve pain symptoms and bowel continuity.
Effect of trophectoderm biopsy for PGT-A on live birth rate per embryo in good prognosis patients
Authors: Awadalla Michael S. , Agarwal Ravi , Ho Jacqueline R. , McGinnis Lynda K. , Ahmady Ali
Published: July 2022

Purpose

To determine if blastocyst trophectoderm biopsy for PGT-A is associated with an increased rate of live birth per embryo in good prognosis IVF patients at a single center.

Methods

We performed a retrospective cohort study of good prognosis embryo transfer cycles at a single center from 1/1/2017 to 12/31/2019. We evaluated the rate of live birth per embryo with and without PGT-A for transfer of embryos in two groups of good prognosis patients: embryos from donor oocytes and embryos from autologous oocytes with maternal age less than 35 years at oocyte retrieval. Two-sided Fisher’s exact tests were used for comparisons between groups.

Results

After transfer of embryos created from donor oocytes the live birth rate per euploid embryo was 70.6% (24/34) compared to 34.3% (35/102) for untested embryos for a rate difference of 36.3% (95% CI 18.4–54.1%, p < 0.01). After transfer of embryos created from autologous oocytes with maternal age less than 35 years at oocyte retrieval the live birth rate per euploid embryo was 70.0% (49/70) compared to 52.5% (53/101) for untested embryos for a rate difference of 17.5% (95% CI 3.0–32.0%, p = 0.03).

Conclusions

In good prognosis patients at our center the live birth rate per euploid blastocyst was higher than for untested blastocysts.
EAHP 2020 workshop proceedings, pediatric myeloid neoplasms
Authors: Leguit R. J. , Orazi A. , Kucine N. , Kvasnicka H. M. , Gianelli U. , Arber D. A. , Porwit A. , Ponzoni M.
Published: July 2022

The first section of the bone marrow workshop of the European Association of Haematopathology (EAHP) 2020 Virtual Meeting was dedicated to pediatric myeloid neoplasms. The section covered the whole spectrum of myeloid neoplasms, including myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), and acute myeloid leukemia (AML). The workshop cases are hereby presented, preceded by an introduction on these overall rare diseases in this age group. Very rare entities such as primary myelofibrosis, pediatric MDS with fibrosis, and MDS/MPN with JMML-like features and t(4;17)(q12;q21); FIP1L1::RARA fusion, are described in more detail.
Sodium assessment in neonates, infants, and children: a systematic review
Authors: Corsello Antonio , Malandrini Sabrina , Bianchetti Mario G. , Agostoni Carlo , Cantoni Barbara , Meani Francesco , Faré Pietro B. , Milani Gregorio P.
Published: July 2022

Hyponatremia is a common disorder in childhood. The indirect and the direct potentiometry are currently the most popular techniques employed for sodium assessment, although discrepancies between the two techniques may be > 10 mmol/L. It is known that < 20% of the recently published articles report information about the technique used for sodium analysis, but no data are available on pediatric studies. This study aimed at investigating the laboratory technique employed for sodium measurement in studies conducted in childhood. A systematic literature search in PubMed, Embase, and Web of Science was undertaken to identify articles containing the word “hyponatremia” in the title between 2013 and 2020. Papers with < 10 subjects were excluded. A total of 565 articles were included. Information on the laboratory technique used for sodium analysis was more commonly (p = 0.035) reported in pediatric (n = 15, 28%) than in non-pediatric (n = 81, 16%) reports. The frequency of reports with and without information on the technique for sodium assessment was not different with respect to the study characteristics, the quartile of the journal where the paper was published, the country income setting, and the inclusion of neonates among the 54 pediatric studies.

   Conclusion: Most pediatric papers do not report any information on the technique used for sodium analysis. Although international authorities have recommended the implementation of direct potentiometry, a low awareness on this issue is still widespread in pediatric research.

What is Known:

• Direct potentiometry and indirect potentiometry are currently employed for sodium analysis in blood.

• Direct potentiometry is more accurate.

What is New:

• Less than 30% of pediatric articles provide information on the technique employed for sodium analysis in blood.

• Indirect potentiometry is more frequently employed than direct potentiometry in pediatric studies.
Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential
Authors: Amato Federico , Guignard Fabian , Walch Alina , Mohajeri Nahid , Scartezzini Jean-Louis , Kanevski Mikhail
Published: July 2022

With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1) uses machine learning to reconstruct a spatio-temporal field of wind speed on a regular grid from spatially irregularly distributed measurements and (2) transforms the wind speed to wind power estimates. Estimates of both model and prediction uncertainties, and of their propagation after transforming wind speed to power, are provided without any assumptions on data distributions. The methodology is applied to study hourly wind power potential on a grid of$$250\times 250$$250×250 m$$^{2}$$2for turbines of 100 m hub height in Switzerland, generating the first dataset of its type for the country. We show that the average annual power generation per turbine is 4.4 GWh. Results suggest that around 12,000 wind turbines could be installed on all 19,617 km$$^{2}$$2of available area in Switzerland resulting in a maximum technical wind potential of 53 TWh. To achieve the Swiss expansion goals of wind power for 2050, around 1000 turbines would be sufficient, corresponding to only 8% of the maximum estimated potential.
Thermodynamic assessment of heat stress in dairy cattle: lessons from human biometeorology
Authors: Foroushani Sepehr , Amon Thomas
Published: July 2022

A versatile meteorological index for predicting heat stress in dairy cattle remains elusive. Despite numerous attempts at developing such indices and widespread use of some, there is growing skepticism about the accuracy and adequacy of the existing indices as well as the general statistical approach used to develop them. At the same time, precision farming of high-yielding animals in a drastically changing climate calls for more effective prediction and alleviation of heat stress. The present paper revisits classical work on human biometeorology, particularly the apparent temperature scale, to draw inspiration for advancing research on heat stress in dairy cattle. The importance of a detailed, mechanistic understanding of heat transfer and thermoregulation is demonstrated and reiterated. A model from the literature is used to construct a framework for identifying and characterizing conditions of potential heat stress. New parameters are proposed to translate the heat flux calculations based on heat-balance models into more tangible and more useful meteorological indices, including an apparent temperature for cattle and a thermoregulatory exhaustion index. A validation gap in the literature is identified as the main hindrance to the further development and deployment of heat-balance models. Recommendations are presented for systematically addressing this gap in particular and continuing research within the proposed framework in general.
Gramian-based model reduction for unstable stochastic systems
Authors: Redmann Martin , Jamshidi Nahid
Published: July 2022

This paper considers large-scale linear stochastic systems representing, e.g., spatially discretized stochastic partial differential equations. Since asymptotic stability can often not be ensured in such a stochastic setting (e.g., due to larger noise), the main focus is on establishing model order reduction (MOR) schemes applicable to unstable systems. MOR is vital to reduce the dimension of the problem in order to lower the enormous computational complexity of for instance sampling methods in high dimensions. In particular, a new type of Gramian-based MOR approach is proposed in this paper that can be used in very general settings. The considered Gramians are constructed to identify dominant subspaces of the stochastic system as pointed out in this work. Moreover, they can be computed via Lyapunov equations. However, covariance information of the underlying systems enters these equations which is not directly available. Therefore, efficient sampling-based methods relying on variance reduction techniques are established to derive the required covariances and hence the Gramians. Alternatively, an ansatz to compute the Gramians by deterministic approximations of covariance functions is investigated. An error bound for the studied MOR methods is proved yielding an a priori criterion for the choice of the reduced system dimension. This bound is new and beneficial even in the deterministic case. The paper is concluded by numerical experiments showing the efficiency of the proposed MOR schemes.
The Hadwiger theorem on convex functions, III: Steiner formulas and mixed Monge–Ampère measures
Authors: Colesanti Andrea , Ludwig Monika , Mussnig Fabian
Published: July 2022

A complete family of functional Steiner formulas is established. As applications, an explicit representation of functional intrinsic volumes using special mixed Monge–Ampère measures and a new version of the Hadwiger theorem on convex functions are obtained.
Standardising the management of open extremity fractures: a scoping review of national guidelines
Authors: Berner Juan Enrique , Ali Stephen R. , Will Patrick A. , Tejos Rodrigo , Nanchahal Jagdeep , Jain Abhilash
Published: July 2022

Introduction

Open extremity fractures can be life-changing events. Clinical guidelines on the management of these injuries aim to standardise the care of patients by presenting evidence-based recommendations. We performed a scoping systematic review to identify all national clinical practice guidelines published to date.

Materials and methods

A PRISMA-compliant scoping systematic review was designed to identify all national or federal guidelines for the management of open fractures, with no limitations for language or publication date. EMBASE and MEDLINE database were searched. Article screening and full-text review was performed in a blinded fashion in parallel by two authors.

Results

Following elimination of duplicates, 376 individual publications were identified and reviewed. In total, 12 clinical guidelines were identified, authored by groups in the UK, USA, the Netherlands, Finland, and Malawi. Two of these focused exclusively on antibiotic prophylaxis and one on combat-related injuries, with the remaining nine presented wide-scope recommendations with significant content overlap.

Discussion

Clinical practice guidelines serve clinicians in providing evidence-based and cost-effective care. We only identified one open fractures guideline developed in a low- or middle-income country, from Malawi. Even though the development of these guidelines can be time and resource intensive, the benefits may outweigh the costs by standardising the care offered to patients in different healthcare settings. International collaboration may be an alternative for adapting guidelines to match local resources and healthcare systems for use across national borders.