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Analysis of attacking styles and goal-scoring in the 2021/22 Women’s Super League
The rapid rise in elite women’s football has increased the demand for female specific research to enable more accurate and appropriate assessments of tactical performance. Thereby, this study aimed to describe goal scoring in relation to different attacking styles during a Women’s Super League (WSL) season. Specifically, 1179 attacking sequences leading to shots on target performed by all 12 teams in the 2021/22 WSL season were analysed. The style of attack for each attacking sequence was characterised by research guided key performance indicators and recorded with the outcome of the subsequent shot on target. Descriptive results indicated that most shots (27.23%) were originating from combinative organised attacks, while set plays accounted for the most goals (27.08%), with fast organised attacks demonstrating the best goal conversation rates (53.33%). Outcomes of a chi-square test highlighted a significant (but weak) association between attacking styles and shot outcomes (χ42 = 9.87, P = 0.043) in the considered WSL season, with shots originating from set plays resulting in significantly more goals than expected (AR = 2.45). Overall, the results can be useful for practitioners when formulating tactical game plans and training sessions, while also providing multiple opportunities for future research in tactical analysis of women’s football.
The Future of Gender-Affirming Care — A Law and Policy Perspective on the Cass Review
Impact of micromechanical properties of organic matter on the micro-mesopore structures of the over-mature shale in the Niutitang Formation
Fund style drift and fund performance: Evidence from China
This study selects the quarterly data of all equity and equity-oriented hybrid open-end funds in China from 2007 to 2022 as the research sample, and examines the impact of fund style drift on fund returns through a two-ways fixed effect model. Our results show that overall style drift tends to improve fund performance. However, after differentiating the type of style drift, we find that fund drift based on stock picking abilities enhanced performance, whereas fund drift based on chasing market trends reduced fund performance. This study presents a new measurement method based on industry allocation, providing an empirical foundation for research on industry-specific theme funds. It also offers fund managers insights for optimizing performance evaluation and incentive systems, while serving as a reference for regulators to develop flexible, effective policies for market stability.
Integrated analysis of proteomics and metabolomics in infantile epileptic spasms syndrome
James Fraser Stoddart obituary: chemist and nanotechnology pioneer who built molecular machines
Genome-wide association study reveals major loci for resistance to septoria tritici blotch in a Tunisian durum wheat collection
Septoria tritici blotch (STB) is a devastating fungal disease affecting durum and bread wheat worldwide. Tunisian durum wheat landraces are reported to be valuable genetic resources for resistance to STB and should prominently be deployed in breeding programs to develop new varieties resistant to STB disease. In this study, a collection of 367 old durum and 6 modern wheat genotypes previously assessed using single Tunisian Zymoseptoria tritici isolate TUN06 during 2016 and 2017 and TM220 isolate during 2017 were phenotyped for resistance to a mixture of isolates (BULK) under field conditions. Significant correlations for disease traits using the three different inoculums were observed. Using 7638 SNP markers, fifty-one marker-trait associations (MTAs) for STB resistance were identified by genome-wide association study (GWAS) at Bonferroni correction threshold of -log10(P) > 5.184 with phenotypic variance explained (PVE) reaching up to 58%. A total of eleven QTL were identified using TUN06 isolate mean disease scoring (TUNMeanD and TUNMeanA) including threeQTL controlling resistance to both isolates TUN06 and TM220. A major QTL was identified on each of chromosomes 1B, 4B, 5A, and 7B, respectively. The QTL on 7B chromosome colocalized with Stb8 identified in bread wheat. Four QTL including the major QTL identified on chromosome 1B were considered as novel. SNP linked to the significant QTL have the potential to be used in marker-assisted selection for breeding for resistance to STB.
Decoherence in spin wave propagation via precursor pulses during signal equilibration
Abstract The processes for generation, amplification, and protraction of oscillating signals—often for information transfer—are inherently associated with dispersive decoherence and nonlinear phenomena. One such example is the case of wave patterns of non-negligible amplitudes that can emerge due to dispersion of a signal propagating through matter; for a continuously applied drive these patterns precede the main signal. Here, we investigate how spin wave generation inherently results in dispersive decoherence in the form of precursors. By quantifying three different frequency regimes, we investigate how decoherence is affected, or predetermined by the shape of the spin wave dispersion relation and, what is perhaps most interesting, it does not require non-linearity. Understanding the relationship between spin wave dispersion and decoherence can enable engineering magnonic devices supporting well-resolved signals for magnonic computing and signal processing.
What lies beneath Europa’s icy surface? Perhaps a heart of metal
Innovative approach of nomography application into an engineering educational context
Nomography is considered a branch of mathematics introduced by Maurice d’Ocagne in 1884 in France. The past century saw nomography grow as a graphical computing method used by scientists and engineers wishing to solve complex problems to a practical precision. Even though nomography has declined with the introduction of calculators and computers, it still offers potential in an educational setting. The recent development of open-source software is helping promote the use of nomograms among scholars in engineering courses who are aware of nomography’s capabilities. The main reason for this apparent and renewed interest in nomography is the capability of open-source software to generate customized and precise nomograms in seconds without the previously required mathematical background. In this work, we introduce Nomogen, a Python package able to build reliable and scalable 3-variable nomograms while avoiding past drawbacks such as manipulating determinants or manually drawing the scales. In this way, some nomograms generated by Nomogen have been tested on undergraduate and graduate students from different engineering backgrounds. Subsequently, a Likert scale survey was conducted, which showed that students had a great and renewed interest in nomography and found it helpful in the engineering learning process. Even though 78.4% of the respondent had never used nomograms, 86.5% believed that these analogical graphs allow a reasonable interpretation of the phenomenon when there are many variables, and, as a result, nomography with the assistance of open-source software, such as Nomogen or PyNomo, should be incorporated in the teaching process as part of their engineering education syllabus.
Evaluation of 3D seed structure and cellular traits in-situ using X-ray microscopy
Vegetation restoration effectiveness with main factors in the Beijing-Tianjin sandstorm source region during 2000–2020, China
The Beijing-Tianjin Sandstorm Source Region (BTSSR), a region with significant vegetation degradation in China, has been subjected to ecological engineering intended to curb vegetation browning. Nevertheless, few studies have used multisource data to quantitatively evaluate the vegetation restoration effectiveness in the BTSSR, and the relationship between ecological engineering and vegetation restoration effectiveness in this region from statistical evidence has received little attention so far. Here, we employed the comprehensive vegetation parameters to describe the vegetation restoration effectiveness, and examined the driving mechanism of natural and human factors in different sub regions. First, we evaluated the vegetation restoration effectiveness in the BTSSR using an index that combined Fractional Vegetation Coverage (FVC) and Net Primary Productivity (NPP). Our results showed that the vegetation restoration effectiveness has significantly increased over time. From 2000 to 2020, 60.9% of the area achieved significant vegetation restoration, and the area with higher vegetation restoration effectiveness was concentrated in the southern part of the study area. Then, we used the Geodetector Model to explore the main factors and their interactions affecting vegetation restoration effectiveness. We found that the vegetation restoration effectiveness in the entire area was dominated by annual precipitation, in the northern part of the study area was led by climate, and in the southern part of the study area was dominated by ecological engineering. We further demonstrated that the interaction between ecological engineering and climate, soil conditions, geographical background and socioeconomic had the synergistic effect on vegetation restoration effectiveness, and the interaction between ecological engineering and annual precipitation had the greatest impact. We recommend that the northern region of the BTSSR continue to build low-density wind and sand control forests, while the southern region needs to be strengthened to prevent soil erosion problems caused by the expansion of human activities.
Research on comprehensive characterization of deep coal full aperture structure and burial depth effect
Basic fibroblast growth factor helps protect facial nerve cells in a freeze-induced paralysis model
Severe axonal damage in the peripheral nerves results in retrograde degeneration towards the central side, leading to neuronal cell death, eventually resulting in incomplete axonal regeneration and functional recovery. Therefore, it is necessary to evaluate the facial nerve nucleus in models of facial paralysis, and investigate the efficacy of treatments, to identify treatment options for severe paralysis. Consequently, we aimed to examine the percentage of facial nerve cell reduction and the extent to which intratympanic administration of a basic fibroblast growth factor (bFGF) inhibits neuronal cell death in a model of severe facial paralysis. A severe facial paralysis model was induced in Hartley guinea pigs by freezing the facial canal. Animals were divided into two groups: one group was treated with gelatin hydrogel impregnated with bFGF (bFGF group) and the other was treated with gelatin hydrogel impregnated with saline (control group). Facial movement scoring, electrophysiological testing, and histological assessment of facial neurons were performed. The freezing-induced facial paralysis model showed a facial neuronal cell death rate of 29.0%; however, bFGF administration reduced neuronal cell death to 15.8%. Facial movement scores improved in the bFGF group compared with those in the control group. Intratympanic bFGF administration has a protective effect on facial neurons in a model of severe facial paralysis. These findings suggest a potential therapeutic approach for treating patients with refractory facial paralysis. Further studies are required to explore the clinical applicability of this treatment.
Publisher Correction: Exposure to Lead (Pb) influences the outcomes of male-male competition during precopulatory intrasexual selection
Evaluating the impact of added greenery on perceived factors of an urban environment in virtual reality
The wellbeing effects of urban greenspace are well established, but may be more attributable to pedestrians’ perceptions than objective levels of greenery. An immersive virtual environment was designed with three levels of roadside greenery: no trees, 200 trees, and 400 trees. Participants were asked to rate each for several perceived and objective factors, and gave their years lived experience in urban, rural, and suburban environments. Trees impacted perceptions of beauty and greenness, and, slightly, building heights. Controlling for urban experience significantly lessened the impact of trees, but showed that perceived greenness had higher significant correlations to all other outcomes.
Author Correction: Two decades of occurrence of non-pathogenic rabbit lagoviruses in Italy and their genomic characterization
Will bird flu spark a human pandemic? Scientists say the risk is rising
An intelligent spam detection framework using fusion of spammer behavior and linguistic
The diverse types of fake text generation practices by spammer make spam detection challenging. Existing works use manually designed discrete textual or behavior features, which cannot capture complex global semantics of text and reviews. Some studies use limited features while neglecting other significant features. However, in case of a large number of features set, the selection of all features leads to overfitting the model and expensive computation. The problem statement of this research paper revolves around addressing challenges concerning feature selection and evolving spammer behavior and linguistic features, with the goal of devising an efficient model for spam detection. The primary objective of this endeavor was to identify the most efficacious subset of features and patterns for the task of spam detection. Spammer behavior features and linguistic features often exhibit complex relationships that influence the nature of spam reviews. The unified representation of features is another challenging task in spam detection. Various deep learning approaches have been proposed for spam detection and classification but these methods are specialized in extracting the features but lack to capture feature dependencies effectively with other features but there is a lack of comprehensive models that integrate linguistic and behavioral features to improve the accuracy of spam detection. The proposed spam detection framework SD-FSL-CLSTM used the fusion of spammer behavior features and linguistic features which automatically detect and classify the spam reviews. Fusion enables the proposed model to automatically learn the interactions between the features during the training process, allowing it to capture complex relationships and make predictions based on both types of features. SD-FSL-CLSTM framework apparently shows the promising result by obtaining a minimum accuracy 97%.