Miracle-dz/UniFAR-FTUs huggingface dataset:
Injury risk and maturity status in Italian elite young football player

groups were statistically significant (p = 0.039); moreover, the effect size between PHV and Post-PHV was moderate. The mean of exposure shows a significant increase from the year before PHV to the year after PHV (p = 0.0089), furthermore, the effect size between Pre-PHV and Post-PHV was large. Conclusions. The results of this study on young Italian football players confirm the overall increased vulnerability of the athletes in the PHV period. Great attention should be given to the maturity status of the subjects. To try to limit injuries, training programs should be calibrated on the state of maturity of the players and not on their chronological age.
The Last 1200 Years of Rainfall/Runoff Variability along the Central Mexico Pacific Coast Associated with the North American Monsoon

This study presents new evidence for long-term variability in the late Holocene North American Monsoon (NAM), Pacific coast of Mexico. We have carried out a rock magnetic study on two deep-sea sediment cores from the Pacific coast Pescadero Basin. The magnetic intensities estimate total magnetic material and are a proxy for total clastic sediment. Ratios of magnetic intensities estimate the grain size of magnetic material. The rock magnetic data show a decimeter scale, multi-decadal oscillation with fourteen cycles (A-N) over the last 1200 years. These oscillations reflect alternating intervals of stronger/coarser magnetic/clastic flux to the coastal ocean and intervals of weaker/finer magnetic flux. We think these variations are caused by variations in long-term dominance of the NAM; summer (wet) monsoons produce rainy conditions (with runoff) while winter (dry) monsoons produce significant offshore winds, increased upwelling/biological productivity. We can correlate our variability to two other published studies southeast of Pescadero Basin, coastal lake sediments in Laguna de Juanacatlan and a Juxtlahuaca Cave stalagmite. Both of these studies estimate local rainfall. We see evidence of the same pattern of multi-decadal rainfall-runoff variability in these records as we see in Pescadero Basin, which is synchronous to within ±25 years over the last 1200 years. The multi-dacadal pattern of hydrologic variability in all three records varies in cycle duration from ~90-years wet/dry cycles in the Little Ice Age (1400–1850 AD) to ~60-years cycles in the Medieval Climate Optimum (1100–1400 AD). This variability in cycle duration suggests some chaotic nature to the regional NAM climate pattern or some long-term non-linear forcing (PDO? ).
Improving Polyglot Speech Synthesis through Multi-task and Adversarial Learning

It is still quite challenging for polyglot speech synthesis systems to synthesise speech with the same pronunciations and accent as a native speaker, especially when there are fewer speakers per language. In this work, we target an extreme version of the polyglot synthesis problem, where we have only one speaker per language, and the system has to learn to disentangle speaker from language features from just one speaker-language pair. To tackle this problem, we propose a novel approach based on a combination of multi-task learning and adversarial learning to help the model produce more realistic acoustic features for speaker-language combinations for which we have no data. Our proposed system improves the overall naturalness of synthesised speech achieving upto 4.2% higher naturalness over a multispeaker baseline. Our qualitative listening tests also demonstrate that system produces speech which sounds less accented and more natural to a native speaker.
THE EFFECTS OF INJECTING INTRA ARTICULAR PLATELET-RICH PLASMA ON PAIN SCORES IN KNEE JOINT OSTEOARTHRITIS

Objective: To determine the effects of intra articular platelet rich plasma (PRP) injection on level of pain of knee joint in patients with knee osteoarthritis. Study Design: Prospective observational study. Place and Duration of Study: Combined Military Hospital Rawalpindi, from Aug 2018 to Jan 2019. Methodology: Forty patients with pain due to knee osteoarthritis for intra-articular injection were included in this observational clinical trial after informed consent and fulfilling inclusion criteria. After measuring pain by numerical rating scale, patients received intra articular platelet rich plasma (10ml) therapy in affected knee under fluoroscopy. Using the Numerical rating scale level of pain was evaluated and recorded for each patient at different intervals. Results: There was dramatic improvement in pain score after first and second injection of platelet rich plasma. There was 33.33% improvement at 3rd week, 54.32% at 6th week and 71.60% at 6th month. Conclusion: Platelet rich plasma is safe, cost effective and minimally invasive in reducing pain scores and functional limitation in knee osteoarthritis.
Physical-physiological quality and early performance of sorghum plants under different boron doses via seed

Boron (B) deficiency is commonly found in tropical crops, among which sorghum stands out for its limited yield under B shortage. However, as a micronutrient, the range between its adequate and toxic levels is narrow, thus fertilization should be cautiously applied. Our goal was to evaluate the physiological and physical quality, as well as early performance, of grain sorghum seedlings under different B doses applied via seeds. Treatments consisted of six doses of boric acid (0.0, 2.5, 5.0, 7.5, 10.0, and 12.5 g Kg-1 seeds) plus a control (uncoated seeds). Boron doses were applied to seeds by coating, in which dolomitic limestone and glue were used for pellet formation. Coated seeds were assessed for physical, physiological, and nutritional characteristics in the laboratory, and early seedling performance was evaluated in a greenhouse. Seed tests were carried out in a fully randomized design, while seedling evaluations were carried out in a randomized block design. Boron application to seeds had no significant effect on seed physical characteristics, despite increases in coated seed sizes. Boron doses had a negative effect on the length and dry mass accumulation of both plant shoot and root. Seed-applied B accumulated mainly in roots and significantly interfered with Ca and Mg accumulations in sorghum seeds, shoots, and roots.
Comparison between deep learning and fully connected neural network in performance prediction of power cycles: Taking supercritical CO2 Brayton cycle as an example

AI is becoming increasingly important in promoting the energy revolution of carbon‐neutral to achieve sustainable development. Induced by the large implementation of renewable energy, the more complexities and uncertainties in the future carbon‐neutral energy systems make their designs hard accessible to the conventional methods, so machine learning (ML) especially the neural network becomes under focus. Here, we design a deep learning architecture based on convolutional neural networks (DL‐CNN) known for its powerful predicting ability, and first utilize it in a case study of performance prediction of supercritical CO2 Brayton cycle. The design paradigm of DL‐CNN architecture for performance prediction of power cycle is proposed. We also summarize the commonly used fully connected neural network (FC‐NN) in related studies of power cycle design. Through systematically comparing the prediction performance of DL‐CNN and FC‐NN, their respective advantages and application scenarios in energy system design are discussed. In addition, a multiobjective design approach based on DL‐CNN combined with random search is proposed and proved to be feasible by comparing with genetic algorithm. The results show that our proposed DL‐CNN model is much more competitive than FC‐NN model when the training data is sufficient and the prediction condition is complex, in which the prediction accuracy can achieve 99.6%. In the future, our deep learning model may help solve the complex design problems of hybrid carbon‐neutral energy systems.
In Vitro Antimicrobial and Antibiofilm Activity of S-(-)-Limonene and R-(+)-Limonene against Fish Bacteria

Brazilian fish farming goes together with the emergence of numerous bacterial diseases, with Aeromonas hydrophila being the main bacterial pathogen. As a consequence, antimicrobials are excessively used. Considering that antimicrobials are relatively stable and nonbiodegradable, medicinal plants and their phytochemicals have been used as alternative treatments of bacteriosis in fish farming. Limonene is a monoterpene available in two enantiomers: R-(+)-limonene and S-(-)-limonene. This study analyzed the antibacterial activity of the phytochemicals S-(-)-limonene and R-(+)-limonene against some bacteria isolated from silver catfish (Rhamdia quelen). Furthermore, by means of spectrophotometry and atomic force microscopy, we also investigated the combination therapy of phytochemicals with antimicrobials and their activity in terms of inhibiting biofilm formation. Six clinical isolates and a standard strain were selected for antimicrobial activity testing. Biofilm formation was tested in 96-well plates and nylon cubes. The most sensitive of the strains tested was the A. hydrophila strain (MF 372510). S-(-)-limonene and R-(+)-limonene had high minimum inhibitory concentrations; however, they strongly inhibited A. hydrophila biofilm formation. R-(+)-limonene and S-(-)-limonene had an additive effect when combined with florfenicol and an antagonistic effect with oxytetracycline. In general, the phytochemicals tested showed strong antibiofilm activity against A. hydrophila, and when in combination therapy with florfenicol, they showed an additive effect against the treatment of A. hydrophila.
Bridging the GAP Between Outputs: Domain Adaptation for Lung Cancer IHC Segmentation

Lung cancer is globally the most common cause of cancer-related death and immunotherapy is one of the most promising treatments for various cancers. Since there are some differences in characteristics between lung cancer immunohistochemistry (IHC) image datasets collected by different hospitals or laboratories, and relabeling new datasets is time-consuming and laborious, we thus propose an effective domain adaptation framework for IHC image segmentation. In our method, maximum mean discrepancy of segmentation outputs is extracted and combined with generative adversarial networks to bridge the gap between the outputs from source and target domains. To make the networks more instructive, pseudo target labels generated by the source model are employed. Meanwhile, adaptive hyperparameter tuning offers much flexibility for the training process. The proposed model has been evaluated over our own datasets and other public medical benchmarks, and a comparison with other recent works demonstrates its effectiveness and robustness. Furthermore, the model is exerted to extract quantitative and spatial features of conventional lung cancer IHC images, which can be utilized for overall survival (OS) and relapse-free survival (RFS) predictions.
Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior

Word embedding approaches represent data sequences to handle their contextual meaning in the NLP tasks. Nowadays, there is an emerging need to understand the user behavior patterns over navigational clickstream data. However, representing the URL data sequences utilizing existing embedding approaches to cluster users' behavior with unsupervised machine learning tasks is a challenging task. This study introduces the Patter2Vec embedding approach using a representation vector to construct contextual, precise, and interpretable clusters over the hidden and popular navigational patterns. To test the usability of the proposed representation in clustering tasks, we conduct an experimental study, which indicates that Pattern2Vec outperforms existing embedding approaches.
Principle and Topology Derivation of Integrated Cascade Bidirectional Converters for Centralized Charge Equalization Systems

This article presents a systematic topology derivation method for developing integrated cascaded bidirectional (ICB) converters, and the centralized charge equalization system can be optimized with the proposed family of converters. On the basis of the traditional centralized system, the proposed ICB converters are generated by integrating the polarity switches and different bidirectional dc–dc converters. Compared with existing methods, the ICB converter-based centralized equalization system obtains the advantages of high voltage gain, high efficiency, high reliability, and low cost, which is more attractive in engineering applications. Since different topology structures have various performances, a half-bridge-based ICB converter with a bidirectional switch is analyzed in detail as an example to validate the feasibility and effectiveness of the proposed topology family. The operation principles, design guidelines, and soft-switching conditions are discussed in more depth. Finally, the experimental results and comparative analysis are conducted to verify the advantages of the proposed ICB converter and prove the evident performance improvement compared with conventional methods.
Bioavailability of mesalazine from two coated formulation tablets

HPLC methodology with a fluorescent detector is suitable for bioavailability studies of investigated generic tablets Mesalazine 250 mg (Jelfa, Poland) versus standard Salofalk tablets (Dr. Falk, Germany). The investigations were completed in ten healthy subjects in a double way crossover design. Only one bioavailability parameter ñ time for maximum mesalazine plasma concentration (tmax ) and overall elimination rate constants are significantly greater for the above standard tablets. The parameters like maximum plasma drug concentration (Cmax), lag time for absorption (Tlag), biological half-life time (t1/2) are also more favorable for the standard, but these values are not significantly different.
An N4-Tetradentate Hydrazone Ligand That Binds in a Neutral, Mono- and Bisdeprotonated Form to Iron(II) and Zinc(II) Metal Ions

The coordination chemistry of butane-2,3-dione bis (2′-pyridylhydrazone) towards the divalent first-row transition metals zinc and iron has been explored. Depending upon the conditions, the ligand in the six complexes was found to be either neutral, mono, or doubly deprotonated. The zinc(II) and iron(II) complexes were fully characterized by elemental analysis, mass spectrometry, and X-ray diffraction methods.
Effect of fuel spatial resolution on predictive wildfire models

Computational models of wildfires are necessary for operational prediction and risk assessment. These models require accurate spatial fuel data and remote sensing techniques have ability to provide high spatial resolution raster data for landscapes. We modelled a series of fires to understand and quantify the impact of the spatial resolution of fuel data on the behaviour of fire predictive model. Airborne laser scanning data was used to derive canopy height models and percentage cover grids at spatial resolutions ranging from 2 m to 50 m for Mallee heath fire spread model. The shape, unburnt area within the fire extent and extent of fire areas were compared over time. These model outputs were strongly affected by the spatial resolution of input data when the length scale of the fuel data is smaller than connectivity length scale of the fuel. At higher spatial resolutions breaks in the fuel were well resolved often resulting in a significant reduction in the predicted size of the fire. Our findings provide information for practitioners for wildfire modelling where local features may be important, such as operational predictions incorporating fire and fuel breaks, and risk modelling of peri-urban edges or assessment of potential fuel reduction mitigations.
A Corpus Analysis of Online Education Tweets During Covid-19

The current study investigates the writing or register style of 0.2 million tweets on the topic of online education during Covid-19 in July and August 2020. This pandemic delivered a massive jolt not only to the global economy, health, and business, but also to our educational system. According to the most recent UNESCO report, 1.3 billion students worldwide have been unable to attend schools or universities since March 2020. (McCarthy, 2020). As a result, online education has been regarded as grasping at straws. The study intends to highlight frequently used language structure and frequently used lexis from individual users' lexicons via tweets. Furthermore, a deliberately used stock of vocabulary items may be useful in scrutinizing and measuring learners' attitudes toward this pandemic around the world. Style was defined by Biber and Conrad (2009) as "frequent language forms used by speakers" and "an art of effective and forceful communication. " As a result, the most frequent words/lemmas were extracted from the entire corpus first, and then a reduced vocabulary with statistical measures was rigorously studied. Furthermore, some other morpho-syntactic linguistic/grammatical features were investigated using the MAT tagger, a statistical computational tool for determining the text type.
A decentralized microgrid considering blockchain adoption and credit risk

Abstract Blockchain and smart contract technology is advancing rapidly and, as many companies and industries are stepping up to adopt the technology, it is a worthy subject of research. This study presents a decentralized microgrid model that considers blockchain and smart contract technology. We consider a microgrid consisting of two players: a power distribution company (DC) and an electricity prosumer. The cost-benefit analysis of utilising blockchain and smart contract technology is assessed through an evaluation model for the two players. Further, to encourage the prosumer to generate renewable energy, a credit period that allows the prosumer to defer payments is provided by outside banks. A game-theoretical method is applied to analyse the decision-making by the two players, seeking to maximise their own profits. The DC acts as a leader who determines the electricity price and blockchain technology investment level, and the prosumer is a follower who determines how much renewable energy should be generated. The results show that the adoption of blockchain and smart contract technology benefits both the DC and prosumer in a decentralized microgrid. Further, several examples are provided to illustrate the model and obtain managerial insights.
A redescription of Stenopora and the type species Stenopora tasmaniensis Lonsdale, 1844 (Trepostomata, Bryozoa)

Abstract. Type material for Stenopora tasmaniensis Lonsdale, 1844 was lost in the late nineteenth century, and subsequent descriptions of the genus have been based on material incorrectly assigned to the type species. A neotype is erected for S. tasmaniensis from the original type locality and the genus redescribed. The genus exhibits ramose, frondescent, encrusting, and massive colony morphologies, diaphragms are absent, and acanthostyles of a single size surround each aperture. This single size of acanthostyles aligns with the original type species description; however, it differs from the subsequently accepted genus description and may result in existing species being removed from the genus. Analysis of zooecial characters of a single colony exhibiting both frondescent and ramose morphologies reveals statistically significant differences between subsampled sections, despite being from the same colony. Differences relate to details of zooecial parameters and are not controlled by colony morphology. This variation within a single colony confirms the importance of using qualitative characters alongside quantitative measures in defining Paleozoic bryozoan species.
Explainable Prediction Of Renal Cell Carcinoma From Contrast-Enhanced CT Images Using Deep Convolutional Transfer Learning And The Shapley Additive Explanations Approach

The prediction of renal cell carcinoma (RCC) is an important cancer screening step. Existing state-of-the-art methods focus on developing machine/deep learning networks with one or more optimization strategies for higher identification accuracies. Such developments ignore the interpretability and clinical utility of models, which are still quite opaque to clinicians. This paper introduces deep convolutional transfer learning and SHapley Additive exPlanations (SHAP) to the classification model and proposes an explainable RCC prediction model. The model evaluates the risks and benefits using decision curve analysis (DCA). Specifically, multiscale feature extraction and compensation are proposed to enrich the representations. By combining the high-importance features in a parallel manner, the models’ performances are gradually enhanced. Our model achieves an accuracy of 73.87% and an area under the curve (AUC) of 0.8030 on the Hunan Cancer Hospital dataset. To demonstrate the generalizability, our model yields an accuracy of 99.81% on the public COIL-100 dataset.
Design and Optimization of Smart Factory Control System Based on Digital Twin System Model

Aiming at the problems of irrational allocation of resources, low efficiency caused by unbalanced production line layout, and slow production line upgrade of the smart factory, this paper builds a real physical smart factory platform through the optimal control strategy and uses the GRAFCET algorithm to optimize the logistics scheduling during the actual system operation. The genetic algorithm is used to optimize the layout effect of the production line; the digital twin technology is used to provide predictive analysis technical support for the upgrading and reengineering of the production line. Through the analysis and comparison of the production capacity and equipment utilization of the physical smart factory and the virtual smart factory processing scheme, practice shows that the design of the digital twin system can effectively improve the effect and accuracy of the lean production method in the production process reorganization. Quantitative analysis of manufacturing industry provides powerful theoretical and technical support.
Rare‐Earth Permanent Magnet SmCo5 for Chiral Interfacial Spin‐Orbitronics

Interfacially asymmetric magnetic multilayers made of heavy metal/ferromagnet have attracted considerable attention in the spintronics community for accommodating spin‐orbit torques (SOTs) and meanwhile for hosting chiral spin textures. In these multilayers, the accompanied interfacial Dzyaloshinskii–Moriya interaction (iDMI) permits the formation of Néel‐type spin textures. While significant progresses have been made in Co, CoFeB, Co2FeAl, CoFeGd based multilayers, it would be intriguing to identify new magnetic multilayers that could enable spin‐torque controllability and meanwhile host nanoscale skyrmions. In this report, first, thin films made of permanent magnet SmCo5 with perpendicular magnetic anisotropy are synthesized, in which the deterministic SOT switching, enabled by the spin Hall effect, in Pt/SmCo5/Ta trilayer is demonstrated. Further, the stabilization of room‐temperature skyrmions with diameters ≈100 nm in [Pt/SmCo5/Ta]15, together with a skyrmionium‐like spin texture in [Pt/SmCo5/Ir]15 multilayers is shown. Based on the material specific parameters, micromagnetic simulations are also carried out. The results confirm the presence of chiral spin textures in this new material family. Through interfacial engineering, the results thus demonstrate that rare earth permanent magnets could be a new platform for studying interfacial chiral spintronics.