Discrete Hamstring: Quadriceps Strength Ratios Do Not Represent Angle-Specific Ratios in Premier League Soccer Players.

ABSTRACT: Lunn, DE, Nicholson, G, Cooke, M, Crespo, R, Robinson, T, Price, RJ, and Walker, J. Discrete hamstring: quadriceps strength ratios do not represent angle-specific ratios in Premier League soccer players. J Strength Cond Res 37(12): 2417-2422, 2023-This study compared angle-specific hamstring:quadriceps (H:Q) ratios with their discrete counterparts during strength testing in professional male soccer players. Twenty-seven professional English Premier League soccer players were recruited for this study (age: 22 ± 4 years; stature: 1.81 ± 0.08 m; body mass: 74.7 ± 6.5 kg). Isokinetic testing of the knee flexors and extensors was conducted concentrically at two angular velocities (60° and 240°·s -1 ) and eccentrically (for the knee flexors only) at 30°·s -1 . Conventional H:Q ratio was calculated as the ratio between peak joint moment in the flexors and extensors at 60°·s -1 . Functional H:Q ratio was calculated as the peak joint moment in the flexors during the eccentric condition and the extensors at 240°·s -1 . Discrete conventional and functional H:Q ratios were 0.56 ± 0.06 and 1.28 ± 0.22, respectively. The residual differences between discrete values and angle-specific residual values were 13.60 ± 6.56% when normalized to the magnitude of the discrete value. For the functional ratios, the normalized residual was 21.72 ± 5.61%. Therefore, neither discrete ratio was representative of angle-specific ratios, although the conventional ratio had lower error overall. Therefore, practitioners should consider H:Q ratio throughout the full isokinetic range of motion, not just the discrete ratio calculated from peak joint moments, when designing and implementing training programs or monitoring injury risk, recovery from injury, and readiness to return to play.

Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink.

In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I1+I2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.

Coronavirus fake news detection via MedOSINT check in health care official bulletins with CBR explanation: The way to find the real information source through OSINT, the verifier tool for official journals.

This research aims to design and prototype a tool to perform intelligence on open sources (OSINT), specifically on official medical bulletins for the detection of false news. MedOSINT is a modular tool that can be adapted to process information from different medical official bulletins. From the processed information, intelligence is generated for decision making, validating the veracity of the COVID-19 news. The tool is compared with other options and it is verified that MedOSINT outperforms the current options when analyzing official bulletins. Moreover, it is complemented with an expert explanation provided by a Case-Based Reasoning (CBR) system. This is proved to be an ideal complement because it can find explanatory cases for an explanation-by-example justification.

A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak.

Measuring the spread of disease during a pandemic is critically important for accurately and promptly applying various lockdown strategies, so to prevent the collapse of the medical system. The latest pandemic of COVID-19 that hits the world death tolls and economy loss very hard, is more complex and contagious than its precedent diseases. The complexity comes mostly from the emergence of asymptomatic patients and relapse of the recovered patients which were not commonly seen during SARS outbreaks. These new characteristics pertaining to COVID-19 were only discovered lately, adding a level of uncertainty to the traditional SEIR models. The contribution of this paper is that for the COVID-19 epidemic, which is infectious in both the incubation period and the onset period, we use neural networks to learn from the actual data of the epidemic to obtain optimal parameters, thereby establishing a nonlinear, self-adaptive dynamic coefficient infectious disease prediction model. On the basis of prediction, we considered control measures and simulated the effects of different control measures and different strengths of the control measures. The epidemic control is predicted as a continuous change process, and the epidemic development and control are integrated to simulate and forecast. Decision-making departments make optimal choices. The improved model is applied to simulate the COVID-19 epidemic in the United States, and by comparing the prediction results with the traditional SEIR model, SEAIRD model and adaptive SEAIRD model, it is found that the adaptive SEAIRD model's prediction results of the U.S. COVID-19 epidemic data are in good agreement with the actual epidemic curve. For example, from the prediction effect of these 3 different models on accumulative confirmed cases, in terms of goodness of fit, adaptive SEAIRD model (0.99997) ≈ SEAIRD model (0.98548) > Classical SEIR model (0.66837); in terms of error value: adaptive SEAIRD model (198.6563) < < SEAIRD model(4739.8577) < < Classical SEIR model (22,652.796); The objective of this contribution is mainly on extending the current spread prediction model. It incorporates extra compartments accounting for the new features of COVID-19, and fine-tunes the new model with neural network, in a bid of achieving a higher level of prediction accuracy. Based on the SEIR model of disease transmission, an adaptive model called SEAIRD with internal source and isolation intervention is proposed. It simulates the effects of the changing behaviour of the SARS-CoV-2 in U.S. Neural network is applied to achieve a better fit in SEAIRD. Unlike the SEIR model, the adaptive SEAIRD model embraces multi-group dynamics which lead to different evolutionary trends during the epidemic. Through the risk assessment indicators of the adaptive SEAIRD model, it is convenient to measure the severity of the epidemic situation for consideration of different preventive measures. Future scenarios are projected from the trends of various indicators by running the adaptive SEAIRD model.

Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction.

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.

Outbreak of francisellosis (Francisella noatunensis subsp. orientalis) in cultured neon jewel cichlids Hemichromis bimaculatus from Morelos, Mexico.

Francisellosis is a disease caused by different species of the bacterial genus Francisella and has been diagnosed in a wide variety of animals, including fish. Francisellosis in fish is characterized by the development of non-specific clinical signs as well as the presence of numerous granulomas in several organs (mainly spleen and kidney). Ten neon jewel cichlids Hemichromis bimaculatus were submitted for diagnosis from a farm located in Morelos, Mexico. Gross examination, wet preparations, cytology, histopathology and PCR were performed. Affected fish showed lethargy, erratic swimming, imbalance and gasping. At the post mortem examination, multiple granulomas were observed in the kidney and spleen. Microscopically, granulomatous inflammation was observed in several organs. Species-specific PCR assay using DNA from the affected tissues of H. bimaculatus as a template demonstrated the presence of F. noatunensis subsp. orientalis (Fno) by amplifying a hypothetical protein gene of the Fno species. The end diagnosis of francisellosis is important for Mexican ornamental aquaculture, since it is necessary to implement measures for treatment, prevention, control and diagnosis. This is the first report of francisellosis in the neon jewel cichlid.

Advanced Sensors Technology in Education.

The topic presented will show how different kinds of sensors can help to improve our skills in learning environments. When we open the mind and let it take the control to be creative, we can think how a martial art would be improved with registered sensors, or how a person may dance with machines to improve their technique, or how you may improve your soccer kick for a penalties round. The use of sensors seems easy to imagine in these examples, but their use is not limited to these types of learning environments. Using depth cameras to detect patterns in oral presentations, or improving the assessment of agility through low cost-sensors with multimodal learning analytics, or using computing devices as sensors to measure their impact on primary and secondary students' performances are the focus of this study as well. We hope readers will find original ideas that allow them to improve and advance in their own researches.

A Clustering WSN Routing Protocol Based on k-d Tree Algorithm.

Clustering in wireless sensor networks has been widely discussed in the literature as a strategy to reduce power consumption. However, aspects such as cluster formation and cluster head (CH) node assignment strategies have a significant impact on quality of service, as energy savings imply restrictions in application usage and data traffic within the network. Regarding the first aspect, this article proposes a hierarchical routing protocol based on the k-d tree algorithm, taking a partition data structure of the space to organize nodes into clusters. For the second aspect, we propose a reactive mechanism for the formation of CH nodes, with the purpose of improving delay, jitter, and throughput, in contrast with the low-energy adaptive clustering hierarchy/hierarchy-centralized protocol and validating the results through simulation.

Web Page Design Recommendations for People with Down Syndrome Based on Users' Experiences.

At present, there is a high number of people with Down syndrome interested and trained to be an active part of society. According to the data extracted by our surveys we know that only 6% of the population with Down syndrome feels isolated in daily activities. However, when the activity requires the use of a computer, the percentage of people who feel isolated increases to 18%. This means that there are obvious website accessibility barriers that make it difficult for users with Down syndrome. To solve this problem, it is considered necessary to make an exhaustive study about Down syndrome. We know that the trisomy of chromosome 21 causes a series of symptoms that directly affect ones Internet browsing capabilities. For example, speech disturbances make communication and speed difficult. This guide is based on a neurological study of Down syndrome. Alterations in listening make understanding audio, retention of audio concepts and speed difficult. The alterations in the physiognomy of movement make it difficult for them to act quickly. Many of these alterations are caused by cognitive disability. After assessing the needs, the benefits of Web Content Accessibility Guidelines 2.0 (WCAG 2.0), and the existing usability guidelines are analyzed and those that may be useful for this profile are extracted. User tests are carried out through two websites developed specifically for this study with the aim of demonstrating the level of effectiveness of each of the planned guidelines. Considering the neurological characteristics of this intellectual disability, research is developed that seeks to extract a list of useful accessibility and usability guidelines for web developers.

Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems.

Planning tasks performed by a robotic agent require previous access to a map of the environment and the position where the agent is located. This creates a problem when the agent is placed in a new environment. To solve it, the RA must execute the task known as Simultaneous Location and Mapping (SLAM) which locates the agent in the new environment while generating the map at the same time, geometrically or topologically. One of the big problems in SLAM is the amount of memory required for the RA to store the details of the environment map. In addition, environment data capture needs a robust processing unit to handle data representation, which in turn is reflected in a bigger RA unit with higher energy use and production costs. This article presents a design for a system capable of a decentralized implementation of SLAM that is based on the use of a system comprised of wireless agents capable of storing and distributing the map as it is being generated by the RA. The proposed system was validated in an environment with a surface area of 25 m 2 , in which it was capable of generating the topological map online, and without relying on external units connected to the system.

PULMONARY LESIONS CAUSED BY THE LUNGWORM (DIDELPHOSTRONGYLUS HAYESI) IN THE OPOSSUM (DIDELPHIS VIRGINIANA) IN COLIMA, MEXICO.

Didelphostrongylus hayesi is an important and prevalent pulmonary nematode in the opossum ( Didelphis virginiana ). An in-depth description of the pulmonary lesions caused by this nematode is lacking. The objective of this investigation was to make a detailed account of the gross, subgross, and microscopic changes that occur in the lungs of opossums naturally infected with D. hayesi. Forty-four opossums trapped in the state of Colima, Mexico, were euthanized by an overdose of barbiturates. Following a postmortem examination, the right lung was cut from the main bronchi and placed in a Petri dish containing a saline solution for the detection and identification of live parasites. The left lung was fixed and cut serially for subgross microscopic examination and sections of lung were cut and stained for histopathologic examination. The most remarkable gross change in parasitized lungs was a poorly collapsible pulmonary parenchyma and mild emphysema. The right lung tested positive for lungworms on gross examination in 20/44, and 11/44 (25%) of the left lungs showed tan nodules on the pleural surface. Microscopically, the bronchi of 20/44 animals harbored adult and larval stages of D. hayesi (left lung), the same 20 opossums from which nematodes were grossly evident at necropsy (right lung). Adults and larvae were present in bronchi, bronchioles, and alveoli mixed with desquamated cells and many eosinophils, and to a lesser extent neutrophils, alveolar macrophages, and giant cells. Bronchi and bronchioles exhibited goblet cell hyperplasia and metaplasia respectively, and infiltration of lymphoplasmacytic cells in the interstitium and lamina propria. The tan nodules consisted of focal alveolar endogenous lipidosis, which likely resulted from parasitic airway obstruction. The lungs of 3/20 parasitized opossums also showed alveolar bronchiolization (Lambertosis). The absence of Eucoleus aerophilus or bacterial pneumonia incriminates D. hayesi as the putative cause of pulmonary lesions in these opossums.

Road traffic noise prediction model based on artificial neural networks.

This paper proposes a model based on machine learning for the prediction of road traffic noise for the city of Bogota-Colombia. The input variables of the model were: vehicle capacity, speed, type of flow and number of lanes. The input data were obtained through measurement campaigns in which audio and video recordings were made. The audio recordings, made with a measuring microphone calibrated at a height of 4 meters, made it possible to calculate the noise levels through software processing. On the other hand, by processing the video data, the capacity, and speed of the vehicle were obtained. This process was carried out by means of a classifier trained with images of vehicles taken in the field and free databases. In order to determine the machine learning algorithm to be used, five models were compared, which were configured with their respective hyperparameters obtained through mesh search. The results showed that the Multilayer Perceptron (MLP) regression had the best fit with an MAE of 0.86 dBA for the test data. Finally, the proposed MLP regressor was compared with some classical statistical models used for road traffic noise prediction. The main conclusion is that the MLP regressor obtained the best error and fit indicators with respect to traditional statistical models.

Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data.

Big data is a combination of large structured, semistructured, and unstructured data collected from various sources that must be processed before using them in many analytical applications. Anomalies or inconsistencies in big data refer to the occurrences of some data that are in some way unusual and do not fit the general patterns. It is considered one of the major problems of big data. Data trust method (DTM) is a technique used to identify and replace anomaly or untrustworthy data using the interpolation method. This article discusses the DTM used for univariate time series (UTS) forecasting algorithms for big data, which is considered the preprocessing approach by using a neural network (NN) model. In this work, DTM is the combination of statistical-based untrustworthy data detection method and statistical-based untrustworthy data replacement method, and it is used to improve the forecast quality of UTS. In this study, an enhanced NN model has been proposed for big data that incorporates DTMs with the NN-based UTS forecasting model. The coefficient variance root mean squared error is utilized as the main characteristic indicator in the proposed work to choose the best UTS data for model development. The results show the effectiveness of the proposed method as it can improve the prediction process by determining and replacing the untrustworthy big data.

Rabbit Fibroma Virus infection in domestic rabbits (Oryctolagus cuniculus) in Mexico.

Rabbit Fibroma is a Leporipoxviral disease and is considered the third most common cutaneous neoplasm in pet rabbits. Two domestic rabbits (Oryctolagus cuniculus) were submitted to the veterinary clinic due to the presence of a nodule on the lip. Histologically, epithelial cells of the epidermis and hair follicles showed mild to moderate ballooning degeneration, spongiosis, and several eosinophilic intracytoplasmic inclusion bodies. The dermis was expanded by atypical spindle cells that also showed eosinophilic intracytoplasmic inclusion bodies. The tissues were evaluated by using transmission electron microscopy. In both cases, keratinocytes exhibit several electron dense and pleomorphic intracytoplasmic viral particles consistent with Poxviruses. To our knowledge, this is the first case report of Rabbit Fibroma Virus infection in Domestic Rabbits in Mexico.

Audio-Visual Automatic Speech Recognition Towards Education for Disabilities.

Education is a fundamental right that enriches everyone's life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition.

Grizzlies and gazelles: Duty factor is an effective measure for categorizing running style in English Premier League soccer players.

English Premier League soccer players run at multiple speeds throughout a game. The aim of this study was to assess how well the duty factor, a dimensionless ratio based on temporal variables, described running styles in professional soccer players. A total of 25 players ran on an instrumented treadmill at 12, 16, and 20 km/h. Spatiotemporal and ground reaction force data were recorded for 30 s at each speed; video data (500 Hz) were collected to determine footstrike patterns. In addition to correlation analysis amongst the 25 players, two groups (both N = 9) of high and low duty factors were compared. The duty factor was negatively correlated with peak vertical force, center of mass (CM) vertical displacement, and leg stiffness (k leg) at all speeds (r ≥ -0.51, p ≤ 0.009). The low duty factor group had shorter contact times, longer flight times, higher peak vertical forces, greater CM vertical displacement, and higher k leg (p < 0.01). Among the high DF group players, eight were rearfoot strikers at all speeds, compared with three in the low group. The duty factor is an effective measure for categorizing soccer players as being on a continuum from terrestrial (high duty factor) to aerial (low duty factor) running styles, which we metaphorically refer to as "grizzlies" and "gazelles," respectively. Because the duty factor distinguishes running style, there are implications for the training regimens of grizzlies and gazelles in soccer, and exercises to improve performance should be developed based on the biomechanical advantages of each spontaneous running style.

Inter-limb asymmetries and kicking limb preference in English premier league soccer players.

The aims of this study were: (1) to quantify interlimb asymmetries in EPL soccer players in the context of kicking limb preference and (2) to establish the relationship between interlimb asymmetries and measures of physical performance. Twenty-two players (age: 21.8 ± 4.4 years) from an EPL club performed a running gait assessment (20 km/h) and unilateral countermovement jumps, a CoD assessment (modified 505 test), and an isokinetic knee extension/flexion protocol using each leg. Asymmetries were quantified using the percentage difference method and Pearson's correlations were used to quantify the association between variables. Players displayed the greatest level of asymmetry in isokinetic strength measures (5.9-12.7%) and lower levels of asymmetry in gait (1.6-7.7%), jump (0.9-7.0%) and CoD (1.9-3.5%) assessments. The influence of the preferred kicking limb was most evident in the isokinetic assessment with the players showing dominance in the preferred limb for knee flexor strength and in the non-preferred limb for knee extensor strength. These manifested in the asymmetry values calculated for the hamstring:quadricep (H:Q) ratios at 60°/s (8.80 ± 7.82%) and 240°/s (11.22 ± 7.04%) and in the functional H:Q ratio (12.67 ± 8.25%). The asymmetry values for peak extensor moment at 240°/s showed a significant correlation (ρ = -0.55, p = 0.034) with 10 m time in the CoD assessment. These findings provide benchmark asymmetry data for soccer practitioners and reveal that kicking limb preferences may bring about interlimb differences in the H:Q ratio which raises important considerations in the design of testing batteries and injury reduction interventions.

Internet of things-based cloud computing platform for analyzing the physical health condition.

BACKGROUND: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient's healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health. OBJECTIVE: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients. METHOD: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient's eye movement. The collected data are used in the cloud database to evaluate the patient's health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers. RESULTS: The experimental results show that patient health monitoring is a reliable way to improve health effectively.

Impact of scaling up SARS-CoV-2 vaccination on COVID-19 hospitalizations in Spain.

BACKGROUND: The advent of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines has been associated with a significant decline in coronavirus disease 2019 (COVID-19) hospitalizations and deaths. However, little is known about the benefits experienced by different population groups and/or using distinct vaccines. METHODS: The Spanish public registry was analyzed to examine associations between weekly vaccination scale-up and the incidence of COVID-19 hospitalizations by age, sex, and vaccine modality. The study period extended from January 2020 to June 2021. RESULTS: A total of 363 960 COVID-19 hospitalizations were recorded in Spain during the study period, with three peaks in March 2020, November 2020, and January 2021. The incidence of COVID-19 hospitalizations per 100 000 population increased exponentially with age, on average 71.5% for each decade older. Overall, individuals older than 60 years of age accounted for 65% of all COVID-19 hospitalizations. The speedy vaccination rollout since the end of 2020, with prioritization of the elderly groups, resulted in a rapid fall in COVID-19 hospitalizations starting in February 2021. The benefit was already noticed 3-4 weeks after the first dose, regardless of the vaccine modality. CONCLUSIONS: COVID-19 hospitalizations increased exponentially with age in all three peaks of SARS-CoV-2 infection in Spain. Early mass vaccination of people over 60 years of age prevented a fourth wave of COVID-19 hospitalizations during the spring of 2021.