Total Number Research Articles

The Role of Biosensors in Detection of SARS-CoV-2: State-of-the-Art and Future Prospects

Abstract: The world is fighting a pandemic so grave that perhaps it has never been witnessed before; COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2). As of August 31st, 2022, the WHO declared the total number of confirmed cases was 599,825,400, with 6,469,458 confirmed deaths from 223 countries under the scourge of this deadly virus. The SARS-CoV-2 is a β-coronavirus, which is an enveloped non-segmented positive- sense RNA virus. It is a close relative of the SARS and MERS viruses and has probably entered humans through bats. Human-to-human transmission is very rapid. People in contact with the patient or even the carriers became infected, leading to a widespread chain of contamination. We are presenting a mini-review on the role of biosensors in detecting SARS-CoV-2. Biosensors have been used for a very long time for viral detection and can be utilized for the prompt detection of the novel coronavirus. This article aims to provide a mini-review on the application of biosensors for the detection of the novel coronavirus with a focus on costeffective paper-based sensors, nanobiosensors, Field effect transistors (FETs), and lab-on-chip integrated platforms. Background: Biosensors have played a crucial role in viral detection for a long time. Objective: To present a comprehensive review of the biosensor application in SARS-Cov-2. Method: We have presented state-of-the-art work in the biosensors field for SARS-Cov-2 detection. Results: The biosensors presented here provide an innovative approach to detecting SARS-Cov- 2 infections early. Conclusion: Biosensors have tremendous potential in accurately detecting viral infections in pandemics requiring rapid screening.

Byzantine-Resilient Second-Order Consensus in Networked Systems.

This article studies the second-order consensus problem in networked systems containing the so-called Byzantine misbehaving nodes when only an upper bound on either the local or the total number of misbehaving nodes is known. The existing results on this subject are limited to malicious/faulty model of misbehavior. Moreover, existing results consider consensus among normal nodes in only one of the two states, with the other state converging to either zero or a predefined value. In this article, a distributed control algorithm capable of withstanding both locally bounded and totally bounded Byzantine misbehavior is proposed. When employing the proposed algorithm, the normal nodes use a combination of the two relative state values obtained from their neighboring nodes to decide which neighbors should be ignored. By introducing an underlying virtual network, conditions on the robustness of the communication network topology for consensus on both states are established. Numerical simulation results are presented to illustrate the effectiveness of the proposed control algorithm.

Applications of Silver Nanoparticles in Patent Research.

Silver nanoparticles (AgNPs) have been widely applied in research and industrial fields, finding applications in nanomedicine, drug delivery, biomedical devices, electronics, the energy sector, and environmental protection. Patents provide information on the industrial viability of product technologies, and the number of patent documents provides an estimate of the evolution of a specific technological field. The present work aims to describe the current trends in AgNPs patent applications. In addition, a retrospective study of published patents in Brazil is presented. Analyses of AgNPs-related patents were conducted using the free platform for patent search Lens® in 2010-2019 and articles published in same period using the Scholar® base. The patent applications and their evolution over time, major depositors and holders, and the main technological areas associated with AgNP applications have been described. China and United States are the major patent applicants for nanotechnologies. The worldwide distribution of publications of journal articles shows that China, India, and the United States are the leading countries in the total number of articles published, in that order. Our study of patent applications and published articles confirmed the growing global increase in new technologies involving NPs and AgNPs, particularly in the biotechnology area, in the fields of medicine and agriculture.

Bibliometric Analysis of the Research Status and Global Trends in Behavioral and Psychological Symptoms of Dementia in Alzheimer's Disease from 2002 to 2022.

Several reviews on behavioral and psychological symptoms (BPSDs) in patients with Alzheimer's disease (AD) have summarized the current state of this field, but global trends are unclear. This study utilized CiteSpace to provide a global overview of the current state of research on AD and its BPSDs and to predict future research trends in the field. Data were retrieved from the Web of Science Core Collection. Bibliometric and cooccurrence analyses were performed using CiteSpace software. In total, 787 valid publications were included in the analysis. Publications on AD and BPSD have shown an increasing trend since 2002. The United States and the University of Toronto were the countries and institutions with the highest total number of publications, respectively. Japan and China were the second and third most influential in the field. Clive Ballard was the top author in terms of the number of publications. Journal of Alzheimer's Disease had the highest number of publications on this topic. Co-occurrence analysis showed that AD, behavioral symptoms, cognitive impairment, and early markers are hot topics in this area. Non-drug management of BPSDs, pharmacological treatment, and physiotherapy will be a hot topic in this field in the future. Our study visualized the relevant articles over the past 21 years to detect global hotspots and trends. Our findings may help researchers to identify research hotspots in this field and will help in the selection of appropriate research topics, while possibly leading to cross-regional cooperation.

Accuracy of death risk prediction models for acute coronary syndrome patients: a systematic review and meta-analysis.

This study systematically evaluates the accuracy of several death risk prediction models for patients with acute coronary syndrome (ACS) through evidence-based methods. We identify the most accurate and effective ACS death risk prediction model and provide an evidence-based basis for clinical healthcare personnel to evaluate their choice of death risk prediction model for ACS patients. An evidence-based approach was used to study the current death risk prediction model for ACS. First, a literature search was carried out using computer-based and manual searching. The literature databases searched include Cochrane Library, MEDLINE, EMBASE, PubMed, Web of Science, WanFang Data, CNKI, VPCS, and SinoMed. The search period was limited to 2009 to 2022. Screening, quality evaluation and data extraction were carried out for the included articles. The PROBAST was used to conduct a migration risk assessment. RevMan 5.3 and Meta-DiSc 1.4 were used in combination to determine the model effect sizes. A descriptive analysis was conducted for the data that could not be meta-analyzed. A total of 8277 articles were initially included in this study. After screening, 25 articles were finally included, involving 11 different risk prediction models. A total of 306,390 patients with ACS were included of which 158,080 (51.6%) were male and 147,793 (48.4%) were female. The patients stemmed from 11 different countries (e.g., China, the USA, Spain, the UK, etc.). The total number of deaths was 23,601. The sensitivity of the GRACE risk prediction model was 0.78, with a specificity of 0.76 and an AUC value of 0.86. The sensitivity of the CAMI risk prediction model was 0.78, with a specificity of 0.70 and an AUC value of 0.85. The sensitivity of the TIMI risk prediction model was 0.51, with a specificity of 0.81, and an AUC value of 0.64. The sensitivity of the REMS risk prediction model was 0.78, with a specificity of 0.46 and an AUC value of 0.41. Eight different risk prediction models (EPICOR, CRUSADE, SAMI, GWTG, LNS, SYNTAX II, APACHE II) that could not be combined with the effect size were also included, with sensitivities ranging from 0.77-0.95, specificities ranging from 0.22-0.99, and AUC values ranging from 0.71-0.92. The GRACE and CAMI risk prediction models demonstrate good accuracy for evaluating the death risk of ACS patients. The accuracy of the TIMI risk prediction model is similar to that of the REMS risk prediction model. The APACHE II, SYNTAX II, EPICOR, and CAMI risk prediction models also show good accuracy for estimating the risk of death in ACS patients, although further validation is needed due to limited evidence. For improved predictive accuracy and to help advance medical interventions, the author recommends that clinical medical staff use the GRACE model to predict the death risk of ACS patients.

High Density Soil Survey of CRC -1 by the Use of Laboratory Methods

A field experiment was carried out during the year 2024 in the Department of Soil Science & Agricultural Chemistry, School of Agriculture, ITM, Gwalior. Surface (0-15 cm) and sub surface (15-30 cm) soil sample were collected by systematic survey with location data using global position system from CRC-1 farm, ITM, Gwalior District, Madhya Pradesh. A total numb of collected soil sample was 120 sample. pH was varied between 7.21 to 8.69 at 0–15 cm depth and 7.18 to 8.57 at 15–30 cm depth. The pH is slightly alkaline. The Electrical conductivity (EC) ranging from 0.23 to 0.52 dsm-1 at 0 -15 cm depth and 0.31 to 0.59 dsm-1 at 15 -30 cm depth emphasizing that the fact that the soil is safe limit of EC. Organic carbon was varied between 0.31 to 0.39% in 15–30 cm depth and 0.35 to 0.42% in 0–15 cm depth. The OC status is low to medium. Nitrogen (N) was ranged between 134.5 to 139.9 kg ha-1 at a depth of 0–15 cm and 132.4 to 135.5 kg ha-1 at a depth of 15–30 cm. Phosphorus(P) ranging from 8.42 to 12.20 cm at 0–15 cm depth and 6.28 to 10.68 cm at 15–30 cm depth. Potassium (K)ranging from 8.42 to 12.20 kg ha -1 at 0–15 cm depth and 6.28 to 10.68 kg ha -1at 15–30 cm depth. Sulphur(S) was ranged between 15.36 to 20.54 kg ha-1 at a depth of surfaced and 13.64 to 18.74 kg ha -1 at a depth of sub surfaced.

Six Square Miles of Urban America: Association Between Firearm Discharge, Injury, and Fatality.

Despite the increase in firearm injury observed across the country, significant gaps remain relevant to our understanding of how firearm exposure translates to injury. Using acoustic gunshot detection and a collaborative hospital and law enforcement firearm injury database, we sought to identify the relationship between firearm discharge and injury over time. From 2018 to 2021, instances of firearm discharge captured via acoustic detection in 6 square miles of Louisville, KY, were merged with data from the collaborative firearm injury database. Key outcomes included the total number of rounds fired, injury and fatality rates per round, and the percentage of rounds discharged from automatic weapons and high-capacity magazines. During the study period, 54,397 rounds of ammunition were discharged resulting in 914 injuries, 435 hospital admissions, 2,442 hospital days, 155 emergent operations, and 180 fatalities. For each round of ammunition fired, the risk of injury and fatality was 1.7% and 0.3%, respectively. The total number of rounds fired per month nearly tripled (614 vs 1,623, p < 0.001) leading to increased injury (15 vs 37, p < 0.001) and fatality (3 vs 7, p < 0.001). The percentage of rounds fired from automatic weapons (0 vs 6.8%, p < 0.001) and high-capacity magazines (7.6 vs 28.9%, p < 0.001) increased over time. The increased burden of firearm injury is related to an overall increase in firearm exposure as measured by the total number of rounds discharged. High-capacity magazines and automatic weaponry are being used with increasing frequency in urban America.

Experience of percutaneous closure of small to large patent ductus arteriosus with Nit-Occlud® device in a tertiary referral hospital in Colombia.

The decision to close patent ductus arteriosus should always be individualized and taken together with the child's family once the risks and benefits of both choices have been exposed. This study aims to report the experience and outcomes in patients undergoing endovascular closure of small to medium-size PDA with a Nit-Occlud® device in a tertiary referral hospital in Colombia. Longitudinal descriptive study, which included all patients under 18 years of age who underwent percutaneous ductal closure with Nit-Occlud® device between January 1, 2011, and February 1, 2023. Patients with associated complex congenital heart disease requiring surgical management, pregnant patients, and patients with incomplete data regarding studied variables were excluded from the study. Eighty-seven patients were documented, with a mean age, weight, and height at closure of 51 months, 14 kg, and 95.83 cm, respectively. About 70% of the patients (n = 61) were female, 76% were under 6-years-old and only one patient was over 15. The average size of the ductus at the pulmonary end was 2 mm. Four of the total number of patients did not achieve PDA closure during the procedure. Of the remaining 83, complete immediate closure was achieved in 81 patients. A device exchange for a larger device was required during the same procedure in one of the cases. Two patients presented residual shunt of 0.5 mm during follow-up, and one required a new procedure for device closure 10 months later. Only one device presented repeatedly embolization to the aorta, requiring surgical removal. As a technical difficulty, one device presented repeated passage into the aorta, so it was decided to remove it before releasing it to avoid complications, and given the complex anatomy of the ductus, surgical closure was indicated. Among the complications, one patient presented a hematoma of the subcutaneous tissue in the right thigh, which improved with medical management, and no deaths related to the procedure were registered. Using the Nit-Occlud® device to close small to moderate-sized ductus remains a safe and effective strategy with successful closure rates at 1-year follow-up irrespective of age, weight, height, or whether it involves a small or medium-sized duct. Despite our limitations, results concerning adverse effects are comparable to those observed in multicentric studies conducted in other regions.

Functional parcellation of the cingulate gyrus by electrical cortical stimulation: a synthetic literature review and future directions

BackgroundThe cingulate gyrus (CG), a brain structure above the corpus callosum, is recognised as part of the limbic system and plays numerous vital roles. However, its full functional capacity is...

Detection of Sarcopenia in a Community-Dwelling Older Population in China

Background and Purpose: Sarcopenia is a common muscle disease among the older population, posing an increased risk for functional decline and leading to loss of independence in daily living. Early detection of sarcopenia among older people before functional decline would be beneficial in enhancing their quality of life. The Asian Working Group for Sarcopenia (AWGS) 2019 recommends the use of 3 screening methods for community-based sarcopenia detection: calf circumference (CC), or the Strength, Assistance in walking, Rise from a chair, Climb stairs, and Falls (SARC-F) questionnaire, or the SARC-F in combination with CC (SARC-CalF) questionnaire. This study aims to compare the relative performance of these 3 methods as screening tools for sarcopenia within a community-dwelling older population. Methods: A total number of 700 community-dwelling older adults participated in the current study. Muscle mass, muscle strength, and physical performance were measured with bioelectrical impedance analysis, handgrip strength, and gait speed, respectively. The AWGS 2019 criteria were considered the criterion standard. The sensitivity/specificity, receiver operating characteristic (ROC) curve, and area under the receiver operating characteristic curve (AUROC) analyses were determined for CC, SARC-F, and SARC-CalF to determine their relative diagnostic performance. Results: Sarcopenia was identified in 21.4% of participants according to the AWGS2019 criteria. The overall prevalence of sarcopenia was 56.6%, 14.7%, and 22.9% according to CC, SARC-F, and SARC-CalF, respectively. Calf circumference showed the highest sensitivity but lowest specificity based on AWGS 2019 as the gold standard regardless of age, gender, and body mass index. The SARC-CalF showed better sensitivity but similar specificity than the SARC-F. The AUROC of CC was significantly better than that of SARC-F and SARC-CalF. The AUROCs of CC, SARC-F, and SARC-CalF were statistically significant in all populations, as well as in the categories of age, gender, and body mass index (P &lt; .05). Conclusions: Calf circumference is useful in ruling out the presence of sarcopenia while the SARC-F is more effective in ruling in sarcopenia, especially in the context of population-based screening. Future studies should be carried out to investigate the value of population-based sarcopenia detection using these screening tools.

Dry Eye Disease in Systemic Lupus Erythematosus

: Systemic lupus erythematosus is a multi-organ autoimmune disease of unknown etiology characterized by widespread inflammation and significant morbidity and mortality. Organ affection includes joint and cutaneous involvement, pulmonary, neurological, and cardiac problems, renal and hematological involvement as well as ocular comorbidities, both posterior but mostly anterior ocular involvement. Therefore, we aimed to evaluate dry eye involvement in lupus patients. A cross-sectional study was performed with the aid of a web-based, anonymous questionnaire that was distributed to both patients diagnosed with lupus erythematosus and to a control group. A total number of 123 patients diagnosed with lupus and 200 responders in the control group completed the questionnaire. Dry eye disease has already been diagnosed in 25 lupus patients (28.9%) compared to 44 (21.9%) individuals in the control group. Dry eye-related symptomatology was reported in 43 (34.60%) patients compared to 57 (28.35%) healthy responders. Upon statistical analysis, more symptoms were reported in the lupus group (p=0.01), in responders diagnosed with dry eye syndrome (p 0.01), and in responders using lubrication (p 0.01) but no association was obtained between the incidence of dry eye symptoms and the presence of the disease. The number of episodes of lupus reactivation since diagnosis was correlated with dry eye syndrome (p=0.15) and the use of lubricating tear drops (p 0.01). Dry eye symptoms and disease are more frequent in lupus patients and further research should be performed to understand the connection between these two disease entities.

Balanced Federated Semisupervised Learning With Fairness-Aware Pseudo-Labeling.

Federated semisupervised learning (FSSL) aims to train models with both labeled and unlabeled data in the federated settings, enabling performance improvement and easier deployment in realistic scenarios. However, the nonindependently identical distributed data in clients leads to imbalanced model training due to the unfair learning effects on different classes. As a result, the federated model exhibits inconsistent performance on not only different classes, but also different clients. This article presents a balanced FSSL method with the fairness-aware pseudo-labeling (FAPL) strategy to tackle the fairness issue. Specifically, this strategy globally balances the total number of unlabeled data samples which is capable to participate in model training. Then, the global numerical restrictions are further decomposed into personalized local restrictions for each client to assist the local pseudo-labeling. Consequently, this method derives a more fair federated model for all clients and gains better performance. Experiments on image classification datasets demonstrate the superiority of the proposed method over the state-of-the-art FSSL methods.

A QTL on Maize Chromosome 5 is Associated with Root-Knot Nematode Resistance.

This study provides the first report of a quantitative trait locus (QTL) in maize (Zea mays) for resistance to the southern root-knot nematode (SRKN) (Meloidogyne incognita). The SRKN can feed on the roots of maize in the U.S. Southern Coastal Plain region and can cause yield losses of 30% or greater in heavily infested fields. Increases in SRKN density in the soil may reduce the yield for subsequently planted susceptible crops. The use of maize hybrids with resistance to SRKN could prevent an increase in SRKN density, yet no genetic regions have been identified that confer host resistance. In this study, a B73 (susceptible) x Ky21 (resistant) S5 recombinant inbred line (RIL) population was phenotyped for total number of eggs (TE) and root weight. This population has been previously genotyped using single nucleotide polymorphisms (SNPs). By utilizing the SNP data with the phenotype data, a single QTL was identified on chromosome 5 that explained 15% of the phenotypic variation (PV) for the number of eggs and 11% of the PV for the number of eggs per g of root (EGR). Plants that were homozygous for the Ky21 allele for the most associated marker PZA03172.3 had fewer eggs and fewer EGR than the plants that were homozygous or heterozygous for the B73 allele. Thus, the first QTL for SRKN resistance in maize has been identified and could be incorporated into maize hybrids.

Immunological and molecular study in children with combined immunodeficiency.

Background. Severe combined immunodeficiency (SCID) is a form of immunodeficiencies (PID), caused by molecular defects. These defects can restrict the development and function of lymphocytes. Early diagnosis and treatment of SCID can lead to disease-free survival. This study aims to investigate some of the possible underlying genetic defects in a group of Egyptian infants and children with clinical and immunological profiles suggestive of SCID. Methods. This study included eighty patients who showed clinical warning signs of immunodeficiency. Subjects were thoroughly examined clinically. Laboratory evaluation included immunoglobulins serum levels and flow cytometric assessment of immune cells. This testing showed an altered immune profile in thirty patients. They had decreased T and/or B lymphocytes or natural killer cells. DNA extraction was done for those cases. The coding regions of the RAG1 gene and RAG2 gene was investigated for hot spot mutations by sequencing technique guided by the patient clinical evaluation, inheritance pattern, immunophenotyping by flow cytometric analysis of lymphocyte subsets, and serum immunoglobulins level detection. Results. Results showed novel and previously reported variants (mutation, polymorphism), they were found in 18 cases which include variants in the RAG1 gene (E880K, A960A, H249R, S913R, K820R, V782G), and variants in the RAG2 gene (P501T, L514M, rs10836573, cDNA.2129A>T). Conclusions. To evaluate SCID patients completely, mutation gene analysis is highly required and recommended.

Cost prediction of building projects using the novel hybrid RA-ANN model

PurposeAccurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.Design/methodology/approachThe proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.FindingsThe results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.Originality/value(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

DMR_Kmeans: Identifying Differentially Methylated Regions Based on k-means Clustering and Read Methylation Haplotype Filtering

Introduction:: Differentially methylated regions (DMRs), including tissue-specific DMRs and disease-specific DMRs, can be used in revealing the mechanisms of gene regulation and screening diseases. Up until now, many methods have been proposed to detect DMRs from bisulfite sequencing data. In these methods, differentially methylated CpG sites and DMRs are usually identified based on statistical tests or distribution models, which neglect the joint methylation statuses provided in each read and result in inaccurate boundaries of DMRs. Methods:: In this paper, a method, named DMR_Kmeans, is proposed to detect DMRs based on kmeans clustering and read methylation haplotype filtering. In DMR_Kmeans, for each CpG site, the k-means algorithm is used to cluster the methylation levels from two groups, and the methylation difference of the CpG is measured based on the different distributions in clusters. Methylation haplotypes of reads are employed to extract the methylation patterns in a candidate region. Finally, DMRs are identified based on the methylation differences and the methylation patterns in candidate regions. Result:: Comparing the performance of DMR_Kmeans and eight DMR detection methods on the whole genome bisulfite sequencing data of six pairs of tissues, the results show that DMR_Kmeans achieves higher Qn and Ql, and more overlapped promoters than other methods when given a certain threshold of methylation difference greater than 0.4, which indicates that the DMRs predicted by DMR_Kmeans with accurate boundaries contain less CpGs with small methylation differences than those by other methods. Conclusion:: Furthermore, it suggests that DMR_Kmeans can provide a DMR set with high quality for downstream analysis since the total length of DMRs predicted by DMR_Kmeans is longer and the total number of CpG sites in the DMRs is greater than those of other methods.

On the impacts of higher tobacco taxes in Argentina: a computable general equilibrium approach

ObjectivesWe analyse the employment effects of increasing tobacco taxation in Argentina by building a computable general equilibrium (CGE) model.MethodsIn line with recent changes in tobacco taxation in the country, the...

InfusedHeart: A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events

In the undertaken study, we have used a customized dataset termed ``Cardiac-200'' and the benchmark dataset ``PhysioNet.'' which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, ``cardiac-200,'' is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.

Absence of Monotony and Strain Effects on Referees' Physical Performance During International Basketball Federation World Cup Basketball Competition.

The study aimed to conduct a comparative analysis of physical performance indicators and assess the levels of performance monotony and strain experienced by basketball referees. The study involved the participation of 12 basketball referees (mean age: 40.0 [4.9]y) affiliated with the International Basketball Federation. The investigation was carried out throughout 2 density tournaments, wherein the maximum heart rate, average heart rate, performance monotony, and performance strain were documented for 3 variables. The findings indicated significant variations in the mean heart rate, maximum heart rate, total distance monotony, total distance strain, the total number of sprints monotony, the total number of sprints strain, calories monotony, and calories strain (P < .05). Decreasing the density of elite-level basketball competitions has been observed to reduce the monotony and strain experienced by referees. However, this action does not increase motion distance or speed zones. The environmental stressors experienced by senior-level athletes (World Cup) differ from those encountered by younger athletes (World Cup Under 19). Further investigation is required to ascertain the potential effects of competition monotony and strain on decision-making processes and the overall quality of refereeing.

Contaminated Gibbs-Type Priors

Gibbs-type priors are combinatorial processes widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be predominant priors in species sampling problems and mixture modeling. We introduce a new family of processes which extends the Gibbs-type one, by including a contaminant component in the model to account for an excess of observations with frequency one. We first investigate the induced random partition, the associated predictive distribution, the asymptotic behavior of the total number of blocks and the number of blocks with a given frequency: all the results we obtain are in closed form and easily interpretable. A remarkable aspect of contaminated Gibbs-type priors relies on their predictive structure, compared to the one of the standard Gibbs-type family: it depends on the additional sampling information on the number of observations with frequency one out of the observed sample. As a noteworthy example we focus on the contaminated version of the Pitman-Yor process, which turns out to be analytically tractable and computationally feasible. Finally we pinpoint the advantage of our construction in different applications: we show how it helps to improve predictive inference in a species-related dataset exhibiting a high number of species with frequency one; we also discuss the use of the proposed construction in mixture models to perform density estimation and outlier detection.