Kendall's Tau Research Articles

Automated differentiation of calcifications and their clusters on the mammography image: the outcomes of the computer aided diagnosis system module

INTRODUCTION: Previously we developed the computer aided detection system (CAD) for mammography MammCheck II that increased the detection rate of small and difficult to detect breast carcinomas (BC). However this system was not specifically designed for calcification detection and discrimination. On the other hand, the calcifications had no influence on the CAD capability to detect BCs that appeared as a focal lesions.OBJECTIVE: To develop the approach for automated differentiation of benign and suspicious calcifications on the mammography images and assess its clinical value.MATERIALS AND METHODS: For the developed software testing we used a set of 390 mammography images with calcifications of all possible types (278 images with benign and 112 images with suspicious calcifications). For classification we used linear support vector machine (SVM) model, that was trained on the set of 126 images (70 — benign and 56 — suspicious). We developed two SVM models: with no vascular calcification analysis and with it. Statistics: for comparison between the normally distributed samples we used the Student’s T-test, for non-normally distributed — Wilcoxon signed-rank or Chi-square tests. For correlation testing of normally distributed samples the Paerson coefficient was calculated, for non-normally distributed samples — the Spearman or Kendall correlation coefficients. The statistical significance corresponded to Р-values <0,05.RESULTS: During the testing of the first model version with no vascular calcification analysis we discovered the similarity of small early vascular calcifications and the suspicious ones. As a result this model falsely classified 14 of 23 (60.87%) vascular calcification clusters as suspicious. Therefore the model was improved. The final discrimination results for all calcification types (both benign and suspicious) obtained with the help of improved model were the following: true positive conclusions — 375/390 (96.15%), false positive conclusions — 15/390 (3.84%). In both cases when suspicious calcifications were classified as benign the wrong results were een only on one mammography view. At the same time, on another view the suspicious calcifications were correctly classified.DISCUSSION: During the CAD development it seems important not only mark the suspicious areas but also suppress false positive markings corresponding to the obviously benign lesions. However it is important during this operation not to suppress the true positive markings. Therefore such systems are inevitably characterized by a certain shift to decreased prognostic value of suspicious markings at the expense of the highest possible prognostic value of benign markings. In our viewpoint, the developed approach meets this requirement. Moreover, its integration into the CAD allows to suppress the markings of soft tissue lesions associated with typical benign calcifications, appeared on the previous processing steps. This capability may decrease the false positive rate of the main CAD module.CONCLUSION: The developed approach to benign and suspicious calcification discrimination (version with vascular calcification analysis) on the mammography image provided the sensitivity — 98.21%, specificity — 95.32%, negative predictive value (benign marking) — 99.25%, positive predictive value (suspicious marking) — 89.43%.

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

Perilymphatic fistula: a historical overview of publication trends

BackgroundPerilymph fistula (PLF) can cause symptoms of dizziness, vertigo, and fluctuating hearing. We hypothesized that publications on PLF have decreased in recent years relative to other inner ear disorders with overlapping symptoms.MethodsWe performed a Scopus search using the terms “perilymphatic fistula” OR “perilymph fistula,” limited to original studies or reviews involving human subjects published in English before 2022. We noted the senior author's institution, country affiliation, and publishing journal. The Kendall rank correlation coefficient test was used to analyze the trend of each variable over the past 30 years. Using the same search strategy, we compared these data to those from vestibular neuritis (VN), Meniere's disease (MD), superior semicircular canal dehiscence (SSCD), and benign paroxysmal positional vertigo (BPPV).ResultsSix hundred and ninety-eight PLF publications were returned. The top publishing country was the United States (n = 334), the top publishing journal was the American Journal of Otology/Otology and Neurotology (n = 68), and the top publishing institution was the University of Pittsburgh (n = 15). In the past 30 years (n = 501), there was no trend in the frequency of annual PLF publications (τ = −0.150, p = 0.265). Still there were positive trends in publications on vestibular neuritis (τ = 0.724, p < 0.001), Meniere's disease (τ = 0.587, p < 0.001), superior canal dehiscence syndrome (τ = 0.840, p < 0.001), and benign paroxysmal positional vertigo (τ = 0.882, p < 0.001) 5,398 PLF cases were identified, of which 4,356 specified the etiology; the majority identified an inciting insult (74.8%, n = 3,257).ConclusionsThe absence of diagnostic criteria for PLF may hinder its appeal to researchers as similar syndromes have had diagnostic consensus documents set forth. Given the greater number of cases with inciting insults, efforts to define a diagnostic criterion should focus on this subtype.

Effect of asymptomatic intervertebral flexion patterns on lumbar disc pressure: A finite element analysis study.

Movement patterns may be a factor for manipulating the lumbar load, although little information is yet available in the literature about the relationship between this variable and intervertebral disc pressure (IDP). A finite element model of the lumbar spine (49-year-old asymptomatic female) was used to simulate intervertebral movements (L2-L5) of 127 asymptomatic participants. The data from participants that at least completed a simulation of lumbar vertebral movement during the first 53% of a movement cycle (flexion phase) were used for further analyses. Then, for each vertebral angular motion curve with constant spatial peaks, different temporal patterns were simulated in two stages: (1) in lumbar pattern exchange (LPE), each vertebral angle was simulated by the corresponding vertebrae of other participants data; (2) in vertebral pattern exchange (VPE), vertebral angles were simulated by each other. The k-mean algorithm was used to cluster two groups of variables; peak and cumulative IDP, in both stages of simulations (i.e., LPE and VPE). In the second stage of the simulation (VPE), Kendall's tau was utilized to consider the relationship between different temporal patterns and IDPs for each individual lumbar level. Cluster analyses showed that the temporal movement pattern did not exhibit any effect on the peak IDP while the cumulative IDP changed significantly for some patterns. Earlier involvement in lumbar motion at any level led to higher IDP in the majority of simulations. There is therefore a possibility of manipulating lumbar IDP by changing the temporal pattern with the same ROM, in which optimal distribution of the loads among lumbar levels may be applied as preventive or treatment interventions. Evaluating load benefits, such as load, on biomechanically relevant lumbar levels, dynamically measured by quantitative fluoroscopy, may help inform interventional exercises.

Research Attitude, Achievement Motivation, and Self-Efficacy Among Post-Graduate Students Specializing in Primary Education

This study investigates the research attitude, achievement motivation, and self-efficacy concepts among post-graduate students specializing in primary education. The research aims to understand the interplay between these constructs and their implications for academic success and research engagement. A quantitative research design was employed, and data were collected using structured questionnaires from a sample of 62 post-graduate students. Descriptive statistics, and correlation analyses were conducted to examine the relationships between research attitude, achievement motivation, and self-efficacy. Statistical analyses showed non-significant correlations: between research attitude and achievement motivation (Kendall's tau_b = -.048, p = .881; Spearman's rho = -.036, p = .939), achievement motivation and self-efficacy (Kendall's tau_b = -.109, p = .708; Spearman's rho = -.204, p = .629), and research attitude and self-efficacy (Kendall's tau_b = .293, p = .362; Spearman's rho = .360, p = .427) among post-graduate students. The findings contribute to a better understanding of the factors influencing post-graduate students' research experiences and offer insights for academic institutions to enhance support mechanisms for student researchers. The implications of the study underscore the importance of fostering positive research attitudes, strengthening achievement motivation, and enhancing self-efficacy among post-graduate students specializing in primary education. Targeted interventions can optimize academic support and promote professional development in research-intensive environments

Aggressive Behaviour Risk Assessment Tool for Hospitalised Patients in Non-Psychiatric Inpatient Units.

To refine and validate an electronic version of the Aggressive Behaviour Risk Assessment Tool (ABRAT) and determine the sensitivity and specificity for identifying potentially violent patients in non-psychiatric inpatient units. A prospective cohort study design was used. All patients admitted or transferred to three inpatient units of an acute care hospital in Nebraska, USA, from 7 February to 9 April 2023, were included. The 10-item ABRAT assessments were performed daily for the first 3 days of admission. The violent events were collected until discharge in three categories: Physical aggression towards others, physical aggression towards property and verbal intimidation/threat towards others. Kendall's tau tests and a multivariate logistic regression procedure were performed to select a parsimonious set of items that best predict violent events. Of 1179 patients, 69 had ≥1 violent event (5.9%). The revised six-item tool with item weighting was named ABRAT for Hospitalised Patients (ABRAT-H). The area under the curve from the Receiver Operating Characteristics analysis was 0.82. The sensitivity and specificity at a cutoff score of two were 68.1% and 85.2%, respectively. As ABRAT-H scores increased, the percentage of violent patients also increased and for patients with scores ≥5, 55.2% became violent. ABRAT-H appears to be useful for identifying potentially violent patients in non-psychiatric inpatient units with satisfactory sensitivity and specificity. The availability of ABRAT-H may help provide focused preventive measures that target patients at high risk for violence and reduce violent events. A majority of the nursing workforce is employed in acute care hospital setting, and the availability of ABRAT-H can further enhance the culture of a safe work environment and have positive impacts not only on the nurses' physical and mental health but also on the quality of patient care. We have adhered to relevant STROBE guidelines for reporting observational studies. No Patient or Public Contribution.

Dollars and Disparities: Unraveling Executive Compensation Patterns in American Non-Profit Healthcare.

This study examines trends in executive compensation at non-profit hospitals across the United States from 2018 to 2022. The research aims to address gaps in the current literature by providing an up-to-date analysis of compensation practices and their relationship to hospital financial metrics. A descriptive longitudinal study was conducted using data from Form 990 filings of 20 randomly selected non-profit hospitals across four U.S. regions. Executive compensation data, along with hospital financial metrics, were extracted and analyzed. Statistical analyses included descriptive statistics, non-parametric tests for trend analysis, and correlation studies using Kendall's Tau B. The median increase in executive compensation over the five-year period was 23.4%, with a median annual increase of 6.2%. Significant positive correlations were found between executive compensation and hospital revenue (τ = 0.676, p < 0.001), net assets (τ = 0.600, p < 0.001), and net income (τ = 0.273, p < 0.001). However, percentage changes in these variables over time were not significantly correlated. The study reveals significant growth in executive compensation at non-profit hospitals, with notable regional variations. While compensation correlates strongly with hospital financial metrics, the lack of correlation in their respective changes over time suggests a complex relationship. These findings raise important questions about resource allocation and compensation practices in non-profit healthcare institutions, highlighting the need for further research and policy discussions.

The Relationship between Social Support and Self-Esteem with Academic Resilience of Junior High School Students

This study aims to examine the relationship between social support and self-esteem and academic resilience in junior high school (SMP) students. Academic resilience is a student's ability to endure and adapt to academic challenges and stress, which is important to their academic success. Social support and self-esteem are thought to play an important role in increasing students' academic resilience. This research uses quantitative methods with a correlational design. The research subjects were junior high school students at several schools in Krian, totaling 351 students. The research instrument is a questionnaire that measures students' levels of social support, self-esteem and academic resilience. The data analysis used was Kendalls Tau B. The results of the study showed that there was no significant positive relationship between social support and students' academic resilience. In addition, self-esteem was also found to have a significant positive relationship with academic resilience. Students who have strong social support from family, friends and the school environment and have high self-esteem tend to have better academic resilience. The conclusion of this research is that social support and self-esteem play an important role in increasing the academic resilience of junior high school students. Therefore, it is important for schools and families to provide adequate support and build students' self-esteem to help them overcome academic challenges and achieve academic success.

If EQ-5D-5L Mobility Dimension Ratings Are High, Is Life-Space Assessment a Clinically Sensible Next Step? Data from a Population Survey.

Background: Impaired health states can limit a person's mobility, often progressively for people with life-limiting illnesses. Quantifying mobility changes is crucial for individual clinical care and service planning. Objective: To explore any correlation between EQ-5D-5L's mobility dimension ratings and Life-Space Assessment (LSA) from a population sample. Methods: An online population survey of Australian adults, nationally-representative by key demographics. An analysis of variance examined each level of the EQ-5D-5L mobility dimension rating against its LSA scores; Kendall's Tau assessed correlation. Results: Participants (n = 6366) were 53% women, mean age 46.1 years (SD 18.6), and mean LSA score 78.0 (SD = 27.5; possible range 0-120). At each EQ-5D-5L mobility dimension level there was a significant difference between LSA scores (p < 0.001), and a moderate negative correlation (Kendall's tau b = -0.342) between the two measures. Conclusion: Given the relationship defined, EQ-5D-5L mobility dimension ratings may prompt clinicians to consider further evaluation with the more detailed Life-Space Assessment. .

Microbial source tracking and identification of fecal contamination patterns in saltwater estuaries between base- and storm-flow events using dual genome-based approaches

Fecal contamination from natural and anthropogenic sources poses significant threats to saltwater estuaries, particularly after storms or heavy rainfall. Monitoring fecal contamination is essential for protecting these vulnerable ecosystems having important ecological and economic values. In this study, we investigated the abundance, sources, and potential causes of fecal contamination at three marine and seven freshwater stations across Vaughn Bay (WA, USA), a shellfish growing district, during base- and storm-flow events. Additionally, we evaluated the performance of fecal indicator bacteria (FIB) quantification, optical brightener assessment, and qPCR analysis for fecal contamination quantification. We compared the effectiveness of qPCR-based microbial source tracking (MST), which targeted a broad range of hosts including, such as humans, birds, cows, horses, ruminants, dogs, and pigs, with sequencing-based MST in identifying fecal contamination sources. Both MST analysis approaches identified birds and humans as the primary sources of fecal contamination. For marine water stations, freshwater creeks VBU001, VBU002, and VB047, along with drain VB007, were identified as the main sources of human-derived fecal contamination in Vaughn Bay, based on Kendall's tau analysis (τ: 0.58–0.97). This information indicates that the septic systems in the catchment areas of these creeks and drains require further investigation to achieve effective fecal contamination control. Optical brightener, FIB enumeration and qPCR quantification results were generally higher during storm-flow events, although they showed poor correlation with each other (Pearson r < 0.40), likely due to physiological and phylogenetic differences among the target organisms of these methods. However, the sequencing-based method faces challenges in precise quantitative identification of differences in fecal contamination between base- and storm-flow events. Due to its high-throughput and cost-effectiveness, we recommend using sequencing-based analysis for large-scale identification of the primary sources of fecal contamination in water environments, followed by targeted qPCR quantification of MST markers for more precise assessments.

Hierarchy and mobility of Latin America and Caribbean container ports

Research on port hierarchies within a maritime region is crucial for comprehending the dynamics of seaborne trade and the broader trends within the maritime sector and related supply chains. This study investigates the evolution of the Latin America and the Caribbean (LAC) container ports and regional sub-systems from 2000 to 2022, analyzing traffic dynamics, shifts in port throughput rankings, and market concentration trends in and among the different coastal regions. Conceptually, this analysis benefits from the notion of evolutionary phases, revealing the stages of a port system's development. Methodologically, 49 LAC ports of international interest are categorized into different coastal regions and tiers based on throughput sizes. Various metrics, including the Gini coefficient, Lorenz curve, Herfindahl-Hirschman Index (HHI), and Kendall Tau (τ), assess port hierarchy and related mobility since 2000. The observed shifts in the hierarchy and market structures, coupled with the fluctuating positions of critical ports, underscore the transition that LAC container ports dynamics experienced in the 21st century. Following a particularly dynamic and volatile evolution observed in the 2000s, the financial crisis of 2008/09 was a critical juncture leading to the transition of the LAC port dynamics from the ‘emerging’ to the ‘maturity’ phase. On the contrary, the pandemic (2019) has not affected the pre-existing hierarchy or the moderate market concentration that was already in progress. The LAC region comprises port sub-systems (coastal ranges) subjected to different tendencies towards (de)concentration, yet mobility in port rankings within each sub-system decreases. The study also reveals the coexistence of this transition with a structural transformation of port governance structures, i.e., granting of concessions of operations to third parties. In 20 of these ports and the above-average mobility of ports are operated by Global Terminal Operators (GTOs), and calls for further research on the (non) causality of these phenomena.

A novel and effective method for characterizing time series correlations based on martingale difference correlation.

Analysis of correlation between time series is an essential step for complex system studies and dynamical characteristics extractions. Martingale difference correlation (MDC) theory is mainly concerned with the correlation of conditional mean values between response variables and predictor variables. It is the generalization and deepening of the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and other statistics. In this paper, on the basis of phase space reconstruction, the generalized dependence index (GDI) is proposed by using MDC and martingale difference divergence matrix theories, which can measure the degree of dependence between time series more effectively. Moreover, motivated by the theoretical framework of the refined distance correlation method, the corresponding dependence measure (DE) is employed in this paper to construct the DE-GDI plane, so as to comprehensively and intuitively distinguish different types of data and deeply explore the operating mechanism behind the relevant time series and complex systems. According to the performances tested by the different simulated and real-world data, our proposed method performs relatively reasonably and reliably in dependence measuring and data distinguishing. The proposal of this complex data clustering method can not only recognize the features of complex systems but also distinguish them effectively so as to acquire more relevant detailed information.

Estimating Students' Academic Success in the Preclinical Stage of Undergraduate Medical Education Using the Admission Test Approach.

Medical schools face substantial challenges in objectively selecting the best applicants, and the admission process can impact medical students' academic performance. This study aimed to estimate the students' academic success in the preclinical stage of undergraduate medical education using admission tests. This cross-sectional study was conducted on 1,193 students' records from the 2014 to 2019 cohorts. The students' admission data comprised the cohort, sex, admission track, psychological test, and academic tests. The academic success was based on the student's end-year academic evaluation. Data were analyzed using contingency and Kendall's tau b tests with IBM® SPSS® Statistics version 16.0 for Windows. Most of the 1,193 preclinical medical students' records included in the study were females (68.1%), from the regular admission track (78.5%), from the considered psychology test category (52.8%), and had an academic admission test of less than or equal to the median. (51.6%). Most students (89.7%) met all the academic requirements to pass the end-year evaluation. The bivariate analyses showed significant correlations between academic success and cohort (p<0.001), psychology test (p=0.005), and academic test (p<0.001). The analyses showed no significant correlation between academic success and sex (p=0.324), and admission track (p=0.128). This study indicated that cohort and psychology tests could estimate the student's academic success at the preclinical stage of undergraduate medical education. The admission criteria related to the academic tests during the admission process should be re-evaluated, so that the academic tests could select the best students among the applicants.

Voice Type Component Profile Model of Glottal Gap Voice in Ex Vivo Canine Larynges

This paper aimed to create a voice type component profile (VTCP) to model a glottal gap during phonation. VTCP was generated from phonation from eight excised canine larynges with induced posterior glottal gaps. Glottal gaps were induced using metal shims with widths ranging from 0.5 to 3.5mm, in 0.5 mm increments. Acoustic data was collected under each gap condition, and a custom MATLAB software was used to analyze the data for VTCP. The trends of each VTC were analyzed with changes in glottal gap size. Increasing glottal gap led to a decrease in VTC1 and an increase in VTC4. Post-Hoc Tukey tests found that for both VTC4 and VTC1, The 1.5mm group differed from the 2, 2.5, and 3.5mm group. Additionally, in both VTC1 and VTC4 there was a significant difference between both the control and 0.5mm shim groups and the 2, 2.5, 3, and 3.5mm shim groups. VTC2 followed a similar trend as VTC1. Average VTC3 was highest at control, and the 1, 2, 2.5, 3, and 3.5mm shim groups significantly differed from the control group. Kendall correlation tests showed that there was a significant weak inverse relationship with glottal gap size and VTC1, VTC2, and VTC3 and a significant weak positive correlational relationship with glottal gap size and VTC4. As glottal gap width increased, phonation exhibited increased turbulence associated with increased breathiness. Periodicity decreased as turbulence increased. Increased turbulence was shown to be derived from the glottal level. The size of the induced glottal gap was the cause of the turbulent signals. The study highlights the potential of VTCP analysis in detecting subtle changes in glottal closure and suggests avenues for further research in diagnosing voice disorders.

Nexus of Classroom Environment and English Language Learning Skills in Higher Education in Pakistan

This study has been designed to examine the nexus between classroom environment and English language learning skills among students in higher education in Pakistan. It has been observed that the classroom environment has a favourable effect on students' language learning not only at school and college levels but also at the university level. A quantitative approach was used for this study. The students of the BS (4 Years) program constitute the population of the study. Cross-sectional research has been conducted, and a well-structured questionnaire has been used as a tool for data collection. The measuring tool consists of different variables, including identification, socio-demographic characteristics, classroom environment, and English language learning skills. A pilot test was done for 30 randomly selected students and the value Alpha was reported to be above .700. A statistics analysis was conducted, including the Normality test and Kendall’s tau_b test. The study findings revealed that the classroom environment favourably affects the English language learning skills of the students at the tertiary level. The results of Kendall's tau_b test confirm the positive correlation between variables (classroom environment and English language learning skills).

IoT and AI: a panacea for climate change-resilient smart agriculture

The application of Internet of Things (IoT) and Artificial Intelligence (AI) for disaster preparedness and sustainable agriculture has been a topic of great interest lately. In the last few years, extreme weather swings due to climate change caused by global warming have caught the farming community off guard, especially in the developing world. One of the key objectives of smart agriculture is optimal use of freshwater, which has become an increasingly scarce resource around the world. Reference Evapotranspiration (ETo), an estimation of total flux of water evaporating from a reference surface is an important parameter for irrigation management. IoT & AI-based location-specific estimation of ETo for crop water requirements augments the decision-making process. In this work, we utilize the Hargeaves and Samani (H–S) model and six regression algorithms for the estimation of ETo. We create a location-specific dataset with locally sensed IoT data from a flood warning system and remotely sensed meteorological data, spanning over 5 years. We train and test Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR), Bagging and Random Forest (RF) algorithms on the locally curated dataset with 20 basic, extracted, and derived attributes. We gradually reduce number of attributes in the dataset from 20 to 3 and compare performance of the six algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Coefficient of Determination R2, Kendall Tau and Spearman Rho metrics. SVR shows superior performance with an MAE of 0.03 and an RMSE of 0.05, followed closely by MLP with an MAE of 0.04 and RMSE of 0.06 with a dataset of 12 attributes. The performance of Bagging and RF algorithms remains relatively unchanged with feature reduction whereas RBF shows slight improvement in performance when number of attributes is reduced to 3. Finally, we develop a novel ensemble hybrid model using the Stacked Generalization technique, which outperforms all individual models in prediction accuracy when using reduced-feature datasets. This work clearly delineates the performances of a diverse set of ML algorithms for feature-rich and feature-scarce scenarios and demonstrates the efficacy of our hybrid ensemble ML algorithm for estimating ETo under limited availability of data in resource-constrained environments.

AGRO PRODUCTS EXPORTS OF UKRAINE WITHIN ITS TRADE AGREEMENTS WITH THE COMMONWEALTH OF INDEPENDENT STATES

The aim of the research is to work out the recommendations on the geography of the agricultural products trade of Ukraine under the circumstances of the on-going war through the analysis of the trends and dynamics of the agro exports from Ukraine to the CIS, the agricultural products exports from Ukraine to the CIS countries possible correlation with each other, its direction and level. The data are the values of the agricultural products exports from Ukraine to the CIS countries from 2001 to 2023 included with the special identification of the years 2021-2023 as the year 2001 is the earliest one with the available data, 2021 is the pre-war year and 2023 – the year with the latest data available and the second year of the war. Such methods of scientific research as empirical, comparative and statistical analyses, including data mining, cleaning and processing, methods of correlation measuring like Pearson, Spearman and Kendal correlation tests, the method of exclusion, as well as textual and tabular methods for better data presentation and comparison were used while conducting the research presented in the given article. The conducted research indicated, that the agro exports of Ukraine to the following country – pairs appeared to be positively correlated: 1) Armenia – with Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic and Tajikistan, 2) Azerbaijan – with Belarus, Georgia, Kyrgyz Republic, Tajikistan and Uzbekistan, 3) Belarus – with Uzbekistan, 4) Georgia – with Kazakhstan, Kyrgyz Republic, Tajikistan and Uzbekistan, 5) Kazakhstan – with Kyrgyz Republic, Tajikistan and Turkmenistan, 6) Kyrgyz Republic – with Tajikistan, and 7) Tajikistan – with Turkmenistan.

Validation of the German version of the Perceived Cohesion Scale.

Team or group cohesion is a multifaceted construct with a variety of definitions and measurement instruments. However, most of these measures are context and group-specific and/or time-consuming. There is no adaptable and economic measure of group cohesion in Germany. Therefore, the aim of this study was to validate the German version of the 6-item Perceived Cohesion Scale (PCS) in a sample of adult nurses in a German University Hospital. The German version of the PCS was generated according to existing guidelines. Confirmatory factor analysis was conducted to assess factor structure. Reliability was tested via internal consistency. To assess convergent and divergent validity, the authors applied the Copenhagen Psychosocial Questionnaire (COPSOQ), the Enhancing Recovery in Coronary Heart Disease Patients (ENRICHD) Social Support Inventory (ESSI), the Effort-Reward Imbalance (ERI) scale and the Patient Health Questionnaire (PHQ-4). The confirmatory factor analysis confirmed a 2-factor structure. Psychometric properties of the German PCS prove satisfactory. Internal consistency was excellent for the whole scale (α = 0.95), as well as for both subscales: sense of belonging (SOB) (α = 0.94) and morale (MOR) (α = 0.93). Moderate to strong correlations with the subscales of COPSOQ (Kendall's Tau (τ) = 0.239 - 0.471) indicated very good convergent validity. Regarding divergent validity, the correlations of the PCS subscales and the PHQ-4 were low (τ = -0.109-[-0.143]), as were the correlations with ESSI (τ = 0.045-0.136). Correlations with ERI were low to moderate (τ = -0.181-0.283). The German version of the PCS showed good psychometric properties. Due to its economic and universal deployment, group cohesion can be measured in a variety of contexts and settings. Int J Occup Med Environ Health. 2024;37(4).

Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station.

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters-humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K), during the vegetative growth stage, which are essential for assessing soil health and optimizing crop growth. Kendall's correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-nearest neighbors (KNN), support vector machines (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were evaluated. A novel hybrid algorithm, 'Bayesian optimization with KNN', was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.

Fractal Dimension and Lacunarity Analysis in the Dentulous and Edentulous Mandibular Posterior Region Using Cone-beam Computed Tomography: A Cross-sectional Retrospective Study.

This cross-sectional retrospective study was conducted to assess the differences in the microarchitecture of the trabecular bone of the posterior mandibular region at dentulous and edentulous sites with the help of fractal dimension (FD) and lacunarity using cone-beam computed tomography (CBCT). Ninety CBCT scans were analyzed for the purpose of the present study. Inclusion criteria included subjects with unilaterally missing mandibular molars or premolars and an with intact contralateral opposing tooth. The coronal view of the dentulous and edentulous sites was used, and the region of interest (ROI) was selected 2.6 mm below the apex of the tooth present. These images were then transferred to ImageJ Software, and fractal analysis was done using the box-counting method of the FracLac plug-in. A paired samples t-test was performed to compare the means of FD and lacunarity, and a Kendall correlation was performed to check correlations. A p-value less than 0.05 was considered to indicate statistical significance. Statistical analysis revealed that the mean FD of the edentulous side was significantly greater than that of the dentulous side (p-value = 0.011). Additionally, the mean lacunarity of the edentulous side was marginally significantly greater than that of the dentulous side (p-value = 0.089). A significant negative correlation was detected between the FD and lacunarity of the edentulous region (p-value = 0.017), and a marginally significant negative correlation was detected between edentulous lacunarity and dentulous lacunarity (p-value = 0.081). The differences in occlusal forces exerted in dentulous and edentulous regions can lead to a change in the trabecular pattern of the bone in these regions. This change in the microarchitecture of bones can be detected by FD and lacunarity, which can further help us assess changes pre- and post-implant. The advanced technology, the assessment of microarchitecture of the bone has been made easy, using FD and lacunarity, as done in the present study. This analysis can further aid us in both pre- and post-implant analysis to prevent failure of the implant. How to cite this article: Bhoraskar M, Denny C, Srikant N, et al. Fractal Dimension and Lacunarity Analysis in the Dentulous and Edentulous Mandibular Posterior Region Using Cone-beam Computed Tomography: A Cross-sectional Retrospective Study. J Contemp Dent Pract 2024;25(6):581-587.