High Correlation Values Research Articles

Development of a surfactant-assisted lanthanum anchored hydroxyapatite@polyaniline hybrid composite for the elimination of chromium ions from aqueous solution

The predominant cause of the global clean water deficit is water contamination caused by heavy metals discharged from various industrial effluents. Innovating low-cost adsorbents that can eliminate heavy metals from polluted water is essential to address this challenge. Human health can be negatively impacted by water contamination caused by the increased levels of carcinogenic heavy metals, such as chromium (Cr), found in wastewater. The main goal of this research is to remove chromium ions from an aqueous environment by utilizing the surfactant-assisted lanthanum anchored hydroxyapatite@polyaniline (CTAB-LaHAp@PANI) hybrid composite. Various analytical techniques including FTIR, XRD, SEM, EDX, TEM, TGA, and XPS analysis were utilized for the characterization of the prepared adsorbents. Different influencing factors such as contact time, solution pH, adsorbent dosage, and initial chromium concentration were systematically optimized through the batch method to achieve maximum removal efficiency of the CTAB-LaHAp@PANI hybrid composite. Remarkably, the CTAB-LaHAp@PANI demonstrated a maximal adsorption capacity of 98.2 mg/g, with an optimal contact time of 120 min at pH 3. The isotherm and kinetic analyses indicated that chromium adsorption onto the CTAB-LaHAp@PANI hybrid composite followed the Langmuir and pseudo-second-order kinetic models, supported by high correlation coefficient values. Moreover, the possible removal mechanism of chromium ions on the prepared hybrid composite was explained based on the results obtained from various analytical techniques. The as-prepared CTAB-LaHAp@PANI hybrid composite demonstrates a superior adsorption capacity, exceptional water stability, cost-effectiveness, and various other characteristics that render it an attractive choice for removing chromium ions from the aqueous phase.

Measurement properties of activity monitoring for a rehabilitation (AMoR) platform in post-stroke individuals in a simulated home environment

ABSTRACT Aim The aim of this study was to evaluate the measurement properties of activity monitoring for a rehabilitation (AMoR) platform for step counting, time spent in sedentary behavior, and postural changes during activities of daily living (ADLs) in a simulated home environment. Methods Twenty-one individuals in the post-stroke chronic phase used the AMoR platform during an ADL protocol and were monitored by a video camera. Spearman’s correlation coefficient, mean absolute percent error (MAPE), intraclass correlation coefficient (ICC), and Bland–Altman plot analyses were used to estimate the validity and reliability between the AMoR platform and the video for step counting, time spent sitting/lying, and postural changes from sit-to-stand (SI-ST) and sit-to-stand (ST-SI). Results Validity of the platform was observed with very high correlation values for step counting (rs = 0.998) and time spent sitting/lying (rs = 0.992) and high correlation for postural change of SI-ST (rs = 0.850) and ST-SI (rs = 0.851) when compared to the video. An error percentage above 5% was observed only for the SI-ST postural change (7.13%). The ICC values show excellent agreement for step counting (ICC3, k = 0.999) and time spent sitting/lying (ICC3, k = 0.992), and good agreement for SI-ST (ICC3, k = 0.859) and ST-SI (ICC3, k = 0.936) postural change. Values of the differences for step counting, sitting/lying time, and postural change were within the limits of agreement according to the analysis of the Bland–Altman graph. Conclusion The AMoR platform presented validity and reliability for step counting, time spent sitting/lying, and identification of SI-ST and ST-SI postural changes during tests in a simulated environment in post-stroke individuals.

The evaluation of the antioxidant activity of selected Eryngium species based on thin-layer chromatography fingerprints using chemometric tools

The purpose of the study was to confirm the utility of multivariate technique to predict antioxidant activity of selected Eryngium extracts based on chromatographic data. Five Eryngium species were analyzed using Thin Layer Chromatography (TLC) with silica gel to obtain their fingerprints. The highest relative content of quercitrin (marker) was observed for E. giganteum. The chemical similarity between extracts was evaluated by Principal Component Analysis (PCA) and Hierachical Cluster Analysis (HCA), and the results confirm the similarity between the same three E. planum from various natural sites. The total phenolic content with Folin–Ciocalteu reagent and antioxidant activity with DPPH reagent by spectrophotometry were determined, and the highest content of phenolics (concentration of gallic acid, mg⋅mL−1) was obtained for E. campestre, E. amethystinum and E. planum T. The highest antioxidant activity (concentration of gallic acid and inhibition %) were obtained for E. planum T. and E. amethystinum, while the lowest was obtained for E. planum Dębówka. High value of correlation coefficient between TLC and spectrophotometric data using Partial Least Squares Regression (PLS) confirms the usefulness of statistical method for the predicting the biological activity. In conclusion, the chromatographic and chemometric methods were suitable for prediction of antioxidant activity.

The Influence of Marketing Activities of Companies on Social Networks on the Purchase Decisions of Students

The study in this paper sought to assess how the company's marketing initiatives influence the purchasing choices of students. By surveying 230 students from the Faculty of Economics in Belgrade and analysing their responses, the researchers evaluated various aspects of the company's marketing strategies and the students' buying behaviour. Descriptive statistics revealed that the variables related to updating content on social media and utilising sponsored advertisements had the most significant impact, along with the variable of making purchasing decisions. The results of the regression and correlation analysis established the existence of statistically significant relationships and the influence of the variables of the company's marketing activity on the variables of students' purchase decisions. Based on the regression model of influence, it was determined that the variables of the company's marketing activities had the greatest impact on the variable of making purchase decisions, with the variables of sponsored ads and updating content on social networks making the greatest individual contribution. A two-component solution was obtained by factor analysis, where the first component gathered all the variables of the company's marketing activities and the purchasing decision variable, while the second component gathered the other variables of the students' purchasing decisions. Between these two components, increasingly high correlation values were found, which only indicates that there is a certain degree of variability between the company's marketing activities and students' purchasing decisions. The research conducted in this study aimed to determine the extent to which social media marketing is beneficial for students in finding products and services they need, as well as how influential it is in their purchasing decisions. It underscores the importance for companies to integrate social media marketing activities and determine the most suitable types of social media marketing strategies.

Оценка хозяйственных рисков при реализации технологий угледобычи в современных условиях недропользования

Introduction. This paper presents research in the field of methodology of assessment and monitoring of associated under ground coalmining risks in order to level their impact on the resulting technical and economic indicators of production and economic activities in modern conditions of subsoiluse. The functioning of technological systems of coal mining enterprises at the present stage of subsurface use occurs in a highly dynamic and highly variable functional environment with a fair degree of uncertainty, which is generated by a significant variety of parameters and characteristics of the initial mining, geological and mining engineering environment, which, in turn, generates the emergence of various groups and risk factors for subsurface use. The established procedures and methods of risk forecasting currently do not give satisfactory results due to the globalization of world markets and high volatility of prices for coal products. Leveling the negative degree of influence of the risk component on the resulting indicators – indicators of technical and economic activity can be achieved through the introduction of an operational system for assessing and monitoring various groups of risk factors. Materials and methods. For the purpose of evaluation, it is proposed to use the methodological and methodological foundations of the method called “vector norms”, which involves the use of Affine space, the constituent areas of compromises and the introduction of utility estimates. A complete model representation of the assessment and monitoring of the impact of various risk factors of subsurface use, taking into account their clustering and ranking, is presented. The algorithmic support of this method is reduced to the following components-iterations: 1. Formation of criteria-based assessment matrices of various factors of subsurface use risk groups. 2. Assignment of optima to evaluation criteria. 3. In order to ensure the legality of the use and implementation of mathematical procedures and operations, the reduction of multi-dimensional evaluation criteria to a relative form, taking into account the assigned optima. 4. Formation of the objective function of the “norm of the vector” (integral indicator) and the development of a procedure for selecting the optimal option. Since the use of the methodological and methodological foundations of the method called “vector norms” allows us to form quantitative estimates of the presented factors-types of risk of subsurface use, this makes it possible to implement the clustering procedure or ranking of a selected set of coal mining enterprises in conjunction with existing risk areas. Results. Quite high values of correlation coefficients were obtained with a significant closeness of the relationship of inversely proportional dependencies of generalizing integral indicators of the degree of risk and the level of achievement of planned indicators and development of production capacity with the level of operational profitability from the conduct of production and economic activities of coal mining enterprises with a directly proportional dependence on the operating costs of mining 1t of coal. Discussion. Сomplete model representation of the assessment and monitoring of the impact of various risk factors of subsurface use, taking into account their clustering and ranking, is presented. As a practical component of the research, variable series of areas of integral degree of risk in the functional environment of Kuzbass coal mines are presented. The use of software modules for correlation and regression analysis, taking into account three-year retrospective dynamics, made it possible to establish the legality and objectivity of the proposed methodological apparatus for integrated risk assessment of the implementation of coal mining technologies in modern conditions of subsurface use. Conclusion. The results of the implementation of the theoretical components of the methodology and model representations of the developed conceptual foundations for the integrated risk assessment of the implementation of coal mining technologies in modern conditions of subsurface use, taking into account five-year retrospective dynamics, showed that in the Kuznetsk basin there was a steady tendency to reduce the cluster of coal mines, which is characterized by high risks of carrying out production and economic activities in the presence of a disproportion of resource potential (the emergent part). The accompanying processes of formation of this trend are manifested in a quantitative increase in the functional units of the rating cluster of group 2B with an average resource potential and inherent moderate risk. It is within this cluster that strategies for prioritizing investments for the development and renewal of technological systems and structures of coal mining are being formed, since only they are able to provide the fastest possible return on invested funds. Resume. The article presents the results of research in the field of methodology for assessing and monitoring the risks associated with underground coal mining in order to level out their impact on the resulting technical and economic indicators of production and economic activity in modern conditions of subsoil use. The research results can be useful in the implementation of coal mining technology in modern conditions of subsoil use.

Correlation Between Body Weight and Carcass Weight in the Selection of Village Chicken Three-Generation

This study aims to estimate the correlation between body weight and carcass weight in selected native chickens in the third generation (G3). The material used in this study was 200 DOC managed with a free-range system. The samples used to obtain carcass weight were determined by purposive sampling, consisting of 38 male chickens and 26 female chickens. The data obtained were analyzed using correlation and regression analysis. The observed variables were body weight (BW) at 2, 4, 6, 8, and 10 weeks of age (independent variable) and carcass weight (CW) at 10 weeks of age (dependent variable). The results showed that the average body weight (BW) of male and female chickens up to 6 weeks of age showed no significant difference (P>0.05). After eight weeks of age, the male free-range body weights were significantly different (P<0.05) higher than female chickens, while at the period of 10 weeks, the body weights of male chickens were significantly different (P<0.01) and showed higher than female chickens. Male chickens' average free-range carcass weight was significantly higher (P<0.01) than the carcass weight (CW) of female chickens. The correlation between body weight at 2, 4, 6, 8, and 10 weeks and carcass weight showed a close correlation, ranging from 0.71 to 0.97. The highest correlation coefficient values were found in male and female native chickens at ten weeks of age, with 0.94 and 0.97. It can be concluded that BW at 2, 4, 6, 8, and 10 weeks of age in male and female chickens strongly correlated with carcass weight (CW). Hence, body weight (BW) could be used as a selection criterion to increase chickens' carcass weight (CW) under study.

Evaluation of Nine Planetary Boundary Layer Turbulence Parameterization Schemes of the Weather Research and Forecasting Model Applied to Simulate Planetary Boundary Layer Surface Properties in the Metropolitan Region of São Paulo Megacity, Brazil

This study evaluates nine Planetary Boundary Layer (PBL) turbulence parameterization schemes from the Weather Research and Forecasting (WRF) mesoscale meteorological model, comparing hourly values of meteorological variables observed and simulated at the surface of the Metropolitan Region of São Paulo (MRSP). The numerical results were objectively compared with high-quality observations carried out on three micrometeorological platforms representing typical urban, suburban, and rural land use areas of the MRSP, during the 2013 summer and winter field campaigns as part of the MCITY BRAZIL project. The main objective is to identify which PBL scheme best represents the diurnal evolution of conventional meteorological variables (temperature, relative and specific humidity, wind speed, and direction) and unconventional (sensible and latent heat fluxes, net radiation, and incoming downward solar radiation) on the surface. During the summer field campaign and over the suburban area of the MRSP, most PBL scheme simulations exhibited a cold and dry bias and overestimated wind speed. They also overestimated sensible heat flux, with high agreement index and correlation values. In general, the PBL scheme simulations performed well for latent heat flux, displaying low mean bias error and root square mean error values. Both incoming downward solar radiation and net radiation were also accurately simulated by most of them. The comparison of the nine PBL schemes indicated the local Mellor-Yamada-Janjic (MYJ) scheme performed best during the summer period, particularly for conventional meteorological variables for the land use suburban in the MRSP. During the winter field campaign, simulation outcomes varied significantly based on the site’s land use and the meteorological variable analyzed. The MYJ, Bougeault-Lacarrère (BouLac), and Mellor-Yamada Nakanishi-Niino (MYNN) schemes effectively simulated temperature and humidity, especially in the urban land use area. The MYNN scheme also simulated net radiation accurately. There was a tendency to overestimate sensible and latent heat fluxes, except for the rural land use area where they were consistently underestimated. However, the rural area exhibited superior correlations compared to the urban area. Overall, the MYJ scheme was deemed the most suitable for representing the convectional and nonconventional meteorological variables on the surface in all urban, suburban, and rural land use areas of the MRSP.

Ensemble empirical mode decomposition based deep learning models for forecasting river flow time series

River flow forecasting is important for flood prediction and effective utilization of water resources. This study proposed a comprehensive methodology that simultaneously enables the creation of multiple datasets and a comparison of multiple learning models for forecasting river flows. First, the data characteristics of the river flow time series were assessed based on its nature and pattern characteristics. Second, data decomposition was made for simplifying the complex data into decomposed modes. Ensemble Empirical Mode Decomposition (EEMD) was used for this purpose that provides intrinsic mode functions (IMFs). In addition, as benchmark, traditional time series decomposition was also applied over the complex data. Third, based on their data characteristics, the IMFs were combined into different components. These components along with the traditional time series decomposition create multiple datasets. Then, the components from the different datasets were forecasted using deep learning models, mainly BPNN (Back-Propagation Neural Network), CNN (Convoluted Neural Network) and LSTM (Long-Short Term Memory). Then, ensemble prediction was used to get the final output. For verification and benchmark purpose, we have also used statistical models like SARIMA over the datasets. A comprehensive evaluation and model comparisons were made using Diebold-Mariano (DM) test, k-fold cross validation and with the results of Prophet. In addition, the temporal convolution network (TCN) and the gated recurrent unit (GRU) were also modelled. Finally, all the DL modelling was performed for two data situations: with missing data deleted and missing data imputed. An empirical case study was done considering the average of daily time series data of Neeleswaram Hydrological Observation (HO) station over the Periyar river of South India for the period 2017–2021. The empirical assessment was made using several tests for all the different datasets and different learning models for further insights. Our results indicate that the deep learning-based models offer better predictions for complex time series in comparison to traditional statistical models. The DL models applied on the original time series data provided robust predictions with good performance such as low RMSE and high correlation values. However, the DL models’ performances are not enhanced through integration with time series decomposition and EEMD. Furthermore, the empirical results and the k-fold cross validation-based test indicate that all the DL models show equivalent performances. Interestingly, the DL models show superior performance over Prophet.

Analysis Of Factors In Tenders For Procurement Of Construction Goods/Services Using The E-Catalogue System

The government is updating regulations for procuring goods and services by introducing an e-catalogue system in the auction/tender process. Construction companies need to study the factors in tenders for the Procurement of construction goods/services using the e-catalogue system to ensure that the tender process using the e-catalogue system runs well and optimally. This research aims to analyze the factors in tenders for the Procurement of construction goods/services using the e-catalogue system. The method used was distributing questionnaires to small construction companies in Jombang Regency to collect data for further analysis. Based on data analysis, four factors were obtained, namely human resource factors (X1), principles of Procurement of goods and services (X2), infrastructure condition factors and e-catalogue support system arrangements (X3), and benefits factors for implementing e-catalogues (X4). The results were then tested for validity and reliability to ensure the validity and reliability of the data. Further analysis was carried out using descriptive analysis methods and Pearson correlation analysis. The results of the descriptive analysis show that the highest factor in tenders for the Procurement of construction goods/services using the e-catalogue system is the human resources factor (X1), with a mean value of 3.620 and a standard deviation value of 0.047. According to the research outcomes regarding Pearson correlations the relationship between factors that has the highest correlation value is (X1) with (X3) both have a strong correlation with a correlation value of 0.771.

Aerobic granular sludge sequencing batch reactor for high strength domestic wastewater treatment: Assessing kinetic models and microbial community dynamics

The study aims to treat high strength domestic wastewater (HSDW) and cultivate microbial granular sludge in a sequencing batch reactor (SBR). A bench-scale bioreactor was used for startup. Response surface methodology (RSM) was used to determine the ideal parameters for removing organic materials from HSDW. Organic loading rate (OLR) and cycle time were the independent variables taken into consideration, while the response variable was COD removal. Results from RSM revealed that the optimal conditions for achieving > 98 % COD and TP (>80) removal were a cycle length of 6 h and an OLR of 1.5 kgCOD/m3d. The Modified-Kincannon and the Grau second-order kinetic models were compared to the COD removal rate for the SBR system. The saturation rate constant (Ni) and maximal substrate removal rate (Vc) were proposed to be 6.8 g L−1 d−1 and 0.83 g L−1 d−1, respectively, based on the modified Stover-Kincannon model. The modified Stover-Kincannon (R2 = 0.93673) and Grau second order (R2 = 0.99219) models high correlation coefficient values show that the experimental results and the prediction for the microbial aerobic granular based SBR system accord well. It was discovered that the modified Stover-Kincannon model was less suitable than the Grau second order model. The system microbial community changed because of the granulation process, as the reactor design operation had a big impact on the community structure. Decreased filamentous bacteria favoured several taxa that have been found to exist in granular biomass that is appropriate for treating wastewater. This study sheds important light on the complex interactions that occur between microbial populations and operational variables all through the granulation process in SBR.

Dual-responsive drug carrier based on zeolitic imidazolate framework-8 incorporated into gold nanoparticles/chitosan/copper sulfide for controlled drug delivery

Currently, two major obstacles in effective treatment of breast cancer are development of resistance to chemotherapeutic drugs by cancer cells and the off-target cytotoxicity of these drugs. The main aim of this study was to synthesize a dual responsive nanocarrier by incorporating the zeolitic-imidazolate framework-8 into the gold nanoparticles/chitosan/copper sulfide for controlled release of doxorubicin. The prepared nanocarrier was characterized using various techniques, including Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis, X-ray diffraction, and zeta potentials. The optimum experimental conditions, including adsorbent dosage (0.01–0.03 g), pH of solutions (5–9), contact time (5–15 min), and temperature (25–45 °C) at three levels were studied using the central composite design based on the response surface methodology. Our results indicated highest response (99 %) with 0.03 g of nanocarrier, at pH of 5, time of 10 min, and temperature of 35 °C. The obtained data were further studied with seven isotherm models. Based on the lower error sum of square values (0.059–0.514) and higher correlation coefficient values (0.9857–0.9972), Langmuir and Sips isotherm models were observed to be the best fitted. The in-vitro drug release study indicated 37.97 % of the drug being released in the simulated environment with physiological conditions, while 71.29 % of the drug was released in the simulated cancer cells condition at higher temperature. This data confirms the pH and temperature sensitivity of the nanocarrier. Enhanced drug release from the nanocarrier indicated low cell viability in the breast cancer cell line (MCF-7) under near-infrared laser irradiation. The IC50 values of nanocarrier and pure drug in MCF-7 cells were 243.95 and 4.442 μg L−1, respectively. Based on these data, we are concluding that our dual responsive nanocarrier has the potential to overcome the problems development of resistance of cancer cells and toxicity associated with current treatment of breast cancer.

On the influence of coastal structures on shoreline variability and beach interconnection

Approximately 24 % of sandy beaches worldwide are experiencing erosional processes often driven by natural forces that are intensified by human activities. Erosion becomes a significant issue, particularly when it affects areas of socioeconomic and ecological importance. Thus, this study aimed to utilize coastline data from a city in southern Brazil, extracted from satellite images using free computational tools, with three main objectives: (1) identify spatial and temporal trends and cycles; (2) assess the impact of coastal structures; and (3) identify beach interconnection indicating bypassing processes in sandy beaches. For the first two objectives, shoreline data from 2015 to 2023, as well as statistical data, were extracted using the CASSIE tool. These data were filtered to minimize tidal effects and remove outliers. Subsequently, they were visualized using a Hovmöller diagram to provide a systemic overview. Through this diagram, it was possible to identify sediment transport toward the north and a sub-annual cycle of seasonality, with erosion occurring during the southern hemisphere's spring months, a period characterized by high-energy events. The relationship between erosional/accretional stretches and anthropogenic structures highlighted the negative impact of longitudinal structures, which exacerbated erosional processes, and the limited effectiveness of transverse structures in retaining necessary sediment. Moreover, the detrimental effect of building a jetty on its adjacent beaches was identified due to the alteration of the refraction control point and consequently, the equilibrium state of the beaches, which are unable to adapt due to coastal urbanization. For the third objective, shoreline data detected by the CoastSat tool from 1984 to 2023 were used. These data were subjected to Pearson correlation analysis. Perpendicular barriers to the shoreline that exhibited low correlation indicate limits of circulation cells that prevent the continuous bypassing process. Conversely, high correlation values suggest that the barrier allows for the occurrence of this process.

ПИТОМИЙ ЕЛЕКТРИЧНИЙ ОПІР ЯК ПОКАЗНИК УПОРЯДКУВАННЯ СТРУКТУРИ КОКСУ

The influence of the coking process temperature on the resistivity of coke was studied. Taking into account that the resistivity and post-reaction strength are affected by some common factors, such as temperature level and coke readiness, which indicates the orderliness of its structure, the relationship between these indicators was established. The analysis of dependencies and their statistical evaluation indicates that the studied relationships are characterized by high values of correlation coefficients (0.77–0.84) and determination (59.29–70.56 %).

Leachate treatment via electrocoagulation–coal‐based powdered activated carbon process: Efficiencies, mechanisms, kinetics, and costs

AbstractThis study aims to improve COD, NH3‐N, and turbidity removal from Bingöl's leachate using a single‐reactor integrated electrocoagulation (EC)–coal‐based powdered activated carbon (CBPAC) process under various experimental conditions. In the EC‐CBPAC process, three stainless‐steel cathodes and three aluminum electrodes were connected to the negative and positive terminals of the power supply, respectively. The initial concentrations in the leachate were 1044 mg O2/L for COD, 204 mg/L for NH3‐N, and 57 NTU (or 71.25‐mg (NH2)2H2SO4/L) for turbidity, respectively. After a 40‐min EC‐CBPAC process, with a CBPAC dosage of 5 g/L and pH of 5 for COD and turbidity, and 9.5 for NH3‐N, the optimum removal efficiencies for COD, NH3‐N, and turbidity were achieved at 92%, 40%, and 91%, respectively. When the EC process was applied without CBPAC under the same experimental conditions, the removal efficiencies of COD, NH3‐N, and turbidity were 87%, 28%, and 54%, respectively. Before and after the EC‐CBPAC process, the Brunauer–Emmett–Teller (BET) surface area, pore volume, and mean pore diameter of the CBPAC were found to be (888 m2/g, 0.498 cm3/g, and 22.28 Å) and (173 m2/g, 0.18 cm3/g, and 42.8 Å), respectively. The optimum pseudo‐first‐order (PFO) rate constants for COD, turbidity, and NH3‐N were determined to be 3.15 × 10−2, 4.77 × 10−2, and 8.8 × 10−3 min−1, respectively. With the current density increasing from 15 to 25 mA/cm2, energy consumption, unit energy consumption, and total cost increased from 68.7 to 122.4 kWh/m3, 6.948 to 15.226 kWh/kg COD, and 0.85 to 1.838 $/kg COD, respectively.Practitioner points EC‐CBPAC process has greater COD, NH3‐N, and turbidity removal efficiency than EC process. COD and turbidity achieved their optimum disposal efficiencies at 92% and 91%, respectively, at pH 5 The most efficient disposal efficiency for NH3‐N was observed to be 40% at pH 9.5. EC‐CBPAC process increased removal efficiencies for COD, NH3‐N, and turbidity by 20%, 19%, and 38%, respectively, compared with EC alone. The turbidity, NH3‐N, and COD disposal fitted PSO model due to high correlation values (R2 0.94–0.99).

Classroom Management in Improving the Quality of Learning in the Education Units

This study focuses on classroom management in education units as a basis to improve the quality of learning. This process relates to the instruments used for data collection. The instrument consisted of thirty-five related to classroom management. The sample was 142 people who were spread in the education unit of Riau Province, Indonesia. Sampling was undertaken by random sampling in accordance with the readiness of the respondent. Data were analysed by substituting the respondents’ results through descriptive statistics, then using the Product Moment correlation method, the contribution of indicators in realizing the success of implementing learning through ANOVA. The average demographic data of the respondents was 4.273 (very high); compile a lesson plan was 4.304 (very high); carry out learning was 4.352 (very high); assess achievement was 4.233 (very high); carry out follow-up the results of the assessment of learning achievement was 3.813 (high). Quality did not vary with a very high correlation value so that it contributed a role in building class management up to 100%.

Klasterisasi Kabupaten/Kota di Provinsi Papua Berdasarkan Indikator Pembangunan Manusia Tahun 2022

The Human Development Index (HDI) is a human development metric empowered by several main elements that reflect the quality of life, namely HLS, RLS, PKK, and UHH. High HDI is one of the basic capital for a country's sustainable development. The improvement of human development in Indonesia continues to increase every year. However, there is still uneven development between regions in Indonesia. The HDI of Papua Province has always been the lowest in Indonesia in the last 10 years and each indicator is still unevenly distributed between districts/cities. This needs to be a special concern by the government. Thus, this study was carried out with the aim of grouping districts / cities in Papua Province based on human development indicators with the application of the agglomerative hierarchy cluster method. Based on the highest correlation value, Average Linkage is the best method with the optimal number of clusters formed is five clusters (Very High, High, Medium, Low, and Very Low) according to the Calinski-Harabasz Index test conducted. The findings of this study can be the basis for designing equitable development policies in districts/cities in Papua Province.

Single-beat mapping during programmed ventricular stimulation identifies infarct-related ventricular tachycardia isthmus sites

Abstract Introduction Complete characterization of reentrant ventricular tachycardia (VT) circuits using classical electrophysiological criteria and high-density mapping during VT is complex. Often, VT episodes cannot be fully characterized because of hemodynamic instability during the mapping procedure. This common limitation has motivated the implementation of mapping strategies during sinus rhythm or ventricular pacing aiming to identify scar regions associated with VT isthmuses or potential arrhythmogenicity. However, these strategies still require time-consuming catheter-based mapping. Purpose We aimed to develop and validate a single-beat mapping strategy during programmed ventricular stimulation (PVS) to identify scar regions associated with VT isthmus sites. We hypothesize that protected VT isthmuses within scar regions are characterized by larger activation time dispersion than surrounding scar areas not related to critical isthmus sites. Methods Experimental study including 16 pigs with established myocardial infarction (MI) in the anterior wall. Ten-to-twelve weeks after MI, pigs underwent left ventricular characterization using 3D late-gadolinium enhancement cardiac magnetic resonance imaging, and further invasive electroanatomical mapping to obtain high-density sequential activation maps during ventricular pacing and VT. High-density maps were compared with single-beat maps using a 64-pole basket catheter positioned in the left ventricle. After VT induction, high-density activation maps in well-tolerated VT episodes were used to identify VT isthmus sites. Simultaneous activation data from the 64-pole basket catheter were also obtained during PVS and VT. Off-line analysis was performed to: (i) validate single-beat activation maps during the baseline drive pacing cycle length (S1), and (ii) identify basket-catheter electrodes with the larger local activation time-dispersion score (LAT-DS) during PVS including up to 4 coupled extrastimuli (S2, S3, S4, S5) (Figure 1). LAT-DS was defined as the normalized product of the LAT of each electrode and the activation dispersion among its neighboring electrodes. Results Basket-catheter based single-beat activation maps during S1 pacing showed high correlation values with high-density sequential activation maps (N=16. R²=0.91). In 7 pigs, the VT isthmus site was fully characterized using high-density activation maps. During any of the coupled extrastimuli of the PVS protocol, basket catheter electrodes colocalizing with documented VT isthmus sites on high-density maps showed higher LAT-DS values compared to surrounding electrodes located on scar or healthy regions. Conversely, LAT values alone could not differentiate scar regions relevant for VT maintenance during PVS protocol (Figure 2). Conclusions Sixty-four pole single-beat activation maps during PVS identify left ventricular scar areas with higher than surrounding LAT-DS values that are associated with VT isthmus sites.Figure 1.Experimental modelFigure 2.A-Example. B-C-Full results

Cross-shelf transport of high chlorophyll-a coastal waters by frontal eddies in the south of Java sea

Intense mesoscale eddy activity has been observed off the southern Java coast (SJC), yet its impact on local ecosystems remains largely unknown. To investigate this, we examined remotely sensed altimetry, chlorophyll-a (Chl-a), and sea surface temperature (SST) data, focusing on their response to eddies in the region. Our eddy detection and tracking analysis revealed a unique cyclonic frontal eddy near the SJC coast and a large anticyclonic eddy offshore, active from July to September 2019. The cyclonic frontal eddy induced water transport through eddy filaments, upwelled subsurface cold water, and enhanced Chl-a concentrations by horizontally entraining Chl-a-rich shelf water offshore. The anticyclonic eddy then contributed to further distributing this enriched water southward. The mean cross-shelf transport associated with the frontal eddy was estimated at 1.80–2.33 Sv offshore, exporting approximately 1.87–2.40 × 103 tons of Chl-a to the Indian Ocean during its lifetime. Additionally, the spatial cross-correlation analysis of zonal and meridional wind stress with Chl-a revealed relatively high correlation values (0.6–1) and short lag times (<5 days) in offshore areas, indicating that the role of wind in the Chl-a advection cannot be ignored. We propose a three-stage mechanism to explain the presence of high Chl-a offshore:1) Wind-driven upwelling intensifies coastal nutrients, elevating Chl-a concentrations in coastal waters, 2) Frontal cyclonic eddy facilitates the retention and offshore export of these upwelling-enriched waters. and 3) Anticyclonic eddy advects these nutrient-rich waters further south. The combination of enhanced coastal upwelling and eddies can explain nutrient-rich coastal waters in offshore regions.

Correlation between serum 1,25-dihydroxyvitamin D and 25-hydroxyvitamin D in response to analytical procedures; a systematic review and meta-analysis

Abstract Objectives In this study, the aim is to provide a more detailed understanding of vitamin D metabolism by evaluating the correlation between 1,25-dihydroxyvitamin D (1,25(OH)2D) and 25-hydroxyvitamin D (25(OH)D) according to the variations in measurement methods and clinical conditions. Methods We searched PubMed, Embase, and Web of Science for studies reporting correlation results between 1,25(OH)2D and 25(OH)D. We performed a meta-analysis based on the correlation results of 1,25(OH)2D and 25(OH)D in different clinical conditions. We included a total of 63 studies and our laboratory’s results in the meta-analysis. The studies were categorized into high-quality methods group (HQMG), medium-quality methods group (MQMG), and low-quality methods group (LQMG) based on the 25(OH)D and 1,25(OH)2D measurement. Results In the healthy, renal disease, and other disease groups, the highest correlation values were observed in the studies categorized as HQMG, with values of 0.35 (95 % CI; 0.23–0.48), 0.36 (95 % CI; 0.26–0.42), and 0.36 (95 % CI; 0.22–0.48), respectively. Significant statistical heterogeneity was observed in the healthy, renal disease, and other disease groups, with I2 values of 92.4 , 82.7, and 90.7 %, respectively (p&lt;0.001). Both Funnel plots and the results of Egger’s and Begg’s tests indicated no statistically significant bias across all studies. Conclusions A significantly low correlation was found between 25(OH)D and 1,25(OH)2D. However, higher correlations were found in the studies categorized as HQMG. Various factors, including methodological inadequacies and disparities, might contribute to this. In the future, with more accurate and reproducible measurements of 1,25(OH)2D, a clearer understanding of vitamin D metabolism will be achieved.

Forecasting of the Dental Workforce with Machine Learning Models

The aim of this study is to determine the factors affecting the dental workforce in Turkey to estimate the dentists employed with machine learning models. The predicted results were obtained by applying machine learning methods; namely, generalized linear model (GLM), deep learning (DL), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM) were compared. The RF model, which has a high correlation value (R2=0.998) with the lowest error rate (RMSE=656.6, AE=393.1, RE=0.025, SE=496115.7), provided the best estimation result. The SVM model provided the worst estimate data based on the values of the performance measurement criteria. This study is the most comprehensive in terms of the dental workforce, which is among the healthcare resources. Finally, we present an example of future applications for machine learning models that will significantly impact dental healthcare management.