Value Of Criteria Research Articles

Genetic evaluations of dairy goats with few pedigree data: different approaches to use molecular information.

One of the limitations of implementing animal breeding programs in small-scale or extensive production systems is the lack of production records and genealogical records. In this context, molecular markers could help to gain information for the breeding program. This study addresses the inclusion of molecular data into traditional genetic evaluation models as a random effect by molecular pedigree reconstruction and as a fixed effect by Bayesian clustering. The methods were tested for lactation curve traits in 14 dairy goat herds with incomplete phenotypic data and pedigree information. The results showed an increment of 37.3% of the relationships regarding the originals with MOLCOAN and clustering into five genetic groups. Data leads to estimating additive variance, error variance, and heritability with four different models, including pedigree and molecular information. Deviance Information Criterion (DIC) values demonstrate a greater fitting of the models that include molecular information either as fixed (genetic clusters) or as random (molecular matrix) effects. The molecular information of simple markers can complement genetic improvement strategies in populations with little information.

Methodical apparatus for risk assessment in decision-making to combat illegal migration at the state border of Ukraine

This study proposes a methodical apparatus for risk assessment when making decisions on combating illegal migration at the state border of Ukraine. This apparatus includes a risk assessment methodology, a set of risk assessment indicators and criteria for combating illegal migration, a model of illegal migration risk assessment and a model for choosing the processing method. The goal that was set during the methodology development involves the following main stages: forming a set of indicators and criteria for risk assessment, assessment of the threat impact level (assessment of the threat probability level and the level of the threat’s negative consequences), vulnerability assessment, determining the risk level and the processing method. An expert survey of analysts from the Ukrainian Border Guard Agency was carried out to develop this set of risk assessment indicators and criteria. The proposed set includes new indicators, mathematical assessment models, quantitative values of assessment criteria and considers the influence weight of the main factors on the threat and vulnerability levels. This provides an opportunity to bring the qualitative characteristics of the risk level indicator to the provisions of modern risk management theory to obtain the quantitative value of the risk level, to exclude a subjective factor in assessment and to determine the risk level more accurately. The improved methodical apparatus allows developing of an action strategy aimed at minimizing the negative consequences of risk when making management decisions to combat illegal migration to assess a risk management result using an economic effect indicator.

A machine learning method based on stacking heterogeneous ensemble learning for prediction of indoor humidity of greenhouse

Efficient production management, high productivity, and improved product quality are essential for the success of greenhouse production in producing sustainable agricultural products. Several environmental factors, including air temperature, humidity, CO2 levels, and light levels, have a major influence on this. Managing internal humidity is critical to preventing climate variation, disease, and pests in glasshouses that can cause significant damage if not properly controlled. This article assesses the performance of machine learning models in predicting indoor humidity levels in a greenhouse using a dataset from Guilan University's greenhouse located in Rasht City, Iran. Seven regression models were used to make predictions: multiple linear regression (MR), polynomial regression (PR), decision tree regression (DT), k-nearest neighbors regression (KNN), support vector regression (SVR), random forest regression (RF), and extreme gradient boosting regression (XGBoost). Evaluation criteria including coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used to evaluate each model. The best machine learning models were selected based on these criteria values (R2 > 0.94) and combined using the stacking method, a popular ensemble learning technique, to create a metamodel for accurately predicting internal humidity within the greenhouse. The metamodel showed exceptional performance, with significantly improved evaluation criteria on the test dataset, specifically R2 of 0.96515, MAE of 0.01395, MSE of 0.03205, and RMSE of 0.00102.

A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC.

As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown. The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features. In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs. The new risk model may be a new method to predict the prognosis and immune status of ccRCC.

Turbid Waters and Clearer Standards: Refining Water Quality Criteria for Coastal Environments by Encompassing Metal Bioavailability from Suspended Particles.

Suspended particulate matter (SPM) carries a major fraction of metals in turbid coastal waters, markedly influencing metal bioaccumulation and posing risks to marine life. However, its effects are often overlooked in current water quality criteria for metals, primarily due to challenges in quantifying SPM's contribution. This contribution depends on the SPM concentration, metal distribution coefficients (Kd), and the bioavailability of SPM-bound metals (assimilation efficiency, AE), which can collectively be integrated as a modifying factor (MF). Accordingly, we developed a new stable isotope method to measure metal AE by individual organisms from SPM, employing the widely distributed filter-feeding clam Ruditapes philippinarum as a representative species. Assessing SPM from 23 coastal sites in China, we found average AEs of 42% for Zn, 26% for Cd, 20% for Cu, 8% for Ni, and 6% for Pb. Moreover, using stable isotope methods, we determined metal Kd of SPM from these sites, which can be well predicted by the total organic carbon and iron content (R2 = 0.977). We calculated MFs using a Monte Carlo method. The calculated MFs are in the range 9.9-43 for Pb, 8.5-37 for Zn, 2.9-9.7 for Cu, 1.4-2.7 for Ni, and 1.1-1.6 for Cd, suggesting that dissolved-metal-based criteria values should be divided by MFs to provide adequate protection to aquatic life. This study provides foundational guidelines to refine water quality criteria in turbid waters and protect coastal ecosystems.

Pembelajaran daring kemampuan berpikir kritis materi jenis usaha dan kegiatan ekonomi di indonesia pada siswa kelas V SD Atta’zhimiyah kota Bandung dengan metode kontekstual menggunakan video pembelajaran

This study aims to examine the learning of critical thinking skills on the type of business and economic activity in Indonesia in class V students with contextual methods using online-based learning video media.. The research method used is descriptive qualitative. The subjects in this study were fifth grade students of SD Atta'Zhimiyah Bandung City with a total of 25 students. The instruments used were teacher and student observation sheets, test questions, and teacher and student interview questionnaires. The results showed that there was an increase in the quality of learning when the learning video media was applied. This is indicated by the average value of IPS understanding in the class of 89.20 which is classified in the good category, the highest score of students is 100 and the lowest value is 60. Student learning mastery shows 92.0% of students getting scores above the minimum completeness criteria and 8, 0% still gets a score below the minimum completeness criteria value.

Advanced Thermo-Hydraulic Assessment of Helical Pipes with Different Shapes of Jackets Using Single-Phase and Hybrid Nanofluids

This study investigates the efficiency and flow characteristics of helical pipes with different jacket shapes, utilizing single and hybrid nanofluids to enhance heat transfer. The research work presented here provides a unique, coherent method to enhance heat transfer efficiency by concurrently incorporating geometrical alterations and improving operating fluid efficiency. A computational framework analyzes turbulent flow in three-dimensional helical pipes with Reynolds numbers ranging from 5000 to 30,000 and a constant heat flux of 1000 W/m2. This investigation involves evaluating the thermo-hydrodynamic performance of spiral pipes with circular, triangular, and square-shaped jackets, using various volume fractions of single-phase nanofluids (water-based Al2O3 and CuO nanofluids) and a water-based hybrid nanofluid comprising Al2O3/Cu. The results indicate that using different nanofluids significantly improves the Nusselt number value and heat transfer coefficients, ranging from 36 % to 60 % compared to water in a smooth coiled pipe. The strategic design of the jacket enhances the Nusselt number values by generating vortices and increasing turbulence. The Bejan number indicates greater irreversibility in the heat transfer process, leading to substantial energy dispersion. The performance evaluation criterion (PEC) value exceeding one suggests that the modified geometries have the potential to outperform smooth channels.

A Trustworthy and Explainable Framework for Benchmarking Hybrid Deep Learning Models Based on Chest X-Ray Analysis in CAD Systems

Evaluating the trustworthiness of deep learning-based computer-aided diagnosis (CAD) systems is challenging. There is a need to optimize trust and performance in model selection. A wide range of models based on evaluation metrics can make it challenging to choose the best one, especially for complex multi-criteria decision-making problems. In the case of COVID-19 diagnosis, using both physicians’ evaluation and AI techniques to establish trust is essential. In this study, 1551 chest X-rays were analyzed using deep transfer learning (DTL) with six models and four SVM kernels. This resulted in 24 hybrid DTL–SVM models. Seven metrics were evaluated using fuzzy multiple-criteria decision-making (MCDM), which uses a dynamic decision matrix to select the best model. This matrix incorporates fuzzy-weighted zero-inconsistency (FWZIC) for weight coefficients and Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for benchmarking. The Grad-CAM technique compared the best model with 16 images, ensuring explainability. Top-performing models were identified, including SqueezeNet-SVM linear, VGG19-SVM linear, and VGG19-SVM. Sensitivity analysis was used to quantify the impact of changing weighted criteria values. A physician expert validated fuzzy MCDM through Grad-CAM analysis, a new aspect of this study. The framework presented in this study was benchmarked against seven other studies and achieved a perfect score in four crucial areas. Trustworthiness is essential for CAD systems, and this study effectively addresses trust and performance challenges in AI model-based CAD systems. The study systematically evaluated the requirements for trustworthiness, including accountability, fairness, robustness, accuracy, and reproducibility, and the results supported this.

INTERNET-BASED HOTEL MARKETING STRATEGY IN INDONESIA: EMPIRICAL STUDY WITH VECTOR AUTOREGRESSION MODEL

Purpose: The purpose of this study is to determine how the Indonesian hotel sector may use the internet as a productive and successful marketing tool. Theoretical Framework: The opportunities offered by the internet include ease of accessing and disseminating information about hotel products and services, the ability to reach a wider and more diverse market, and the opportunity to improve the quality and efficiency of services (Kapoor &amp; Kapoor, 2021). However, the internet also poses challenges, such as increasingly fierce competition, changes in consumer preferences and behavior, and threats to data security and privacy (Bandara, Fernando, &amp; Akter, 2020). Design/Methodology/Approach: This research uses secondary data from the World Bank and UNWTO regarding Internet users, Number of hotel rooms, Total guests, and Tourism arrivals from 2000 to 2022. This research applies a vector autoregression (VAR) model with two lags to test the relationship between five time variables. series, namely Total Guests, Tourism Arrivals, Room Rate, Number Of Rooms, and Internet Users. The VAR model was chosen because it has the lowest information criteria values, namely AIC, SC, and HQ, which shows a high fit to the data. Findings: Number of Rooms is the variable that has the most influence on other variables, which indicate the capacity and quality of hotel accommodation. Therefore, hotels are advised to increase the Number Of Rooms in order to attract more tourists and increase revenue. Apart from that, Total Guests, Tourism Arrivals, and Room Rate also have a significant effect on several other variables, which reflect the dynamics of market demand and supply, as well as hotel competitiveness. Therefore, hotels are advised to maintain and improve these variables with appropriate marketing strategies, such as promotions, distribution channels and social networks. Meanwhile, Internet User is the weakest variable in the VAR model, which has a negative relationship with Total Guests, Tourism Arrivals, and Room Rate, which reflects the influence of information technology on consumer behavior. Research, Practical &amp; Social Implications: This research makes a practical contribution by providing solutions for the hotel industry in Indonesia to increase performance and income by utilizing the internet as an effective and efficient marketing medium. Originality/Value: This research provides a theoretical contribution by examining the influence of the internet as an effective and efficient marketing medium for the hotel industry in Indonesia, which is a topic that has rarely been researched before.

Modeling of deionization of aqueous compounds using electrodialysis process (data collection for enhanced clarity)

ABSTRACT The deionization process is increasingly recognized as a solution to global water shortages. This study aims to investigate the impact of various parameters on the deionization efficiency. Specifically, volume flow rates (F) were tested (1, 2.5, 5, 10, 15, and 20 ml min−1), solute concentrations (C) (200, 500, and 1000 mg l−1), temperatures (T) (50, 60, and 70°C), voltages (V) (10, 20, and 30 V), and pressures (P) (200, 400, and 800 Pa). In another experiment examined F (100, 250, and 500 mol l−1 m2), V (0.1, 0.5, and 1 V), C (100, 200, 300, 400, and 500 moles m3), and time durations (D) (16, 32, 48, 64, 80, and 96 min). These experiments aimed to determine the separation percentage of potassium chloride cations and anions and were based on data extracted from scientific articles. In the subsequent step, the ANFIS model with the FCM network creation method and the error backpropagation training algorithm was employed to evaluate the impact of each cell input on the separation percentage, output flux, and the separation percentage of potassium chloride cations and anions. This evaluation was conducted through 31 and 15 iterations, respectively. Based on the evaluation criteria, it was observed that the volume flow rate as the sole input yielded the highest prediction accuracy for the separation percentage and output flux. Additionally, the FCTVP model results indicated a slight reduction in the error criteria compared to the FCTV, FCT, FC, and single models. Consequently, it can be concluded that the cell pressure factor may play a decisive role, either reducing or increasing the separation process (R2 = 0.93,MBE = −0.3, and RMSE = 6.4), as well as the output flux (R2 = 0.85, MBE = 0.0003, and RMSE = 0.07). Furthermore, the statistical criteria values of the FVCT model for the separation percentage of potassium chloride cations and anions demonstrated a proper correlation (R2 = 0.96 and 0.95), slight underestimation, and low error MBE = −0.57 and −1.2) (RMSE = 15.2 and 18.5, respectively).

Adipose Tissue Quantification Improves the Prognostic Value of GLIM Criteria in Advanced Gastric Cancer Patients.

The present study investigated whether the risk of recurrence after curative surgery could be further stratified by combining the Global Leadership Initiative on Malnutrition (GLIM) criteria and changes in subcutaneous (SAT) and visceral (VAT) adipose tissue mass after surgery in patients with advanced gastric cancer (AGC). This study retrospectively analyzed 302 patients with AGC who underwent curative surgery. Based on the GLIM criteria, patients were classified into malnourished and non-malnourished groups. The cross-sectional areas of SAT and VAT were measured from preoperative and 6-month post-operative computed tomography (CT) images. Multivariate survival analyses demonstrated that GLIM-defined malnutrition (p = 0.008) and loss of VAT after surgery (p = 0.008) were independent risk factors for recurrence-free survival (RFS). Evaluation of the prognostic value of combining the two independent predictors showed that malnourished patients with a marked loss of VAT had the worst 5-year RFS rate of 35.2% (p < 0.001). Preoperative GLIM-defined malnutrition and a loss of VAT during the first 6 months after surgery were independent predictors for RFS in patients with AGC. Changes in the VAT area after surgery could further enhance the prognostic value of the GLIM criteria for predicting the risk of gastric cancer recurrence.

Relationship Between Tenofovir Diphosphate Concentrations in Dried Blood Spots and Virological Outcomes After Initiating Tenofovir-Lamivudine-Dolutegravir as First-Line or Second-Line Antiretroviral Therapy.

Tenofovir diphosphate (TFV-DP) concentration in dried blood spots is a marker of long-term adherence. We investigated the relationship between TFV-DP concentrations and virological outcomes in participants initiating tenofovir-lamivudine-dolutegravir (TLD) as first-line or second-line antiretroviral therapy. Three primary care clinics in Khayelitsha, Cape Town, South Africa. We conducted a post hoc analysis of 2 randomized controlled trials of participants initiating TLD. TFV-DP concentrations and viral loads were measured at 12, 24, and 48 weeks. Multivariable logistic regression was performed to assess the association with virological suppression (<50 copies/mL) per natural logarithm increase in TFV-DP concentration. Generalized estimating equations with logit link were used to assess associations with virological rebound. The Akaike Information Criterion and Quasi-likelihood Information Criteria were used to compare models built on continuous TFV-DP data to 4 previously defined concentration categories. We included 294 participants in the analysis, 188 (64%) of whom initiated TLD as second-line therapy. Adjusted odds ratios (95% CIs) of virological suppression were 2.12 (1.23, 3.75), 3.11 (1.84, 5.65), and 4.69 (2.81, 8.68) per natural logarithm increase in TFV-DP concentration at weeks 12, 24, and 48, respectively. In participants with virological suppression at week 12, the adjusted odds ratio for remaining virologically suppressed was 3.63 (95% CI: 2.21 to 5.69) per natural logarithm increase in TFV-DP concentration. Models using continuous TFV-DP data had lower Akaike Information Criterion and Quasi-likelihood Information Criteria values than those using categorical data for predicting virological outcomes. TFV-DP concentrations in dried blood spots exhibit a dose-response relationship with viral load. Analyzing TFV-DP concentrations as continuous variables rather than conventional categorization may be appropriate.

Households catastrophic health expenditure among older people with noncommunicable diseases: A cross-sectional study from Iran.

Noncommunicable diseases are one of the main difficulties faced by older adults in many countries. The study aimed to investigate the households'catastrophic health expenditure (CHE) among older people with noncommunicable diseases. This cross-sectional study was conducted between October and December 2022 in Semnan, Iran, with the participation of 400 older individuals suffering from diabetes and hypertension. The Content Validity Ratio(which is calculated based on necessity criterion) and Content Validity Index(which calculated based on relevance, transparency, and simplicity criteria) values of 0.94 and 0.89, respectively, confirmed the content validity of the researcher-made questionnaire. The occurrence of CHE was estimated using the World Health Organization's rule of "allocating at least 40% of non-food household costs to healthcare."Due to the qualitative nature of the data, the χ 2 test was utilized to assess the statistical association between demographic and background variables and occurrence of CHE. Older people with diabetes and hypertension had an annual direct medical costs of $821 averagely, which includes 9.7% for diagnosis, 23.9% for doctor visits, and 66.4% for treatment. Direct medical costs account for around 0.26 of nonfood costs, with 12.5% of seniors facing high medical charges. Age, marital status, type of basic health insurance, diabetes, the time elapsed since initial diabetes diagnosis, the severity of diabetes complications, and the development of diabetes-related visual impairments, are associated with CHE(p < 0.05). While the prevalence of CHE among elderly individuals with diabetes and hypertension is reasonably manageable, targeted promotional efforts are still necessary to protect those at high risk.

MODELING THE MANY EARTHQUAKES IN SUMATRA USING POISSON HIDDEN MARKOV MODELS AND EXPECTATION MAXIMIZATION ALGORITHM

Sumatra Island is one of the islands that are prone to earthquakes because Sumatra Island is located at the confluence of three plates, namely the large Indo-Australian plate, the Eurasian plate and the Philippine plate. In general, the number of earthquake events follows the Poisson distribution, but there are cases where there is overdispersion in the Poisson distribution. The Poisson Hidden Markov Models (PHMMs) method is used to overcome overdispersion, then applying the Expectation-Maximization Algorithm (EM algorithm) to each model to obtain the estimated parameters. From the models obtained, the best model will be selected based on the smallest Akaike Information Criterion (AIC) value. The data used is secondary data on earthquake events on the island of Sumatra from January 2000 to December 2022 with a depth of ≤ 70 Km and a magnitude of ≥ 4.4 Mw. From the research, the model with m = 3 is the best estimation model with an AIC value of 1503,286. From the best model, estimates are obtained for Poisson Hidden Markov Models with an average occurrence of earthquakes of 5.7633 ≈ 6 events within one month.

A Data-driven Approach for Generating Leaf Tissue Nutrient Interpretation Ranges for Greenhouse Lettuce

In the absence of controlled sufficiency studies, foliar interpretations for many horticultural crops are based on survey concentrations from small data sets. In addition, both survey and sufficiency ranges provide little interpretation regarding zones that are above or below the concentration range deemed “sufficient.” While providing a critical initial set of ranges, it was based on a limited set of data and therefore improvements in interpretation of data are needed. This study presents a novel method based on 1950 data points to create data-driven nutrient interpretation ranges by fitting models to provide more refined ranges of deficient (lowest 2.5%), low (2.5% to 25%), sufficient (25% to 75%), high (75% to 97.5%), and excessive (highest 2.5%). Data were analyzed by fitting Normal, Gamma, and Weibull distributions. Corresponding P values were calculated based on the Shapiro-Wilk test for normality for the Normal and Gamma distributions, and the Kolmogorov-Smirnov test was used for the Weibull distribution. The optimal distribution was selected based on the lowest Bayesian Information Criterion (BIC) value and visual fitness. The Weibull distribution best represented nitrogen, phosphorus, potassium, calcium, manganese, zinc, and copper, and the Gamma distribution best represented magnesium, sulfur, iron, and boron. Using the selected distributions, we propose a refined set of nutrient evaluation ranges for greenhouse-grown lettuce. These refined standards will aid growers and technical specialists in more accurately interpreting leaf tissue sample data.

Lawson Criterion Analysis of D-3He Fusion Reaction

The Lawson criterion constitutes a pivotal condition for the viability of sustained fusion reactions. This study employs numerical methods and data analysis in Microsoft Excel to ascertain the Lawson criterion for D-³He fusion at temperatures ranging from 75 to 150 keV. Furthermore, the investigation examines the influence of bremsstrahlung radiation and evaluates fusion reactivity based on Hively and Bosch-Hale parameters. Hively’s reactivity value falls within the range of 1.121 × 10−22 – 6.750 × 10−22 m3/s, the instability occurs at temperatures < 100 keV. The Bosch-Hale value differs significantly from the Hively reactivity, which is 1.208 × 10−22 – 2.340 × 10−22 m3/s. The bremsstrahlung radiation within the range of 4.819 × 10−20 – 6.802 × 10−20 keV exhibits minimal impact on the reaction. The Lawson criterion value within the scope of this study typically falls within the range of 0.437 × 1021 – 1.452 × 1021 s/m3. This range signifies the necessary combination of confinement time and plasma density to facilitate the generation of clean energy within a temperature range of 75 to 150 keV, thus propelling the fusion process toward the ignition phase.

Influence of reinforcement and its omission on trial-by-trial changes of response bias in perceptual decision making.

Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and nonsensory response bias. In the framework of signal detection theory, these aspects of discrimination performance are quantified through separate measures, sensitivity (d') for sensory discriminability and decision criterion (c) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single-trial level as a consequence of reinforcement history. We subjected rats to a two-stimulus two-response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three signal-detection-theory-based criterion learning models with respect to their ability to fit experimentally observed fluctuations of response bias on a trial-by-trial level. These models shift the criterion by a fixed step (1) after each reinforced response or (2) after each nonreinforced response or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady-state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial-based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies.

Comparison of four performance models in quantifying the inequality of leaf and fruit size distribution.

The inequality in leaf and fruit size distribution per plant can be quantified using the Gini index, which is linked to the Lorenz curve depicting the cumulative proportion of leaf (or fruit) size against the cumulative proportion of the number of leaves (or fruits). Prior researches have predominantly employed empirical models-specifically the original performance equation (PE-1) and its generalized counterpart (GPE-1)-to fit rotated and right-shifted Lorenz curves. Notably, another potential performance equation (PE-2), capable of generating similar curves to PE-1, has been overlooked and not systematically compared with PE-1 and GPE-1. Furthermore, PE-2 has been extended into a generalized version (GPE-2). In the present study, we conducted a comparative analysis of these four performance equations, evaluating their applicability in describing Lorenz curves related to plant organ (leaf and fruit) size. Leaf area was measured on 240 culms of dwarf bamboo (Shibataea chinensis Nakai), and fruit volume was measured on 31 field muskmelon plants (Cucumis melo L. var. agrestis Naud.). Across both datasets, the root-mean-square errors of all four performance models were consistently smaller than 0.05. Paired t-tests indicated that GPE-1 exhibited the lowest root-mean-square error and Akaike information criterion value among the four performance equations. However, PE-2 gave the best close-to-linear behavior based on relative curvature measures. This study presents a valuable tool for assessing the inequality of plant organ size distribution.

The Assessment of Hapanasan Hot Spring Nature Tourism Based on the Analysis of Working Areas for Nature Tourism Objects and Attractions

Hapanasan Hot Spring Nature Tourism (HHSNT) is located in Rambah Tengah Baru Village, Rambah Sub-district, Rokan Hulu District, Riau Province. The area of HHSNT is 9 ha. This research aims to identify existing facilities in this area and assess the attractiveness, accessibility, environmental conditions, accommodation, infrastructure, and availability of clean water at this nature tourist location. In-depth interviews were conducted with 13 key informants. Direct observation was carried out by systematically observing and recording this region's potential nature tourist attractions. The analysis of working areas for nature tourism objects and attractions (Analisis Daerah Operasi-Objek Daya Tarik Wisata Alam/ADO-ODTWA) is used to determine the feasibility value of this tourist attraction. The results of this research show that the criteria value for attractiveness is 83.33%, the criteria for accessibility is 95.83%, the criteria for accommodation is 83.33%, the criteria for supporting facilities and infrastructure is 80%, the criteria for availability of clean water is 73.88%, and the criteria for socio-economic environmental conditions is 66.66%. Overall, the HHSNT area is worthy of development, with a percentage of 80.50%. The development of HHSNT can bring numerous benefits to the community, economy, and environment of Rokan Hulu. It can contribute to sustainable development while preserving the region's nature and cultural heritage for future generations

Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.

This study aimed to create and validate a radiomics nomogram for non-invasive preoperative Ki-67 expression level prediction in patients with bladder cancer (BCa) using contrast-enhanced CT radiomics features. A retrospective analysis of 135 patients was conducted, 79 of whom had high levels of Ki-67 expression and 56 of whom had low levels. For the dimensionality reduction analysis, the best features were chosen using the least absolute shrinkage selection operator and one-way analysis of variance. Then, a radiomics nomogram was created using multiple logistic regression analysis based on radiomics features and clinical independent risk factors. The performance of the model was assessed using the Akaike information criterion (AIC) value, the area under the curve (AUC) value, accuracy, sensitivity, and specificity. The clinical usefulness of the model was assessed using decision curve analysis (DCA). Finally, to establish a radiomics nomogram, the best 5 features were chosen and integrated with the independent clinical risk factors (T stage) and Rad-score. This radiomics nomogram demonstrated significant correction and discriminating performance in both the training and validation sets, with an AUC of 0.836 and 0.887, respectively. This radiomics nomogram had the lowest AIC value (AIC = 103.16), which was considered to be the best model. When compared to clinical factor model and radiomics signature, DCA demonstrated the more value of the radiomics nomogram. Enhanced CT-based radiomics nomogram can better predict Ki-67 expression in BCa patients and can be used for prognosis assessment and clinical decision making.