Value Of Criteria Research Articles

Multi-Criteria Decision Making (MCDM) Approach for Identifying Optimal Solar Farm Locations: A Multi-Technique Comparative Analysis

This study explores the spatial suitability for solar farm placement using Geographic Information System (GIS) techniques (ArcGIS Pro 3.0.2) coupled with Muti-Criteria Decision-Making (MCDM) methods. Nine evaluation criteria significantly impacting solar farm placement in Erbil, Iraq were selected, and the Analytic Hierarchy Process (AHP) was utilized to assign proper weights to these criteria. Suitability maps were derived and classified using three MCDM methods: Weighted Overlay (WO), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighting) to establish comparisons. Discrepancies were noticeable between the outcomes of these methods, with distinctions observable across most categories. The validation process illustrated superior performance by SAW model, closely followed by TOPSIS model, while the weighted overlay model showed significant differences. The southeastern region of Erbil governorate was identified as the most suitable area for solar farm construction due to favorable criteria values and potential for future expansion. This approach massively facilitates locating and evaluating ideal areas for solar farm construction, thereby minimizing the time, financial resources, and efforts dedicated towards this endeavor.

Pulsatile Vascular Afterload Measurement Improves Prediction of Right-Sided Heart Dysfunction in Undifferentiated and Left-Sided Pulmonary Hypertension.

The association between raised pulmonary artery pressures, pulmonary vascular resistance (PVR), and right-sided heart dysfunction is well recognized. The added value of pulsatile indices, specifically pulmonary arterial compliance (PAC) and characteristic impedance, remains unclear. This study aimed to identify the optimal vascular afterload model for predicting right-sided heart dysfunction in newly diagnosed pulmonary hypertension (PH). This was a prospective cohort study of 86 patients with suspected PH (60±16 years; 66 women) who underwent right-sided heart catheterization and cardiac magnetic resonance imaging. Right ventricular end-diastolic volume index (RVEDVi) and right atrial (RA) volume index were measured using cardiac magnetic resonance imaging. Vascular resistance, PAC, and characteristic impedance were measured using pulmonary artery pressure from right-sided heart catheterization and pulmonary artery flow from cardiac magnetic resonance imaging. Optimal model fit was determined by the lowest Akaike Information Criterion value. A total of 58% of patients had a right ventricular ejection fraction <50%, 63% had an increased RVEDVi, and 56% had an increased RA volume index. Overall, PVR was the best predictor of right ventricular ejection fraction <50% (P<0.01), increased RVEDVi (P<0.01), and RA volume index (P=0.02). However, for patients with a PVR <240 dynes.s.cm-5, PACclinical outperformed PVR in predicting right ventricular ejection fraction <50% (P=0.02) and increased RVEDVi (P=0.05). A 3-component model (PVR, PACclinical, and characteristic impedance) best predicted RA dilatation (P=0.05). In paired analysis, alternative methods of PAC quantification better predicted increased RVEDVi (PACdecay P=0.02) and RA volume index (PACarea P=0.02). PACclinical <2.3 mL/mm Hg optimally predicted right-sided heart dysfunction (area under the curve, 0.768) and increased the AUC for PVR in undifferentiated/left-sided PH (0.89 versus 0.72). Pulsatile vascular afterload measurement improves right-sided heart dysfunction prediction in undifferentiated/left-sided PH. More accurate PAC measurement methods could further enhance prognostic accuracy, while a PVR ≥240 dynes.s.cm-5 remains a valuable threshold for precapillary PH.

Four New Patient-Reported Outcome Measures Examining Health-Seeking Behavior in Persons With Type 2 Diabetes Mellitus (REDD-CAT): Instrument Development Study.

The management of type 2 diabetes mellitus (T2DM) includes mastery of complex care activities, self-management skills, and routine health care encounters to optimize glucose control and achieve good health. Given the lifelong course of T2DM, patients are faced with navigating complex medical and disease-specific information. This health-seeking behavior is a driver of health disparities and is associated with hospitalization and readmission. Given that health-seeking behavior is a potentially intervenable social determinant of health, a better understanding of how people navigate these complex systems is warranted. To address this need, we aimed to develop new patient-reported outcome (PRO) measures that evaluate health-seeking behavior in persons with T2DM. These new PROs were designed to be included in the Re-Engineered Discharge for Diabetes-Computer Adaptive Test (REDD-CAT) measurement system, which includes several other PROs that capture the importance of social determinants of health. Overall, 225 participants with T2DM completed 56 self-report items that examined health-seeking behaviors. Classical Test Theory and Item Response Theory were used for measurement development. Exploratory factor analysis (EFA; criterion ratio of eigenvalue 1 to eigenvalue 2 being >4; variance for eigenvalue 1 ≥40%) and confirmatory factor analysis (CFA; criterion 1-factor CFA loading <.50; 1-factor CFA residual correlation >.20; comparative fit index ≥0.90; Tucker-Lewis index ≥0.90; root mean square error of approximation <0.15) were used to determine unidimensional sets of items. Items with sparse responses, low-adjusted total score correlations, nonmonotonicity, low factor loading, and high residual correlations of high error modification indices were candidates for exclusion. A constrained graded response model was used to examine item misfit, and differential item functioning was examined to identify item bias. Cronbach α was used to examine internal consistency reliability for the new PROs (criterion ≥0.70), and floor and ceiling effects were examined (criterion ≤20%). Four unidimensional sets of items were supported by EFA (all EFA eigenvalue ratios >4; variance for eigenvalue 1=41.4%-67.3%) and CFA (fit statistics all exceeded criterion values). This included (1) "Health-Seeking Behavior: PCP-Specific" (6 items); (2) "Health-Seeking Behavior: General Beliefs" (13 items); (3) "Health-Seeking Behavior: Family or Friends-Specific" (5 items); and (4) "Health-Seeking Behavior: Internet-Specific" (4 items). All items were devoid of differential item functioning for age, sex, education, or socioeconomic status factors. "Health-Seeking Behavior: General Beliefs" was developed to include both a computer adaptive test and a 6-item short form version; all other PROs were developed as static short forms. The psychometric reliability of these new PROs was supported; internal consistency ranged from acceptable to excellent (Cronbach α=.78-.91), and measures were free of significant floor or ceiling effects (floor effects range: 0%-8.9%; ceiling effects range: 0%-8.4%). The new REDD-CAT Health-Seeking Behavior PROs provide reliable assessments of health-seeking behaviors among those with T2DM.

Understanding Sociodemographic Determinants of Voluntary Cesarean Section Deliveries in India through Geospatial Modeling using Multiscale Geographically Weighted Regression (MGWR)

IntroductionOver the period of time, the increasing trend of voluntary cesarean section (CS) delivery has become a serious maternal health concern. Previous studies have shown wide geographic variations in the prevalence of voluntary CS delivery constraints by several sociodemographic determinants. However, none of them have attempted to disentangle this phenomenon by using spatially varying coefficient models to consider local factors. This study aims to identify the spatially heterogeneous relationships among voluntary CS deliveries against the backdrop of sociodemographic determinants in India.Data and MethodsThis study utilized data from the National Family Health Survey (NFHS-5), 2019-21. The ordinary least square (OLS) model was used as a base model, and multiscale geographically weighted regression (MGWR) was employed at the district level to consider the local factors associated with voluntary CS delivery. An appropriate model diagnostic check was also performed to make our model robust and reliable. Several maps were produced using ArcGIS Pro to visualize the distribution of coefficients of several factors.ResultsMore than half (58%) of the total CS deliveries were voluntary; however, choropleth maps have shown that southern peninsular and northeastern districts have a higher prevalence than the rest. The distribution of voluntary CS deliveries with a Moran’s Index of 0.22 was also spatially autocorrelated. Moreover, the values of Jarque–Bera statistics and Koenker (BP) Statistics show that the OLS model residuals were not normally distributed and reflected heteroskedasticity. The estimates from the MGWR show that four and above ANC visits and mass media exposure show a positive relationship with voluntary CS deliveries. The local R-squared map explains nearly half of the variation for voluntary CS deliveries for all northeastern districts. The model fit metric shows higher R square and lower corrected Akaike’s Information Criterion (AICc) values for the MGWR model, which validates that the MGWR model is more robust and parsimonious.ConclusionThe present study revealed that although voluntary CS deliveries are at an increasingly alarming pace, the geographic distribution of voluntary CS deliveries is, albeit unequally. Therefore, this investigation has made an effort to examine how spatial analysis can help identify the geographic patterns and factors that explain this variation in voluntary CS deliveries across space. These findings add value to our understanding of how geographic space and scale matter when health is examined. Public health practitioners can recognize such variations when devising targeted interventions for CS deliveries in India and other developing nations.

An Arima Model-Based Approach to Improve Electricity Reliability

Objective: The objective of this study was to forecast the number of critical days, for the period from July 2020 to December 2022, based on time series analysis, to model changes in the number of critical power days in a prospective way. Related Studies: This section provides an overview of the use of forecasting models and methods for measuring electricity demand, applied as decision-making support and as a tool for identifying anomalous phenomena. Method: The methodology for this research involves analysis and forecasting using the Autoregressive Integrated Moving Average (ARIMA) model of order (p,d,q)(p,d,q), applied to the time series of critical days. Model development and performance evaluation followed these steps and strategies: Specification (Pre-processing), Identification and Estimation, Verification, and Forecasting. Data collection was performed by analyzing time series observations from a Brazilian electric utility company responsible for energy supply in Rio Grande do Sul. Results and Discussion: The results indicated that the seasonal ARIMA (2,1,1)(2,1,1) model performed best, with the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, as well as the lowest mean square error among the tested models. With the selected ARIMA model, forecasts were made for the following 30 months, estimating the number of monthly critical days. Research Implications: The paper discusses the practical and theoretical implications of forecasting critical days for power quality. Practically, it presents an ARIMA model to estimate these days, aiding power companies in projecting interruptions and improving preventive management and resource allocation. Theoretically, it contributes to the time series forecasting literature in the power sector, confirming the effectiveness of ARIMA models with seasonal components for grid management and adverse event forecasting in complex systems. Originality/Value: The study contributes to the literature by applying the ARIMA model with seasonal components to predict critical days of interruption in the distribution of electricity, an approach that has not yet been explored for specific adverse events. The originality lies in the application of the model in a context of quality and reliability in the supply and detailed seasonal analysis. The methodology proposes a solution for forecasting and managing these events, bringing new perspectives for proactive management in the sector. The study stands out for its potential to improve professional practice, allowing informed decisions for resource allocation and maintenance, increasing reliability and reducing costs and financial compensation.

Method for determining the effect of destabilizing factors of digital filters on the multicriteria synthesis of radio communications of aviation robotic devices

The purpose of the research is development of a method for determining the influence of destabilizing factors, such as the unevenness of the amplitude-frequency response (AFC) and the nonlinearity of the phase-frequency response (FFC), in non-recursive digital frequency selection filters on the target function of multicriteria structural-parametric synthesis of radio communications from the payload of aviation robotic devices.Methods. In conducting scientific research, probability theory, mathematical statistics, statistical radio engineering and computational mathematics were used. The study obtained mathematical expressions of the dependence of the probability of error per bit (BER) on the average signal-to-noise ratio when signals pass through symmetrical non-recursive digital filters with quadrature amplitude modulation with a positivity of no more than 4096, signal constellation irregularities of no more than 5, which are the basis for the energy balance of radio lines with aviation robotic devices. The expressions obtained are one of the "foundations" for the implementation of a multi-criteria synthesis of radio systems adaptive to a cognitively formed flight task. In this paper, all the results of the probability of reliability of the received data by means of communication are reflected for the case of rigid decision-making (Gray coding).Results. In the course of the conducted research: 1) a mathematical model of the structural-parametric synthesis of radio communications with aviation robotic devices is demonstrated, which is based on the criteria for minimizing: electronic computing resources with a given noise immunity; radiation outside the operating frequency band; peak factor signals; the effects of narrowband, broadband and structural interference in the radio channel at a given bandwidth; the use of structural and functional nodes specific to the existing information and communication environment; 2) the analytical dependence of the influence of the parameters of one of several structural and functional nodes - digital filters of frequency selection, on the criteria for the synthesis of communication means has been revealed. The paper considers the most common approximating functions - Kaiser, Tukey, Barlett-Khan, Chebyshev, and also shows the influence of the filter order on the equivalent of energy loss in information communication lines of aviation robotic devices.Conclusion. The scientific article presents a method for determining the effect of the uneven frequency response of a symmetrical non-recursive digital filter in devices for receiving and processing radio signals of multicriteria means of communication with the payload of aviation robotic devices. The proposed approach increases the reliability of determining the values of criteria for minimizing electronic computing resources for a given noise immunity, radiation outside the operating frequency band, as well as the use of structural and functional nodes characteristic of the existing information and communication environment.

Predicting the body weight of crossbred Holstein × Zebu dairy cows using multivariate adaptive regression splines algorithm.

This study aimed to estimate live body weight from body measurements for Holstein × Zebu dairy cows (n = 156) reared under conditions of humid tropics in Mexico using multivariate adaptive regression splines algorithm (MARS) with several train-test proportions. The body measurements included withers height, rump height, hip width, heart girth, body length and diagonal body length. The data were divided into 65:35, 70:30 and 80:20 split data for training and testing sets, respectively. The MARS algorithm was used to construct a prediction model, which predicted the body weight from the body measurements of the test dataset. The results emphasized that the MARS algorithm had an explanation rate for 80:20 train and test set of 0.836 and 0.711, respectively, with minimum Akaike information criterion values. This indicates that it is a reliable way of predicting body weight from body measurements. The results suggest that body weight prediction can be performed with the MARS algorithm in a reliable way, therefore, this algorithm may be a useful tool for animal breeders and researchers in the development of feeding and selection-aimed approaches.

Comparison of Patient-Specific Quality Assurance Devices in Radiation Therapy

This study aimed to compare the results of patient-specific quality assurance (PSQA) between MapCHECK and Octavius for radiation therapy. A total of 50 patients with passing PSQA results were included. The point dose difference (DD) and gamma passing rate (GPR) were analyzed to evaluate the agreement between the measured and planned data, and the PSQA results from the two quality assurance (QA) devices were compared for all cases. The DDs and GPRs were within the tolerance ranges in all of the cases. The average GPR difference between the two detectors was statistically significant for the brain and the H&amp;N values for the gamma criteria were 3%/3 mm, 2%/2 mm, and 1%/1 mm. These PSQA systems can be used interchangeably for routine PSQA during the delivery of radiation therapy.

A Spectrophotometric Method to Determine Minimal Erythema Dose for Ultraviolet Radiation in Human Skin.

Background/Objectives: Ultraviolet radiation (UVR) induces oxidative stress in the skin by generating reactive oxygen species (ROS), which can lead to inflammatory conditions including erythema (a sign of sunburn). This clinical study aims to develop an instrumental evaluation method to determine the minimal erythema dose (MED) for UVR. Methods: Fourteen human subjects aged 27 to 57 years (48.93 ± 8.54) participated in this study. Six subsites were designated on the test skin site of each subject and irradiated with UVR at different doses. The examiner visually assessed erythema, determining the 'visual MED'. Additionally, the a* value (a chrominance parameter presenting greenness to redness) was measured using a spectrophotometer as an indicator of erythema. The a* values of the UVR-irradiated subsites were compared to the non-irradiated control value, and the differences were referred to as Δa*. The Δa* value of the subsites irradiated with UVR at the 'visual MED' was referred to as the Δa*VMED for each subject. The mean of the Δa*VMED values of all subjects was chosen as a criterion value for the 'instrumental MED'. The 'instrumental MED' was defined as the lowest dose of UVR that causes an Δa* value equivalent to the criterion value. The 'visual MED' and 'instrumental MED' values of all subjects were subjected to correlation analysis. Results: The mean of the Δa*VMED values of all subjects was 1.88 ± 0.8. The means of the 'visual MED' and 'instrumental MED' values (in J m-2 unit) of all subjects were 300.14 ± 84.16 and 303.29 ± 77.99, respectively. In Pearson correlation analysis, the 'instrumental MED' and 'visual MED' values had a very strong positive correlation with each other (r = 0.864, p = 0.000). Conclusions: This study suggests that the instrumental evaluation method of MED based on the spectrophotometric measurement of the a* values can complement or replace the visual evaluation method and that this method will be useful in monitoring skin tolerance to oxidative stress affected by prooxidant factors and defensive factors.

Topology optimization of rectangular parallel plate heat exchanger unit

This research develops computational optimization applied, for the first time, to Rectangular Parallel Plate Heat Exchanger unit (RPP-HEX-U). This is by employing Finite Element Analysis coupled to topology design method. New innovative fins design are obtained that enhance importantly the overall thermal performance and efficiency of the RPP-HEX-U. The new design indicates that the new topology-optimized fins outperform traditional ones (cylindrical, rectangular, and chevron-type plate fins) in enhancing heat exchange performance and preventing local dead zones formation. The obtained Nusselt number (Nu) of the topology-optimized fins design is significantly higher, with an increase of 5.77% to 8.23% compared to traditional cylindrical and rectangular fins, respectively. The Performance Evaluation Criterion (PEC) values of the topology-optimized fins range from 1.71 to 2.12, thus indicating superior overall performance. Three-dimensional numerical simulations confirmed the effectiveness of these fins in disrupting boundary layer development and enhancing heat transfer within the RPP-HEX-U. The innovative fin design, feasible for manufacturing via stamping technology, adds additional value thanks to the industrial feasibility of the present approach.

Investigation of the differences in designers' spatial perception in interior spaces using Kokedama

The word kokedama consists of the Japanese words "koke" (moss) and "dama" (ball). Due to its easily accessible materials and easy construction, it has become an application that almost everyone can do, and its use is expected to become widespread. The aim of this study is to investigate the differences in the spatial perceptions of designers who have received basic design education in different faculties in the case of interiors that including kokedama. To measure the differences in spatial perception, photographs of 8 different public interiors were processed and a survey was administered to the subjects. The survey results were analyzed statistically. According to the results of the study, the increase in the value of the perceptibility and simplicity criteria also increases the value of other criteria. As the value of perceptibility and simplicity increases in kokedama applications, the perception of complexity decreases. This data explains that the use of kokedama contributes positively to spatial perception. The criteria that have the most effect when they are together are the structure/identity and perceptibility criteria. The highest scorers on the criteria affecting spatial perception were graduates of the faculty of fine arts, while the lowest scorers were graduates of the faculty of agriculture.

Full-field Modal Analysis of a Tensegrity Column Using a Three-dimensional Scanning Laser Doppler Vibrometer with a Mirror

Abstract Tensegrity structures become important components of various engineering structures due to their high stiffness, light weight, and deployable capability. Existing studies on their dynamic analyses mainly focus on responses of their nodal points while overlook deformations of their cable and strut members. This study proposes a non-contact approach for experimental modal analysis of a tensegrity structure to identify its three-dimensional (3D) natural frequencies and full-field mode shapes, which include modes with deformations of its cable and strut members. A 3D scanning laser Doppler vibrometer is used with a mirror for extending its field of view to measure full-field vibration of a novel three-strut metal tensegrity column with free boundaries. Tensions and axial stiffnesses of its cable members are determined using natural frequencies of their transverse and longitudinal modes, respectively, to build its theoretical model for dynamic analysis and model validation purposes. Modal assurance criterion (MAC) values between experimental and theoretical mode shapes are used to identify their paired modes. Modal parameters of the first 15 elastic modes of the tensegrity column identified from the experiment, including those of the overall structure and its cable members, can be classified into five mode groups depending on their types. Modes paired between experimental and theoretical results have MAC values larger than 78%. Differences between natural frequencies of paired modes of the tensegrity column are less than 15%. The proposed non-contact 3D vibration measurement approach allows accurate estimation of 3D full-field modal parameters of the tensegrity column.

Ecological factors associated with fox feces density in an Echinococcus multilocularis endemic zone in Japan.

Human alveolar echinococcosis caused by Echinococcus multilocularis is an important zoonotic disease in the northern hemisphere. The life cycle of E. multilocularis is maintained primarily in wild animals and requires an intermediate host (mainly small mammals). Human can become an intermediate host through accidental ingestion of E. multilocularis eggs. Hokkaido Prefecture is the only area of Japan in which human alveolar echinococcosis is endemic. The purposes of this study were to elucidate the land use ecological factors associated with the density of red fox feces along paved roads and the relationship between the distributions of red fox (Vulpes vulpes) populations and fox feces, which determine the level of hazard from eggs. A series of surveys was conducted in the central part of the Nemuro Peninsula of Hokkaido, excluding urban areas, over a total of 4 years in May-June in 2014 and 2016-2018 when red foxes remain with their cubs around the dens. Transects of 500 m were set up on paved roads, and feces within the transects were counted. Univariable and multivariable analyses were performed to examine ecological factors including the principal components (PCs) of five land use-type occupancy proportions within 500 m and 1 km, respectively, as explanatory fixed-effect variables. The number of feces in each transect was examined as the response variable using integrated nested Laplace approximation with negative binomial errors with a spatio-temporal autocorrelations structure to separate the effects of similarities of neighboring locations and annual variation. The multivariable models with the lowest widely applicable information criterion values were selected. The feces density was explained by the PC of the 500- m buffer (-0.27, 2.5th and 97.5th percentiles: -0.44, -0.10) characterized by mixed forests (-0.82) and scarcity of residential areas (0.29) and the proximity to the nearest livestock farm house (-0.35, 2.5th and 97.5th percentiles: -0.53, -0.17). This suggested that foxes defecate in the areas where prey is abundant, avoiding humans. Policy discussions regarding bait distribution design targeting these conditions should be initiated.

Meadow degradation reduces microbial β diversity and network complexity while enhancing network stability

Continuing meadow degradation under the dual impacts of climate change and human activities has altered microbially-mediated biogeochemical cycles. However, microbial diversity and network stability in response to meadow degradation and their relationships to environmental variables remain insufficiently understood. This study focused on alpine meadows with varying degrees of degradation in the hinterland of the Qinghai-Tibetan Plateau. It analyzed the soil microbial diversity (α and β diversity) and ecological network topology characteristics under the degradation of alpine meadows. Furthermore, the extent to which these factors elucidate the environmental changes observed during degradation was explored. The results demonstrated that: (1) the α diversity of soil microbial under degradation of alpine meadows exhibited an increasing trend, although not significantly. The β diversity (community dissimilarity) exhibited a significant decreasing trend. These indicated that meadow degradation resulted in microbial homogenization intra- and inter-community. In the light degradation stage, the β diversity of bacteria and fungi significantly increased with geographic and environmental distance rise. Only bacterial β diversity significantly increased with geographical distance in the moderate degradation stage and environmental distance in the heavy degradation stage, respectively. This indicated that meadow degradation possibly altered microbial assembly, especially for fungi. (2) The degree of soil bacterial nodes exhibited a significant decreasing trend with degradation and fungal eigencentrality demonstrated a significant decreasing trend. After removing the identical nodes, the natural connectivity of the fungal network exhibited a significant decreasing trend with degradation. This suggested that the stability of the soil microbial network increased while the network complexity decreased under meadow degradation. These changes possibly marked the irreversible course of alpine meadow degradation. (3) The Mantel test indicated that bacterial diversity and network topological features were more strongly associated with environmental factors than those of fungi. Akaike information criterion (AIC) values and the upset matrix plots of variance partitioning based on Redundancy analysis (RDA) indicated that higher rates of soil factors, such as soil pH, organic carbon (SOC) content, and total potassium (TK), elucidated changes in microbial community diversity and network topological features. This study highlights the importance of soil microorganisms for ecological stability and the role of microorganisms should be emphasized in future efforts to restore alpine grasslands.

The Movement Disorder Society Criteria: Its Clinical Usefulness in Multiple System Atrophy.

Objective In 2022, Wenning et al. proposed the Movement Disorder Society Criteria (MDS criteria) for the Diagnosis of Multiple System Atrophy (MSA). These criteria were expected to provide useful alternatives to the second consensus statement. We examined trends in these diagnostic criteria. Methods We used patient data registered with the Hokkaido Rare Disease Consortium for Multiple System Atrophy, which has been recruiting patients with MSA through medical facilities in Hokkaido since November 2014. Patients were evaluated according to the MDS criteria based on neurological examinations and imaging findings at three separate times: the first evaluation, the time of enrollment (diagnosis), and the most recent evaluation (final evaluation). Results The MDS criteria were examined in 68 of 244 patients enrolled between November 2014 and July 2022. At the initial evaluation, the classifications were as follows: clinically established (n=27; 39.7%); clinically probable (n=13; 19.1%); possible prodromal (n=12; 17.6%); and negative [did not meet criteria (n=16; 23.5%)]. At the time of diagnosis, the classifications were as follows: clinically established (n=45; 66.2%); clinically probable (n=12; 17.6%); possible prodromal (n=4; 5.9%); and negative (n=7; 10.3%). At the final evaluation, the classifications were as follows: clinically established (n=52; 76.5%); clinically probable (n=9; 13.2%); possible prodromal (n=2; 2.9%); and negative (n=5; 7.4%). Conclusion We were able to clarify the changes in the criteria values and transition of patients due to the clarification of imaging and supportive findings in the MDS criteria.

Comparative prognostic value of tumor volume in IOIO and IOTKI treatment for metastatic renal cancer

ObjectivesWe aimed to investigate the prognostic significance of tumor size in metastatic renal cell carcinoma (mRCC) by comparing the effectiveness of dual immune checkpoint inhibitor (IOIO) and immune checkpoint inhibitor combined with tyrosine kinase inhibitor (IOTKI) therapies. MethodsThis retrospective observational study included patients with mRCC diagnosed between October 2014 and February 2024 who received IOIO or IOTKI treatment at Kobe University Hospital and 5 affiliated hospitals. Clinical and imaging data were collected, and target lesions were measured according to RECIST v.1.1 criteria. Time-dependent ROC curve analysis was performed to evaluate the prognostic value of tumor size, nephrectomy status, and IMDC risk criteria for progression-free survival (PFS) and overall survival (OS). ResultsThe study included 180 mRCC patients, consisting of 99 receiving IOIO therapy and 81 receiving IOTKI therapy. Time-dependent AUC analysis showed that tumor size had a higher predictive ability for PFS and OS in the IOIO group than the IOTKI group. In multivariate analysis, tumor size was a significant independent prognostic factor for PFS (HR: 1.010, 95% CI: 1.004–1.016, P < 0.001) in the IOIO group. Moreover, the AUC for tumor size was consistently superior in predicting outcomes compared to nephrectomy status and IMDC risk classification in the IOIO group. Kaplan-Meier curves indicated that tumor size effectively stratified PFS in both nephrectomized and non-nephrectomized cases. ConclusionTumor size significantly impacts the prognosis of mRCC patients treated with IOIO therapy, demonstrating greater predictive ability than nephrectomy status and IMDC risk classification. These findings suggest that tumor volume should be considered a critical factor in treatment decision-making for renal cancer, particularly in patients undergoing IOIO therapy.

Constraints on Interacting Dark Energy Models from the DESI Baryon Acoustic Oscillation and DES Supernovae Data

Abstract The recent results from the first-year baryon acoustic oscillations (BAO) data released by the Dark Energy Spectroscopic Instrument (DESI), combined with cosmic microwave background (CMB) and Type Ia supernova (SN) data, have shown a detection of significant deviation from a cosmological constant for dark energy. In this work, we utilize the latest DESI BAO data in combination with the SN data from the full 5 yr observations of the Dark Energy Survey and the CMB data from the Planck satellite to explore potential interactions between dark energy and dark matter. We consider four typical forms of the interaction term Q. Our findings suggest that interacting dark energy (IDE) models with Q ∝ ρ de support the presence of an interaction where dark energy decays into dark matter. Specifically, the deviation from ΛCDM for the IDE model with Q = β H 0 ρ de reaches the 3σ level. These models yield a lower value of Akaike information criterion than the ΛCDM model, indicating a preference for these IDE models based on the current observational data. For IDE models with Q ∝ ρ c, the existence of interaction depends on the form of the proportionality coefficient Γ. The IDE model with Q = β H ρ c yields β = 0.0003 ± 0.0011, which essentially does not support the presence of the interaction. In general, whether the observational data support the existence of interaction is closely related to the model. Our analysis helps to elucidate which type of IDE model can better explain the current observational data.

Risk stratification using the academic research consortium high bleeding risk criteria in patients with atrial fibrillation undergoing percutaneous coronary intervention

Abstract Background Previous studies have reported that the academic research consortium high bleeding risk (ARC-HBR) criteria are useful to predict the risk of bleeding events in patients with coronary artery disease undergoing percutaneous coronary intervention (PCI). However, there are few studies investigating the predictive value of the ARC-HBR criteria in patients with atrial fibrillation (AF) undergoing PCI. Purpose The purpose of this study was to investigate whether the ARC-HBR criteria can be applied to long-term risk stratification in patients with AF undergoing PCI. Methods Patients with AF undergoing PCI at 15 institutions between January 2015 and March 2021 were enrolled. The ARC-HBR score was calculated by assigning 1 point for each major criterion and 0.5 points for each minor criterion. Clinical outcomes within 1 year, including major adverse cardiovascular events (MACE: all-cause death, myocardial infarction, stent thrombosis, and stroke) and major bleeding events (BARC 3 or 5), were evaluated. Results A total of 723 patients were included in this analysis. The best cut-off values of J-HBR score determined by ROC curve analysis to predict MACE and major bleeding events were both 3.0. Of 723 patients, 161 (22.3%) had ARC-HBR score ≥3.0. In Kaplan-Meier curve analyses, the incidence of MACE as well as major bleeding events was significantly higher (log-rank p&amp;lt;0.001 and p=0.010) in patients with ARC-HBR score ≥3.0 compared to those with ARC-HBR score &amp;lt;3 (Figure 1). Conclusion The ARC-HBR score may be useful in assessing the risk of MACE, including thrombotic events, as well as bleeding events in AF patients undergoing PCI.Figure 1

Statistical Models and Evaluation Criteria Used in Poultry Farming

In this thesis study, the statistical models and evaluation criteria used in poultry breeding were examined comprehensively, taking into account live weight gain, egg weight and egg numbers. For this purpose, as an example of live weight increase; 9-week live weight gains of Ross-308 broiler line, as an example of egg weight; The average of 20-week-old egg weights of brown Lohmann laying hens and 9-week egg yields of Japanese quail were used as an example of the number of eggs. In modeling, for live weight gain; 10 different models for egg production; 11 different models and 8 different models for egg weight were considered. In evaluating the models; error mean squares, coefficient of determination, corrected coefficient of determination, Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, accuracy factor, deviation factor and Durbin-Watson autocorrelation values were taken into consideration. As a result of the study, in terms of live weight gain; The cubic piecewise regression model is the best model in terms of egg yields; It was determined that the modified Compartmental model and the logistic model gave better results than the others in terms of egg weight. The worst models are in live weight gains; Brody, egg yields; It was concluded that there was a quadratic linear and Von Bertalanffy model for egg weight.

Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.