Set Of Values Research Articles

Indigenous Experiences and Contributions to Western Scientific Knowledge Systems: An Ethnographic Exploration

Knowledge systems are embedded in sets of values, worldviews, and cosmologies that affect the whole process of knowledge production. Employing an ethnographic method that integrates participant observation, interviews, and focus groups, I explore the contributions, ideas, points of view, and metaphors that individuals of Indigenous origins introduce to the scientific method when they enter Western scientific knowledge systems. This ethnographic exploration focuses on a group of students with a variety of Indigenous backgrounds participating in a field course on introduction to Ecology. Native-born students contribute innovations to the knowledge production process by shaping research questions, methodologies, and result interpretations. These innovations stem from diverse worldviews and epistemologies, and while they could significantly impact scientific knowledge production, the students may not fully appreciate their own relevance. This work may serve as a testimony of the processes of reflection and negotiation with the scientific methods, practices, and values that native students undergo when participating in a Western scientific context.

High‐precision numerical simulation method for thermal–mechanical coupling in rubbers

AbstractImproving the precision of numerical simulations in thermal–mechanical coupling for amorphous polymer materials is crucial for their effective utilization. To enhance the precision of rubber numerical simulations, this study proposed a method considering the temperature and strain rate to establish tensile testing conditions based on the principle of time–temperature equivalence. Additionally, to obtain more precise parameters for hyperelastic constitutive equations, stress relaxation experiments were conducted to determine a set of stress values under complete rubber stress relaxation, which were used to fit the hyperelastic components of the hyper‐viscoelastic algorithm. A finite element model of rubber rebound was established, and a hyperelastic model based on loss factor and a hyperelastic model based on Prony series (hyper‐viscoelastic) were used to calculate the temperature changes caused by rubber rebound coefficient and energy dissipation during the rebound process at 110, 120, and 130°C. We compared the calculation results of these two algorithms with experimental data to verify their accuracy. The research results show that the average error of the rebound coefficient of the hyperelastic algorithm at three temperatures is 3.63%, and the average error of the rebound coefficient of the hyper‐viscoelastic algorithm at these three temperatures is 0.93%. The comparison with the rebound test results shows that the hyper‐viscoelastic method is more accurate and better captures the viscoelastic hysteresis of rubber, but the model calculation time is longer. In addition, at the moment of rebound sample impact, the maximum temperature rise amplitude of the hyperelastic algorithm sample impact position is 2.60, 1.75, and 0.91°C, which are 110, 120, and 130°C, respectively; The maximum heating amplitudes of the hyper‐viscoelastic algorithm are 2.05, 1.35, and 0.61°C. The hyper‐viscoelastic algorithm is closer to the actual test results.Highlights Applied the principle of time–temperature equivalence. Determination of material parameters using the hyper‐viscoelastic algorithm. Calculated the temperature change during the rubber rebound process.

Introducing listening as a weak method for advancing sustainability and interdisciplinary scholarship

AbstractThere is now widespread recognition of the need for inter/transdisciplinary (I/TD) approaches to solving global problems like climate change and biodiversity. Yet methods for successfully integrating knowledge across disciplines, and between research and practice, are in need of further development, particularly approaches that can ameliorate epistemological and ontological divides. Here we propose a framework for good listening as a ‘weak method’ that can provide guidance and structure to I/TD collaborations, but does not assume the form and goals a given collaboration will take. Synthesising the results of a scoping, interdisciplinary literature review, we highlight four key components of listening—receiving, processing, interpretation, and feedback/response—and provide a set of normative values regarding ‘good listening’ for each. Our goal is to provide a framework that is grounded in detailed scholarly discussions of listening politics and practice, but that is specifically formulated in response to the needs and concerns of I/TD researchers. We then apply our framework to four commonly encountered challenges in sustainability science, drawing on our collective experience in the field to explore how good listening can aid I/TD collaboration in regards to inclusion, group dynamics, format and pace. In doing so, we hope to inspire those working in sustainability science to approach I/TD collaboration in a new way and provide a tool for facilitating caring and transformational approaches to solving the world’s most pressing sustainability crises.

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy. This retrospective study included 154 patients (38 female; mean age 66.8years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests. A total 168 HCCs (mean size 2.9 ± 1.7cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001). The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.

The Short Form 6 Dimensions (SF-6D): Development and Evolution.

This paper considers the development and evolution of the short-form 6 dimensions (SF-6D), a generic preference-weighted measure consisting of a health classification with accompanying value set that was developed from one of the widest used health related quality of life measures, the SF-36 health survey. This enabled health state utility values to be directly generated from SF-36 and SF-12 data for a range of purposes, including to produce quality adjusted life years for use in economic evaluation of healthcare interventions across a range of different conditions and treatments. This paper considers the rationale for the development of the measure, the development process, performance and how the SF-6D has evolved since its conception. This includes the development of an updated version, SF-6D version 2 (SF-6Dv2), which was generated to deal with some criticisms of the first version, and now includes a standalone version for inclusion in studies without relying on use of SF-36 or SF-12. Valuation methods have also evolved, from standard gamble in-person interviews to online discrete choice experiment surveys. International work related to the SF-6Dv1 and SF-6Dv2 is considered. We also consider recommendations for use, highlighting key psychometric evidence and reimbursement agency recommendations.

Identification of risk factors and establishment of prediction models for mortality risk in patients with acute kidney injury: A retrospective cohort study.

This study investigated factors influencing death in patients with Acute Kidney Injury (AKI) and developed models to predict their mortality risk. We analyzed data from 1079 AKI patients admitted to Changsha First Hospital using a retrospective design. Patient information including demographics, medical history, lab results, and treatments were collected. Logistic regression models were built to identify risk factors and predict 90-day and 1-year mortality. The 90-day mortality rate among 1079 AKI patients was 13.8% (149/1079) and the one-year mortality rate was 14.8% (160/1079). For both 90-day and 1-year mortality in patients with AKI, age over 60, anemia, hypotension, organ failure, and an admission Scr level above 682.3 μmol/L were identified as independent risk factors through multivariate logistic regression analysis. Additionally, mechanical ventilation was associated with an increased risk of death at one year. To ensure the generalizability of the models, we employed a robust 5-fold cross-validation technique. Both the 90-day and 1-year mortality models achieved good performance, with area under the curve (AUC) values exceeding 0.8 in the training set. Importantly, the AUC values in the validation set (0.828 for 90-day and 0.796 for 1-year) confirmed that the models' accuracy holds true for unseen data. Additionally, calibration plots and decision curves supported the models' usefulness in predicting patient outcomes. The logistic regression models built using these factors effectively predicted 90-day and 1-year mortality risk. These findings can provide valuable insights for clinical risk management in AKI patients.

Comparison of urinary 3-hydroxybenzo(a)Pyrene (3-OHBaP) and trans-anti-7,8,9,10-tetrahydroxy-7,8,9,10-tetrahydrobenzo(a)Pyrene (TetraolBaP) as biomarkers of exposure to carcinogenic BaP

IntroductionBiomonitoring of exposure to carcinogenic Benzo(a)Pyrene is generally based on measurement of urinary 3-hydroxybenzo(a)pyrene (3-OHBaP), but its analysis is complex and only reflects the BaP detoxification pathway. TetraolBaP, another BaP metabolite resulting from the metabolic activation pathway, is now available but has not yet been studied in occupational settings or compared with 3-OHBaP. MethodsBiomonitoring was carried out on 118 subjects working in the aluminium smelting industry. 3 urine samples were collected from each subject at the beginning and end of the working week. Pyrene metabolite (1-hydroxypyrene) and the two BaP biomarkers (3-OHBaP and TetraolBaP) were analysed using LC-Fluorescence and GC-NCI-MS-MS. ResultsThe workers studied were found to be highly exposed, with 1-OHP and 3-OHBaP frequently exceeding maximum recommended values in occupational settings. Maximum concentrations were measured at end of shift+16h for all biomarkers, highlighting dermal exposure and/or temporary storage. Correlations were strong between 1-OHP and 3-OHBaP (r = 0.68–0.75) as well as between 3-OHBaP and TetraolBaP (r = 0.67–0.78), and moderate between 1-OHP and TetraolBaP (r = 0.59–0.76). While TetraolBaP levels were higher at low PAH exposures, TetraolBaP increased much more slowly at high exposures, indicating progressive saturation of the bioactivation pathway. The [3-OHBaP]/[TetraolBaP] ratio was found to be significantly lower in chronically exposed workers. Urinary TetraolBaP levels corresponding to 1-OHP (2.5 μg/L or 1 μmol/mol creatinine) or 3-OHBaP (0.4 nmol/mol creatinine) guidance values were found to range between 0.84 and 0.95 nmol/mol creatinine. ConclusionsTetraolBaP, resulting from carcinogenic BaP's metabolic activation pathway, was shown to be a diagnostically specific and sensitive biomarker for determining subjects' toxic internal exposure to PAHs in different contexts (occupational settings, environment) and assessing health risks.

Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process.

A-optimal experimental designs for Michaelis – Menten model

Kinetics of simple and complex kinetic reactions is usually described by exponential and rational models. The fractional rational models include the well-known Michaelis – Menten model of enzymatic kinetics. In the presented article A-optimal designs are proposed for the Michaelis – Menten model. The elimination method used to construct A-optimal designs, allowed us to determine the nodes and weights of A-optimal designs separately and thereby extend this approach to other criteria and models of the rational type. It is shown that the nodes of A-optimal designs determined by this method are the roots of the 4th degree algebraic equation with coefficients depending on the model parameters. If the nodes of the A-optimal design are already known, then the weights of the corresponding nodes are determined analytically by the Pukelsheim formula. The properties of the roots as well as the properties of the nodes of A-optimal designs were studied using the Sturm system constructed in the general form for the resulting equation. It is shown that with a certain combination of parameters of the Michaelis – Menten model, the degree of the resulting algebraic equation is reduced to three. A partition of the set of values of the parameters of the Michaelis – Menten model into two subsets has been found. In one of them, the A-optimal design is determined uniquely, whereas for the other one it is necessary to select the optimal node from two possible options. It is revealed that the degree of the algebraic equation is equal to three for points belonging to the curve which is the common boundary of the indicated subsets. Corresponding numerical examples are given to illustrate the results obtained.

CORPORATE CULTURE AS A FACTOR IN PERSONNEL EFFICIENCY

The article is devoted to the study of the influence of corporate culture on the efficiency of personnel through the prism of work psychology. The main focus is on the analysis of key components of corporate culture, such as corporate values, norms and traditions, and their impact on staff motivation. Corporate culture is defined as a set of values and beliefs that determine the behavior of employees and the effectiveness of the organization as a whole. The psychological climate in the organization is considered as a critical factor affecting job satisfaction and the emotional state of employees. The article substantiates the importance of maintaining a healthy psychological climate for ensuring high productivity. Various aspects of personnel motivation are considered, including intrinsic and extrinsic incentives. It has been found that a positive corporate culture promotes motivation, improves communication and strengthens trust between employees. In particular, factors such as open communication, support from management and opportunities for professional development are key to ensuring high performance. The study shows that a positive psychological climate helps reduce stress levels and increase overall job satisfaction. It is noted that corporate values, which are shared by all members of the organization, create a basis for successful cooperation and achievement of common goals. The authors provide recommendations for implementing effective management practices to improve corporate culture. Special attention is paid to the role of leadership in the formation of a favorable working environment. The conclusions emphasize the need for a strategic approach to the formation of corporate culture. It is emphasized that investment in the development of corporate culture has a positive effect on the efficiency of the staff in general. Keywords: corporate culture, work psychology, personnel motivation, psychological climate, corporate values

Design and development of low‐weight and high‐strength electromagnetic shielding nanocomposite in a microwave frequency range

AbstractIn this novel work, we fabricated the multiwalled carbon nanotubes (MWCNTs)/polypyrrole (PPy) nanocomposite by employing the novel ultrasonic dual mixing (UDM) method. Subsequently, the atomistics and structural characterizations of the fabricated MWCNT/PPy nanocomposite (PM) were performed using SEM, Raman, and XRD. Furthermore, we experimentally evaluated the mechanical properties of the fabricated PM by reinforcing the PPy matrix with MWCNTs at different wt.%. The experimental study of PM shows that the mechanical properties depend upon the amount and dispersion quality of MWCNTs in the PPy matrix. In order to validate the experimental outcomes, the conservative fully coupled finite element (FE) models were developed using the ANSYS Workbench and novel FEAST software. The electromagnetic interference (EMI) shielding effectiveness (SE) of developed PM was also calculated at different thicknesses of PM by taking the effect of SE due to absorption (SEA) and SE reflection (SER) in the microwave frequency range of the C‐band (5.37–8.2 GHz) into consideration. The total SE (SET), the cumulative sum of SEA and SER, is the strong function of the thickness of the PM. Additionally, our study's outcomes reveal that as the thickness of the PM increased, the SET also increased by dominating the SEA rather than the SER. The SET value of 75 dB was observed for a sample with a thickness of 3 mm, indicating a high level of shielding. Thus, material becomes a highly attractive option for achieving high‐performance EMI SE in the C‐band for satellite communications, Wi‐Fi devices, weather radar systems, surveillance, and cordless telephones. A complex permittivity and permeability were also studied using the Nicolson–Ross–Weir (NRW) method to understand the mechanism behind the EMI SE.Highlights Nanocomposite fabricated using the novel ultrasonic dual mixing method. FE models were developed to validate experimental results. The EC and EMI SE are improved with the increase in the sample thickness. Absorption, rather than reflection, dominated the shielding mechanism. SE was observed dB with good mechanical properties.

Crimes against Palestinian Civilians Under Zionist Occupation

Throughout its long history, humanity has coexisted with conflicts, power, influence, policies of violence and distress, bloodbaths, massacres, racial calamities and famines. Its present and future visions are still subject to horrors, hardships and tragedies. International humanitarian law has come to express these values in legal language that imposes the obligation on the States parties that make up these agreements. The basic rules and principles of this law have become a set of values accepted by the entire international community and to know that the civilian population is the first to be exposed in most cases to acts of aggression and violence. In different parts of the world, we are witnessing the process of ethnic cleansing, mass killings and atrocities committed against civilians and destroying their homes and property. This is what prompted the researcher to choose his research ( crimes committed against Palestinian civilians under the Zionist occupation) to answer many questions, including knowing who are civilians through the concept of crimes against civilians. The international trend of retaliation against civilians was rejected during the military operations. In the UN General Assembly resolution adopted in 1970, it is not permissible to retaliate against the civilian population, whether they are a group or individuals in armed conflicts. Civilians, in particular, within the framework of international humanitarian law, which is a newly established law that includes a set of international legal rules targeting situations of armed conflict and the protection of civilians suffering from the scourge of this conflict , and the subsequent military occupation. The rules of international humanitarian law are characterized by moral highness, as they codify the rules of morality that must prevail during armed conflict and military occupation

ARE INTELLECTUAL CAPITAL AND ITS COMPONENTS AFFECTIVE ON FINANCIAL PERFORMANCE?

The factors that affect intellectual capital creation at firms and how these factors influence the financial performance of firms have been core questions of extant work in the channels of finance for many years. In this period, therefore, the efforts that firms make to increase their intellectual capital to create value and studies that examine the impact of the increased intellectual capital on the firm’s financial performance are notable. Thus, this study aims to explore the influence of intellectual capital and its components on the financial performance of the BIST Sustainability Index companies for the period between 2018:Q4-2024:Q1. A set of Intellectual Value Added Coefficient (VAIC) and its components Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Capital Employed Efficiency (CEE) are employed as indicators of intellectual capital. The financial performance is measured through Return on Assets (ROA) and Return on Capital Employed (ROCE). Control variables included in each model are Market to Book Value (MV/BV), Logarithm of Total Assets (SIZE), and Asset Turnover (ATR). The fixed effects regression results show that an increase in the intellectual capital of the companies listed in the BIST Sustainability Index has a positive effect on their financial performance.

Mapping wild nature areas to identify priority areas for urban rewilding in cities: A process-oriented approach

Urban rewilding initiatives are gaining traction in cities, yet little remains known about the spatial opportunities for transitioning greenspaces into rewilding sites. This paper advances a process-oriented framing of urban wild nature areas (WNAs) and proposes a novel methodological approach for mapping existing WNAs at the city-scale, thereby shedding light on the potentials for urban rewilding across greenspace types and urban environments. Using Vienna as a case study, we identify WNAs as greenspaces predominantly shaped by natural succession processes and devoid of vegetation management. NDVI timeseries derived from Sentinel-2-satellite data for the period 2017–2022 are processed using a Random Forest algorithm to distinguish between unmanaged and managed vegetation annually and integrated into a multi-year composite map of ‘urban vegetation management intensity’. Based on this map and a set of objective cut-off values, 1298 WNAs are identified, representing 29.5 % of the city’s total urban greenspace and 14.9 % of the city’s total area. WNAs are distributed across diverse urban settings and greenspace types, with the majority being found in formal greenspaces such as forests, meadows and parks, and low-density built-up areas. The key strength of the process-oriented approach lies in its ability to detect WNAs dominated by natural succession, regardless of historic origin, greenspace type, and governance structure, thereby providing a baseline against which the full potential for urban rewilding can be evaluated. We demonstrate the methods’ utility in identifying potentials for urban rewilding, emphasising the importance of directing rewilding efforts towards residential greenspaces, urban parks, and street greenery, especially in densely built inner-city districts.

From Simulations to Inference: Using Machine Learning to Tune Patient-Specific Finite-Element Models of the Middle Ear Towards Objective Diagnosis.

Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. A major challenge in tuning these models is the large number of parameters involved. Traditional methods, such as one-by-one sensitivity analyses, are time-consuming, subjective, and often return only a single set of parameter values, focusing on reproducing averaged data rather than capturing the full variability of experimental measurements. In this study, we applied simulation-based inference (SBI) using neural posterior estimation (NPE) to tune an FE model of the human middle ear. The training dataset consisted of 10,000 FE simulations of stapes velocity, ear-canal (EC) input impedance, and absorbance, paired with seven FE parameter values randomly sampled within plausible ranges. The neural network learned the association between parameters and simulation outcomes, returning the probability distribution of parameter values that can reproduce experimental data. Our approach successfully identified parameter sets that reproduced three experimental datasets simultaneously. By accounting for experimental noise and variability during training, the method provided a probability distribution of parameters, representing all valid combinations that could fit the data, rather than tuning to averaged values. The network demonstrated robustness to noise and exhibited an efficient learning curve due to the large training dataset. SBI offers an objective alternative to laborious sensitivity analyses, providing probability distributions for each parameter and uncovering interactions between them. This method can be applied to any biological FE model, and we demonstrated its effectiveness using a middle-ear model. Importantly, it holds promise for objective differential diagnosis of conductive hearing loss by providing insight into the mechanical properties of the middle ear.

Cardiac Troponin Levels in Patients with Chronic Kidney Disease: "Markers of High Risk or Just Noise''?

Kidney disease is linked to the development of cardiovascular disorders, further increasing morbidity and mortality in this high-risk population. Thus, early detection of myocardial damage is imperative in order to prevent devastating cardiovascular complications within this patient group. Over the years, cardiac biomarkers have been identified and are now widely used in everyday clinical practice. More specifically, available data suggest that cardiac troponin and its regulatory subunits (TnT, TnI, and TnC) reflect the injury and necrosis of myocardial tissue. While cTnC is identical in cardiac and skeletal muscle, TnT and TnI constitute cardiac-specific forms of troponin, and, as such, they have been established by international societies as biomarkers of cardiac damage and diagnostic indicators for acute myocardial infarction. Elevations in the levels of both cardiac troponins (cTnT and cTnI) have been also reported in asymptomatic patients suffering from chronic kidney disease. Therefore, if abnormal, they often generate confusion among clinicians regarding the interpretation and clinical significance of their numerical values in emergency settings. The aim of this review is to explore the reasons behind elevated troponin levels in patients with chronic kidney disease and identify when these elevated levels of biomarkers indicate the need for urgent intervention, considering the high cardiovascular risk in this patient group.

Comparing the measurement properties of the EQ-5D-5L, SF-6Dv2, QLU-C10D and FACT-8D among survivors of classical Hodgkin's lymphoma.

This study aimed to evaluate the measurement properties of EQ-5D-5L, SF-6Dv2, QLU-C10D, and FACT-8D in survivors of Classical Hodgkin's Lymphoma (CHL). A cross-sectional, web-based survey was conducted from May to August 2022 to collect data. Chinese value sets were used to estimate the utility scores for EQ-5D-5L, SF-6Dv2, and QLU-C10D, while the Australian value set was used for FACT-8D. The measurement properties assessed included ceiling and floor effects, convergent validity (assessing associations between similar dimensions/utility scores using Spearman's rank correlation and intraclass correlation coefficient), and known-group validity (measures could differentiate health-related quality of life (HRQoL) between risk groups). A total of 534 CHL survivors participated in the survey and completed the questionnaire. All dimensions of EQ-5D-5L, SF-6D (except for vitality), QLU-C10D, and FACT-8D showed ceiling effects, ranging from 18 to 91.6%. The EQ-5D-5L demonstrated the higher ceiling effects compared to other measures, with 33% of patients reporting full health on this scale. All 30 pairs of associations between similar dimensions from the four measures were statistically significant, with correlation coefficients ranging from 0.29 to 0.77. Regarding utility scores, the EQ-5D-5L utility score showed a stronger correlation with SF-6Dv2 than with the other two measures. Statistically significant correlations of utility scores between the four measures were observed. EQ-5D-5L can significantly differentiate HRQoL among all known-groups, while SF-6Dv2, QLU-C10D, and FACT-8D showed a less strong discriminant ability. EQ-5D-5L outperformed SF-6Dv2 in terms of agreement with cancer-specific PRMs and discriminant ability. However, SF-6Dv2 showed stronger associations with similar dimensions of QLU-C10D and FACT-8D, indicating high convergent validity. The generic PBMs are sensitive enough to measure HRQoL in survivors of CHL.

Risk factors analysis and nomogram conduction for major adverse events after lumbar fusion surgery in older patients: a prospective cohort study

This study aimed to identify risk factors for major adverse events (AEs) after lumbar fusion surgery in patients aged 75 and over and evaluate the role of several common geriatric comprehensive assessment items in predicting postoperative major AEs. This is a prospective cohort study of patients aged ≥75 years who underwent open lumbar fusion for degenerative spine disease from August 2019 to August 2022. The primary outcome measure was the incidence of major postoperative AEs within 90 days after surgery. Clavien-Dindo III-IV complications and unplanned readmission were defined as major AEs. Patients' characteristics, laboratory tests, assessment results, and surgery-related variables were compared between the major AEs and the non-major AEs groups. Multivariable logistic regression analysis was used to identify independent risk factors for major AEs. The logistic regression model was evaluated in another prospective cohort of patients from October 2022 to October 2023. A total of 301 patients (mean [SD] age, 79.7 [3.5] years; 60.5% male) were included in the study. Five features, including female (OR 1.99, p = 0.040), higher body mass index (BMI) (OR 1.090, p = 0.024), frailty (OR 2.043, p = 0.032), hypoalbuminemia (OR 2.489, p = 0.040), and higher Charlson comorbidity index (CCI) (OR 1.397, p = 0.024), were independently associated with major AEs and were selected to develop a predictive nomogram of major AEs. The area under the curve values for the development set and validation set were 0.75 and 0.71, respectively. Preoperative frailty, hypoalbuminemia, female sex, greater BMI, and higher CCI grade were risk factors for 90-day major AEs after lumbar fusion surgery in older patients. The predictive nomogram that we developed using this data can enhance preoperative risk counseling and optimization for older patients.

A method for evaluating the regulation capacity of thermostatically controlled load group based on state complementary control strategy

Electric heating load (EHL) is a type of thermostatically controlled load whose power consumption mode can be flexibly adjusted, as long as the customers’ thermal comfort is ensured. The key to unlocking the regulatory potential of EHL lies in characterizing and harnessing its post-aggregation regulatory capacity, given its inherent dispersion and heterogeneity. Firstly, the second-order equivalent thermal parameter (ETP) model, grounded in the thermal equilibrium of the room, is employed to analyze the operational characteristics and to construct a model for aggregating load group. Secondly, leveraging the analysis of individual EHL regulation capacity, the temperature state characteristic quantity is introduced as a sorting index. Considering the dynamic properties of baseline power over time, a control strategy is proposed, based on state complementary characteristics among EHLs, for a more comprehensive and dynamic system management. Finally, the key indexes for reporting and implementation phases are identified to evaluate the regulation capacity of load group. The case study evidences that the newly introduced control strategy and evaluation method can provide more stable regulation power support for power grid dispatching, and more deeply harness the regulation capacity to maximize its potential value. In addition, this paper also discusses the influence of related factors such as the number, the rated power, the inside temperature control interval, the inside temperature setting value and the outside temperature on the regulation ability of the EHL group, offering a robust framework for the dispatching center to devise well-informed and rational regulation instructions.

Parameter identification of rock mass in the time domain

A time domain rock mass parameter identification method considering the Rayleigh damping is proposed in this paper. Starting with a set of initial parameter values, the algorithm solves the finite-element (FE) equations updating the parameters in each iterative step until the result converges to a local optimum. Numerical FE analysis, displacement sensitivity analysis, and the truncated singular value decomposition method are integrated into the solution algorithm for parameter identification. The technique introduced is verified in numerical studies with in-situ experiments of rock mass structures. Our results show that the approach, compared with conventional methods, can reduce ambiguities caused by inadequate data and provide more accurate insights into the subsurface for rock mass quality evaluation.