A guide to large data sets for population-based cancer research: Strengths, limitations, and pitfalls.

With the proliferation of cancer research based on large databases, misalignment of research questions and data set capabilities is inevitable. Nationally maintained databases are appealing to cancer researchers because of the ease of access to large amounts of patient data available for analysis and risk estimation. Data sets that are commonly used in cancer research include the National Cancer Database, the SEER (Surveillance, Epidemiology, and End Results) program of the National Cancer Institute, the SEER-Medicare database, the American College of Surgeons National Surgical Quality Improvement Program, and the Healthcare Cost and Utilization Project databases, among others. Each data set has pros and cons with respect to variable availability and the ability to analyze cancer-specific outcomes. It is critical for researchers to understand the strengths and limitations of each database. Changing variable definitions, the length of postoperative data collection, and the availability of patient-reported outcomes or social determinants of health data are examples of factors that researchers must consider when selecting a data set for research purposes. For the current review, the authors summarized the advantages and disadvantages of various national data sets for cohort studies in cancer populations.

Aggregation operators based on Einstein averaging under q-spherical fuzzy rough sets and their applications in navigation systems for automatic cars.

This study introduces innovative operational laws, Einstein operations, and novel aggregation algorithms tailored for handling q-spherical fuzzy rough data. The research article presents three newly designed arithmetic averaging operators: q-spherical fuzzy rough Einstein weighted averaging, q-spherical fuzzy rough Einstein ordered weighted averaging, and q-spherical fuzzy rough Einstein hybrid weighted averaging. These operators are meticulously crafted to enhance precision and accuracy in arithmetic averaging. By thoroughly examining their characteristics and interrelations with existing aggregate operators, the article uncovers their distinct advantages and innovative contributions to the field. Furthermore, the study illustrates the actual implementation of these newly constructed operators in a variety of attribute decision-making scenarios employing q-SFR data, yielding useful insights. Our suite of decision-making tools, including these operators, is specifically designed to address complex and uncertain data. To validate our approach, this study offers a numerical example showcasing the real-world applicability of the proposed operators. The results not only corroborate the efficacy of the proposed method but also underscore its potential significance in practical decision-making processes dealing with intricate and ambiguous data. Additionally, comparative and sensitivity analyses are presented to assess the effectiveness and robustness of our proposed work relative to other approaches. The acquired knowledge enriches the current understanding and opens new avenues for future research.

Evaluating approach uncertainties of quantitative detection of SARS-CoV-2 in wastewater: Concentration, extraction and amplification.

Substantial uncertainties pose challenges to the accuracy of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quantification in wastewater. We conducted a comprehensive evaluation of two concentration methods, three nucleic acid extraction methods, and the amplification performance of eight primer-probe sets. Our results showed that the two concentration methods exhibited similar recovery rates. Specifically, using a 30 kDa cut-off ultrafilter and a centrifugal force of 2500 g achieved the highest virus recovery rates (27.32 ± 8.06 % and 26.37 ± 7.77 %, respectively), with lower corresponding quantification uncertainties of 29.51 % and 29.47 % in ultrafiltration methods. Similarly, a 15 % PEG concentration with 1.5 M NaCl markedly improved virus recovery (26.76 ± 5.92 % and 28.47 ± 6.74 %, respectively), and reducing variation to 22.16 % and 23.66 % in the PEG precipitation method. Additionally, employing a vigorous bead-beating approach at 6 m/s during viral RNA extraction significantly increased RNA yield, with an efficiency reaching up to 82.18 %. Among the evaluated eight primer-probe sets, the E_Sarbeco primer-probe set provided the most stable and consistent quantitative results across various sample matrices. These findings are crucial for establishing robust viral quantification protocols and enhancing methodological precision for effective wastewater surveillance, enabling sensitive and precise detection of SARS-CoV-2.

Lung-MAP Next-Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400).

INTRODUCTION: Squamous cell cancer (SqCC) is a lung cancer subtype with few targeted therapy options. Molecular characterization, that is, by next-generation sequencing (NGS), is needed to identify potential targets. Lung Cancer Master Protocol Southwest Oncology Group S1400 enrolled patients with previously treated stage IV or recurrent SqCC to assess NGS biomarkers for therapeutic sub-studies. METHODS: Tumors underwent NGS using Foundation Medicine's FoundationOne research platform, which sequenced the exons and/or introns of 313 cancer-related genes. Mutually exclusive gene set analysis and Selected Events Linked by Evolutionary Conditions across Human Tumors were performed to identify mutually exclusive and co-occurring gene alterations. Comparisons were performed with data on 495 lung SqCC downloaded from The Cancer Genome Atlas. Cox proportional hazards models were used to assess associations between genetic variants and survival. RESULTS: NGS data are reported for 1672 patients enrolled on S1400 between 2014 and 2019. Mutually exclusive gene set analysis identified two non-overlapping sets of mutually exclusive alterations with a false discovery rate of less than 15%: NFE2L2, KEAP1, and PARP4; and CDKN2A and RB1. PARP4, a relatively uncharacterized gene, showed three frequent mutations suggesting functional significance: 3116T>C (I1039T), 3176A>G (Q1059R), and 3509C>T (T1170I). When taken together, NFE2L2 and KEAP1 alterations were associated with poorer survival. CONCLUSIONS: As the largest dataset to date of lung SqCC profiled on a clinical trial, the S1400 NGS dataset establishes a rich resource for biomarker discovery. Mutual exclusivity of PARP4 and NFE2L2 or KEAP1 alterations suggests that PARP4 may have an uncharacterized role in a key pathway known to impact oxidative stress response and treatment resistance.

Integrated behavioral health implementation and chronic disease management inequities: an exploratory study of statewide data.

BACKGROUND: People with diabetes, vascular disease, and asthma often struggle to maintain stability in their chronic health conditions, particularly those in rural areas, living in poverty, or racially or ethnically minoritized populations. These groups can experience inequities in healthcare, where one group of people has fewer or lower-quality resources than others. Integrating behavioral healthcare services into primary care holds promise in helping the primary care team better manage patients' conditions, but it involves changing the way care is delivered in a clinic in multiple ways. Some clinics are more successful than others in fully integrating behavioral health models as shown by previous research conducted by our team identifying four patterns of implementation: Low, Structural, Partial, and Strong. Little is known about how this variation in integration may be related to chronic disease management and if IBH could be a strategy to reduce healthcare inequities. This study explores potential relationships between IBH implementation variation and chronic disease management in the context of healthcare inequities. METHODS: Building on a previously published latent class analysis of 102 primary care clinics in Minnesota, we used multiple regression to establish relationships between IBH latent class and healthcare inequities in chronic disease management, and then structural equation modeling to examine how IBH latent class may moderate those healthcare inequities. RESULTS: Contrary to our hypotheses, and demonstrating the complexity of the research question, clinics with better chronic disease management were more likely to be Low IBH rather than any other level of integration. Strong and Structural IBH clinics demonstrated better chronic disease management as race in the clinic's location became more White. CONCLUSIONS: IBH may result in improved care, though it may not be sufficient to resolve healthcare inequities; it appears that IBH may be more effective when fewer social determinants of health are present. Clinics with Low IBH may not be motivated to engage in this practice change for chronic disease management and may need to be provided other reasons to do so. Larger systemic and policy changes are likely required that specifically target the mechanisms of healthcare inequities.

Application of the international classification of functioning, disability, and health in autism and attention-deficit hyperactivity disorder: A scoping review.

LAY ABSTRACT: The International Classification of Functioning, Disability, and Health (ICF) is a framework designed by the World Health Organization (WHO) to help different sectors, such as healthcare, social services, education, and policy, understand how people with health-related issues function (do the things they want to and need to do) in their daily lives. This framework has also been used to guide clinical practice and research in autism and attention-deficit hyperactivity disorder (ADHD). To make it more practical, shorter versions of the ICF called Core Sets have been developed. We wanted to explore how the ICF and the ICF Core Sets have been used in research relating to autism and ADHD. We looked at the research that had been previously published on this topic by conducting a systematic search and review. Seventy-eight studies meeting our criteria were included in our review. Results show that the ICF has been applied in many ways across various contexts. However, most of the research has focused on autism, mainly involving children. The review highlights that although the ICF was used in some studies, the underlying philosophies of the framework were not always followed. The medical perspective still influenced the way research was done and interpreted. Nevertheless, using the ICF in the right way can help shift research on neurodevelopmental conditions like autism and ADHD toward a more holistic approach, moving away from solely focusing on medical aspects.

Evaluation of psychosocial risks affecting academician using the sine trigonometric pythagorean fuzzy (ST-PFN) DEMATEL method.

BACKGROUND: In the realm of academia, psychosocial risk factors play a pivotal role, exerting considerable influence on the well-being and performance of scholars. Factors such as excessive workloads, unrelenting performance expectations, time constraints, and competitive atmospheres can detrimentally affect their health and overall quality of life. Moreover, issues like social isolation, work-life imbalance, and academic anxiety further compound these challenges, impeding academic progress and success. OBJECTIVE: Thus, it is imperative to identify, mitigate, and manage psychosocial risk factors effectively to foster a conducive academic environment conducive to productivity and achievement. METHODS: This study employs a novel approach, integrating expert opinions and utilizing sine trigonometry Pythagorean fuzzy numbers to ascertain the degrees of importance and rank psychosocial risk factors through the DEMATEL method. This methodology presents a unique contribution to existing literature, offering fresh insights into this critical area of study. RESULTS: Evaluation of 25 factors reveals burnout, job dissatisfaction, precarious working conditions, lack of trust, unfair treatment, job insecurity, and limited developmental opportunities as primary concerns, underscoring the multifaceted nature of these challenges. CONCLUSIONS: In the occupational sphere, active participation from all stakeholders in addressing psychosocial risks is paramount for effective problem resolution. It is incumbent upon parties involved to discharge their duties, foster collaboration, enhance working conditions, ensure equity, and facilitate avenues for professional growth. Each identified risk factor addressed and preempted translates into a tangible reduction in workplace accidents and occupational ailments, underscoring the tangible benefits of proactive risk management.

COVID-19 prevention and control effect of non-pharmaceutical interventions-fuzzy-sets qualitative comparative analysis based on 69 countries in the world.

Introduction: Coronavirus disease 2019 occurred unexpectedly in late December 2019, it was difficult to immediately develop an effective vaccine or propose targeted medical interventions in the early stages of the outbreak. At this point, non-pharmaceutical interventions (NPIs) are essential components of the public health response to COVID-19. How to combine different NPIs in the early stages of an outbreak to control the spread of epidemics and ensure that the policy combination does not incur high socio-economic costs became the focus of this study. Methods: We mainly used the fuzzy set qualitative comparative analysis to assess the impact of different combinations of NPIs on the effectiveness of control in the COVID-19 pandemic early stage, using open datasets containing case numbers, country populations and policy responses. Results: We showed that the configuration of high morbidity results includes one, which is the combination of non-strict face covering, social isolation and travel restrictions. The configuration of non-high morbidity results includes three, one is strict mask wearing measures, which alone constitute sufficient conditions for interpreting the results; the second is strict testing and contact tracing, social isolation; the third is strict testing and contact tracing, travel restriction. The results of the robustness test showed that the number, components and consistency of the configurations have not changed after changing the minimum case frequency, which proved that the analysis results are reliable. Conclusion: In the early stages of the epidemic, the causes of high morbidity are not symmetrical with the causes of non-high morbidity. Strict face covering is the most basic measure required to prevent and control epidemics, and the combination of non-strict face covering and containment is the most important factor leading to poor prevention and control, and the combination of strict containment and proactive pursuit is the way to achieve superior prevention and control, timely and proactive containment strategies have better prevention and control, and should mobilize the public to cooperate.

Confidence sets for intraclass correlation coefficients in test-retest curve measurements.

Evaluating test-retest reliability is crucial in biomechanical research, as it validates experimental results. While methods for reliability of scalar outcome variables are well-established, methods to assess reliability of continuous curve data (such as joint angle trajectories during gait) remain less explored. This study investigates methods for constructing confidence sets for curve-level intraclass correlation coefficients (ICC), which can be expressed as either an ICC curve or an integrated ICC. Currently, no standardised guidelines exist in biomechanics for reporting curve-level ICC uncertainty. Nonparametric bootstrapping techniques are proposed for both the ICC curve's confidence bands and the integrated ICC's confidence intervals, and these methods are validated through Monte Carlo simulations, covering various effect sizes and curve characteristics. Additionally, these methods are applied to assess the test-retest reliability of knee kinematics in three different planes during landing of one-leg hops, where less uncertainty is observed for the ICC curve and integrated ICC in the frontal plane compared to other planes. When the entire time domain is of primary empirical interest, we recommend using a rank-based bootstrap confidence band to express ICC uncertainty, as it yields increasingly precise and valid results as the number of individuals increases, with the coverage rate approaching the correct level of 95%. When a single summary metric is of primary interest, we recommend using the integrated ICC along with a typical bootstrap confidence interval based on the normal distribution, as the coverage rate remains adequately accurate and stable at around the correct level of 95% across varying number of individuals.

Accurate space-group prediction from composition.

Predicting crystal symmetry simply from chemical composition has remained challenging. Several machine-learning approaches can be employed, but the predictive value of popular crystallographic databases is relatively modest due to the paucity of data and uneven distribution across the 230 space groups. In this work, virtually all crystallographic information available to science has been compiled and used to train and test multiple machine-learning models. Composition-driven random-forest classification relying on a large set of descriptors showed the best performance. The predictive models for crystal system, Bravais lattice, point group and space group of inorganic compounds are made publicly available as easy-to-use software downloadable from https://gitlab.com/vishsoft/cosy.

Creating an SF-6Dv2 social value set for New Zealand.

The SF-6D health descriptive system and its second version published in 2020, the SF-6Dv2, is used worldwide for valuing health-related quality of life (HRQoL) for economic evaluation and measuring patient-reported health outcomes. In this study, a valuation tool was developed and applied to create a social value set, comprising 18,750 health state values, for the SF-6Dv2 for New Zealand (NZ). This tool was adapted and extended from the one used to create a social value set for the EQ-5D-5L, a simpler health descriptive system with fewer dimensions and health states. The tool implements the PAPRIKA method, a type of adaptive discrete choice experiment, and a binary search algorithm to identify health states worse than dead and has extensive data quality controls to ensure the validity and reliability of the social value set derived from participants' personal value sets. The tool, accompanied by a short introductory video designed specifically for the SF-6Dv2, was distributed via an online survey to a large representative sample of adult New Zealanders in June-July 2022. The tool's data quality controls enabled participants who failed to understand or sincerely engage with the valuation tasks to be identified and excluded, resulting in the participants being pared down to a sub-sample of 2985 'high-quality' participants whose personal value sets were averaged for the social value set. These results, including participants' positive feedback, demonstrate the feasibility and acceptability of using the tool to value larger health descriptive systems such as the SF-6Dv2. Having successfully created an SF-6Dv2 social value set for NZ, the valuation tool can be readily applied to other countries, used to generate personal value sets for personalised medicine and adapted to create value sets for other health descriptive systems.

Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.

Arboviruses such as dengue, Zika, and chikungunya present similar symptoms in the early stages, which complicates their differential and timely diagnosis. In 2022, the PAHO published a guide to address this challenge. This study proposes a methodological framework that transforms qualitative information into quantitative information, establishing differential weights in relation to symptoms according to the medical evidence and the GRADE scale based on recommendation 1 of the said guide. To achieve this, common variables from the dataset were identified using the PAHO guide, and quality rules were established. A linear interpolation function was then parameterised to assign weights to the symptoms according to the evidence. Machine learning was used to compare the different models, achieving 99% accuracy compared with 79% without the methodology. This proposal represents a significant advancement, allowing the direct application of the PAHO recommendations to the dataset and improving the differential classification of arboviruses.

PGAP3 regulates human bronchial epithelial cell mRNAs present in asthma and respiratory virus reference data sets.

PGAP3 is a glycosylphosphatidylinositol (GPI) phospholipase gene localized within chromosome 17q12-21, a region highly linked to asthma. Although much is known about the function of other chromosome 17q12-21 genes expressed at increased levels in bronchial epithelium such as ORMDL3 and GSDMB, little is known about the function of increased PGAP3 expression in bronchial epithelium in the context of asthma. The aim of this study was therefore to determine whether increased PGAP3 expression in human bronchial epithelial cells regulated expression of mRNA pathways important to the pathogenesis of asthma by utilizing RNA-sequencing and bioinformatic analysis. We performed RNA-sequencing on normal human bronchial epithelial cells transfected with PGAP3 for 24 and 48 hours. PGAP3 regulated genes were compared to asthma and respiratory virus (influenza A, rhinovirus, respiratory syncytial virus) reference data sets to identify PGAP3 target genes and pathways. Approximately 9% of the upregulated PGAP3-induced genes were found in an asthma reference data set, 41% in a rhinovirus reference data set, 33% in an influenza A reference data set, and 3% in a respiratory syncytial virus reference data set. PGAP3 significantly upregulated the expression of several genes associated with the innate immune response and viral signatures of respiratory viruses associated with asthma exacerbations. Two of the highest expressed genes induced by PGAP3 are RSAD2, OASL, and IFN-λ, which are anti-viral genes associated with asthma. PGAP3 also upregulated the antiviral gene BST2, which like PGAP3 is a GPI-anchored protein. We conclude that PGAP3 expression in human bronchial epithelial cells regulates expression of genes known to be linked to asthma, and also regulates the bronchial epithelial expression of genes pertinent to the pathogenesis of respiratory viral triggered asthma exacerbations.

Optimizing bioremediation techniques for soil decontamination in a linguistic intuitionistic fuzzy framework.

Bioremediation techniques, which harness the metabolic activities of microorganisms, offer sustainable and environmentally friendly approaches to contaminated soil remediation. These methods involve the introduction of specialized microbial consortiums to facilitate the degradation of pollutants, contribute to soil restoration, and mitigate environmental hazards. When selecting the most effective bioremediation technique for soil decontamination, precise and dependable decision-making methods are critical. This research endeavors to tackle the aforementioned concern by utilizing the tool of aggregation operators in the framework of the Linguistic Intuitionistic Fuzzy (LIF) environment. Linguistic Intuitionistic Fuzzy Sets (LIFSs) provide a robust framework for representing and managing uncertainties associated with linguistic expressions and intuitionistic assessments. Aggregation operators enrich the decision-making process by efficiently handling the intrinsic uncertainties, preferences, and priorities of MADM problems; as a consequence, the decisions produced are more reliable and precise. In this research, we utilize this concept to devise innovative aggregation operators, namely the linguistic intuitionistic fuzzy Dombi weighted averaging operator (LIFDWA) and the linguistic intuitionistic fuzzy Dombi weighted geometric operator (LIFDWG). We also demonstrate the critical structural properties of these operators. Additionally, we formulate novel score and accuracy functions for multiple attribute decision-making (MADM) problems within LIF knowledge. Furthermore, we develop an algorithm to confront the complexities associated with ambiguous data in solving decision-making problems in the LIF Dombi aggregation environment. To underscore the efficacy and superiority of our proposed methodologies, we adeptly apply these techniques to address the MADM problem concerning the optimal selection of a bioremediation technique for soil decontamination. Moreover, we present a comparative evaluation to delineate the authenticity and practical applicability of the recently introduced approaches relative to previously formulated techniques.

A comparison of morning priming exercise using traditional-set and cluster-set configurations on afternoon explosive performance.

The impact of two priming exercise protocols using traditional (TS) or cluster-set (CS) arrangements on explosive performance 6 hours later were examined. Sixteen male collegiate athletes performed three testing sessions (one baseline without any prior exercise in the morning and two experimental sessions) separated by 72 hours. Participants completed two morning (9-11 am) priming protocols in a randomized order, either using a TS (no rest between repetitions) or CS (30 seconds of rest between repetitions) configuration. The protocols consisted of 3 sets × 3 repetitions of barbell back squat at 85% of 1 repetition maximum, with 4 minutes of rest between sets. In the afternoon (3-5 pm) of each trial, after a 6-hour rest period, a physical test battery was conducted that replicated baseline testing, including countermovement jump, 20-meter straight-line sprint, and T-test abilities. Across both conditions, participants exhibited increased countermovement jump height, 20-meter sprint time and T-test time compared to baseline (P < 0.05). Improvements in countermovement jump height (+4.4 ± 5.4%; P = 0.008) and 20-meter sprint time (+1.3 ± 1.7%; P = 0.022), but not T-test time (+1.1 ± 3.3%; P = 0.585), were significantly greater for CS than TS. In conclusion, compared to a traditional set arrangement, a morning-based priming protocol using a cluster-set configuration led to superior explosive performance benefits in the afternoon.

LogTODIM-PROMETHEE technique for development evaluation of school-enterprise cooperation from the perspective of collaborative education based on the probabilistic linguistic group decision-making.

The integration of industry and education can be literally explained as the role of education in providing intellectual support and production services for the development of industries in a country or region, evolving from an auxiliary role in industrial development to a supporting role in industrial development. The government, enterprises, universities, and intermediaries are all the main bodies of industry education integration, and school enterprise cooperation is the core. The two basic functions of universities, namely "applied scientific research" and "social services", must be highlighted. How to strengthen the cultivation of applied talents and improve the quality of talent cultivation in universities is a problem that universities must face in their transformation. Strengthening school enterprise cooperation and promoting the integration of industry and education is an effective way to solve this problem. The development evaluation of school-enterprise cooperation from the perspective of collaborative education is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Logarithmic TODIM (LogTODIM) and PROMETHEE technique was employed to put forward the MAGDM issues. The probabilistic linguistic term sets (PLTSs) are employed as a technique for characterizing uncertain information during the development evaluation of school-enterprise cooperation from the perspective of collaborative education. In this paper, the probabilistic linguistic LogTODIM-PROMETHEE (PL-LogTODIM-PROMETHEE) technique is constructed to put forward the MAGDM under PLTSs. The MEREC technique is employed to obtain the weight values under PLTSs. Finally, a numerical example for development evaluation of school-enterprise cooperation from the perspective of collaborative education is put forward to validate the LogTODIM-PROMETHEE technique.

Sketching Methods with Small Window Guarantee Using Minimum Decycling Sets.

Most sequence sketching methods work by selecting specific k-mers from sequences so that the similarity between two sequences can be estimated using only the sketches. Because estimating sequence similarity is much faster using sketches than using sequence alignment, sketching methods are used to reduce the computational requirements of computational biology software. Applications using sketches often rely on properties of the k-mer selection procedure to ensure that using a sketch does not degrade the quality of the results compared with using sequence alignment. Two important examples of such properties are locality and window guarantees, the latter of which ensures that no long region of the sequence goes unrepresented in the sketch. A sketching method with a window guarantee, implicitly or explicitly, corresponds to a decycling set of the de Bruijn graph, which is a set of unavoidable k-mers. Any long enough sequence, by definition, must contain a k-mer from any decycling set (hence, the unavoidable property). Conversely, a decycling set also defines a sketching method by choosing the k-mers from the set as representatives. Although current methods use one of a small number of sketching method families, the space of decycling sets is much larger and largely unexplored. Finding decycling sets with desirable characteristics (e.g., small remaining path length) is a promising approach to discovering new sketching methods with improved performance (e.g., with small window guarantee). The Minimum Decycling Sets (MDSs) are of particular interest because of their minimum size. Only two algorithms, by Mykkeltveit and Champarnaud, are previously known to generate two particular MDSs, although there are typically a vast number of alternative MDSs. We provide a simple method to enumerate MDSs. This method allows one to explore the space of MDSs and to find MDSs optimized for desirable properties. We give evidence that the Mykkeltveit sets are close to optimal regarding one particular property, the remaining path length. A number of conjectures and computational and theoretical evidence to support them are presented. Code available at https://github.com/Kingsford-Group/mdsscope.

Selecting optimal celestial object for space observation in the realm of complex spherical fuzzy systems.

The sensible selection of celestial objects for observation by the James Web Space Telescope (JWST) is pivotal for the precise decision-making (DM) process, aligning with scientific priorities and instrument capabilities to maximize valuable data acquisition to address key astronomical questions within the constraints of limited observation time. Aggregation operators are valuable models for condensing and summarizing a finite set of data of imprecise nature. Utilization of these operators is critical when addressing multi-attribute decision-making (MCDM) challenges. The complex spherical fuzzy (CSF) framework effectively captures and represents the uncertainty that arises in a DM problem with more precision. This paper presents two novel aggregation operators, namely the complex spherical fuzzy Yager weighted averaging (CSFYWA) operator and the complex spherical fuzzy Yager weighted geometric (CSFYWG) operator. Many fundamental structural properties of these operators are delineated, and thereby an improved score function is suggested that addresses the limitations of the existing score function within the CSF system. The newly defined operators are applied to formulate an algorithm for MADM problems to tackle the challenges of ambiguous data in the selection process. Moreover, these strategies are effectively applied to handle the MADM problem of selecting the optimal astronomical object for space observation within the CSF context. Additionally, a comparative analysis is also performed to demonstrate the validity and superiority of the proposed techniques compared to the existing strategies.

Disturbance-rejection control for unbalanced operation of microgrids: Invariant-set approach.

In microgrids, voltage imbalance control is crucial to preserving the required level of power quality. The article presents a tracker design that mitigates the unbalance in the microgrids. The proposed microgrid model includes positive and negative sequences, whereby the positive sequences are controlled, and the negative sequence is treated as an external disturbance (whose effect must be attenuated). The uncertainty in the external disturbance is modelled in the norm-bounded form. The suggested control is based on attracting (driving) the state trajectory into a small region, including the origin (attracting ellipsoid-set). The effect of the negative sequence components is attenuated by minimizing the ellipsoid volume. When the state trajectory enters that region, it will never leave it for the future time (termed invariant-set). Two theorems were formulated for tracker synthesis that follows the desired reference and reduces the negative sequence impact. These theorems are the invariant-set method and the H∞ approach. The validity of the suggested control is demonstrated via testing the system under various operational unbalanced scenarios, such as unbalances or faults at the load side or in the transmission lines. The simulations show the superiority of the suggested method in terms of accuracy and dynamic response when compared with the H∞ . Additionally, a comparison is made between the suggested tracker and Active Disturbance Rejection Control (ADRC).

Effect of Rapid Response Order Panels on Resident and Nurse Satisfaction.

Background: Hospitals are required to have rapid response (RR) systems in place to respond to acute changes in a patient's condition. In high-stress situations like RR, medical residents face decision-making challenges due to time constraints and perceived pressure. Instituting order panels (OPs) can facilitate clinical decision making and improve residents' and nurses' satisfaction and patient safety. Objective: This quality improvement (QI) project aimed to create and institute standardized OPs for common RR clinical scenarios to improve satisfaction of internal medicine residents and nurses with the RR process. Methods: This was a single tertiary care center QI project that developed OPs for 10 common RR scenarios. Resident and nursing satisfaction with RR was assessed before and after OP implementation via survey and qualitative data collection. Results: Residents and nurses expressed high levels of satisfaction across various aspects of the RR process before and after OP implementation in both quantitative and qualitative analysis. Increased satisfaction was observed among residents regarding time spent placing orders (94%; P = 0.02) and time spent correcting wrong orders (87%; P = 0.03) after OP implementation. The nurses' survey revealed no statistically significant differences in satisfaction before and after the implementation of OPs regarding communication, collaboration, efficiency, and organization of the team. Conclusion: The introduction of standardized OPs for RRs resulted in increased satisfaction among internal medicine residents in terms of order placement and correcting wrong orders. Nurse satisfaction based on survey responses remained neutral. Qualitative data from both groups demonstrated a positive impact on communication, efficiency, and teamwork.