Study on Temperature Disaster Indicators of Passion Fruit in Southwest Fujian, China

In China, meteorological forecasting relies on meteorological data obtained from regional and national stations. However, there were discrepancies between the data collected from the meteorological station at the passion fruit growing base and the data from regional and national stations. Consequently, the high and low temperature disaster indicators determined by the meteorological station at the passion fruit growing base cannot be applied to meteorological forecasting. To address this issue and facilitate the monitoring and early warning of high and low temperature disasters in passion fruit cultivation in Fujian, China, we used multi-source hourly temperature data (including the data from meteorological observation stations in passion fruit growing bases, the nearest regional stations, and national surface conventional meteorological observation stations) in three cities in southwestern Fujian (Longyan, Sanming, and Zhangzhou) spanning the years 2020 to 2022. By employing comprehensive statistical analysis methods (0.5 interval division and Cumulative frequency), we identified that passion fruit in southwestern Fujian was susceptible to high temperature disasters during the blooming-fruiting period, as well as low temperature disasters during the sprouting period. Consequently, we developed high and low temperature disaster indicators based on data from regional and national stations for different phenological periods of passion fruit in this region.

Investigating the causal associations between five anthropometric indicators and nonalcoholic fatty liver disease:Mendelian randomization study

BACKGROUND Although the etiology of nonalcoholic fatty liver disease(NAFLD)has not been thoroughly understood,the emerging roles of anthropometric indicators in assessing and predicting the risk of NAFLD have been highlighted by accumulating evidence.AIM To evaluate the causal relationships between five anthropometric indicators and NAFLD employing Mendelian randomization(MR)design.METHODS The Anthropometric Consortium provided genetic exposure data for five anthropometric indicators,including hip circumference(HC),waist circumference(WC),waist-to-hip ratio(WHR),body mass index(BMI),and body fat percentage(BF).Genetic outcome data for NAFLD were obtained from the United Kingdom Biobank and FinnGen Consortium.Genome-wide significant single nucleotide polymorphisms were chosen as instrumental variables.Univariable MR(UVMR)and multivariable MR(MVMR)designs with analytical approaches,including inverse variance weighted(IVW),MR-Egger,weighted median(WM),and weighted mode methods,were used to assess the causal relationships between anthropometric indicators and NAFLD.RESULTS Causal relationships were revealed by UVMR,indicating that a higher risk of NAFLD was associated with a perunit increase in WC[IVW:odds ratio(OR)=2.67,95%CI:1.42-5.02,P=2.25×10^(−3)],and BF was causally associated with an increased risk of NAFLD(WM:OR=2.23,95%CI:1.07-4.66,P=0.033).The presence of causal effects of WC on the decreased risk of NAFLD was supported by MVMR after adjusting for BMI and smoking.However,no causal association between BF and NAFLD was observed.In addition,other causal relationships of HC,WHR(BMI adjusted),and BMI with the risk of NAFLD were not retained after FDR correction.CONCLUSION This study establishes a causal relationship,indicating that an increase in WC is associated with a higher risk of NAFLD.This demonstrates that a suitable decrease in WC is advantageous for preventing NAFLD.

Influence of reduced-port laparoscopic surgery on perioperative indicators, postoperative recovery, and serum inflammation in patients with colorectal carcinoma

BACKGROUND Conventional five-port laparoscopic surgery,the current standard treatment for colorectal carcinoma(CRC),has many disadvantages.AIM To assess the influence of reduced-port laparoscopic surgery(RPLS)on perioperative indicators,postoperative recovery,and serum inflammation indexes in patients with CRC.METHODS The study included 115 patients with CRC admitted between December 2019 and May 2023,52 of whom underwent conventional five-port laparoscopic surgery(control group)and 63 of whom underwent RPLS(research group).Comparative analyses were performed on the following dimensions:Perioperative indicators[operation time(OT),incision length,intraoperative blood loss(IBL),and rate of conversion to laparotomy],postoperative recovery(first postoperative exhaust,bowel movement and oral food intake,and bowel sound recovery time),serum inflammation indexes[high-sensitivity C-reactive protein(hs-CRP),tumor necrosis factor-α(TNF-α),and interleukin-6(IL-6)],postoperative complications(anastomotic leakage,incisional infection,bleeding,ileus),and therapeutic efficacy.RESULTS The two groups had comparable OTs and IBL volumes.However,the research group had a smaller incision length;lower rates of conversion to laparotomy and postoperative total complication;and shorter time of first postoperative exhaust,bowel movement,oral food intake,and bowel sound recovery;all of which were significant.Furthermore,hs-CRP,IL-6,and TNF-αlevels in the research group were significantly lower than the baseline and those of the control group,and the total effective rate was higher.CONCLUSION RPLS exhibited significant therapeutic efficacy in CRC,resulting in a shorter incision length and a lower conversion rate to laparotomy,while also promoting postoperative recovery,effectively inhibiting the inflammatory response,and reducing the risk of postoperative complications.

Quality indicators in respiratory therapy

Quality indicators in healthcare refer to measurable and quantifiable parameters used to assess and monitor the performance,effectiveness,and safety of healthcare services.These indicators provide a systematic way to evaluate the quality of care offered,and thereby to identify areas for improvement and to ensure that patient care meets established standards and best practices.Respiratory therapists play a vital role in areas of clinical administration such as infection control practices and quality improvement initiatives.Quality indicators serve as essential metrics for respiratory therapy departments to assess and enhance the overall quality of care.By systematically tracking and analyzing indicators related to infection control,treatment effectiveness,and adherence to protocols,respiratory care practitioners can identify areas to improve and implement evidence-based changes.This article reviewed how to identify,implement,and monitor quality indicators specific to the respiratory therapy departments to set benchmarks and enhance patient outcomes.

Progress in the Study of Laboratory Indicators Related to Acute Pancreatitis

Acute pancreatitis (AP) is one of the more common gastrointestinal diseases in clinics and is characterized by rapid progression, many complications, and high mortality. When it develops into severe pancreatitis, its prognosis is poor. Therefore, early assessment of the degree of inflammatory response plays a crucial role in the treatment plan and prognosis of patients. More and more studies have shown that the levels of D-dimer (D-D), angiotensin-2 (Ang-2), phosphate, heparin-binding protein (HBP), retinol-binding protein-4 (RBP4), and osteoblastic protein (OPN) are closely related to the severity of acute pan-creatitis and can be used as effective indicators for early assessment of AP. In this paper, the research progress of the above indicators in assessing the severity of AP is summarized.

Evidence-Based Nursing Practice of Reducing Immune-Related Skin Toxicity of Tumor Patients Guided by Sensitive Indicators

Purpose research on nursing sensitive indicators in tumor Patients application effect in immune-related skin toxicity management. Method select our hospital April to June, 202360 cases patients with immune therapy settings as the control group. August-October, 2023 60 cases the patients treated with immune therapy were the experimental group. The control group adopted regular nursing methods, while the experimental group sensitive Indicators, evidence-based give preventive care. The social situation, psychological state, physical function, quality of life score, incidence of skin toxicity caused by immune checkpoint inhibitors, moderate and above of the two groups of patients were compared. Incidence of skin toxicity. Result: experience group SAS score, SDS score higher than the control group, the difference was statistically significant (P < 0.05);The incidence of skin toxic reactions caused by immune checkpoint inhibitors and the incidence of moderate and above skin toxic reactions in the experimental group are lower than those in the control group, and the difference is statistically significant (P < 0.05). Conclusion: sensitive indicator guidance evidence-based preventive care can reduce the degree of immune-related skin toxicity, improve the psychological state and quality of life of tumor patients treated with immune therapy and reduce the incidence of adverse reactions, improve nursing quality and patient satisfaction.

Risk Prediction Model for Hypertensive Disorders of Pregnancy Based on Routine Laboratory Indicators and Risk Factors: A Retrospective Analysis

Background: Hypertensive disorder of pregnancy (HDP) is a group of diseases in which pregnancy and elevated blood pressure coexist. There is still a lack of reliable clinical tools to predict the incidence of HDP. The purpose of this study was to establish and validate a nomogram prediction model for assessing the risk of HDP in pregnant women based on laboratory indicators and HDP risk factors. Method: A total of 307 pregnant women who were hospitalized in the obstetrics and gynecology department of our hospital were included in this study, and were randomly divided into a training cohort and validation cohort at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for the development of HDP on laboratory indicators as well as risk factors for HDP in the training cohort of patients. The results of the multivariate regression model were visualized by forest plots. A nomogram was constructed based on the results of multivariate logistic regression to predict the risk of HDP in pregnant women. The validity of the risk prediction model was evaluated by the area under the receiver operating characteristic curve (AUC), the consistency index (C-index), the calibration curve and the decision curve analysis (DCA). Results: BMI ≥ 25 Kg/m2, total cholesterol in early pregnancy, uric acid and proteinuria in late pregnancy were independent risk factors for HDP. The AUC and C-index of the nomogram constructed by the above four factors were both 0.848. The calibration curve is closely fitted with the ideal diagonal, showing a good consistency between the nomogram prediction and the actual observation of HDP. The DCA has demonstrated the great clinical utility of nomogram. Internal verification proves the reliability of the predicted nomograms. Conclusion: The BTUP nomogram model based on laboratory indicators and risk factors proposed in this study showed good predictive value for the risk assessment of HDP. It is expected to provide evidence for clinical prediction of the risk of HDP in pregnant women.

Optimization Management of Industrial Organizations Based on Performance Indicators

This paper proposes an intelligent management system (IMS) to help managers in their delicate and tedious task of exploiting the plethora of data (indicators) contained in management dashboards. This system is based on intelligent agents, ontologies and data mining. It is implemented by PASSI (Process for Agent Societies Specification and Implementation) methods for agent design and implementation, the Methodology for Knowledge Modeling and Hot-Winters for data prediction. Intelligent agents not only track indicators but also store the knowledge of managers within the company. Ontologies are used to manage the representation and presentation aspects of knowledge. Data mining makes it possible to: make the most of all available data;model the industrial process of data selection, exploration and modeling;and transform behaviors into predictive indicators. An instance of the IMS named SYGISS, currently in operation within a large brewery organization, allows us to observe very interesting results: the extraction of indicators is done in less than 5 minutes whereas manual extraction used to take 14 days;the generation of dashboards is instantaneous whereas it used to take 12 hours;the interpretation of indicators is instantaneous whereas it used to take a day;forecasts are possible and are done in less than 5 minutes whereas they did not exist with the old management. These important contributions help to optimize the management of this organization.

Comparative Study of Evaluation Indicators for Learning Cities and the Implications

With the development of society,more and more cities are participating in the initiative to build learning cities.Constructing an evaluation indicator system for learning cities to monitor the progress and promote their growth has become increasingly important.This paper analyzes the preliminary framework of the UNESCO Global Learning City Index and R3L+Quality Framework.The comparison is made from the aspects of design philosophy,criteria of indicator,and the cycle of evaluation process.The findings suggest that the construction of an evaluation indicator system should be focused more on the diversity of learning city development,the construction of an evaluation process cycle,and the significance of building cooperative networks.

Development and Application of Integrated Indicators for Assessing Healthcare Waste Management Systems in Kenyan Hospitals

Healthcare waste management (HCWM) is an important aspect of healthcare delivery globally because of its hazardous and infectious components that have potential for adverse health and environmental impacts. The paper introduces a set of indicators for assessing HCWM systems in hospitals. These indicators are: HCWM policies and standard operating procedures, management and oversight, logistics and budget support, training and occupational health and safety, and treatment, disposal and waste treatment equipment housing. By plotting a mark on a continuum which is defined as good and poor on the extremes and is connected with all other marks in a spoke arrangement, it’s possible to describe a baseline for HCWM in any specific hospital. This baseline can be used to improve awareness of the actors and policy-makers, compare the same hospital at a different point in time, to compare observations by different evaluators and to track improvements. Results suggest that in Kenya, the application of such indicators is useful for evaluating which priorities should be addressed to improve outcomes in HCWM systems. Systematic sampling technique was used to identify and collect data by use of observational checklist, interviews, visual verification and review of documents and a HCWM assessment tool. The objective is to suggest an integrated management tool as a method to identify prevailing problems with a HCWM system. The method can be replicated in other contexts worldwide, with a focus on the developing world. The integrated indicators focus on management of HCW and not its potential impact on human health and environment, an area recognized to be critical for future research.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Indicator Selection for Quality Measurement in Maternal Neonatal and Child Health Services: Application of Random Forest Classifier

Quality of Maternal, Neonatal and Child (MNCH) care is an important aspect in ensuring healthy outcomes and survival of mothers and children. To maintain quality in health services provided, organizations and other stakeholders in maternal and child health recommend regular quality measurement. Quality indicators are the key components in the quality measurement process. However, the literature shows neither an indicator selection process nor a set of quality indicators for quality measurement that is universally accepted. The lack of a universally accepted quality indicator selection process and set of quality indicators results in the establishment of a variety of quality indicator selection processes and several sets of quality indicators whenever the need for quality measurement arises. This adds extra processes that render quality measurement process. This study, therefore, aims to establish a set of quality indicators from a broad set of quality indicators recommended by the World Health Organization (WHO). The study deployed a machine learning technique, specifically a random forest classifier to select important indicators for quality measurement. Twenty-nine indicators were identified as important features and among those, eight indicators namely maternal mortality ratio, still-birth rate, delivery at a health facility, deliveries assisted by skilled attendants, proportional breach delivery, normal delivery rate, born before arrival rate and antenatal care visit coverage were identified to be the most important indicators for quality measurement.

Extraction and imaging of indicator elements for non-destructive,in-situ,fast identification of adverse geology in tunnels

The lag in quantitative methods and detection techniques for geologic information has resulted in time-consuming and human-experienced geologic analysis in tunnels.Geochemical indicators of rocks can be used to identify adverse geology and to explain the intrinsic causes of damage to normal rocks.This study proposes a method to identify adverse geology by extracting and imaging the indicator elements.The mapping relationship between rock components and geologic bodies is quickly determined by indicator element extraction based on factor analysis,and then the data are gridded for image output.The location and size of the target adverse geology are visually identified through the distribution images of the indicator elements,thus reducing data dimensions and analysis time.A non-destructive,in-situ and fast element detection technique in tunnels was adopted to speed up the process of geology identification.The accuracy of the detection was validated by comparing field and laboratory test results.This study further confirms and refines the previous research,and the results provide references for geological,mining and underground projects.

Development,Characterization and Application of a Three-Layer Intelligent pH-Sensing Indicator Based on Bromocresol Green(BCG)for Monitoring Fish Freshness

As a novel food quality monitoring technology,intelligent freshness indicator has received wide attention in recent years.However,its poor safety and stability are the main problems hindering its practical application.Hence a new pH-sensing indicator based on bromocresol green(BCG)was developed in this study for nondestructive and real-time monitoring the freshness of marine fishes.The indicator was designed with a three-layer structure,using the polytetrafluoroethylene(PTFE)membrane with high hydrophobicity and air permeability as the inner layer to isolate the moisture in the package,BCG-coated filter paper as the colorchanging layer to indicate the freshness of fish,and a transparent unidirectional permeable(TUP)membrane with moisture resistance as the out layer to isolate the moisture in the environment.This contributed to weaken the influence of humidity and prevent dye migration,so as to improve the accuracy and safety of the indicator.Therefore,a highly sensitive and distinguished color variation response to trimethylamine(TMA)standard solution with different concentrations was observed on the indicator.Additionally,the indicator showed a high color stability at different storage temperatures up to 14 days with total color differences(ΔE)less than 5.0.The indicator presented visible color variations from yellow to green then eventually to blue when applied to monitor the freshness of sea bass and salmon stored at 4℃,implying that fish was spoiled.Meanwhile,indicatorsΔE value was significantly positively correlated with total volatile basic nitrogen(TVB-N)and total viable count(TVC)in sea bass and salmon samples.Thus,the pH-sensing indicator can be applied as a cost-effective and promising intelligent indicator for monitoring fish freshness.

MaOEA/I:Many-objective Evolutionary Algorithm Based on Indicator I_(ε+)

Balancing the diversity and convergence of the population is challenging in multi-objective optimization. The work proposed a many-objective evolutionary algorithm based on indicator I_(ε+)(MaOEA/I) to solve the above problems. Indicator I_(ε+)(x,y) is used for environmental selection to ensure diversity and convergence of the population. I_(ε+)(x,y) can evaluate the quality of individual x compared with individual y instead of the whole population. If I_(ε+)(x,y) is less than 0, individual x dominates y. If I_(ε+)(x,y) is 0, individuals x and y are the same. If I_(ε+)(x,y) is greater than 0, no dominant relationship exists between individuals x and y. The smaller I_(ε+)(x,y), the closer the two individuals. The dominated individuals should be deleted in environmental selection because they do not contribute to convergence. If there is no dominant individual, the same individuals and similar individuals should be deleted because they do not contribute to diversity. Therefore, the environmental selection of MaOEA/I should consider the two individuals with the smallest I_(ε+)(x,y). If I_(ε+)(x,y) is not greater than 0, delete individual y;if I_(ε+)(x,y) is greater than 0, check the distance between individuals x, y, and the target point and delete the individual with a longer distance. MaOEA/I is compared with 6 algorithms until the population does not exceed the population size. Experimental results demonstrate that MaOEA/I can gain highly competitive performance when solving many-objective optimization problems.

Does financial development moderate the link between technological innovation and environmental indicators?An advanced panel analysis

This study analyzes the role of financial development(FD)on the impact of technologi-cal innovation(TI)on six environmental quality indicators for the 25 economies that are part of the Organization for Economic Cooperation and Development for the period from 2000 to 2019.We use a two-step dynamic generalized method of moments approach to understand this relationship.The results show that FD augments the posi-tive effects of TI on four of the six environmental indicators,namely ecological foot-print,adjusted net savings,pressure on nature,and environmental performance.However,no significant effects on environmental sustainability and environmental vulnerability indices were found.When considering all of the environmental quality indicators,TI appears to enhance environmental quality.We find evidence to support the existence of the environmental Kuznets curve in the context of each environmen-tal indicator and economic growth.Moreover,FD and energy consumption appear to accelerate environmental degradation.Based on these results,FD should be viewed as an important parameter in designing policies for innovation to achieve the goal of net-zero carbon emissions.Highlights.Technological innovation and environmental quality nexus is studied.The moderating role of financial development is analyzed.Six different environmental quality indicators are used for OECD countries.Financial development intensifies the environmental benefits of innovation.•The EKC hypothesis is confirmed for all six environmental indicators.

Effect of Storage Time on Main Quality Indicators of Cotton

The quality indicators of cotton will change during storage.Taking the 5.89 million t of Xinjiang cotton from 2016 to 2021 as a sample,this paper analyzed the main fiber quality indicator data of warehouse-in and warehouse-out cotton for storage of 1.5,3.0,4.0,5.0,6.0,and 7.0 years.It was found that the color grade of cotton decreased with the extension of storage time.The cotton with storage time of 5.0 years mainly changed from white cotton grade 2 and white cotton grade 3 to light yellow stained cotton grade 1 and yellow stained cotton grade 1.Among them,the increase of light yellow stained cotton grade 1 was the largest,and the change to yellow stained cotton grade 1 was the largest at the storage 6.0-7.0 years.In addition,there were no significant changes in moisture regain,Micronaire value,upper half mean length,length uniformity index and fiber strength.

Determination of Main Design Indicators of Patrol Road in Nature Reserves

In principle,nature reserves are managed and controlled according to the core area and the general control area.At present,there is no relevant design specification for the design system of patrol road in various protected areas.This paper analyzed the factors to be considered in determining the grade,horizontal and vertical design indicators,and cross section indicators of the patrol road in the protected area,and came up with the corresponding design indicators and parameters,so as to provide a certain reference for the subsequent patrol road design.

Understanding Patients’ Symptoms Management Using Objective and Subjective Indicators among Patients with Systemic Lupus Erythematosus

Background: Descriptive statistics have been used to document the high prevalence of sleep disorders, fatigue, and pain in patients with systemic lupus erythematosus (SLE). Objectives: To describe day-to-day variations in sleep indicators, physical activity level, pain, and fatigue measured using objective and subjective indicators in lupus patients with sleep disorders. Methods: We selected three patients with sleep disorders, measured by the Pittsburgh Sleep Quality Index score (≥9), out of 20 participants followed up for 4 weeks. We compared the self-rated quality of life (QoL) measured using the Pittsburgh Sleep Quality Index (PSQI) with that of objectively measured sleep indicators monitored using the Nemuri scan. The detailed data collection methods have been previously published. Results: The sleep status generated by the Nemuri scan revealed that Case A with a flare and Case B without a flare required frequent daytime rest. The PSQI sleep duration and sleep latency were generally in agreement with the monitored data, although the PSQI failed to capture the complexity of sleep disorders, particularly the fluctuations in the quality of sleep indicators. Patient C approximately 4 hours on weekdays and 10 hours on Saturday nights. All three cases had high fatigue levels, and their moderate-to-vigorous physical activity duration was less than one-half of duration that recommended by the World Health Association. Conclusion: Visual display for objective monitoring of sleep quality is an excellent tool for understanding patients who require frequent resting and irregular sleep indicators. Objective monitoring of sleep quality, along with self-rated pain and fatigue, promotes an understanding of how patients with SLE cope with severe symptoms.