Tracking Changes Research Articles

Analysis of uterine evacuation methods in postabortion care after implementation of a surveillance network (CLAP MUSA-Network) at a university hospital

Background Management of uterine evacuation is essential for increasing safe abortion care. Monitoring through surveillance systems tracks changes in clinical practice and provides information to improve equity in abortion care quality. Objective This study aimed to evaluate the frequency of manual vacuum aspiration (MVA) and medical abortion (MA), and identify the factors associated with each uterine evacuation method after surveillance network installation at a Brazilian hospital. Methods This cross-sectional study included women admitted for abortion or miscarriage to the University of Campinas Women’s Hospital, Brazil, between July 2017 and November 2020. The dependent variables were the use of MVA and MA with misoprostol. The independent variables were the patients’ clinical and sociodemographic data. The Cochran–Armitage, chi-square, and Mann–Whitney U tests, as well as multiple logistic regression analysis, were used to compare uterine evacuation methods. Results We enrolled 474 women in the study, 91.35% of whom underwent uterine evacuation via uterine curettage (78.75%), MVA (9.46%), or MA (11.54%). MVA use increased during the study period (Z = 9.85, p < 0.001). Admission in 2020 (odds ratio [OR] 64.22; 95% confidence interval [CI] 3.79–1086.69) and lower gestational age (OR 0.837; 95% CI 0.724–0.967) were independently associated with MVA, whereas the only factor independently associated with MA was a higher education level (OR 2.66; 95% CI 1.30–5.46). Conclusion MVA use increased following the installation of a surveillance network for good clinical practice. Being part of a network that encourages the use of evidence-based methods provides an opportunity for healthcare facilities to increase access to safe abortions.

Serial attentional resource allocation during parallel feature value tracking.

The visual system has evolved the ability to track features like color and orientation in parallel. This property aligns with the specialization of processing these feature dimensions in the visual cortex. But what if we ask to track changing feature-values within the same feature dimension? Parallel tracking would then have to share the same cortical representation, which would set strong limitations on tracking performance. We address this question by measuring the precision of color representations when human observers track the color of two superimposed dot clouds that simultaneously change color along independent trajectories in color-space. We find that tracking precision is highly imbalanced between streams and that tracking precision changes over time by alternating between streams at a rate of ~1 Hz. These observations suggest that, while parallel color tracking is possible, it is highly limited, essentially allowing for only one color-stream to be tracked with precision at a given time.

Test-Retest reliability and measurement error of the uncontrolled manifold analysis: A step towards the clinical translation

The uncontrolled manifold (UCM) analysis has gained broad application in biomechanics and neuroscience for investigating the structure of motor variability in functional tasks. The UCM utilizes inter-trial analysis to partition the variance of elemental variables (e.g., finger forces, joint angles) that affect (VORT) and do not affect (VUCM) a performance variable (e.g., total force, end-effector position). However, to facilitate the translation of UCM into clinical settings, it is crucial to demonstrate the reliability of UCM estimates: VORT, VUCM, and their normalized difference, ΔV. This study aimed to determine the test–retest reliability using the intraclass correlation coefficient (ICC3,K), Bland-Altman plots, the standard error of measurement (SEM), and the minimal detectable change (MDC) of UCM estimate. Fifteen healthy individuals (24.8 ± 1.2 yrs old) performed a finger coordination task, with sessions separated by one hour, one day, and one week. Excellent reliability was found for VORT (ICC3,K = 0.97) and VUCM (ICC3,K = 0.92), whereas good reliability was observed for ΔV (ICC3,K = 0.84). Bland-Altman plots reveled no systematic differences. SEM% values were 24.57 %, 26.80 % and 12.49 % for VORT, VUCM and ΔV respectively, while the normalized MDC% values were 68.12 %, 74.30 % and 34.61 % for VORT, VUCM and ΔV respectively. Our results support the use of UCM as a reliable method for investigating the structure of movement variability. The excellent measurement properties make the UCM a promising tool for tracking changes in motor behavior over time (i.e., effects of interventions in prospective studies).

Challenges in the application of the Tshivenda scientific register in the teaching and learning of state of matter and the kinetic molecular theory in grade 10 physical sciences classrooms.

Background: This paper investigated some of the challenges in the application of Tshivenda scientific register (TSR) during classroom practices of some physical sciences teachers in some of the public secondary schools in the Vhembe West District, South Africa. Methods: It was an interpretative qualitative case study wherein three physical sciences teachers and 40 learners took part in the study. The study conducted at schools of Vhuronga 2 circuit in Vhembe West District between January 2022 to November 2022. Semi-structured interviews and classroom observations were used for data collections. Researchers analyzed their data through Data Analysis Scheme (DAS) which comprised of themes, categories, and characteristics in this qualitative study. Texts that belong to a particular theme were highlighted using same colour and track changes was also used to codify categories and characteristics of a theme. Results: The research findings had shown numerous challenges in the teaching and learning of physical sciences including teachers and learners not used to physical sciences being taught and learnt through TSR, not familiar with some scientific words in TSR, difficulties in understanding scientific term in TSR as well as absence of Tshivenda physical sciences resources beside TSR. Consequently, this has impacts on teacher and learner's ability to implement TSR in the teaching and learning of Physical Sciences. Moreover, the findings also show that teachers and learners participated in the study sometimes switch from Tshivenda Scientific words to English Scientific words during Physical Sciences lessons. Conclusions: Therefore, it is suggested that the above-mentioned challenges in the development and application of TSR for Physical Sciences teaching need to be addressed so that teachers can teach learners Physical Sciences through language they know best. Hence, physical sciences teachers must be developed, trained, and furnished with essential language skills for them to develop Tshivenda scientific language registers on other sciences topics.

Systematic Review on Prediction and Monitoring of Climate Change

Climate change poses a severe and very bad threat to human society and global ecosystems, necessitating careful planning, monitoring, and forecasting. This paper reviews spread a range of monitoring techniques and predictive models to better understand, anticipate, and mitigate the effects of climate change. Predictive models combine a wide and large range of methods, from statistical and machine learning-based models to complex Earth system models that integrate atmospheric, oceanic, and terrestrial components. These models look at historical data of history, greenhouse gas emissions, and various climate drivers to anticipate future climatic scenarios. Tracking changes in temperature, precipitation patterns, sea level rise, and other climate change indicators over time also requires the use of monitoring tools that include satellite observations, ground-based sensors, and remote sensing technology.. Creating sustainable behaviors to counteract the negative consequences of climate change, educating politicians, and integrating reliable monitoring systems and predictive models all help in the development of adaptive systems. To address the complexities, difficulties, and breakthroughs in climate change prediction and monitoring, scientists, policymakers, and stakeholders should work together.

Tracking the push towards extinction: combining dispersal and management data to monitor Asian longhorned beetle eradication in the U.S.

Based on the threat posed by the Asian longhorned beetle (Anoplophora glabripennis Motschulsky), many countries including the United States have adopted policies of eradication. The eradication of infestations that cover hundreds of square kilometers can require multiple visual surveys of millions of individual trees. At these scales, eradication may take several decades and span multiple beetle generations. During this period the infestation of new trees adds spatially-explicit risk to the landscape while surveys and the removal of infested trees reduce it. To track dynamic risk on the landscape we have developed the Asian Longhorned Beetle Hazard Management and Monitoring Tool. The geospatial tool combines data documenting; the locations, levels of infestation, and dates of detection of infested trees; the locations, methods, and timing of survey and host removal activities; and a reconstruction of beetle movement within the infested landscape to generate annual spatial estimates of infestation risk based on the combination of beetle dispersal and survey and host removal activities. The analyses of three eradication programs highlight similar patterns in risk through time with risk peaking at the time infestations are detected and declining as management activities slow beetle spread and reduce risk through surveys. However, the results also highlight differences in risk reduction among the eradication programs associated with differences in beetle dispersal among infestations and the size of the infested landscape, highlighting the importance of applying local information to structure eradication programs. The Asian Longhorned Beetle Hazard Management and Monitoring Tool provides a quantitative repeatable approach to tracking changes in infestation risk using local beetle behavior and management efforts. In addition to this, the tool may provide a structure to optimize eradication efforts by allowing managers to estimate expected risk reduction based on proposed survey and host removal strategies.

Longitudinal growth in college student self-efficacy and intercultural competence attenuated by anxiety/depression

IntroductionA wide range of stakeholders, including prospective students, parents, accreditors, future employers, and the general public, require detailed data on college outcomes. However, there are many challenges to producing such complex research tracking change over time in the higher education setting.MethodsThis multi-method longitudinal study at three different colleges was grounded in Input-Environment-Output and Social Cognitive theoretical frameworks. It examined: potential change on five different key psychosocial outcomes (i.e., self-efficacy, anxiety, intercultural competence, ethnic identity, and cognitive empathy), associations between these variables, and the role of race/ethnicity.ResultsMultilevel growth modeling revealed within and between subject changes over time. The findings provide evidence that liberal arts colleges focused on global learning can produce significant growth for students of all races on self-efficacy and intercultural competence, both outcomes that are valued for workforce readiness. However, the results also show that anxiety and depression symptoms attenuate growth in intercultural competence, which is concerning given other recent data on students’ mental health and wellness concerns.DiscussionWe discuss implications for student development practitioners and faculty alongside potential future directions for research in other higher education settings.

Does religiosity in early adulthood predict change in filial eldercare norms after midlife among baby boomers?

This study investigated whether patterns of religious change from early to middle adulthood is associated with patterns of change in filial norms from midlife to later life. Tracking change across 45 years of the adult life span, we link developmental processes occurring at two stages of life using midlife as the point of inflection. Respondents consisted of 436 individuals in the Baby Boom generation who participated in the Longitudinal Study of Generations from Waves 1 (1971) to 9 (2016). We conducted latent class and latent transition analysis to identify religious classes and their transitions over several decades, and latent growth curve modeling to identify change in filial norms. We identified three religiosity classes in Waves 1 and 5-strongly religious, weakly religious, and moderately religious-and five patterns of religious transitions. These transitions were then used to predict change in filial norms between Waves 5 and 9. Respondents who remained weakly religious from early to mid-adulthood reported weaker filial norms in midlife, compared to those who became more religious, and declined more rapidly in their strength of filial norms after middle age. Those who stayed weakly religious also declined more rapidly post-middle age. Our findings link dynamics in religiosity and filial norms across disparate stages of the adult life span and suggest that religious orientations earlier in adulthood are linked to filial norms at time of life when responsibilities for eldercare become a concern for one's parents as well as oneself. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Comprehensive Assessment of Anthropogenic and Natural Sources and Sinks of Australasia's Carbon Budget

AbstractRegional carbon budget assessments attribute and track changes in carbon sources and sinks and support the development and monitoring the efficacy of climate policies. We present a comprehensive assessment of the natural and anthropogenic carbon (C‐CO2) fluxes for Australasia as a whole, as well as for Australia and New Zealand individually, for the period from 2010 to 2019, using two approaches: bottom‐up methods that integrate flux estimates from land‐surface models, data‐driven models, and inventory estimates; and top‐down atmospheric inversions based on satellite and in situ measurements. Our bottom‐up decadal assessment suggests that Australasia's net carbon balance was close to carbon neutral (−0.4 ± 77.0 TgC yr−1). However, substantial uncertainties remain in this estimate, primarily driven by the large spread between our regional terrestrial biosphere simulations and predictions from global ecosystem models. Within Australasia, Australia was a net source of 38.2 ± 75.8 TgC yr−1, and New Zealand was a net CO2 sink of −38.6 ± 13.4 TgC yr−1. The top‐down approach using atmospheric CO2 inversions indicates that fluxes derived from the latest satellite retrievals are consistent within the range of uncertainties with Australia's bottom‐up budget. For New Zealand, the best agreement was found with a national scale flux inversion estimate based on in situ measurements, which provide better constrained of fluxes than satellite flux inversions. This study marks an important step toward a more comprehensive understanding of the net CO2 balance in both countries, facilitating the improvement of carbon accounting approaches and strategies to reduce emissions.

The Impact of Projected Changes in Hurricane Frequencies on U.S. Hurricane Wind and Surge Damage

Abstract We use a simple risk model for U.S. hurricane wind and surge economic damage to investigate the impact of projected changes in the frequencies of hurricanes of different intensities due to climate change. For average annual damage, we find that changes in the frequency of category-4 storms dominate. For distributions of annual damage, we find that changes in the frequency of category-4 storms again dominate for all except the shortest return periods. Sensitivity tests show that accounting for landfall, uncertainties, and correlations leads to increases in damage estimates. When we propagate the distributions of uncertain frequency changes to give a best estimate of the changes in damage, the changes are moderate. When we pick individual scenarios from within the distributions of frequency changes, we find a significant probability of much larger changes in damage. The inputs on which our study depends are highly uncertain, and our methods are approximate, leading to high levels of uncertainty in our results. Also, the damage changes we consider are only part of the total possible change in hurricane damage due to climate change. Total damage change estimates would also need to include changes due to other factors, including possible changes in genesis, tracks, size, forward speed, sea level, rainfall, and exposure. Nevertheless, we believe that our results give important new insights into U.S. hurricane risk under climate change. Significance Statement We investigate how changes in the frequencies of hurricanes of different intensities as a result of climate change may contribute to changes in U.S. economic damage due to wind and surge. We find that economic damage will likely increase as a result of projected increases in the frequency of landfalling hurricanes. Analysis of our results shows that increases in the frequency of category-4 storms are the main driver of the changes. Our best estimate results, based on a multimodel ensemble, give modest increases in damage, but within the ensemble there are individual scenarios that give much larger increases in damage. The large range of individual damage estimates is a motivation for continuing efforts to reduce the uncertainty around hurricane projections under climate change.

A decade of corporate governance in Brazil: 2010-2019

We take advantage of the Brazilian mandatory corporate governance (CG) reporting system to build an overall Brazil Corporate Governance Index (BCGI) and subindices (CGIs), and track changes in firms' scores over the 10-year period from 2010-2019. We show that overall CG level improved significantly between 2010 and 2019, with most of the improvement over the first part of this period. The improvement has two sources: an increase in the proportion of high-standard listings (Novo Mercado and Level 2, NML2) versus low-standard listings (Level 1 and regular, L1R), and within-firm improvement in CG practices. In the first half of the sample period, both NML2 and L1R firms improved CG practices considerably. Overall improvement in the second half of the sample period reflects an increasing proportion of NML2 firms, plus gradual improvement in L1R CG levels; with nearly constant NML2 levels. Improvements were stronger for Board Procedure and Disclosure subindices. Firms in both listings improved their CG. Overall improvement was stronger in NML2 than in L1R, but was concentrated in the period from 2010-2015.

Effect of the telemedicine-supported multicomponent exercise therapy in patients with knee osteoarthritis: study protocol for a randomized controlled trial

IntroductionThe rising prevalence of knee osteoarthritis is placing a considerable strain on the global healthcare system. To address this issue, telemedicine-supported multicomponent exercise therapy has emerged as a promising approach. This therapy combines exercise, patient education, and health coaching to empower knee osteoarthritis patients to manage their condition from the comfort of their homes. Nevertheless, there are some existing limitations in the current research on this approach, including challenges related to patient compliance and the absence of objective evaluation methods.Methods and analysisPatients diagnosed with knee osteoarthritis, who have not undergone knee surgery in the past year, will be recruited for a randomized controlled trial. The trial will include an intervention group and a control group. The intervention group will receive an mHealth app-based multicomponent exercise therapy, consisting of exercise therapy, patient education, and health coaching. Meanwhile, the control group will receive usual care, involving drug therapy and patient education. The primary outcome of the trial will be the measurement of pain intensity, assessed using a visual analog scale at baseline and at 4, 8, and 12 weeks of the post-intervention. To analyze the data, a two-factor, four-level repeated measures ANOVA will be used if the assumptions of homogeneity of variance and sphericity are met. If not, a mixed effects model will be employed.DiscussionThe aim of the study is to evaluate the effectiveness of multicomponent exercise therapy aimed at enhancing pain self-management for knee osteoarthritis patients in the comfort of their own homes. The intervention incorporate wearable devices equipped with advanced deep learning systems to monitor patients' adherence to the prescribed at-home exercise regimen, as well as to track changes in outcomes before and after the exercise sessions. The findings from this trial have the potential to enhance both the accessibility and quality of care provided to knee osteoarthritis patients, offering valuable insights for future improvements in their treatment and management.Trial registrationChinese Clinical Trials Registry, ChiCTR2300073688. Registered on 19 July 2023, https://www.chictr.org.cn/bin/project/edit?pid=199707. World Health Organization International Clinical Trials Registry Platform, https://trialsearch.who.int/Trial2.aspx?TrialID=ChiCTR2300073688.

Evaluating the Impact of Data Transformation Techniques on the Performance and Interpretability of Software Defect Prediction Models

The performance of software defect prediction (SDP) models determines the priority of test resource allocation. Researchers also use interpretability techniques to gain empirical knowledge about software quality from SDP models. However, SDP methods designed in the past research rarely consider the impact of data transformation methods, simple but commonly used preprocessing techniques, on the performance and interpretability of SDP models. Therefore, in this paper, we investigate the impact of three data transformation methods (Log, Minmax, and Z-score) on the performance and interpretability of SDP models. Through empirical research on (i) six classification techniques (random forest, decision tree, logistic regression, Naive Bayes, K-nearest neighbors, and multilayer perceptron), (ii) six performance evaluation indicators (Accuracy, Precision, Recall, F1, MCC, and AUC), (iii) two interpretable methods (permutation and SHAP), (iv) two feature importance measures (Top-k feature rank overlap and difference), and (v) three datasets (Promise, Relink, and AEEEM), our results show that the data transformation methods can significantly improve the performance of the SDP models and greatly affect the variation of the most important features. Specifically, the impact of data transformation methods on the performance and interpretability of SDP models depends on the classification techniques and evaluation indicators. We observe that log transformation improves NB model performance by 7%–61% on the other five indicators with a 5% drop in Precision. Minmax and Z-score transformation improves NB model performance by 2%–9% across all indicators. However, all three transformation methods lead to substantial changes in the Top-5 important feature ranks, with differences exceeding 2 in 40%–80% of cases (detailed results available in the main content). Based on our findings, we recommend that (1) considering the impact of data transformation methods on model performance and interpretability when designing SDP approaches as transformations can improve model accuracy, and potentially obscure important features, which lead to challenges in interpretation, (2) conducting comparative experiments with and without the transformations to validate the effectiveness of proposed methods which are designed to improve the prediction performance, and (3) tracking changes in the most important features before and after applying data transformation methods to ensure precise and traceable interpretability conclusions to gain insights. Our study reminds researchers and practitioners of the need for comprehensive considerations even when using other similar simple data processing methods.

Progression of definite and non-definite Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) and risk of major adverse cardiac events

Abstract Background In patients with Arrhythmogenic right ventricular cardiomyopathy (ARVC, also called"Arrhythmogenic Cardiomyopathy"), it is a challenge to predict those at greatest risk of major adverse cardiac events (MACE). Our hypothesis is that risk of arrhythmia may be preceded by change in ventricular structure and function. Purpose To assess the association between change in ventricular structure and function over time and risk of (MACE) in ARVC. Methods 101 definite and non-definite ARVC patients (defined as per Revised Task Force criteria (TFC)) were included with at least two sequential echocardiography and ECGs between 2010 and 2022. Structural progression was defined as development of new 2010 TFC on echocardiography during follow-up. MACE were defined as ventricular fibrillation (VF), sustained ventricular tachycardia (Sus VT), appropriate implantable cardio-defibrillator (ICD) therapy (shock/anti-tachycardia pacing (ATP)), heart failure, cardiac transplantation and death. Results Of the 101 patients, 51 had a definite diagnosis of ARVC, and 50 had a non-definite ‘early’ diagnosis, of whom a total of 66 (68%) carried a pathogenic variant associated with ARVC. Most were male 58 (57%), and the median age at presentation was 39 years (IQR: 27 - 53). Palpitations 41 (41%) and syncope 25 (25%) were the most common symptoms at baseline. 21 (23%) of the entire population were engaged in competitive exercise. At baseline, 22/101 (22%) patients presented with MACE, in which 20 (91%) definite patients met either minor or major imaging criteria at inclusion. During 4 years follow up (IQR: 2 – 6), 22 patients experienced MACE, in which 11 with history of MACE and 11 without, of those (15 (68%)) had structural progression. Additionally, 9 patients had structural progression of which 5 patients had history of MACE and no second event during follow up and 4 patients had no MACE at baseline or during follow up. Results showed that the odds of structural progression were significantly higher in patients with MACE than without (Table 1). Conclusion Structural progression was strongly associated with MACE events. This finding raises the possibility that tracking change over time identifies those patients at greatest risk of arrhythmia, with significant changes in right ventricular measurements.

Evaluating individual participant data plans for ICMJE compliance: A case study at University of Michigan

Introduction: The International Committee of Medical Journal Editors (ICMJE) requires researchers to post individual participant data (IPD) plans for interventional clinical trials with registration in order to be eligible for publication in member journals. Objectives: We aimed to gain insight into how researchers interpret these requirements and the related prompts for information used by ClinicalTrials.gov. To accomplish this, we explored the contents of the IPD plans that University of Michigan (U-M) researchers submitted with trial registrations for the first 27 months that the 2019 requirement was in effect. Methods: Clinical trial registration data for U-M was downloaded using the ClinicalTrials.gov API. 198 interventional trials were analyzed using a data collection form to categorize their characteristics and responses to the ClinicalTrials.gov IPD fields. Descriptive statistics were used to examine the data.Results: More than two-thirds of trial registrations (138, 70%) answered “No,” they would not share IPD, while an additional 21 (10%) left the question blank or answered “Undecided.” Only 39 trial registrations (20%) indicated plans to share IPD. Most trials that indicated they would share IPD provided additional information in the other fields in the form.Conclusions: The vast majority (90%) of trials provided an acceptable answer that complies with the ICMJE policy. Additional education and resources from ICMJE would help researchers provide more useful responses in the IPD Sharing Statement fields. Future research includes tracking changes to the information provided in IPD plans and examining how the plans are assessed by ICMJE journal editors.

Abstract 18113: O 2 Pulse is Not Stroke Volume: Changes in Oxygen Pulse Do Not Reliably Track Changes in Stroke Volume During Exercise in Health or Heart Failure

Background: Oxygen Pulse (O2P) is often used to index peak stroke volume (SV) during cardiopulmonary exercise testing (CPET) when measures of cardiac output are not possible. O2P is calculated as Oxygen uptake (V˙O 2 ) / heart rate (HR) and based upon rearrangement of the Fick equation (V˙O 2 = [HR x SV] x arterial-venous O 2 difference [Δa-vO2]; Δa-vO2 is not included in the O2P calculation). As little data exist to validate the use of O2P, we assessed whether O2P tracks changes in SV during exercise in health and disease. Methods: We studied 401 individuals: 232 healthy adults (healthy; 51% males; age: 61 ± 11 [28-88] years); 119 adults at high-risk for future development of heart failure (high-risk; 47% males; age: 51 ± 6 [40-64] years); and 50 adults with heart failure with preserved ejection fraction (HFpEF; 36% males; age: 71 ± 7 [56-86] years). Participants were studied at rest, during light (~30% V˙O 2 max), moderate (Mod; ~60% V˙O 2 max) and maximal exercise (Max). V˙O 2 (indirect calorimetry) and cardiac output (Q˙c; acetylene rebreathing) were measured, enabling calculation of SV (Q˙c/HR) and O2P. Data were compared via two-way (measure*stage) ANOVA. Results: We observed a significant measure*stage interaction for all three groups (all, P< 0.001; Figure), suggesting different responses to exercise between measures. Specifically, SV and O2P increased from rest to light exercise in all groups (all, P <0.001; Figure). Thereafter, O2P continued to increase from light-Mod (all, P<0.001) and Mod-Max (all, P <0.001; other than for Mod-Max in HFpEF, P =0.124). However, actual SV did not increase from light-Mod exercise (all groups, P>0.917) or from Mod-Max exercise in high-risk (Δ0±13 ml, P =0.999) and HFpEF (Δ-3±14 ml, P =0.564), but fell slightly in healthy individuals from Mod-Max (Δ-5±13 ml, P <0.001). Conclusions: Changes in O2P do not reflect changes in SV between exercise stages during CPET in health or disease and should therefore be used with caution.

Application of Remote Sensing in Desertification Monitoring

Desertification has become one of the greatest international concerns in recent decades. This study mainly focuses on the significance and necessity of applying remote sensing to improve efficiency in desertification monitoring. Results show that applications of remote sensing in desertification monitoring have shown advantages and promises of development in the future. Satellite images and unmanned aerial vehicle technology are two major sources of remote sensing data. This study also contains specific applications of remote sensing techniques, including normalized difference vegetation index (NDVI), land cover classification, and spectral mixture analysis (SMA). NDVI measures the vegetation cover to reflect potential desertification but fails to offer a comprehensive picture of the desertification status of an area. While land cover classification identifies different types of land use to monitor desertification, track changes over time, and find potential environmental stressors, the technique requires huge investments in technology and labor. SMA, the sub-pixel classification technique, provides a detailed analysis while consuming significant computing resources and time. This study also provides prospects for future developments of data acquisition and analysis approaches in desertification monitoring using remote sensing. Making suggestions on future research topics and possible methodologies, this study hopes to inspire further research in this area to better deal with the global issue of desertification.

Insights in Pharmaceutical Pollution: The Prospective Role of eDNA Metabarcoding.

Environmental pollution is a growing threat to natural ecosystems and one of the world's most pressing concerns. The increasing worldwide use of pharmaceuticals has elevated their status as significant emerging contaminants. Pharmaceuticals enter aquatic environments through multiple pathways related to anthropogenic activity. Their high consumption, insufficient waste treatment, and the incapacity of organisms to completely metabolize them contribute to their accumulation in aquatic environments, posing a threat to all life forms. Various analytical methods have been used to quantify pharmaceuticals. Biotechnology advancements based on next-generation sequencing (NGS) techniques, like eDNA metabarcoding, have enabled the development of new methods for assessing and monitoring the ecotoxicological effects of pharmaceuticals. eDNA metabarcoding is a valuable biomonitoring tool for pharmaceutical pollution because it (a) provides an efficient method to assess and predict pollution status, (b) identifies pollution sources, (c) tracks changes in pharmaceutical pollution levels over time, (d) assesses the ecological impact of pharmaceutical pollution, (e) helps prioritize cleanup and mitigation efforts, and (f) offers insights into the diversity and composition of microbial and other bioindicator communities. This review highlights the issue of aquatic pharmaceutical pollution while emphasizing the importance of using modern NGS-based biomonitoring actions to assess its environmental effects more consistently and effectively.

Automated system for high-accuracy quantity takeoff using BIM

Reliable construction project cost estimation relies on accurate quantity takeoffs (QTOs), traditionally done with error-prone 2-D drawings, but the emergence of Building Information Modeling (BIM) has improved QTO speed, accuracy, and reliability. However, the quality of BIM models can influence quantity accuracy, an aspect often overlooked in research on automating QTO with BIM. This paper develops an integrated framework to automatically extract and visualize construction quantities from BIM models. The system includes a Quantity Precision Check (QPC) module for automating more accurate QTO outputs. These outputs are then automatically transferred to an external database and visualized through a user-friendly dashboard on the Microsoft Power BI platform. This dashboard allows for comparisons between 2-D and BIM quantities and helps track changes in quantities over time due to design changes. The system was successfully validated in an infrastructure construction project in Canada, where it identified a 39% inconsistency in wall elements' material quantities and reduced steel quantity by 10% due to design changes. Overall, the system offers a speedy, reliable, and efficient tool for construction estimators, site managers, and cost control teams to provide accurate quantities to the procurement team and make swift decisions in response to design change impacts on construction costs.

Malmquist index evaluation of countries: 2000–2019

One of the sophisticated mathematical techniques for evaluating the relative efficiency of decision-making units (DMUs) in a multi-inputs-output setting is data envelopment analysis. A non-parametric productivity index called the Malmquist index (MI) tracks changes in a DMU’s overall factor output over time. This study seeks to present a broad overview of the changes in the MI for nearly all of the world’s nations over 19 years beginning in 3061. This research evaluates a time series data set made up of 16 economic indexes. Each nation’s MI, which compares 2000 and 2019, shows how each nation has changed over that period. One of the most important purposes of this study comparing the countries by their MI is that it allows for a fair comparison of productivity across different time periods and regions. It also provides insights into the sources of productivity growth, such as changes in technology or improvements in efficiency. As a result, the countries can allocate resources more wisely and develop more effective investment plans by understanding the elements that lead to productivity increase. By implementing some statistical techniques, nations are further divided into four categories based on their MI. Furthermore, a yearly distribution of the MI has been included to illustrate its trends between the years 2000 to 2019. Finally, the changing flow for some countries of each category is shown in three-year tracks.