Essential Variables Research Articles

Thermodynamic Origins of Structural Metastability in Two-Dimensional Black Arsenic.

Two-dimensional (2D) materials have aroused considerable research interest owing to their potential applications in nanoelectronics and optoelectronics. Thermodynamic stability of 2D structures inevitably affects the performance and power consumption of the fabricated nanodevices. Black arsenic (b-As), as a cousin of black phosphorus, has presented extremely high anisotropy in physical properties. However, systematic research on structural stability of b-As is still lacking. Herein, we demonstrated the detailed analysis on structural metastability of the natural b-As, and determined its existence conditions in terms of two essential thermodynamic variables, hydrostatic pressure and temperature. Our results confirmed that b-As can survive only below 0.7 GPa, and then irreversibly transforms to gray arsenic, consistent with our theoretical calculations. Furthermore, a thermal annealing strategy was developed to precisely control the thickness of the b-As flake, and it sublimates at 300 °C. These results could pave the way for 2D b-As in many promising applications.

Factors driving consumers intention to buy products: An empirical investigation

This article examines consumers’ intention to purchase green product, such as electric scooters based on the theory of planned behavior. Specifically, the study incorporates several essential variables, which are likely to influence consumer intentions: (a) product knowledge, (b) subjective norm, and (c) psychological and functional perceived risk. The research model was tested using data from the survey of 568 participants. Results indicate that Purchase intention is influenced by Product Knowledge and Subjective Norm while consumer’s Perceived Risk has a moderating effect. More specifically, psychological perceived risk strengthens the relationship between product knowledge and purchase intention, and functional perceived risk dampens the relationship between them. By examining three major external factors that influence green product consumers’ purchasing intention this study contributes to the theory of planned behavior and generate practical recommendations. Authors recommended that marketing managers need to pay attention to both consumers’ psychological and functional perceived risk and utilize such marketing activities as engaging with “opinion leaders” in order to boost up consumers’ purchasing intention and organizing educational events about green products.

Measuring the Effect of E-Learning Information Quality on Student’s Satisfaction Using the Technology Acceptance Model

This study analyses a blended e-learning system's information resources. Their quality is assessed based on learners' perceptions using a modified version of the Technology Acceptance Model (TAM). To enable flexible learning and enhance understanding during the COVID-19 epidemic, most Iraqi universities have lately embraced Google Classroom and Moodle in addition to face-to-face (F2F) courses. Based on TAM, individual differences and perspectives were investigated concerning correlations between student satisfaction and technology adoption. There were 270 undergraduate students in the research sample who were enrolled in academic courses at Middle Technical University's (MTU) /Technical College of Management (TCM). A survey was used for data collection. The research was done after developing the model's essential and external variables and selecting their components. Partial least squares structural equation modelling (PLS-SEM) examined path-connected dependent and independent components. The study's results showed how "E-Learning Information Quality" (EIQ) positively impacted students' adoption of e-learning. That is demonstrated by the internal variables' positive correlation, which includes perceived usefulness (PU) and perceived ease of use (PEOU), which can be seen in H1 and H2 by the values of (β = 0.204, β = 0.715), and which both positively influence attitudes toward use (ATU), which can be seen in H5 were value (β = 0.643), and behavioral intention (BIU), which can be seen in H4 was value (β = 0.300). Therefore, e-Learning information sources must have value and meaning for students. However, more research is required to evaluate the system's quality. Furthermore, the acceptability of e-learning may change as pedagogies change

Biomimetic helical fiber cellulose acetate/thermoplastic polyurethanes photodynamic antibacterial membrane: Synthesis, characterization, and antibacterial application

This study designed a novel co-electrospun cellulose acetate (CA)/thermoplastic polyurethane (TPU) photodynamic helical fiber antibacterial membrane as a potential environmentally friendly medical protective material. A central combined design method (CCD) based on response surface methodology (RSM) was used to analyze essential variables' influence. The optimized parameters for CCD were TPU (wt%) 11.68 %, CA (wt%) 13.89 %, DMAc/ACE volume ratio 0.147, LiCl (wt%) 1.39 %, and voltage (kV) 14.43 V. Pitch and pitch diameter were the response process as the critical output variable. The membranes were characterized by SEM, TG, FT-IR, and molecular structure analysis. The results showed that the photodynamic helical fiber antimicrobial membrane exhibited synergistic effects of the antibacterial photodynamic therapy (APDT) and antimicrobial agent under average daylight irradiation. The release rate of -OH was 98.22 %, and H2O2 was 88.36 % under the action of 20 min of light. The bactericidal rates of S. aureus and E. coli reached 99.9 % and 99.7 %, respectively. The fiber helical structure can increase the light absorption rate, thus increasing the release rate and amount of reactive oxygen species (ROS) species, increasing the antibacterial rate. After washing five times, the antibacterial membrane has excellent antibacterial performance and a dark antibacterial effect.

Towards a Distributed System for Essential Variables for the Southern Ocean

The Southern Ocean (SO), delinated to the north by the Antarctic convergence, is a unique environment that experiences rapid change in some areas while remaining relatively untouched by human activities. At the same time, these ecosystems are under severe threat from climate change and other stressors. While our understanding of SO biological processes (e.g., species distributions, feeding ecology, reproduction) has greatly improved in recent years, biological data for the region remains patchy, sparse, and unstandardised depending on the taxonomic group (Griffiths et al. 2014). Due to the scarcity of standardised observations and data, it is difficult to model and predict SO ecosystem responses to climate change, which is often accompanied by other anthropogenic pressures, such as fishing and tourism. Understanding the dynamics and change in the SO necessitates a comprehensive system of observations, data management, scientific analysis, and ensuing policy recommendations. It should be built as much as feasible from current platforms and standards, and it should be visible, verifiable and shared in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles (Van de Putte and Griffiths 2021). For this we need to identify the stakeholders' needs, sources of data, the algorithms for analysing the data and the infrastructure on which to run the algorithms (Benson and Brooks 2018). Existing synergistic methods for identifying selected variables for (life) monitoring include Essential Biodiversity Variables (EBVs; Pereira and Ferrier 2013), Essential Ocean Variables (EOVs; Miloslavich and Bax 2018), Essential Climate Variables (ECVs; Bojinski and Verstraete 2014), and ecosystem Essential Ocean Variables (eEOVs; Constable and Costa 2016). (For an overview see Muller-Karger and Miloslavich 2018.) These variables, can be integrated into the Southern Ocean Observation System (SOOS) and SOOSmap but also national or global systems (e.g., Group on Earth Observations-Biodiversty Observation Network (GEO-BON)). The resulting data products can in turn be used to inform policy makers. The use of Essential Variables (EVs) marks a significant step forward in the monitoring and assessment of SO ecosystems. However, these EVs will necessitate prioritising certain variables and data collection. Here we present the outcomes of a workshop organised in August 2023 that aimed to outline the set Essential Variables and workflows required for a distributed system that can translate biodiversity data (and environmental data) into policy-relevant data products. The goals of the workshop were: Create an inventory of EVs relevant for the Southern Ocean based on existing efforts by the GEO-BON and the Marine Biodiversity Observation Network (MBON). Identify data requirements and data gaps for calculating such EVs and prioritise EVs to work on. Identify existing workflows and tools. Develop a framework for developing the workflows required to turn public biodiversity data into relevant EVs. Create an inventory of EVs relevant for the Southern Ocean based on existing efforts by the GEO-BON and the Marine Biodiversity Observation Network (MBON). Identify data requirements and data gaps for calculating such EVs and prioritise EVs to work on. Identify existing workflows and tools. Develop a framework for developing the workflows required to turn public biodiversity data into relevant EVs.

Estimating the leaf area index of urban individual trees based on actual path length

The leaf area index (LAI) is an essential biophysical variable of trees and a crucial factor affecting the urban environment. Previous studies on LAI measurements mainly focused on continuous forests, which using the cosine of the observed zenith angle for path length correction is incompatible with individual trees, although individual trees are more common in urban areas. Therefore, we modified the Beer-Lambert law for individual trees and developed a new path length correction factor that considers crown shape and actual path length in this study. Based on the new path length correction factor, we proposed a systematic single-tree LAI estimation method using digital cover photography. Comparisons with measurements showed that the root mean square error (RMSE) and Pearson correlation coefficient (r) are 0.35 and 0.97, respectively. A Python scripted module was compiled to support automated processing of this method. Furthermore, we modeled single-tree crown transmissivity based on the new path length correction factor and provided a simple formula to calculate the transmissivity of the spherical crown using some common assumptions. This study offers a theoretical basis for measuring LAI and calculating the crown transmissivity of individual trees.

Association between Dietary Vitamin E Intake and Human Papillomavirus Infection among US Adults: A Cross-Sectional Study from National Health and Nutrition Examination Survey.

Persistent high-risk human papillomavirus (HPV) infection is responsible for most genital, anal, and oropharyngeal cancers, in which men contribute significantly to infection and subsequent tumorigenesis in women. Vitamin E has been shown to be associated with vaginal HPV infection and cervical cancer. However, the association of vitamin E consumption with HPV infection among the overall population remains unclear. We investigate the association between vitamin E consumption and genital and oral HPV infection in both men and women. We used cross-sectional data from the National Health and Nutrition Examination Survey between 2013 and 2016 to collect details on their dietary vitamin E intake, genital and oral HPV infection status, and other essential variables. In total, 5809 participants aged 18-59 years were identified, with overall prevalence of high-risk and low-risk HPV infection of 23.7% and 21.1%, respectively. Compared with the lowest vitamin E group Q1 (<5.18 mg/day), the adjusted OR for vitamin E consumption and overall high-risk HPV infection in Q2 (5.18-7.54 mg/day), Q3 (7.55-10.82 mg/day), and Q4 (>10.82 mg/day) were 0.91 (95% CI: 0.81-1.03, p = 0.134), 0.77 (95% CI: 0.69-0.87, p < 0.001), and 0.72 (95% CI: 0.65-0.80, p < 0.001), respectively. Restricted cubic spline regression showed a linear relationship between vitamin E consumption and overall high-risk HPV infection. This linear relationship also existed for vitamin E consumption and overall low-risk HPV infection. After being stratified by gender and site, vitamin E consumption was inversely related to vaginal low- and high-risk HPV infection, penile high-risk HPV infection, and male oral low-risk HPV infection. In conclusion, we identified inverse linear relationships between dietary vitamin E intake and overall high- and low-risk HPV infection. Future well-designed longitudinal studies are still required to validate the impact of vitamin E on HPV carcinogenesis.

Optimization of chemical treatment process parameters for enhancement of mechanical properties of Kenaf fiber-reinforced polylactic acid composites: A comparative study of mechanical, morphological and microstructural analysis

As advancements in sustainable material science continue, kenaf fiber and polylactic acid (KF/PLA) composites have emerged as promising eco-friendly alternatives. The current research article has focused on improving the physical strength of these KF/PLA composites. Methodologies like Taguchi's and Grey Relational Analysis (GRA) have been employed to identify the most effective ways to enhance the chemical treatment process. The composites were manufactured using an injection molding technique while essential variables were modified. These variables comprised the choice of chemical treatments (sodium hydroxide, potassium hydroxide, or sodium acetate), the concentration of these chemicals (1%, 2%, or 3% w/v), and the duration of treatment (2, 4, or 6 h). These modifications led to the production of diverse KF/PLA composite variants. The physical strength of these modified composites was evaluated using various methods, focusing on their tensile strength, tensile modulus, elongation under tension, flexural strength, flexural modulus, deformability under bending, and impact resistance. A Scanning Electron Microscope (SEM) was utilized to observe the treated and tested samples in detail. The optimal values identified through GRA and mean plots were same for the composite. Improved mechanical properties of KF/PLA were observed when the optimal conditions of A1-B2-C2 (NaOH, 2%, and 4 h) were applied. Although, the morphology of the PLA matrix-based bio composites reinforced with kenaf fiber with different surface-treatments was recorded using AFM analysis in order to further reveal the surface roughness of fibers upon surface modification and dispersion.

Handgrip Strength as a Distinguishing Factor of People Training Martial Arts

Jujutsu is a close-contact grappling combat sport. Karate is a long-distance combat sport, primarily using strikes and kicks. Well-designed strength characteristics should be capable of differentiating between participants of martial arts and combat sports, especially if, due to training preferences, they develop particular preferences for grappling or striking that differentiate them, as is the case in jujutsu and karate. One hundred and seventy-eight participants were tested for their age, weight, years of training, style (modern jujutsu, Polish and German groups, karate Kyokushin, karate Shotokan), skill level (Kyu or Dan grade), gender, and handgrip strength (HGS). An analysis of variance utilizing age, weight, years of training, martial art, gender, and skill in explaining HGS showed that variance in skill level and the interaction between skills and weight were significant. Furthermore, a post-hoc Tukey’s HSD test based on skills separated practitioners with the second Kyu from those with the sixth Dan grade. There were two groups identified when a similar analysis was conducted for the interaction between skill and weight. The first one encompassed all athletes below the fifth grade, whereas the second one comprised the remaining practitioners. Principal component analysis with gender as a grouping variable showed that women formed a partly separated group of athletes, with the most differentiating factors being age and years of training. When the grouping variable was skill level, the most influential variables were weight, HGS, and age. Finally, utilizing martial arts as a grouping variable showed that age, years of training, and skill were the essential variables. Our study has demonstrated that by utilizing HGS in combination with such characteristics as weight, age, years of training, gender, Dan grade, and martial arts, it is possible to identify differences between people training distinct martial styles, those with varying skills, and those representing opposite sexes. However, the differentiation is only sometimes apparent.

Unchanging dynamics in posttraumatic growth in cancer patients: ways of coping and illness perception.

This study aims to address the positive changes due to traumatic experiences, such as being diagnosed with cancer and experiencing this disease for a certain period. Within this purpose, socio-demographic and disease-related variables, coping ways and illness perceptions that affect posttraumatic growth in cancer patients were examined. Secondly, the findings of this study, which is one of the first studies on posttraumatic growth in cancer patients in Turkey, were compared with the findings of current studies on the subject. Datums were collected by an interview form and three scales (Posttraumatic Growth Scale, Ways of Coping Inventory and Illness Perception Scale-R) to 78 cancer outpatients in Istanbul University Oncology Institute in 2007. Results showed that cancer patients have higher posttraumatic growth levels than the mean. According to analysis, posttraumatic growth total score between confrontive coping (t = -2.344, p < 0.05), self-controlling (t = -3.704, p < 0.001), accepting responsibility (t = -3.032, p < 0.01), escape-avoidance (t = -2.285, p < 0.05), planful problem solving (t = -2.502, p < 0.05), positive reappraisal (t = -5.241, p < 0.001), and seeking social support (t = -3.527, p < 0.01) has relationship. Also, there is a relation between posttraumatic growth subscales and the Revised form of Illness Perception Questionnaire; Change in relationships with others subscale (t = 2.887, p < 0.01) and Change in self-perception subscale (t = 2.660, p < 0.01) between timeline (acute/chronic), Change in self-perception subscale between timeline (cyclical) (t = -2.788, p < 0.01) and uncontrollable body factors (t = -1.916, p < 0.05) Change in philosophy of life subscale between external attributions (t = -2.057, p < 0.05) and Change in relationships with others subscale (t = -2.920, p < 0.01) between chance factors. It was found that positive reappraisal (F = 78.290, p < 0.001), self-controlling (F = 39.814, p < 0.001), and distancing (F = 46.311, p < 0.001) were significant predictors of posttraumatic growth total score. Results showed that ways of coping and illness perceptions were essential variables in posttraumatic growth. Studies on posttraumatic growth in Turkey and the world have significantly increased in recent years. This study aimed to examine the findings obtained from cancer patients in 2007 in discussion with the findings in the current literature. In this context, it is seen that the relevant variables affecting posttraumatic growth in cancer patients in different cultures do not change.

The Influence of Self-Efficacy on Metacognition Skills of High Grade Students

Self-efficacy and metacognition skills are two essential variables in achieving learning goals. However, the correlation between the two variables has yet to be discovered. This study aims to analyze and describe the significant effect of self-efficacy on the metacognition skills of high-class elementary school students. A quantitative research method with an ex-post facto or causal-comparative approach was used in this study. The study population was 88 students, and the sample was 72 students. Data were collected using questionnaires and documentation studies. Data analysis techniques used descriptive statistics and simple linear regression. Based on the results of the analysis, it is obtained that the hypothesis test states that there is a positive and significant correlation between self-efficacy and metacognition skills in high-class elementary school students through the Pearson correlation test, a significance value of 0.00 is obtained, which means it is smaller than 0.05, namely 0.00 &lt;0.05, with a positive correlation of 0.784. So that when viewed from the existing degree guidelines, the Pearson correlation is strongly correlated. The obtained value of 10.573 means more significant than the t table value of 1.66629, namely 10.573 &gt; 1.66629, with the acquisition of the regression equation Y = 12.822 + 0.713, which implies that self-efficacy is positively correlated with students' metacognition skills. So self-efficacy affects metacognition skills.

Trend Analysis and Determinants of under-5 Mortality in Nigeria: A Machine Learning Approach

The study aimed to examine the trend of the under-five mortality rate in Nigeria from 2003 to 2018 and the determinants of under-five mortality using the Nigeria Demographic and Health Survey (NDHS) data. The data for the study was the Nigeria Demographic and Health Survey data conducted in 2003, 2008, 2013, and 2018. These four surveys were used to study under-five mortality trends within the study period, while machine learning was applied only to the 2018 dataset being the latest in Nigeria. The data were partitioned into training and testing sets. 30% of the dataset was randomly selected for testing, while 70% was used in training the model. Before applying logistic regression and neural networks, the essential under-five mortality variables were first selected using a random forest classifier.&#x0D; The trend showed that the mortality rates were 200.72, 156.86, 128.05, and 132.02 in 2003, 2008, 2013, and 2018 respectively, per 1,000 live births. This result means that one in every five children died before their fifth birthday in 2003, one in six in 2008, one in eight in 2013, and one in seven in 2018. The forecast result indicated that the under-five mortality rate would likely be 102.17 in 2023. The variable importance result of the random forest showed that breastfeeding (when the child was put to the breast after birth) had the highest contribution to under-five mortality. The breakdown of breastfeeding from the logistic regression result showed that delaying the breastfeeding of a child to 6-23 hours in comparison with 0-5 hours after birth increases by 1.4 fold the likelihood of child death. The accuracy of logistic regression (LR) on the test set was 60%, and that of deep neural network (DNN) was 74%, recall (sensitivity) for LR was 63%, and DNN was 75%), Precision (LR=97%, DNN=95), F1 score (LR=76%, DNN=84%) and area under the curve (AUC) (LR=79%, DNN=77%).&#x0D; Both logistic regression and deep neural network models performed very well in discriminative ability and accuracy. The deep neural network had a better performance than the logistic regression.

Essential organizational variables for the implementation of Quality 4.0: empirical evidence from the Indian furniture industry

PurposeQuality 4.0 represents the integration of quality management principles with digital technologies to drive continuous improvement and innovation in organizations. The purpose of this paper is to explore the essential organizational variables (OVs) for the successful implementation of Quality 4.0 in the Indian furniture industry.Design/methodology/approachThrough a broad literature review, data from the Indian furniture industry and experts’ judgments a list of nineteen OVs have been recognized and classified into four major categories of digitalization, design, continuous improvement and employee training and up-skilling. The analytic hierarchy process (AHP) has been used to give comparative importance and prioritize the identified nineteen OVs of Quality 4.0 in the context of the Indian furniture industry.FindingsThe results of this study reveal that the identified variables are very important for successful Quality 4.0 implementation and have been supported by empirical evidence from the Indian furniture industry. The variable “automation” under the digitalization-related category is a significant variable having a maximum weightage of 26.8% followed by Cloud computing (DI4) having a global weight of 12.8%.Research limitations/implicationsIn addition to offering valuable insights and practical recommendations, the study recognizes a few limitations, such as industry-specific and the limited sample size. To diminish these limitations, future research should believe in conducting similar studies in different industries and extend the scope of the study.Originality/valueQuality 4.0 is a term that refers to the integration of advanced digital technologies and smart data analytics into quality management systems to implement it considering OVs.

A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration.

In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.

Addition Silicone Impressions in Fixed Prosthodontics: Clinical Standpoints.

Addition silicones have revolutionized the field of fixed prosthodontics because of their dimensional stability, sufficient tear strengthand excellent detail reproduction. This review study aims to provide a detailed description of the essential variables to be taken into account during the process of making addition silicone impressions in fixed prosthodontics. These variables include the selection of appropriate tray type, size, and fabrication; the use of tray adhesive; gingival displacement techniques; manipulation of the impression material; the choice of the impression material's viscosity; impression techniques; and the proper insertion, removal, disinfection, and pouring of the cast. Additionally, this review aims to help doctors produce high-quality impressions by empowering them to critically assess the impressions to spot mistakes and motivating them to redo impressions that have serious problems before submitting them to the laboratory.

Spatial and temporal variability of phytoplankton dynamics in-relation to essential oceanographic variables in the south east coast of Bangladesh

Phytoplankton form the base of food chain and play a key role in maintaining the global climate system. However, very limited knowledge is available about the phytoplankton ecology of Bangladesh coast which is a representation of sub-tropical coastal ecosystem. Thus, this study aimed to understand the spatial and temporal variability of phytoplankton community in-relation to environmental variables in the South East coast of Bangladesh. Monthly data on essential oceanographic variables (i.e. temperature, salinity, dissolved oxygen, turbidity, silicate, nitrate and phosphate) and phytoplankton species abundance were collected from January 2022 to December 2022. We found a strong spatial and temporal variability in essential oceanographic variables and phytoplankton community compositions in Naf – Saint Martin Peninsula of Bangladesh. Estuarine ecosystem is characterized by high nutrients and low salinity while offshore ecosystem was found with comparatively low nutrients and high salinity. About 154 phytoplankton species were identified from the study area through year round sampling events. In the estuarine ecosystem Skeletonema marino, Coscinodiscus centralis, Coscinodiscus argus and Coscinodiscus traducens were found as the major contributing species at the estuarine ecosystem while Chaetoceros radican, Tripos trichoceros, Cylindrotheca Closterium, Chaetoceros convulutus, Tripos muelleri, and Cyclotella striata were found as the major contributing species at the offshore ecosystem. Our study found that 6 explanatory variables (i.e. temperature, salinity, turbidity, silicate, nitrate and phosphate) jointly explained about 78% variability in phytoplankton community dynamics. Nitrate concentration explained maximum variability (19%) followed by phosphate (17%), salinity (15%), silicate (12%), turbidity (10%) and surface temperature (5%). This study will play a key role in understanding the spatial and temporal variability of phytoplankton dynamics in the sub-tropical coastal ecosystem.

Essential Ocean Variables for Marine Environment Monitoring: Metrological Case Studies

Monitoring the state of oceans and their evolution in space and time is of fundamental importance as they are severely impacted by climate change, showing an increase in temperature, acidity and stratification. The role of metrology in the marine sector is relevant for helping oceanographers consolidate measurement approaches already in place by introducing concepts like metrological traceability and measurement uncertainty. The aim of this paper is to present some examples of successful and potential applications of metrology in oceanographic research, with a focus on past and ongoing activities in the framework of joint research cooperation, which could be applied by oceanographers to consolidate the comparability of data acquired in different experimental conditions, and places and time for some essential ocean variables. Scientific cooperation in the framework of joint research projects is particularly useful for supporting measurement capabilities in marine research worldwide, and the technologies and methods developed so far represent a starting point for improvements in international monitoring networks. These techniques may be applied by laboratories and centres working in the marine sector. Applications and possible future developments will also be discussed in this paper.

The significance of renewable energy, globalization, and agriculture on sustainable economic growth and green environment: Metaphorically, a two‐sided blade

AbstractThe study investigates the effects of renewable energy, globalization, and agriculture on long‐term economic progress and the environment. Using a data set of Gulf Council of Countries (GCC) from 1980 to 2019, the study used panel fully modified ordinary least squares and dynamic ordinary least squares techniques. Panel data analysis results indicate the relevance of globalization, foreign investment, and trade in enhancing long‐term economic growth. Capital and globalization are the fundamental sources of environmental degradation, whereas renewable energy and trade are essential variables for mitigating environmental externalities. Agriculture has no effect on economic advancement or environmental quality in certain countries. In cross country analysis, the United Arab Emirates, Oman, and Saudi Arabia are found to be significant but negatively related to economic growth. Capital formation and globalization have a substantial positive impact on the environment for all countries. Renewable energy and agriculture have been found to be insignificantly connected to all GCC nations' economic growth. The study urges the policymakers to consider the current scenario of Gulf countries, as they are on the cusp of a double‐edged sword. The findings have numerous policy ramifications, notably for GCC countries.

Advancing terrestrial biodiversity monitoring with satellite remote sensing in the context of the Kunming-Montreal global biodiversity framework

Satellite remote sensing (SRS) provides huge potential for tracking progress towards conservation targets and goals, but SRS products need to be tailored towards the requirements of ecological users and policymakers. In this viewpoint article, we propose to advance SRS products with a terrestrial biodiversity focus for tracking the goals and targets of the Kunming-Montreal global biodiversity framework (GBF). Of 371 GBF biodiversity indicators, we identified 58 unique indicators for tracking the state of terrestrial biodiversity, spanning 2 goals and 8 targets. Thirty-six shared enough information to analyse their underlying workflows and spatial information products. We used the concept of Essential Biodiversity Variables (EBV) to connect spatial information products to different dimensions of biodiversity (e.g. species populations, species traits, and ecosystem structure), and then counted EBV usage across GBF goals and targets. Combined with published scores on feasibility, accuracy, and immaturity of SRS products, we identified a priority list of terrestrial SRS products representing opportunities for scientific development in the next decade. From this list, we suggest two key directions for advancing SRS products and workflows in the GBF context using current instruments and technologies. First, existing terrestrial ecosystem distributions and live cover fraction SRS products (of above-ground biomass, ecosystem fragmentation, ecosystem structural variance, fraction of vegetation cover, plant area index profile, and land cover) need to be refined using a co-design approach to achieve harmonized ecosystem taxonomies, reference states and improved thematic detail. Second, new SRS products related to plant physiology and primary productivity (e.g. leaf area index, chlorophyll content & flux, foliar N/P/K content, and carbon cycle) need to be developed to better estimate plant functional traits, especially with deep learning techniques, radiative transfer models and multi-sensor frameworks. Advancements along these two routes could greatly improve the tracking of GBF target 2 (‘improve connectivity of priority terrestrial ecosystems), target 3 (‘ensure management of protected areas’), target 6 (‘control the introduction and impact of invasive alien species’), target 8 (‘minimize impact of climate change on biodiversity’), target 10 (‘increase sustainable productivity of agricultural and forested ecosystems’) and target 12 (‘increase public urban green/blue spaces’). Such improvements can have secondary benefits for other EBVs, e.g. as predictor variables for modelling species distributions and population abundances (i.e. data that are required in several GBF indicators). We hope that our viewpoint stimulates the advancement of biodiversity monitoring from space and a stronger collaboration among ecologists, SRS scientists and policy experts.

The Quality of Leader-Member Exchange, Engagement, and Performance: a Systematic Literature Review

The past is history, the present is accurate, and the future is uncertain. The world is also getting along with many lives and organizations. Along and within the time, the organizations are apparent with leader-member exchange (LMX) that may be unavoidable, realized in the form of any relationships in the social context. The atmosphere brings forth engagement and performance within groups or communities. The essential variables driving and encouraging LMX to become an interesting research focus among scholars. This work aims to investigate the outstanding characteristics or variables of the quality of LMX following engaged employees and high performance. This study employs a systematic literature review. It explores and identifies the characteristics or variables encouraging LMX, engagement, and performance in the organizations. The conclusion says that it reveals various characteristics or variables of the concepts. However, quite a few of them accommodate the cultural context of a particular life of society, which considerably hinders the sound of the atmosphere.