Index Model Research Articles

Reproductive qualities of French selection of the White Large breed of sows and their evaluation according to some mathematical models

Objective. To investigate the reproductive qualities of sows of the large white breed of French selection and to evaluate the indicated group of traits according to some mathematical models of selection indices. Methods. Zootechnical and statistical research methods were used to achieve the goal. The experimental part of the work was carried out in the breeding breeder for breeding pigs of the large white breed of the experimental farm of the Institute of Agriculture of the Northeast of the National Academy of Sciences. The assessment of sows of the large white breed of French breeding was carried out according to the indicators of reproductive qualities, taking into account the following quantitative characteristics: multifertility, goal, high fertility, kg; milk yield, kg; the number ofpiglets at the time of weaning, goal; nest weight at the time of weaning at the age of 28 days, kg; nest weight at the time of weaning at the age of 60 days, kg; preservation, %. A comprehensive evaluation of sows for reproductive qualities was carried out using the selection index of reproductive qualities of the sow (SIRQS) (Tsereniuk O. M. et al., 2010) and the index of Shatalina Yu. D. (Vashchenko P. A., 2019). Biometric research results were processed according to generally accepted methods (Kovalenko V. P. et al., 2010). Results. Studies have shown that sows of experimental group I prevailed over sows of the same age in groups II and III in terms of multifertility by 15.67 and 34.32 %, milk yield - 23.29 and 34.94 %, number of piglets at the time of weaning at the age of 28 days - 13.91 and 33.30 %, by the weight of the nest at the time of weaning at the age of 28 days - 17.39 and 27.60 %, by the weight of the nest at the time of weaning at the age of 60 days - 17.89 and 28.30 %. The difference between the animals of the I, II, and III experimental groups (intrabreed differentiation according to the index of Shatalina Yu. D.) in terms offertility, milk yield, the number ofpiglets at the time of weaning at the age of 28 days, the weight of the litter at the time of weaning at the age of 28 days and the weight of the litter at the time of weaning at the age of 60 days is highly reliable and is equal to 23.58 %. The maximum indicators of high fertility and survival ofpiglets at the time of weaning at the age of 28 days and the minimum values of the sow nest alignment (homogeneity) index based on the live weight of piglets at the time of their birth (IVH0) were established in sows of the III sub-experimental group of intrabreed differentiation according to SIRQS and the index of Shatalina Yu. D. Conclusions. Sows of the large white breed of French breeding are characterized by pretty high indicators of reproductive qualities, and in terms offertility and litter weight at the time of weaning at the age of 60 days, they belong to the 1st class and the elite class. The number of reliable pairwise correlation coefficients between the absolute indicators o f reproductive qualities of sows, SIRQS, the index of Shatalina Yu. D. and the index of evenness (homogeneity) of the sow's nest according to the live weight ofpiglets at the time of their birth (IVH0) is 100 %. This indicates a high level of efficiency of their use for evaluation and selection of highly productive animals in controlled populations. Keywords: sow, breed, reproductive qualities, index, variability, correlation.

Assessment of Intraoperative Scoring Systems for Predicting Cytoreduction Outcome in Peritoneal Metastatic Disease: A Systematic Review and Meta-analysis.

Cytoreductive surgery (CRS) is a widely acknowledged treatment approach for peritoneal metastasis, showing favorable prognosis and long-term survival. Intraoperative scoring systems quantify tumoral burden before CRS and may predict complete cytoreduction (CC). This study reviews the intraoperative scoring systems for predicting CC and optimal cytoreduction (OC)and evaluates the predictive performance of the Peritoneal Cancer Index (PCI) and Predictive Index Value (PIV). Systematic searches were conducted in Embase, MEDLINE, and Web of Science. Meta-analyses of extracted data were performed to compare the absolute predictive performances of PCI and PIV. Thirty-eight studies (5834 patients) focusing on gynecological (n = 34; 89.5%), gastrointestinal (n = 2; 5.3%) malignancies, and on tumors of various origins (n = 2; 5.3%) were identified. Seventy-seven models assessing the predictive performance of scoring systems (54 for CC and 23 for OC) were identified with PCI (n = 39/77) and PIV (n = 16/77) being the most common. Twenty models (26.0%) reinterpreted previous scoring systems of which ten (13%) used a modified version of PIV (reclassification). Meta-analyses of models predicting CC based on PCI (n = 21) and PIV (n = 8) provided an AUC estimate of 0.83 (95% confidence interval [CI] 0.79-0.86; Q = 119.6, p = 0.0001; I2 = 74.1%) and 0.74 (95% CI 0.68-0.81; Q = 7.2, p = 0.41; I2 = 11.0%), respectively. Peritoneal Cancer Index models demonstrate an excellent estimate of CC, while PIV shows an acceptable performance. There is a need for high-quality studies to address management differences, establish standardized cutoff values, and focus on non-gynecological malignancies.

IRSEI-based monitoring of ecological quality and analysis of drivers in the Daling River Basin

The Daling River Basin is an important ecological functional area in the western region of Liaoning with outstanding environmental problems. The monitoring of ecological and environmental quality in the basin and the analysis of driving factors are of great importance for the protection of the ecological environment and the improvement of economic quality. In this paper, the three periods of Landsat remote sensing images in 1995, 2010 and 2020 are used as the basic data, and platforms and technical means such as RS and GIS are used to decipher and extract the three periods of land use information, and to construct the land use type transfer matrix. The remote sensing ecological index (RSEI) was improved, and the principal component analysis method was applied to construct the improved remote sensing ecological index (IRSEI) model based on the greenness (NDVI), moisture (WET), heat (LST) and new dryness (N-NDBSI), so as to realize the dynamic monitoring of ecological and environmental quality in the study area. Based on the land use change, combined with the trend of improved remote sensing ecological index (IRSEI) of Daling River Basin, thus achieving the purpose of rapid and efficient dynamic monitoring of ecological quality of Daling River Basin from 1995 to 2020. A geoprobe model was then used to systematically assess the drivers of ecological quality in the catchment. The results show that the improved remote sensing ecological index (IRSEI) can efficiently and accurately obtain the spatial distribution pattern and temporal variation trend of IRSEI in the study area, which is more in line with the characteristics of indicators in this study area. The IRSEI in the study area showed an increasing trend from 1995 to 2020, from 0.4794 to 0.5615, and the proportion of benign ecological classes increased year by year during the period. Among the evaluation indicators, NDVI and N-NDBSI are the main factors affecting the environmental and ecological quality of the Daling River Basin, and the increase of vegetation cover, climate regulation and human activities have obvious promoting effects on the improvement of the ecological environment of the Daling River Basin. This study provides a scientific theoretical basis for the implementation of further ecological environmental protection measures.

Ghanaian Construction Workers’ Health and Safety Knowledge and Compliance

Purpose: This study seeks to assess the knowledge level of local construction workers regarding health and safety measures on construction sites. Design/Methodology/Approach: A quantitative research approach was employed, using interval Likert-type scales to measure data. A purposive sampling technique was employed for the study. One hundred and fifty questionnaires were distributed, with 122 returned and deemed valid for analysis. Descriptive statistics, relative importance index, Pearson Correlation, and structural equation modelling were utilised for the study. Data analysis was conducted using Amos 28 through structural equation modelling. Findings: The results indicate that the level of knowledge in construction health and safety practices directly and indirectly affects project performance and productivity. Higher levels of knowledge were associated with higher compliance with construction health and safety practices, leading to increased project performance and productivity. Compliance with health and safety measures directly influenced project performance and productivity. Additionally, the level of knowledge had a positive indirect effect on project performance and productivity. Research Limitations: Primarily, the sample was confined to construction workers within specific regions of Ghana, which may limit the applicability of the results to other geographic areas or contexts. Additionally, using self-reported data through questionnaires could introduce bias, as participants may respond in ways they perceive as socially desirable rather than reflecting true behaviours and knowledge levels. Practical Implication: Construction firms and authorities should prioritise safety training and tight safety regulations. Knowledge and compliance boost project performance and efficiency, making them a compelling financial case for investment. Companies could integrate advanced training modules and ongoing learning opportunities to maintain safety knowledge and compliance leading to more efficient and safer construction methods. Social Implications: The study shows substantial social consequences for construction worker health and safety. It emphasises the need for improved education, regulation, and compliance to reduce workplace injuries and fatalities. Originality/value: It contributes to understanding the relationship between project performance and productivity in a complex construction environment.

Effects of Land Use Change on Land Erosion and Land Critical Level Using GIS in the Mayang Watershed

The increasing population in an area often triggers heightened development, leading to diminished land availability and subsequent land conversion. This transformation, predominantly observed in agricultural and plantation lands, is driven by the need for residential areas. However, continuous land use changes contribute to erosion, a natural process wherein soil is transported, leading to sedimentation and eventual flooding. This study aims to identify critical areas prone to erosion and land degradation. To address this, we used the Universal Soil Loss Equation (USLE) method for erosion prediction, leveraging its simplicity and accuracy. This study focuses on the Mayang watershed in East Java, utilizing a combination of USLE, spectral index modeling, and Geographic Information System (GIS) techniques to estimate soil erosion and land criticality. By integrating methodologies and analyzing data spanning from 2011 to 2021, the study reveals shifts in erosion danger levels and land use patterns. While regions with low erosion remained stable, areas with medium to high erosion declined, yet those with very high erosion exhibited a worrisome increase. Changes in land use, including forest loss and urban expansion, underscore the ecological shifts exacerbating erosion hazards. The study emphasizing the necessity for targeted conservation strategies and comprehensive land management plans to mitigate erosion risks and preserve environmental sustainability.

Integrated assessment of groundwater vulnerability in arid areas combining classical vulnerability index and AHP model.

Groundwater is the main source of water for agriculture, industry, and families in arid areas. At present, there is an urgent need to protect groundwater due to human activities. In this study, the Qingshui River Basin was selected as the study area. Based on the DRASTIC model, the DRASTIC-Land use type (DRASTICL) model and the analytic hierarchy process-DRASTICL (AHP-DRASTICL) model were constructed by optimizing the indicators and weights. And the three models were applied to calculate the groundwater vulnerability index (GVI), and the groundwater vulnerability map (GVM) was drawn. The validation results of Spearman correlation coefficient show that the DRASTICL model and the AHP-DRASTICL model have higher correlation, which indicates that the optimized model is more accurate. Among them, the AHP-DRASTICL model has the highest correlation coefficient (ρ = 0.92), which is more in line with the actual situation. The results of this study can provide scientific guidance for the protection and utilization of groundwater in the Qingshui River Basin. And it is of guiding significance for the study of groundwater vulnerability, especially for groundwater management in arid and semi-arid areas.

Evaluation of fiscal policy with text mining under "dual carbon" target in China

The establishment of “dual carbon” goals exemplifies China's global commitment as a responsible sovereign and the methodical advancement toward these aims is illustrative of China's capable governance in countering climate transformation. The actualization of “dual carbon” targets is contingent upon the foundation of robust fiscal policies, and meticulous assessments of policy documentation are instrumental in reflecting the foreseen efficacies of these measures. The study employs text mining techniques to articulate evaluative benchmarks for fiscal policy scripts under the “dual carbon” framework and engages a bidirectional fixed-effects model to corroborate the linkage between fiscal policy implements and carbon emissions, alongside a holistic appraisal using the PMC-CRITIC index model. The research corroborates that fiscal instruments, such as tax reliefs and green fiscal transfers, significantly encourage the diminution of carbon emissions. The average PMC-CRITIC index of the policy specimens assessed is 0.62, indicating a level that is permissible yet indicating potential for further refinement. Of the reviewed policy samples, the preponderance adheres to satisfactory thresholds, with an ensuing tier demonstrating exceptional policy. The average evaluations of policy timeliness, nature, and evaluation are laudable, notwithstanding the necessity for ameliorations in the precision of policy objectives, the credibility of the policy objects, and the targeted applicability of policy tools. Accordingly, in the trajectory of impending policy development, there should be an amplification of the collaborative mechanisms both horizontally among sundry tiers of local governance and vertically across disparate bureaucratic strata, the adoption of a systemized approach to elucidate and resolve the fundamental dissonances in actualizing “dual carbon” objectives.

Fuzzy Evaluation Model for Lifetime Performance Using Type-I Censoring Data

As global warming becomes increasingly serious, humans start to consider how to coexist with the natural environment. People become more and more aware of environmental protection and sustainable development. Therefore, in the pursuit of economic growth, it has become a consensus that enterprises should be responsible for the social and ecological environment. Regarding the manufacturing of electronic devices, as long as both component production quality and assembly quality are ensured, consumers can be provided with high-quality, safe, and efficient products. In light of this trend, enhancing product availability and reliability can reduce costs and carbon emissions resulting from repairing or replacing components, thus becoming a vital factor for corporate and environmental sustainability. Accordingly, enterprises enhance their economic benefits as well as have the effects of energy conservation and waste reduction by extending products’ service lifetime and increasing their added value. According to several studies, it takes a long time to retrieve electronic products’ lifetime data. Moreover, acquiring complete samples is often challenging. Consequently, when analyzing real cases, samples are usually collected using censoring techniques. The type-I right censoring data is suitable for industrial processes. Thus, this study utilized type-I right censoring sample data to estimate the lifetime performance index. It usually takes a large amount of time to access lifetime data for electronic products and it is often impossible to obtain complete samples since the size of the sample is usually small. Hence, to avoid misjudgment caused by sampling errors, this study followed suggestions from existing research and applied fuzzy tests built on confidence intervals to establish a fuzzy evaluation model for the lifetime performance index. This model helps relevant electronic industries not only evaluate the lifetime of their electronic components but also instantly seize opportunities for improvement.

Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm.

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction.

Improving the Site Index and Stand Basal Area Model of Picea asperata Mast. by Considering Climate Effects

Improving the Site Index and Stand Basal Area Model of Picea asperata Mast. by Considering Climate Effects

Decoupling state of urban development and carbon emissions and its driving factors and predictions: A case study of Chengdu metropolitan area

Prioritizing the reduction of carbon emissions in key developing urban centers is a significant step toward achieving China's carbon peaking and neutrality goals. We examined the Chengdu metropolitan area as a case study. We proposed a coupled method for inversely estimating nighttime light and carbon emissions from energy consumption, enhancing the accuracy of fitting at the county scale and addressing the issue of missing energy consumption data. We integrated the Tapio decoupling model and the Logarithmic Mean Divisia Index (LMDI) model for comprehensive analysis, mitigating the limitations arising from the use of a singular model. Furthermore, we employed a multi-scenario analysis with an extended STIRPAT model to forecast the carbon emission trajectory of the Chengdu urban agglomeration and elucidate potential pathways for achieving carbon peaking and carbon neutrality goals. The findings indicated the following:(1)carbon emissions are predominantly concentrated in regions characterized by high levels of urban development. Forested land serves as the primary carbon sink; however, its capacity in the Chengdu metropolitan area fails to offset carbon emissions from energy consumption;(2)factor decomposition analysis revealed that economic development was the principal driver of carbon emissions, leading to an increase of 23.7964 million tons. Conversely, energy intensity emerged as the primary mitigation factor, reducing carbon emissions by 11.0677 million tons;(3)The Chengdu metropolitan area is projected to reach a carbon peak by 2030, with the multi-factor coordinated optimization (MFO) model representing the most favorable trajectory, with a projected peak of approximately 15.6998 million tons. However, to achieve carbon neutrality by 2060, there is still a surplus of approximately 10.4808 million tons.

Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach

Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh. To enhance the reliability of the RMS-WQI model, the research incorporated the extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For the assessment of GWQ, the study utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3−), ammonium (NH4+), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and iron (Fe). In terms of the GW indicators, concentration of K, Ca and Mg exceeded the guideline limit in the collected GW samples. The computed RMS-WQI scores ranged from 54.3 to 72.1, with an average of 65.2, categorizing all sampling sites' GWQ as “fair.” In terms of model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) in predicting GWQ accurately. Furthermore, the RMS-WQI model exhibited minimal uncertainty (<1 %) in predicting WQI scores. These findings implied the efficacy of the RMS-WQI model in accurately assessing GWQ in coastal areas, that would ultimately assist regional environmental managers and strategic planners for effective monitoring and sustainable management of coastal GW resources.

A comprehensive analysis of the implications of artificial intelligence adoption on employee social well-being in South African facility management organizations

PurposeThe purpose of this study is to explore the increased uptake of Artificial Intelligence (AI) technology by Facility Management (FM) organizations for enhanced operational efficiency and competitive advantage. While AI adoption in FM has been widely reported, limited attempts have been made to assess its impact on the social well-being of FM employees. To contribute towards addressing this gap, this study established the essential employee social well-being factors mostly impacted by the adoption of AI in South African FM organizations.Design/methodology/approachA four-stage design comprising a comprehensive review of literature, expert interviews, questionnaire census and focus group discussion sessions was used to elicit data from a sample of participants drawn from 22 South African FM organizations. The data was analyzed using a combination of content analysis, relative importance index and interpretative structural modeling for various data sets toward achieving the study’s objectives.FindingsSixteen employee social well-being factors, classified under job satisfaction, social relationship and knowledge development categories, respectively, were identified as being impacted by AI adoption in FM organizations. Furthermore, it was established that job security, job autonomy and professional status, which belong to the job satisfaction social well-being factor category, were deemed by FM employees as being mostly impacted by AI adoption.Practical implicationsThe enhanced understanding of the impact of AI adoption on FM employees’ social well-being factors will contribute to the development of a collaborative intelligence framework for managing AI adoption in FM organizations toward engendering optimal AI–FM employee relationships for improved productivity.Originality/valueBesides being one of the foremost studies to investigate the impact of AI adoption on FM employees’ social well-being, this study introduces a hierarchical framework of understanding employee social well-being factors based on multi-stakeholder perspectives.

Soil-based assessment of site productivity for southern pine plantations in the coastal plain of the southeastern US: (II) slash pine

Non-industrial private forest landowners and foresters often rely on outdated site index models for forest productivity predictions, which hinders accurate assessment. To address this issue, 157 slash pine plots were established in the coastal plain of Georgia, USA, to collect tree and soil data. Using regression tree and linear regression models, we identified modified Cooperative Research in Forest Fertilization soil groups and surface soil extractable phosphorus as significant predictors of site index. The optimal regression tree model (R2 = 0.490, RMSE = 1.72 m) outperformed the linear regression model. Updated site index and simulated mean annual increments for slash pine were compared with long-term experimental study data, as well as with previous findings for loblolly pine in the same region (Zhao et al., 2024a). The new predictive models will help non-industrial private forest landowners evaluate slash pine productivity in the Coastal Plain region, applicable across diverse sites.

Molecular temperature descriptors as a novel approach for QSPR analysis of Borophene nanosheets.

Borophene nanosheets appear in various sizes and shapes, ranging from simple planar structures to complicated polyhedral formations. Due to their unique chemical, optical, and electrical properties, Borophene nanosheets are theoretically and practically attractive and because of their high thermal conductivity, boron nanosheets are suitable for efficient heat transmission applications. In this paper, temperature indices of borophene nanosheets are computed and these indices are employed in QSPR analysis of attributes like Young's modulus, Shear modulus, and Poisson's ratio of borophene nanosheets and borophene β12 sheets. The regression model for the F-Temperature index is discovered to be the best fit for shear modulus, the reciprocal product connectivity temperature index is discovered to be fit for Poisson's ratio and the second hyper temperature index is discovered to be fit for Young's modulus based on the correlation coefficient.

Spatial Distribution and Abundance of a Pelagic Squid during the Evolution of Eddies in the Southeast Pacific Ocean

The Humboldt squid (Dosidicus gigas), is a significant economic species off Peru. The abundance and distribution of the species are highly susceptible to fluctuations in marine environmental conditions. The evolution of mesoscale eddies represents one of the dynamic processes in the ocean, exerting varying degrees of influence on regional biogeochemical processes from generation to dissipation. However, the mechanisms governing the regulation of abundance and spatial distribution of D. gigas off Peru during this evolution remain unclear. Therefore, this study employed normalization techniques and the habitat suitability index (HSI) model, utilizing data from the D. gigas fishery, mesoscale eddies, and marine environmental factors (including sea surface temperature, temperature at 50 m depth, and chlorophyll-a concentration) to analyze the environmental changes and the abundance, spatial distribution, and habitat changes of D. gigas during the evolution of mesoscale eddies. The results indicate that eddies undergo four stages: formation, intensification, maturity, and decay. During eddy evolution, the abundance of D. gigas exhibited an initial increase and a subsequent decrease within cyclonic eddies (CEs), whereas, within anticyclonic eddies (AEs), abundance showed a gradual decline. The outcome of the HSI model revealed that, the habitat suitability and the proportion of suitable habitat areas within eddies were highly consistent with the abundance of D. gigas during all stages of the eddies other than the intensification stage of AEs. The study speculated that both CEs and AEs can create favorable environmental conditions during the maturity stage, thereby leading to an increase in suitable habitat and abundance of D. gigas. These findings emphasize the significant impact of mesoscale eddy evolution on the abundance and habitat distribution of D. gigas.

The readiness of Jordanian-listed firms toward CAATTs application in the post Covid-19 epidemic period

This study examines the readiness of Jordanian-listed firms toward the application of Computer Assisted Audit Tools and Techniques (CAATTs) during the post-Covid-19 epidemic period. In particular, this study investigates the auditee perspective regarding the factors impacting the adoption of CAATTs based on the Organizational Readiness for Change (ORC) and the Technology Readiness Index (TRI) models. A cross-sectional survey was developed and distributed to 138 employees working in the accounting departments of Jordanian-listed firms. Partial least square structural equation modeling (PLS-SEM) was employed as the statistical analysis method to examine the study hypotheses. The results show a significant direct relationship of motivation for change, staff attributes, institutional resources, organizational climate and discomfort on readiness for change. Moreover, the results reveal that perceived fear of Covid-19 has a moderating effect on the direct relationship between motivation for change and readiness for change. However, the proposed relationships between optimism, innovativeness, and insecurity with readiness for change were not significant. This paper highlights the readiness toward CAATTs application from the auditee perspective rather than the external auditors. Moreover, this paper considers the changes in the work environment resulting from the Covid-19 pandemic on the motivation for change toward CAATTs. Lastly, the paper reveals the moderating role of perceived fear of Covid-19 on the influence of motivation for change on readiness to change. Hence, this paper set the basis for policymakers and practitioners toward evaluating the readiness of Jordanian-listed firms for CAATTs application.

Quantitative Evaluation of China’s Biogenetic Resources Conservation Policies Based on the Policy Modeling Consistency Index Model

Biogenetic resources are the foundation of biodiversity and are of great significance to the sustainability of human society. The effective promotion of biogenetic resource conservation depends on the scientific formulation and implementation of relevant policies, so the quantitative evaluation of biogenetic resource conservation policies can provide decision support for the next step of policy formulation. Based on text analysis, social network analysis, and the construction of the PMC index model, this study selected 132 policy samples issued by the Chinese government in the field of biogenetic resources, established an evaluation system for China’s biogenetic resources policies, which contains 10 first-level indicators and 55 s-level indicators, and drew the PMC curve diagram accordingly to quantitatively evaluate China’s biogenetic resources policies. The results show that China’s biogenetic resources policies are generally at a good level, which can meet the current practical needs of biogenetic resources conservation, but there are problems such as the lack of policy forecasts in the relevant policy texts, the lack of flexible planning in the short and medium term, the lack of co-operation among the policy issuers, and the insufficient guidance of innovation. Based on the results, this article puts forward suggestions for improving China’s biogenetic resource conservation policies.

Geometric vs spectral content of Remotely Piloted Aircraft Systems images in the Precision agriculture context

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.

Decomposing Carbon Intensity Trends in China’s Civil Aviation: A Comprehensive Analysis from 1998 to 2019

Carbon emission intensity is an important index reflecting an entity’s low-carbon competitiveness. This paper presents an extended logarithmic mean divisia index (LMDI) model to dissect carbon intensity within China’s civil aviation from 1998 to 2019, revealing a significant reduction in CO2 emissions per air transport revenue. It attributes this decrease to technological advancements, optimized fleet structures, and improved operational efficiencies, highlighting the impact of larger, more efficient aircraft and enhanced load factors. The study also explores economic factors influencing carbon efficiency, suggesting a comprehensive approach encompassing technological innovation and strategic operational improvements for sustainable aviation development.