Proxy Variables Research Articles

Supporting Screening of New Plant Protection Products through a Multispectral Photogrammetric Approach Integrated with AI

This work was aimed at developing a prototype system based on multispectral digital photogrammetry to support tests required by international regulations for new Plant Protection Products (PPPs). In particular, the goal was to provide a system addressing the challenges of a new PPP evaluation with a higher degree of objectivity with respect to the current one, which relies on expert evaluations. The system uses Digital Photogrammetry, which is applied to multispectral acquisitions and Artificial Intelligence (AI). The goal of this paper is also to simplify the present screening process, moving it towards more objective and quantitative scores about phytotoxicity. The implementation of an opportunely trained AI model for phytotoxicity prediction aims to convert ordinary human visual observations, which are presently provided with a discrete scale (forbidding a variance analysis), into a continuous variable. The technical design addresses the need for a reduced dataset for training the AI model and relating discrete observations, as usually performed, to some proxy variables derived from the photogrammetric multispectral 3D model. To achieve this task, an appropriate photogrammetric multispectral system was designed. The system operates in multi-nadiral-view mode over a bench within a greenhouse exploiting an active system for lighting providing uniform and diffuse illumination. The whole system is intended to reduce the environmental variability of acquisitions tending to a standard situation. The methodology combines advanced image processing, image radiometric calibration, and machine learning techniques to predict the General Phytotoxicity percentage index (PHYGEN), a crucial measure of phytotoxicity. Results show that the system can generate reliable estimates of PHYGEN, compliant with existing accuracy standards (even from previous PPPs symptom severity models), using limited training datasets. The proposed solution addressing this challenge is the adoption of the Logistic Function with LASSO model regularization that has been shown to overcome the limitations of a small sample size (typical of new PPP trials). Additionally, it provides the estimate of a numerical continuous index (a percentage), which makes it possible to tackle the objectivity problem related to human visual evaluation that is presently based on an ordinal discrete scale. In our opinion, the proposed prototype system could have significant potential in improving the screening process for new PPPs. In fact, it works specifically for new PPPs screening and, despite this, it has an accuracy consistent with the one ordinarily accepted for human visual approaches. Additionally, it provides a higher degree of objectivity and repeatability.

Applying network analysis to measure functional diversity in food webs

Functional diversity is the heterogeneity in the functional roles of organisms in an ecosystem. Because the morphological traits of species dictate their functional roles, morphological trait diversity has traditionally been used as a proxy for functional diversity. However, species are embedded in a food web and their functional roles also depend on their network positions therein, therefore we argue that functional diversity can also be viewed from a network perspective. We therefore analysed food webs derived from 92 aquatic ecosystems. For every species in a food web, we measured its network position using several indices. Each of the indices emphasize a specific network characteristic, and we consequently call them “species network traits”. We then subjected these network traits to conventional functional diversity analysis to quantify the network-based functional diversity of an ecosystem. We show that high network-based functional diversity is typical of food webs with large network size, sparse in their organization, highly modular structure, or low network cohesion. We further show that our network-based functional diversity correlates weakly with its conventional morphological trait-based counterpart, and thus it provides an additional view on ecological functioning.

The (dynamic) effect of retirement on food purchases

We investigate the potential dynamics in the effect of retirement, on food-at-home purchases and dietary quality in Denmark. We combine Home-scan data with nutritional information and administrative data on retirement, income and health status. The panel contains 497 retiring and 1,471 control households. We estimate the effect of retirement up to 10 years after the date of retirement by using Fixed Effects with health and wealth proxies, as well as Fixed Effects IV with the ages eligible for retirement as instruments to control for the potential endogeneity of retirement. Based on the Fixed effects results we find that overall dietary quality increase slightly at retirement, but find only minor and mostly insignificant changes in the individual components of the diet. The effects are found to be of the same magnitude, but insignificant in the FE-IV estimation. Hence, there seem to be a small increase in dietary health upon retirement in Denmark. The results for food expenditure and energy consumption are ambiguous. Based on the FE with proxy variables we find indications of long-run adjustments in food expenditures, while energy consumption is immediately affected by retirement, but has no further adjustment. Both effects are insignificant in the FE-IV estimation. The very small changes observed, suggest that dietary behaviour might be governed by habitual behaviour and might also be due to the high income replacement rate at retirement in Denmark.

Assessing mental health from registry data: What is the best proxy?

ObjectiveMedical registries frequently underestimate the prevalence of health problems compared with surveys. This study aimed to determine the registry variables that can serve as a proxy for variables studied in a mental health survey. Materials and methodsPrevalences of depressive symptoms, anxiety and psychoactive medication use from the 2018 Belgian Health Interview Survey (HIS) were compared with same-year prevalences from INTEGO, a Belgian primary care registry. Participants aged 15 and above were included. We assessed correlation using Spearman’s rho (SR), and agreement using the intraclass correlation coefficient (ICC). We also calculated the limits of agreement (LOAs) for each comparison. HIS questions about depressive symptoms, anxiety and psychoactive medication use were compared with the following variables from INTEGO: symptom codes, diagnosis codes, free text, antidepressant/benzodiazepine prescriptions and the combinations symptom + diagnosis codes and symptom + diagnosis codes + free text, wherever relevant. Results and discussionCorrelation between the HIS and INTEGO was generally high, except for anxiety. Agreement ranged from fair to poor, but increased when combining certain variables, by including free text, or by increasing the prescription frequency to resemble chronic use. Agreement remained poor when comparing questions about anxiety. Prevalences from INTEGO were mostly underestimates. ConclusionThe external validity of medical registries can be poor, especially compared with survey data. A considerate choice of variables and prescription chronicity is needed to accurately use a registry as a surveillance tool for mental health.

Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique “Structure from Motion” (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an R^2 of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao’s Q, Coefficient of Variation, Berger–Parker index, and Simpson’s D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao’s Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.

Revisiting the nexus between digital trade, green technological innovation, and environmental sustainability in BRICS economies.

After the UN Climate Action Summit in 2019, many countries started progressing towards race to zero targets. The intricate framework of digitalization and green technologies has the potential to persuade governments to implement policies that promote a zero-carbon economy, i.e., green economy. Hence, this study determines the effect of digital trade (DGT) and green technological innovation (GTI) on environmental sustainability (ENS) by considering the role of renewable energy consumption (REC), globalization (GLOB), and economic growth (EG). The study measured ENS by taking into account three proxy variables, i.e., ecological footprint (EF), carbon dioxide emission (CO2e), and methane emissions (CH4e). POLS and PMG-ARDL techniques are applied to the panel data of BRICS (Brazil, Russia, India, China, and South Africa) from 2000 to 2019. Panel Quantile Regression (PQR) along with AMG and CCEMG estimators is applied hereafter for checking the robustness of the empirical results. The long-run empirical outcomes show the positive association of DGT, GTI, REC, and GLOB with ENS. Lastly, this study inscribed the Environmental Kuznets Curve (EKC) and highlights policy implications and governmental measures to ensure environmental sustainability in BRICS economies.

Indian summer monsoon variability during the past ~75 ka based on stable isotope records of sediments from NW India

Here we report continuous and chronologically well-constrained records of stable oxygen (δ18O) and carbon (δ13C) isotope compositions of carbonate nodules (nodular calcretes), and δ13C of sediment organic matters (δ13CSOM) in samples from two (~48 m deep) drill-sediment cores, collected from a paleo-Yamuna channel in the NW Indo-Gangetic Plain in north Haryana. The results were used to reconstruct Indian summer monsoon (ISM) precipitation variability and paleovegetation pattern during the past ~75 ka and present a longer record of ISM variability in the Indo-Gangetic Plain. The δ18O of carbonate nodules (−7.80‰ to −4.04‰, average −6.01‰) shows a good negative covariation with variability in the model-derived intensity of ISM precipitation, indicating it to be a good proxy for ISM variability. We infer intense ISM precipitation during late marine isotope stage (MIS) 5, early and late MIS 3, and early MIS 1 periods, marked by relatively lower δ18O of carbonates. These changes in the ISM intensity are in-phase with the precession-induced changes in the Northern Hemisphere summer insolation. The δ13CSOM (−27.4‰ to −22.3‰, average −25.3‰) indicates C3-dominated vegetation in the Himalayan catchment with a noticeable increase in δ13CSOM during late MIS 4 to early MIS 3, late MIS 3, and early MIS 1 periods, which are characterized by the intense ISM (warm and moist condition). This suggests an increased abundance of C4 plants during the periods of intensified ISM precipitation. The δ13C of carbonate nodules varies from −3.63‰ to 1.56‰ (average −1.55‰). The enrichment in 13C of the carbonates is most likely due to the influence of dissolved inorganic carbon derived from the Himalayan source and/or atmospheric CO2 on the carbonate nodules. Therefore, we suggest that the δ13C of carbonate nodules from the Indo-Gangetic Plain requires a careful evaluation.

Capital structure, business model innovation, and firm performance: Evidence from Chinese listed corporate based on system GMM model.

This paper aims to verify the impact of capital structure on business model innovation and firm performance and the mediating effect of business model innovation. We use the data of the Chinese growth enterprises market (GME) listed high-tech firms from 2016 to 2022 as a dynamic panel data model with the system-generalized method of moments (sys-GMM), adopting return on asset and earning per share as firm performance. Our results show that capital structure has a lag effect on firm performance. The total debt ratio in the last period has a significant non-linear impact on the performance and business model innovation level of nowadays, presenting a U-shaped relationship. The first-order lag short-term debt ratio positively improves firm performance. Business model innovation significantly promotes better firm performance, and business model innovation does exist in the mediating effect between enterprise capital structure and its performance. These results remain robust to different sample sizes or proxy variables. This paper proposes some suggestions for firm operations and government policies based on the findings.

The impact of overconfidence on stock market valuation: An empirical study on listed firms

Behavioral theory predicts that investor overconfidence leads to overpricing because overconfident investors overestimate the accuracy and quality of their information while underestimating risk (Adebambo & Yan, 2018). The paper investigates the impact of the overconfidence psychology of investors on firm valuation in the Vietnamese stock market. To test this relationship, the secondary data of 264 non-financial firms listed on the Ho Chi Minh Stock Exchange (HOSE) is investigated in the period of seven years from 2016–2022. The current study applies three different regression models, in which two alternative models are used to ensure the results are not sensitive to the variable proxy. This research employs the change in trading volume (CTV) variable as a main variable proxy of overconfidence and applies the feasible generalized least squares (FGLS) method as the main estimation. As a result, this study confirms the positive impact of overconfidence bias on firm valuation on the HOSE, regarding different investor overconfidence proxies since all of the proxies are statistically significant. These empirical results have several implications for market regulators, investors, and academic researchers as well as for investment costs, capital allocation, and market effectiveness.

Changes in Macroeconomic Variables and Financial Performance of Nigerian Deposit Money Banks

This study examined how changes in macroeconomic variables impact financial performance of Nigerian banks from 2010 till 2021 using panel data and estimated utilising panel regression analysis and descriptive analysis. The multiple regression model used Real GDP Growth (RGDPG), lagged Real GDP Growth (RGDPG (-1), Inflation (INF), Exchange Rate (EXR), Interest Rate (PLR), Money Supply Growth (GMS), and Liquidity Ratio (LIQ) as proxies of macroeconomic variables and as independent variables while financial performance, captured by both asset and equity returns (ROA) and (ROE) were dependent variables. With the exception of INF plus RGDPG, the panel regression analysis results showed that changes in most of the macroeconomic variables have negative and significant impacts on the financial performance of Nigerian banks. RoA and RoE are negatively and negligibly impacted by real GDP, whereas both RoA and RoE are positively and negligibly impacted by inflation. The research recommended that when creating and putting into practice strategic plans, Nigerian banks should consider how macroeconomic conditions may affect their operations and financial performance. Furthermore, banks are encouraged to be involved in the development of any policies that may affect their operations.

Effect of governance on investment: Evidence from Sub-Sahara Africa

<abstract> <p>In this study, we investigated the impact of institutional quality on private and public investments in sub-Saharan Africa (SSA) using a sample of forty-one (41) countries between 2002–2021 using a system GMM. Using seven proxies of institutional variables, the results showed that all the institutional proxies had a positive and significant effect on private investment and a negative effect on public investment. We argued that higher institutional quality reduced the costs of doing business, increased investor confidence in the economy, and that property rights enforcement and the legal system increased the attractiveness of private investment in the region. Conversely, for public investment, we argued that better institutional quality exposed the inefficiency of the sector as they were mostly used for rent-seeking and perpetrate corruption, and as such reduced public investment. The results were also robust to different measures of institutions and governance indicators. We recommended that SSA governments improve project oversight and accountability, reduce corruption, promote transparency and citizen participation, strengthen anti-corruption measures, and invest in infrastructure through public-private partnerships in order to improve efficiency in the public sector. Also, governments must ensure lower lending rates, improve the efficiency of credit markets, and ensure better institutional indicators in order to increase private investment.</p> </abstract>

Evaluating patent technology transfer potential : A predictive model based on citation network analysis

Purpose: The citing of patents is an indicator of the transfer of technical knowledge and the influence of one technology on subsequent developments. The objective of this paper is to construct a predictive model based on patent and paper citation network to evaluate the potential for technology transfer of patents Methodology: This paper employs a binary classification algorithm from machine learning, specifically the decision tree algorithm, as its methodology. This has facilitated the creation of a predictive index system comprising 16 structured indicators across three dimensions: the degree of technological innovation, the quality of patent document, and the extent of intellectual property rights (IPR) protection. In order to construct the training sample set, patent transfer data for graphene sensors were employed as proxy variables for technology transfer potential. Findings: The findings of the study indicate that the dynamic characteristics of the citation network are a significant factor in the assessment of the potential for technology transfer. The most significant indicator affecting the transformation value of patents is inward degree centrality. Moreover, the results demonstrate that additional evaluation indicators, namely outward proximity centrality, family size, and selfcitation frequency, are also of significance. The patent technology transfer potential prediction model presented in this paper, which is based on citation network indicators and a decision tree algorithm, exhibits a high degree of predictive accuracy. Value: The value of this research lies in its novel perspective and method for patent valuation and technology transfer strategies. Furthermore, it offers a more comprehensive reflection of the inheritance and flow of patented technology by integrating the citation relationships between patents and scholarly papers. This makes it a valuable contribution to the understanding and promotion of the technology transfer process.

Non Financial Perspectives and Firms’ Value of Deposit Money Banks in Nigeria

The purpose of this study is to examine the effect of non-financial perspective of balanced scorecards and firm value of deposit money bank in Nigeria. Secondary data from respective annual reports of selected deposit money banks in Nigeria for the period of ten years spanning from 2010-2020 were used for analysis while descriptive statistics and panels were employed for data analysis. Three hypotheses were formulated to guide the study. One regression model was examined in order to test the hypotheses included in the research framework of the study. Firm‘s value is the dependent variable of the study and is measured by Tobin‘s Q and non financial perspective is the independent variable proxy with learning and growth (human capital), internal business process (operational efficiency), and Customer satisfaction (turnover). The results show that learning and growth (human capital) and internal business processes are positive and significantly associated with Tobin‘s Q while Customer satisfaction (turnover) is not significantly associated with Tobin‘s Q. Therefore, the study recommended that banks should seriously involve their staff in research and development programme for them to be current with the technology advancement which ultimately improves the worth of the banks.

Exacerbation or suppression? Digital transformation and shadow banking activities of non-financial firms

Using an unbalanced panel dataset from Chinese non-financial firms spanning 2008–2021, this research explores the relationship between digital transformation and shadow banking. Contrary to expectations, the evidence indicates that digital transformation accelerates shadow banking rather than suppressing it. A result resilient to potential endogeneity, proxy variables, and outlier exclusion. Furthermore, shadow banking exacerbates operational risk, a pattern particularly prominent among firms with underdeveloped digital capabilities, intimating that digital maturity may alleviate the detrimental impacts. This study elucidates the economic repercussions of shadow banking in the digital transformation epoch, yielding valuable insights.

Effect of Capital Structure on Shareholder Value: Evidence from Panel ARDL Approach

This study examines the effect of capital structure on the shareholders’ value in Indian pharmaceutical industry collecting data over a period from 2001 to 2020. Further, the study also investigates the long-run and short-run relationship between capital structure and shareholder value. This study employs pooled, fixed, random effect regression models and panel autoregressive distributed lag analysis. The shareholder value of Indian firms is measured by earnings per share (EPS) and market value added (MVA) while the debt-equity ratio, interest coverage ratio, and total liabilities to total assets are the proxy variables for capital structure. The study reveals that capital structure influences shareholder value significantly. The findings also explored the existence of a long-run relationship between capital structure and shareholder value.

Research on the Influence of Confucian Culture on Enterprise Agency Conflicts

With Chinese listed companies from 2008 to 2019 as research samples, this paper takes the number of Confucian academies within a specific radius of the enterprise as the proxy variable of Confucian culture, which found that Confucian culture is negatively correlated with enterprise agency conflicts and can reduce enterprise agency conflicts. Furthermore, the relief effect of Confucian culture on agency conflict is more apparent in private enterprises and areas with high legalization and more openness to the outside world. The conclusion of this paper further enriches the research on the influence of informal institutions on corporate governance, which also provides some reference for regulators when formulating relevant institutions, such as forming an effective mechanism from the perspective of culture, so as to promote the enterprises by cultural restrains. During the promulgation and implementation of legal systems, we should take full advantage of the guiding role of traditional culture and combine formal systems with informal ones, thus leveraging their strengths to promote the healthy development of the market.

Environmental regulation tools and corporate environmental information disclosure: Evidence from a-share-listed companies in Shenzhen and Shanghai, China

Environmental accounting is not only a more comprehensive and effective accounting model but also a more beneficial monitoring model for ecological protection and social development. Environmental accounting enables companies to develop economic benefits while protecting and managing the environment and to assume corresponding social responsibilities and obligations. Based on the importance of environmental regulation, this study attempts to evaluate the role of environmental regulatory tools in corporate environmental information disclosure using the Environmental Information Disclosure Index (EIDI) as a proxy variable. The regression analysis conducted on a panel dataset encompassing all A-share listed companies in the Shanghai and Shenzhen stock markets from 2007 to 2018 reveals a significant relationship between environmental regulatory tools and EIDI. Specifically, the Control command type, Public Participation Type, and Voluntary action-oriented Type have a positive and significant influence on corporate environmental information disclosure, whereas the Market Incentive Type has a negative and significant influence. This evidence suggests that the reasonable use of environmental regulatory tools is an important means of improving the quality of corporate environmental information disclosure.

Gut Microbiota Uniqueness Is Associated with Lake Size, a Proxy for Diet Diversity, in Stickleback Fish.

AbstractOrganismal divergence can be driven by differential resource use and adaptation to different trophic niches. Variation in diet is a major factor shaping the gut microbiota, which is crucial for many aspects of their hosts' biology. However, it remains largely unknown how host diet diversity affects the gut microbiota, and it could be hypothesized that trophic niche width is positively associated with gut microbiota diversity. To test this idea, we sequenced the 16S ribosomal RNA gene from intestinal tissue of 14 threespine stickleback populations from lakes of varying size on Vancouver Island, Canada, that have been shown to differ in trophic niche width. Using lake size as a proxy for trophic ecology, we found evidence for higher gut microbiota uniqueness among individuals from populations with broader trophic niches. While these results suggest that diet diversity might promote gut microbiota diversity, additional work investigating diet and gut microbiota variation of the same host organisms will be necessary. Yet our results motivate the question of how host population diversity (e.g., ecological, morphological, genetic) might interact with the gut microbiota during the adaptation to ecological niches.

Investor Sentiment and The Stock Market

Investor sentiment wields substantial influence within the stock market, reflecting emotional states and beliefs held by investors, encompassing optimism, pessimism, and fear. As attention to its impact surges, the interplay of sentiment, trading decisions, stock prices, and trading volumes gains prominence. This paper examines investor sentiment's mechanisms, exploring its distinct forms and transmission channels, and highlights its critical role in shaping market expectations. Optimistic sentiment fosters a belief in market upswings, potentially leading to over investment and market bubbles. Conversely, pessimistic sentiment drives market pessimism, leading to panic selling and steep market declines. Fear sentiment triggers market volatility, amplifying fluctuations during significant events. The diffusion of investor sentiment through media channels and its interplay with market expectations is explored. Insights into investor sentiment's integration with stock market returns, classification methods, and proxy variables are showcased. Domestic research innovation is underscored, combining foreign insights with China's unique context. However, challenges remain, owing to sentiment's subjectivity and market complexities. Future research avenues are identified: understanding sentiment drivers, enriching theoretical frameworks, and leveraging technology for predictive accuracy. Psychological theories, behavioral finance, and AI-driven analytics offer promising directions. Interdisciplinary collaboration is advocated to unveil the intricate relationships between investor sentiment, market fundamentals, and external factors, enabling more informed decision-making and refined risk management. Investor sentiment's significance is undeniable. By unraveling its complexities, academia and practitioners can fortify their ability to predict market volatility and strategize effectively in an ever-evolving financial landscape.

Does Attenuating Endowment Effect Improve Efficiency? An Overview of Applications

This article explores the impact of the endowment effect on market efficiency, with a specific focus on attenuating this effect to improve overall efficiency. The analysis is grounded in a classical three-dimensional theoretical framework encompassing willingness to pay (WTP), willingness to accept (WTA), and loss aversion. The article discusses strategies to narrow the gap between WTA and WTP and reduce loss aversion as means to mitigate the endowment effect and enhance market efficiency. Drawing on literature reviews and case studies, the paper examines three applications of the endowment effect in the context of P2P payment, organizational co-determination, and the U.S. drinking water supply. The findings suggest that attenuating the endowment effect has the potential to improve efficiency, foster cooperation, and provide practical methods for enhancing market outcomes. To improve the future study, this paper provides insight to mitigate the endowment effect by combining closing the WTA-WTP discrepancy and reducing the loss aversion, introducing proxy variables based on specific contexts for empirical study.