Important Variables Research Articles

THE EFFECT OF INFLATION, EXPORT AND IMPORT ON ECONOMIC GROWTH IN CENTRAL JAVA


 
 
 
 Economic growth is one of the indicators used to measure the success rate of a region's development. The purpose of this study is to determine the effect of inflation, exports and imports on economic growth in Central Java Province. The type of data used is monthly time series data from January 2017 to December 2021. The number of samples used is 60 samples. The analytical method used is multiple linear regression analysis.The results of the data processing carried out, the t test on the inflation variable shows a positive but not significant relationship to economic growth. This is based on a probability value greater than 0.05, namely 0.0731. For the export variable, the probability value is 0.0186, which means that the export variable has a negative and significant effect on economic growth. As for the import variable, the probability value is 0.0000. So that it can be interpreted that the import variable has a positive and significant influence on economic growth. From the results of this study, it is suggested that the government be able to implement policies to control inflation and apply export restrictions and increase imports while still paying attention to price balance and people's needs.
 
 
 

Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States.

Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47-65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose-response to low (<30 μg/L) concentrations of Li.

Dimension reduction for constructing high-dimensional response distributions by accounting for unimportant and important variables

Probability distributions of responses have been widely used in structural analysis and design because of their complete statistical information. In practice, the dimensionality of input variables could easily reach hundreds or thousands, making it computationally expensive to obtain accurate distributions. In this paper, a generalized most probable point (MPP) method is developed to effectively build the response distributions of high-dimensional problems. First, a global index based on one-iteration MPPs is presented for dimension reduction, which is to divide the input variables into important and unimportant variables. After fixing the unimportant variables at their one-iteration MPP components, the MPP components of the important variables are obtained by performing the inverse first-order reliability method (FORM) in the reduced space. Predictive models of the all MPP components are then established to quickly estimate the MPPs of other cumulative distribution function (CDF) values. To accurately calculate CDF points of limit state functions with different shapes, a comprehensive uncertainty analysis method that accommodates the contributions of the important and unimportant variables is proposed by multiple combinations of FORM, second-order reliability method, and second-order saddlepoint approximation. Finally, the response distributions are generated based on Gaussian mixture distribution and all CDF points. The effectiveness of the proposed method is verified by a mathematical example and two engineering cases.

A Study on the Effectiveness and Efficiency of Public or Private Hospitals within Pakistan

Hospitals play a very important role in human lives. People are in search of quality and timely healthcare facilities. This paper talks about the efficiency and effectiveness of healthcare services. One needs to identify the factors and mechanisms to enhance the performance of government and private hospitals. This study aims to find the important variables that were required to find the best solution for public and private hospitals in Pakistan. The major contributions of the paper are as follows. The paper first deliberates on the need for technical excellence as compared to interpersonal excellence for hospital management. It also performs a qualitative review of the literature on the important variables for determining the effectiveness and efficiencies of the hospital. Then, the paper presents a public survey about the effectiveness and efficiency of public and private hospitals using different questionnaires. These questionnaires were filled by the relatives of patients or patients themselves when they visited hospitals. On this dataset, the paper applies a machine learning algorithm i.e., random forest, to predict which hospital type is suitable for them while considering the variables. These variables include; the services of the hospital, admission process, treatment, doctor's behavior, timely treatment, and knowledge of the staff about SOPs. The data was split into 75 % training and 25 % testing dataset. Python’s Library SK-learn was used for implementation. The accuracy of the classifier on the test dataset is 96.91 %. The paper then determines the variables that are contributing the most to the measure of effectiveness and efficiencies of hospitals. The algorithm also ranks these features that can be used to improve a hospital's performance. It also provides a benchmark to the patients in the selection of hospitals for healthcare facilities.

Anaerobic co-digestion of human excreta, food leftovers and kitchen residue: 1 ternary mixture design, synergistic effects and RSM approach

Anaerobic digestion of multiple substrates can generate more biogas while remaining stable, if positive synergistic effects are achieved. The type of co-digested substrates and the mixing ratio used, are the most important variables as each substrate has unique set of characteristics. Optimizing the volume ratios by testing various substrate mixing ratios is a popular method for determining the best-performing ratio of substrate mixture. The ternary mixture design has reportedly been found to quicken the process of testing different mixing ratios with high accuracy without running several experiments. Therefore, a ternary mixture design and a response surface approach are used in this work to ascertain the relationship between substrate mix and responses (biogas yield, methane yield, and synergy). The findings of the experiment revealed that R9 comprising 78.8 % human excreta, 11.8 % food leftovers and 9.4 % kitchen residue, had the highest methane production of 764.79 mLCH4/gVS and a synergistic index of 3.26. Additionally, the 3D response surface plots from the response surface model showed important and shared interactions between Human Excreta, (HE), Food Leftovers (FLO), and Kitchen Residue (KR). HE and KR had a similar positive synergistic effect on biogas yield, methane yield, and synergy, which was not the case for FLO. The response surface plots showed that the predicted responses (methane yield, biogas yield and synergy) increased with increasing HE and KR fractions and decreased with increasing FLO fractions in the substrate mixtures.

Determinants of the customer participation in financial services delivery through technology

In the post-pandemic era, client preparedness, motivation for technological adoption, and appreciation for technology-enabled financial services all need substantial study and analysis. The study investigates the factors that impact customer participation in financial institutions’ fin-tech offerings. To investigate the factors that influence customers’ engagement in fin-tech services, a well-structured questionnaire was sent to 423 respondents through a suitable sample approach. SPSS-25 and SEM analysis were used to determine the determinants and important variables. The factor analysis found that six variables accounted for 72.04% of the variation. Following SEM regression analysis, it was shown that Trust (β =0.44, P =0.000), User Experience (β =0.31, P =0.000), and the regulatory framework (β =0.29, P =0.000) all had a substantial influence on customer engagement. The findings of the study will aid all strategic planners in developing strategies and policies to increase consumer engagement in the provision of financial services.

Intercellular crosstalk shapes purinergic metabolism and signaling in cancer cells

CD73-derived adenosine suppresses anti-cancer immunity, and CD73 inhibitors are currently evaluated in several clinical trials. Here, we have assessed enzyme kinetics of all key purinergic ectoenzymes in five cancer cell lines (Hodgkin lymphoma, multiple myeloma, pancreas adenocarcinoma, urinary bladder carcinoma, and glioblastoma) under normoxia and hypoxia. We found that adenosine metabolism varied considerably between individual cancer types. All cell lines investigated exhibited high ecto-adenosine deaminase (ADA) activity, which critically influenced the kinetics of adenosine accumulation. Combining kinetics data with single-cell RNA sequencing data on myeloma and glioblastoma cancerous tissue revealed that purine metabolism is not homogeneously organized, but it differs in a cancer type-specific fashion between malignant cells, stromal cells, and immune cells. Since purine metabolism in cancerous tissue is most likely spatially heterogeneous and differs between the various cell types, diffusion distances in the microenvironment as well as ADA activity may be important variables that influence the level of bioactive adenosine.

Factors contributing to the burnout of the faculties of a medical university in Iran: A cross-sectional study.

Faculty members confront a variety of obstacles over time, the most recent of which is the coronavirus disease 2019 pandemic, which may increase their vulnerability to burnout (BO). This study aims to examine BO in medical school faculties, as well as the factors that lead to BO and well-being in them. This cross-sectional study was conducted in 2021 using online questionnaires completed by 222 faculty members of a medical university in Iran. The Maslach Burnout Inventory-Human Services Survey (MBI-HSS) and the Well-being index (WBI) were used. Additionally, we gathered individual-level profiles (demographic, well-being) and occupational information (job profile, attitude toward work). A total of 60 (27%) faculties reported having high BO, and 112 (50.5%) reported having low well-being. Being female (odds ratio, OR=2.69), having time to spend with the family (OR=.26), the intent of turnover (OR=8.65), job recommendation to the offspring (OR=.26), and experiencing violence last year (OR=2.97) were some of the individual-level factors and job-related attitudes associated with a higher BO. In the neural network for BO, the most important variables were the intention of turnover, followed by adequate family time. One third of the responding faculty reported severe BO, and BO was found to be significantly associated with lower well-being. The increased levels of BO and a decreased experience of well-being were both associated with a higher intention of turnover. According to the study, it is important to pay attention to both clinical and nonclinical field faculty members, female faculty members, those who have a high workload, and members who have experienced violence in the workplace. By acknowledging the unique challenges and experiences faced by these individuals, tailored measures can be developed to address their specific concerns and foster a supportive and inclusive environment.

What predicts the best 24-month outcomes following surgery for cervical spondylotic myelopathy? A QOD prospective registry study.

The aim of this study was to identify predictors of the best 24-month improvements in patients undergoing surgery for cervical spondylotic myelopathy (CSM). For this purpose, the authors leveraged a large prospective cohort of surgically treated patients with CSM to identify factors predicting the best outcomes for disability, quality of life, and functional status following surgery. This was a retrospective analysis of prospectively collected data. The Quality Outcomes Database (QOD) CSM dataset (1141 patients) at 14 top enrolling sites was used. Baseline and surgical characteristics were compared for those reporting the top and bottom 20th percentile 24-month Neck Disability Index (NDI), EuroQol-5D (EQ-5D), and modified Japanese Orthopaedic Association (mJOA) change scores. A multivariable logistic model was constructed and included candidate variables reaching p ≤ 0.20 on univariate analyses. Least important variables were removed in a stepwise manner to determine the significant predictors of the best outcomes (top 20th percentile) for 24-month NDI, EQ-5D, and mJOA change. A total of 948 (83.1%) patients with 24-month follow-up were included in this study. For NDI, 204 (17.9%) had the best NDI outcome and 200 (17.5%) had the worst NDI outcome. Factors predicting the best NDI outcomes included symptom duration less than 12 months (OR 1.5, 95% CI 1.1-1.9; p = 0.01); procedure other than posterior fusion (OR 1.5, 95% CI 1.03-2.1; p = 0.03); higher preoperative visual analog scale neck pain score (OR 1.2, 95% CI 1.1-1.3; p < 0.001); and higher baseline NDI (OR 1.06, 95% CI 1.05-1.07; p < 0.001). For EQ-5D, 163 (14.3%) had the best EQ-5D outcome and 169 (14.8%) had the worst EQ-5D outcome. Factors predicting the best EQ-5D outcomes included arm pain-only complaints (compared to neck pain) (OR 1.9, 95% CI 1.3-2.9; p = 0.002) and lower baseline EQ-5D (OR 167.7 per unit lower, 95% CI 85.0-339.4; p < 0.001). For mJOA, 222 (19.5%) had the best mJOA outcome and 238 (20.9%) had the worst mJOA outcome. Factors predicting the best mJOA outcomes included lower BMI (OR 1.03 per unit lower, 95% CI 1.004-1.05; p = 0.02; cutoff value of ≤ 29.5 kg/m2); arm pain-only complaints (compared to neck pain) (OR 1.7, 95% CI 1.1-2.5; p = 0.02); and lower baseline mJOA (OR 1.6 per unit lower, 95% CI 1.5-1.7; p < 0.001). Compared to the worst outcomes for EQ-5D, the best outcomes were associated with patients with arm pain-only complaints. For mJOA, lower BMI and arm pain-only complaints portended the best outcomes. For NDI, those with the best outcomes had shorter symptom durations, higher preoperative neck pain scores, and less often underwent posterior spinal fusions. Given the positive impact of shorter symptom duration on outcomes, these data suggest that early surgery may be beneficial for patients with CSM.

Distribution modelling of the Caucasian endemic Fritillaria latifolia against the background of climate change

Current climate change, habitat degradation, pastoralism, shoot and bulb harvesting pose serious threats to the rare Caucasian endemic Fritillaria latifolia throughout its range. Knowledge of the limiting factors, species range dynamics in relation to climate change and the role of Protected Areas in species distribution are necessary to develop an effective conservation system at present and in the future. This was aimed (1) to determine the most suitable set of abiotic predictors for modelling Fritillaria latifolia localisation, (2) to formalise environmental and anthropogenic factors in species distribution models, (3) to predict the possible changes in the species range in relation to climatic changes, (4) to identify refugia with a consistently high probability of the species occurrence despite climatic changes. We applied Maxent software for species habitat modelling to build current and climatic models of the Fritillaria latifolia distribution, considering the abiotic variables and anthropogenic predictors such as the distance to Protected Areas and grasslands. Distances to anthropogenic infrastructure were calculated with the Path Distance measure considering the horizontal straight-line distance, surface distance and vertical factor. We also formalised the area accessibility (movement factor) through the distance to optimal sites (plots with 0.8 threshold of habitat suitability), where the probability of species occurrence was higher than 0.5. The most important abiotic variables in the species distribution were the Emberger's pluviothermic quotient, with optimal values corresponding to humid and perhumid climates, and the terrain roughness index, with optimal values ranging from nearly level (81–116) to intermediately rugged (162–239) slopes. Distance to Protected Areas (0–1 km) was the third important predictor of the Fritillaria latifolia current distribution, while the distance to grasslands contributed less to the model. The distance of suitable areas from optimal habitats (area accessibility) was 15 km. The species current core ranges are localised in the Western and Central Caucasus, Western and Central Transcaucasia, and the northwestern ridges of the Lesser Caucasus within a network of Protected Areas covering most of the highlands. The optimistic socio-economic pathway SSP1-2.6 predicted a 1.6-fold decrease in the area of species optimal habitats from 2021 to 2100. The pessimistic SSP5-8.5 scenario predicted 122-fold habitat area reduction. According to SSP1-2.6 climatic models, by 2100 the refugia area would be 172.4 km2 in the highlands of the western and central parts of the Greater Caucasus, including the Caucasus State Nature Reserve and Teberda National Park. These areas should be prioritised for the conservation of Fritillaria latifolia populations.

The impact of entrepreneurship factors on organization behavior and learning at Jordan Orange Telecommunication firm

The purpose of this study was to evaluate the impact of important entrepreneurship variables, notably self-concept, work motivation, and risk-taking, on organizational behavior and learning within the setting of Jordan Orange Telecommunication firm. Notably, the research was intended to look at how these factors affect organizational behavior and learning. To evaluate the hypotheses that were proposed in this research, a sample size of 294 people participated in the study, and their data were evaluated and debated. It was shown that an individual's perception of themselves as entrepreneurs, their level of drive at work, and their willingness to take risks all had an impact on developing organizational behavior and organization learning. It became clear that self-concept was the most important component, and that it included aspects such as self-confidence, self-perception, and self-definition. Closely following behind was the concept of work motivation, which included concepts such as self-motivation, extrinsic motivation, the setting of goals, and concentration. Risk-taking was the third key factor, and within this category were subcategories such as risk tolerance and risk perception, as well as expectations surrounding the consequences. The findings showed the possibility for improving organizational behavior and fostering innovation by assisting employees in comprehending the roles they play in their jobs, boosting job motivation, and encouraging a predilection for risk-taking. This can be accomplished by providing employees with training and development opportunities. The study adds fresh insights to a field that merits additional exploration, particularly considering the connection between workload, compensation, and the structural demands that are placed on organizations. The fact that the research concentrated on Jordan Orange Telecommunication company makes it both innovative and of major importance. This is because it can improve both the business's operations as well as the overall quality of the company's goods and services.

Quantitative analysis using an artificial neural network of the most important bio-kinematic variables to determine the effectiveness of the long jump

Quantitative analysis using an artificial neural network of the most important bio-kinematic variables to determine the effectiveness of the long jump

Dielectric Spectroscopy Measurements of Foxtail Millet

Dielectric spectroscopy data measured for Foxtail Millet in powder form with temperature variations are presented. The dielectric behavior is discussed with respect to important variables. A brief review of measurements on these materials and description of the methods are included. Single frequency range for the data at 9. 8 5 GHz using X-band Microwave bench used. Values of the dielectric constant and loss factor with frequency and temperature are shown graphically, and the influences of dipolar relaxation, conductivity, dielectric loss factor, dielectric constant are discussed. The Experimental values and Calculated values from LLB formulae are verified and there is good agreement found.

Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

&lt;abstract&gt; &lt;p&gt;Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.&lt;/p&gt; &lt;/abstract&gt;

Cognitive insight in first-episode psychosis: Exploring the complex relationship between executive functions and social cognition

Social cognition (SC) and executive function (EF) have been described as important variables for social functioning and recovery of patients with psychosis. However, the relationship between SC and EF in first-episode psychosis (FEP) deserves further investigation, especially focusing on gender differences. AimsTo investigate the relationship between EF and different domains of SC in FEP patients and to explore gender differences in the relationship between these domains. MethodsA cross-sectional study of 191 patients with new-onset psychosis recruited from two multicenter clinical trials. A comprehensive cognitive battery was used to assess SC (Hinting Task, Face Test and IPSAQ) and EF (TMT, WSCT, Stroop Test and digit span – WAIS-III). Pearson correlations and linear regression models were performed. ResultsA correlation between Theory of Mind (ToM), Emotional Recognition (ER) and EF was found using the complete sample. Separating the sample by gender showed different association profiles between these variables in women and men. ConclusionsA relationship between different domains of SC and EF is found. Moreover, women and men presented distinct association profiles between EF and SC. These results should be considered in order to improve the treatment of FEP patients and designing personalized interventions by gender.

Government environmental protection subsidies and corporate green innovation: Evidence from Chinese microenterprises

This paper explores the impact of government environmental protection subsidies on corporate green innovation using panel data of listed companies from 2007 to 2019. The results show that such subsidies can significantly promote corporate green innovation, and the results are robust. Financing constraints, research and development (R&D) willingness, and resource allocation efficiency are important variables for government environmental protection subsidies to promote corporate green innovation. Further analysis shows that compared with industrial policies at the provincial level, the key supportive industrial policies at the central level have a more obvious reinforcing effect on government environmental subsidies to promote enterprise green innovation. Furthermore, government environmental subsidies in the eastern, middle, and western regions benefit the promotion of enterprise green innovation, and the promotional effect is stronger in the middle and western regions. Compared with state-owned enterprises, government environmental subsidies have a more obvious promotional effect on promoting green innovation of non-state-owned enterprises. This paper provides strong theoretical inspiration for better playing the positive incentive role of government intervention with the help of government environmental protection subsidies.

Hybrid Electrical Vehicle Design by Using Solar and Battery Sources

This paper of the modelling methods and software used to model EVs with BLDC motors are provided in this research. It examines the value of simulation in enhancing control, energy, and performance techniques for vehicles. This research also examines important variables, scenarios, and difficulties in simulating BLDC motor driven EVs, emphasising their significant contribution to the development of EV technology. The battery is charged using a power converter and a DC/DC converter. In order to charge the battery, it also used regenerative braking. This model enables a thorough examination of battery performance under various operating circumstances, such as temperature changes and charge and discharge cycles. The battery efficiency of this lithium-ion battery is outstanding. An electric vehicle with a solar power source and a totally battery-powered design. We create a comprehensive and adaptable simulation environment that faithfully replicates real-world EV behaviour by integrating the Simulink and Simscape libraries from MATLAB. It not only advances the technology of electric vehicles but also looks into the integration of solar power to increase the sustainability of electric mobility. This research highlights the potential for electric vehicles to be a key contributor to a cleaner and more sustainable transportation future by fusing the advantages of lithiumion batteries, BLDC motors, and solar power.

Accurate storm surge prediction in hurricane area of the Atlantic Ocean using a new multi-recursive neural network based on gate recursive unit

This paper proposes an enhanced storm surge forecasting approach utilizing novel neural networks, specifically a model based on gated recurrent units (GRUs), and incorporates a distortion loss function known as Distortion Loss Including Shape and Time (DILATE) for improved prediction accuracy and reliability. Historical data from four stations in the hurricane-prone region of the Atlantic Ocean are utilized for both training and testing purposes. Various models are employed to predict storm surges with lead times of 3, 6, and 12 hours, and their performance is evaluated by comparing the predicted values with the observed ones. Furthermore, the influence of various physical factors on storm surge formation is examined. The findings indicate that the GRU-DILATE model is more suitable for storm surge prediction, particularly for longer lead times. This model effectively reduces prediction delays and accurately captures the trends in wave height associated with storm surges. Among the physical factors studied, wind speed and atmospheric pressure emerge as the most important variables influencing storm surges. Additionally, the significance of wind direction is highlighted, as it plays a crucial role in determining whether the sea level rises or falls during the initial stages of a storm surge. It is expected that the proposed model has the potential to enhance response and preparedness measures in hurricane-prone areas, finding applications in coastal planning, infrastructure design, disaster management, and other aspects of ocean engineering.

Topographic conditions as governing factors of mire vegetation types analyzed from drone‐based terrain model

AbstractQuestionsPrevious studies have shown that mire vegetation responds to fine‐scale differences in topography; however, very few studies have examined the relationship between multiple topographic conditions and vegetation distribution patterns by using topographic information with a resolution finer than microtopographic patches of sub‐meter scale in mires. Is it possible to predict vegetation distribution patterns in a mire using topographic information derived from drone imagery? How do topographic conditions affect the emergence of vegetation types?Location3 km2 of sloping mire in eastern Hokkaido, northern Japan.MethodsDigital surface models (DSMs) and orthomosaics were created using an affordable drone (Phantom3 advanced) and photogrammetry. We calculated 23 variables reflecting topographic conditions from DSMs. The locations of four vegetation types (Moliniopsis japonica–Eriophorum vaginatum, ME; Moliniopsis japonica–Carex lasiocarpa, MC; Sphagnum fuscum–Ledum palustre, SL; and Carex lyngbyei–Thelypteris palustris, CT) were recorded by field survey and orthomosaics. Random forest models that predicted vegetation types from topographic variables were created and we evaluated the prediction accuracy, importance of variables and effect of variables on the occurrence probability of the vegetation types.ResultsAll predictive indicators (recall, precision, overall accuracy, kappa) were very high (&gt;0.9). Important variables for modeling were altitude, relative height to the surroundings, slopes at a scale of several 10 m, soil wetness index and terrain ruggedness. Each vegetation type appeared under different topographic conditions; altitude was a key factor in ME and MC, and slope in CT. SL showed patchy distribution predictions on hummocks.ConclusionsVegetation distribution in the mire was strongly related to topographic information based on the DSMs. Analysis of the variable effects indicates that the emergence of the vegetation types was explained by a combination of multiple topographic conditions. This approach has the outstanding advantages of obtaining many survey points quickly and retrieving topographic conditions difficult to measure using on‐ground field surveys.

Leaf traits linked to structure and palatability drive plant-insect interactions within three forested ecosystems.

Plant traits and insect herbivory have been highly studied within the modern record but only to a limited extent within the paleontological. Preservation influences what can be measured within the fossil record, but modern methods are also not compatible with paleobotanical methods. To remedy this knowledge gap, a comparable framework was created here using modern and paleobotanical methods, allowing for future comparisons within the fossil record. Insect feeding damage on selected tree species at Harvard Forest, the Smithsonian Environmental Research Center, and La Selva were characterized using the damage type system prevalent within paleobotanical studies and compared with leaf traits. Linear models and random forest analyses tested the influence of leaf traits on total, specialized, gall, and mine frequency and diversity. Structural traits like leaf dry mass per area and palatability traits, including lignin and phosphorus concentrations, are important variables affecting gall and mine damage. The significance and strength of trait-herbivory relationships varied across forest types, which is likely driven by differences in local insect populations. This work addresses the persistent gap between modern and paleoecological studies focusing on the influence of leaf traits on insect herbivory. This is important as modern climate change alters our understanding of plant-insect interactions, providing a need for contextualizing these relationships within evolutionary time. The fossil record provides information on terrestrial response to past climatic events and, thus, should be implemented when considering how to preserve biodiversity under current and future global change.