Influence Of Variables Research Articles

GROUNDWATER POLLUTION SOURCE USING PRINCIPAL COMPONENT ANALYSIS IN GUELMA PLAIN, NORTHEAST ALGERIA

This study was conducted in the agricultural region of Guelma, located in northeastern Algeria, where groundwater is the main source of water for human consumption, agriculture, and industry. The objective was to characterize groundwater quality and identify potential pollution sources using multivariate statistical methods, including Principal Component Analysis (PCA), correlation matrix, and the Piper diagram. The analyses revealed strong correlations between certain ions, such as sodium and chloride, suggesting carbonate dissolution processes, such as calcite and dolomite, leading to increased water hardness. The Piper diagram allowed for the classification of water types based on the relative concentrations of major cations (Ca²⁺, Mg²⁺, Na⁺+K⁺) and anions (Cl⁻, SO₄²⁻, CO₃²⁻+HCO₃⁻), showing a predominance of mixed hydrochemical types influenced by both natural and anthropogenic processes.PCA then simplified the interpretation by identifying the most influential variables that could serve as key indicators for the continuous monitoring of water quality. Overall, the results indicate that groundwater chemistry in this region is strongly influenced by human activities and local geological conditions, highlighting the need for sustainable management and continuous monitoring to protect this vital resource.

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H10-25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na+. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca+2, Mg+2, and Na+ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Analisis Faktor-Faktor Yang Mempengaruhi Intergritas Laporan Keuangan Pada Perusahaan Manufaktur Yang Terdaftar Di Bursa Efek Indonesia

Publication of financial reports as accounting information products produced by the company, cannot be separated from the process of its preparation. The purpose of this study is to find empirical evidence of the influence of the Financial Report Integrity variable on Independent Commissioners, Institutional Ownership, Financial Distress, KAP Reputation and Auditor Industry Specialization. The research method used in this research activity uses the following research characteristics: Mixed Research Method refers to a combination of quantitative and qualitative methods. The population in this study were manufacturing companies for the 2019-2022 period on the Indonesia Stock Exchange. Based on the analysis of variable data, Independent Commissioners partially do not have a significant influence on the integrity of financial reports. Based on data analysis, the institutional ownership variable partially does not have a significant influence on the Integrity of Financial Reports.

Analysis of the Share Price of IDX Sharia Growth 30 Companies Listed on the Indonesia Stock Exchange

This study aims to analyze the effect of Net Profit Margin, Return on Asset and Earning Per Share on the stock price of Idx Sharia Growth 30 companies listed on the Indonesia Stock Exchange (IDX). The approach in this study is to use a quantitative approach and based on its objectives, this type of research is causal, namely research that explains the influence of an independent variable on bound variables. The population of this study is 30 Idx Sharia Growth 30 companies listed on the Indonesia Stock Exchange (IDX) in 2020-2023. The procedure for determining samples using the purposive sampling method is a sampling method that uses certain considerations. Based on the results of the selection, all companies meet the criteria. The independent variables in this study are Net Profit Margin, Return on Asset and Earning Per Share and the dependent variable is the stock price. Data analysis uses multiple linear regression analysis. The results of the analysis show that Net Profit Margin, Return on Asset and Earning Per Share have a positive and significant effect on stock prices. The results of the Coefficient of Determination showed that the Adjusted R-squared value was 63.2% and the remaining 36.8% was influenced by other factors that were not present in this study.

Examining Determinants of Transport-Related Carbon Dioxide Emissions by Novel Super Learner Algorithm

Combating carbon dioxide (CO2) emissions across sectors becomes inevitable due to negative impacts. The transport sector takes place among the most important sectors. Accordingly, the study examines transport-related CO2 (TCO2) emissions in the top four emitting countries (namely, the United States, Canada, Saudi Arabia, & Australia) by considering six explanatory variables, using data from 1990/Q1 to 2020/Q4, and performing an artificial intelligence approach. The outcomes show fresh insights that (i) super learner (SL) algorithm overwhelms other machine-learning algorithms in terms of model performance; (ii) energy intensity has an increasing impact on TCO2 emissions, whereas others (e.g., financial development, income, globalization, oil use, & urbanization) have a mixed impact across countries; (iii) the influential variables have some critical thresholds, where the power of impacts differentiate across these limits. Hence, the SL algorithm presents robust outcomes for TCO2 emissions. Accordingly, a set of policy endeavors for the countries examined are also discussed.

Interaction between modified magnetic nanoparticles and human albumin: Kinetics and isotherm studies and application in protein depletion

Magnetic nanoparticles modified with tetraethyl orthosilicate (Fe3O4@TEOS) and bovine serum albumin (Fe3O4@TEOS@BSA) were evaluated as sorbent in albumin depletion from human serum samples by magnetic dispersive solid phase extraction. Characterization studies were carried out by X-ray diffraction, thermogravimetry, Fourier transform infrared spectroscopy, zeta potential, and scanning electron microscopy. Both nanoparticles also showed high thermal stability and pH-dependent surface charges. The human serum albumin adsorption protocol was optimized using a central composite rotatable design. Nanoparticle mass, pH, and albumin concentration were the most influential variables. Avrami's fractional order and Freundlich isotherm models best fitted the data for human albumin adsorption kinetic and isotherm studies for Fe3O4@TEOS and Fe3O4@TEOS@BSA, and the maximum adsorption capacities were 11.93 and 14.89 mg g−1, respectively. The protein desorption was influenced by the pH of samples and eluent volume. Electrophoresis in a polyacrylamide gel containing sodium dodecyl sulfate showed different patterns of serum protein bands when consecutive depletions were performed. The Fe3O4@TEOS showed greater affinity for HSA and efficiency in depletion. The process was versatile, and the depleted albumin proportion could be controlled by the nanoparticle masses. The proposed method is a powerful sample preparation technique for rapid, reliable, and specific depletion of albumin.

Trace organic contaminants in U.S. national park surface waters: Prevalence and ecological context

Surface water samples were collected from 264 sites across 46 U.S national parks during the period of 2009–2019. The number of sites within each park ranged from 1 to 31 and the number of samples collected within each park ranged from 1 to 201. Samples were analyzed for up to 340 trace organic contaminants (TrOCs), including pharmaceuticals, personal care products, pesticides, and various contaminants indicative of anthropogenic influence (e.g., fragrances, surfactants, flame retardants). A total of 155 TrOCs was detected in at least one sample with concentrations ranging from the reporting level of 10 ng/L (multiple contaminants) to 11,900 ng/L (p-cresol). Except for bisphenol A, DEET, theobromine, and gabapentin, TrOCs were detected in <20% of samples. Despite the relatively low detection frequencies, when TrOCs were detected, concentrations were similar to those reported from other regional or national studies. We compared detected concentrations to bioactivity concentrations and water quality benchmarks, when available, to identify occurrences of elevated concentrations and to estimate the potential for biological effects to aquatic biota. Elevated concentrations of 27 TrOCs, mostly pesticides, were detected throughout the study. To gain insight regarding potential sources, we related watershed characteristics (e.g., land cover, presence of point sources) to the number of TrOCs detected at each site. We found that the presence of wastewater treatment plants and the proportion of the watershed classified as agricultural land were the most influential variables for describing the number of pharmaceuticals and the number of pesticides present, respectively. This study represents the largest-scale study characterizing the presence and magnitude of TrOCs in U.S. national park surface waters, to date. These data provide a baseline that can be used to inform future monitoring within the parks and to assess changes in water quality.

Towards Sustainable Strategies: Uncovering the Impact of Environmental Policies on Innovation Activities of Czech Firms

In recent years, there has been an increased emphasis on sustainable development worldwide, which has prompted governments to adopt environmental policies aimed at mitigating the adverse environmental impacts of economic activities. Among these strategies, the implementation of environmental policies has emerged as a key tool for promoting sustainable development. However, although these policies are created with good intentions, their impact on firms' innovation activities remains a matter of debate and research. The aim of this paper is to examine the impact of environmental policies on the innovation activities of Czech firms, with a particular focus on how these policies shape firms' strategies towards sustainable development. Given the growing emphasis on sustainability and environmental issues, it is necessary to examine how environmental policies can influence firms' overall innovation activities. 412 firms of different sizes were surveyed. And EU emissions trading and energy consumption emerged as the most influential variables with a positive impact.

Attitudes towards people living with HIV/AIDS through the EAPVVS-E: A descriptive analysis in nursing students

BackgroundDespite advances in antiretroviral treatment, stigma towards people with HIV/AIDS continues to exist. Nursing students, as future key players in health care, must advocate for the elimination of stigma through education, empathy and the creation of a supportive environment. This holistic approach is crucial to improving their quality of life and moving towards the eradication of HIV/AIDS. AimTo identify nursing students' attitudes towards people living with HIV or AIDS and their relationship with sociodemographic and academic-cultural variables of the participants. DesignA cross-sectional study was performed. SettingsThe research was carried out in the Nursing Degree of the Faculty of Health Sciences of the Ceuta Campus of the University of Granada. ParticipantsConvenience sampling was used to recruit 284 students of the degree in Nursing. MethodsData were collected voluntarily and on an anonymous basis, using the “Attitudes Towards People Living with HIV/AIDS Scale in Nursing Students”. Bivariate and multivariate analyses were performed. ResultsFactors 1 (Professional Practice), 2 (Social Integration) and 3 (Partner and Family) exhibited mean scores considered to be favourable attitudes (>4 points). In contrast, Factor 4, called Benevolent Stigma, had a mean score of 3.68 ± 0.97. Religious beliefs, academic year, sexual orientation, academic practices and age are considered influential variables in the different factors. ConclusionsNursing students present attitudes with favourable scores. However, these results highlight the importance of influencing the different factors, especially in the second year of the Bachelor's Degree in Nursing in our sample. Thus, an educational intervention in this area would be necessary to reinforce the values of humanised care.

Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach.

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system.

Seasonal Variability and Hydrological Patterns Influence the Long-Term Trends of Nutrient Loads in the River Po

This study investigates the long-term trends (1992–2022) of nitrogen and phosphorus loadings exported by the River Po to the Adriatic Sea, to better analyse how changes in hydrology are affecting the timing and magnitude of river nutrient loads. We used 30 years of monitoring data in order to (a) identify the main temporal patterns and their interactions at a decadal, annual and seasonal scale, (b) estimate precipitation effects on load formation and evaluate whether and to which extent the hydrological regime affects nutrient export across the years and (c) analyse the nutrient export regime at a monthly scale and the main transport dynamic of N and P chemical species (hydrological vs. biogeochemical control). The long-term analysis shows a general decrease of both P and N loadings, but the trends are different between the elements and their chemical species, as well as undergoing different seasonal variations. We found a statistically significant relationships between precipitation and loads, which demonstrates that precipitation patterns drive the exported load at the intra- and interannual time scales considered in this study. Precipitation-induced load trends trigger seasonal changes in nutrient deliveries to the sea, peaking in spring and autumn. The nitrogen decrease is mainly concentrated in the summer dry period, while total phosphorus diminishes mainly in spring and autumn. This mismatch of N and P results in variable molar N:P ratios within the year. The effects of extreme drought and flood events, along with the progressive decrease in the snowmelt contribution to water fluxes, are expected to exacerbate the variability in the N and P loadings, which in turn is expected to perturbate the biodiversity, food webs and trophic state of the Northern Adriatic Sea.

Exploring the influence of flexural rigidity variability of aquatic plants on stem deflection geometry and flow characteristics

The existing assumption of a constant flexural rigidity (EI) for the entire height of aquatic plants in the analysis of flow past flexible vegetation may not translate in to real scenario as EI of a flexible plant stem varies along the height due to variation in tissue structure and cross section. This variation is therefore needed to be effectively quantified for better analysis of flow-vegetation interaction. In this regard, the own-weight cantilever method was used to represent the variability of EI along the stem height of three plants: water lily, water chestnut, and lotus. Analytical models are proposed to predict stem deflection geometry and vertical velocity distribution across channel depth incorporating EI as a function of stem height. Analytical and experimental data, obtained from open-channel flume tests, significantly support the developed models and confirm the assumption of variable flexural rigidity. Further investigations show that the flexural rigidity, transcends being a mere constant numeric value; it profoundly influences various aspects of the fluid-vegetation interaction such as: the extent of deflection geometry, velocity reduction, Reynold stress variation, and penetration length. Therefore, this study is crucial for better understanding of the physics of sediment transport, pollutant mixing, and wetland management and restoration.

Influence of stocking density and interaction variability on disease progression of white spot syndrome virus-infected shrimp under different risk scenarios

White spot syndrome virus (WSSV), the causative agent of white spot disease (WSD), is the most significant viral pathogen affecting farmed shrimp. As the culture model for whiteleg shrimp has evolved into a higher-intensity model, the need to study the influence of stocking density on disease progression in pond culture systems has been reinforced. Therefore, we assessed WSD progression by deriving a direct correlation between seasonal disease outbreak risk, stocking density, and viral shedding rate (WSSV genome copies/L/g) using three different stocking densities: high (210 shrimp/m2), medium (140 shrimp/m2), and low (70 shrimp/m2). In Experiment 1 (three stocking densities at 25 °C) and Experiment 2 (stocking density × temperature conditions), shrimp showed relatively high cumulative mortality within the temperature range of optimal WSSV replication (23–29 °C). Notably, the mortality trend, peak viral loads, and viral shedding of WSSV-infected shrimp occurred earlier under high-density conditions, regardless of temperature. Thereafter, this study established WSD risk scenarios based on water temperature conditions and confirmed a significant difference in mortality trends between vulnerable and less vulnerable water temperature scenarios under high- and low-density conditions. The vulnerable water temperature scenario included constant (25 °C), shifting-up (20 °C to 30 °C), and shifting-down (30 °C to 20 °C) conditions, whereas the less vulnerable water temperature scenario included constant 20 °C and 30 °C conditions. Based on mortality trends, the WSD risk scenarios revealed that the times to 50 % mortality for shrimp at high, medium, and low densities were 4, 5, and 6 days post-infection (dpi) in the vulnerable water temperature scenario, and 6, 7, and 9 dpi in the less vulnerable water temperature scenario. Viral shedding variability was evaluated based on the correlation between viral loads and viral shedding rate, and linear regression analysis revealed a significant positive correlation between viral shedding rates and viral loads in shrimp, regardless of WSD risk scenarios or stocking density conditions. Notably, a relatively enhanced viral shedding rate was observed at higher stocking densities (from low- to high-density conditions) and under vulnerable water temperature scenarios. This study suggests that increased stocking density and vulnerable water temperature scenarios could induce greater interactions among WSSV-infected shrimp. It also confirmed the potential role of risk scenarios, stocking density, and viral shedding in WSD progression, providing insights into the disease outbreak mechanisms on shrimp farms.

Influence of subgrade spatial variability on strain-alleviating ability of geogrids and rutting life in flexible pavement

This study employs the Random Field Finite Difference Analysis to assess how subgrade spatial variability impacts geogrid reinforcement’s strain-alleviating ability and the reinforced pavement’s rutting life. The geogrid’s abilities to reduce critical strains are evaluated using a strain-alleviating ratio and compared between deterministic and spatially variable scenarios. The analysis involves six geogrid reinforcement arrangements, considering two kinds of geogrid stiffness (G1 and G2) and three typical positions: top (L1), mid-depth (L1-2) and bottom (L2) of the base course. Key findings include: (a) Subgrade spatial variability significantly amplifies mean critical strains and leads to irregular strain and stress distributions, which in turn impacts the strain-alleviating ability of the geogrid reinforcements and potentially changes the optimal geogrid position. (b) The impacts of subgrade spatial variability on the geogrids’ strain-alleviating ability vary with the type of critical strains, the geogrid position, and the coefficient of variation and scale of fluctuation of subgrade modulus. When the geogrid is located at L2 (G_L2), its ability to alleviate critical subgrade strain is significantly compromised. (c) The adverse effect of subgrade spatial variability on the rutting life of G_L2 reinforced pavement is significant and can be mitigated by homogenising a very thin sublayer at the subgrade surface.

Data-driven shear capacity analysis of headed stud in steel-UHPC composite structures

This study employs machine learning (ML) techniques for shear capacity analysis of headed stud in steel-UHPC composite structure. 194 experimental and numerical results of push-out tests are collected and serve as the dataset for ML models training and testing. Six ML algorithms are implemented to train ML models. Comparison analysis is conducted to compare the performance of three empirical formulae and ML models. The results demonstrate that both the Random Forest and eXtreme Gradient Boosting Trees (XGBoost) models exhibit excellent performance, surpassing an R2 value of 97 % on both training and testing datasets. In contrast, the empirical formulae perform less effectively. Besides, the study incorporates the Shapley additive explanations algorithm to ranking the importance of each feature, and carried out parametric analysis to investigate the correlation between each feature and shear capacity using all samples in the collected dataset. Notably, the most influential variables include diameter and ultimate strength of the headed studs, followed by stud height and thickness of UHPC slab. Cover thickness of UHPC layer and steel fiber volume fractions shows little influence on the shear capacity. Furthermore, based on findings of parametric analysis, design recommendations are provided to avoid shear capacity reduction caused by group effect of studs and UHPC damage. Finally, a user-friendly interactive software is developed and provided to facilitate the shear capacity prediction and design of headed studs in steel-UHPC composite structures.

Climatic controls of fire activity in the red pine forests of eastern North America

Large-scale modes of climate variability influence forest fire activity and may modulate the future patterns of natural disturbances. We studied the effects of long-term changes in climate upon the fire regime in the red pine forests of eastern North America using (a) a network of sites with dendrochronological reconstructions of fire histories over 1700–1900 A.D., (b) reconstructed chronologies of climate indices (1700–1900), and (c) 20th century observational records of climate indices, local surface climate and fire (1950s-2021). We hypothesized that (H1) there are states of atmospheric circulation that are consistently associated with increased fire activity, (H2) these states mark periods of increased climatological fire hazard, and (H3) the observed decline in fire activity in the 20th century is associated with a long-term decline in the frequency of fire-prone states.At the annual scale, years with significantly higher fire activity in the reconstructed and modern fire records were consistently associated with the positive phases of the Pacific North American pattern (PNA), either independently or in combination with the positive phase of the El Niño-Southern Oscillation index (ENSO). During years with both ENSO and PNA in their positive state, the region experienced positive mid-tropospheric heights and temperature anomalies resulting in drought conditions. The fire-prone climate states identified in the reconstructed records became less frequent in 1850 but re-emerged in the 20th century. While our study did not demonstrate a direct influence of climate on the observed decrease in fire activity in the 20th century, it does reveal a clear climate signal embedded within the fire history reconstruction of the region over the past centuries. This study underscores the importance of considering large-scale climatic patterns in understanding historical fire regimes and highlights their role for future fire dynamics in the region and shaping ecological effects of future fires.

INFLUENCE OF ISLAMIC SOCIAL REPORTING (ISR) DISCLOSURE ON THE QUALITY OF PROFITS IN REGISTERED COMPANIES IN JAKARTA ISLAMIC INDEX (JII)

Islamic Social Reporting (ISR) is one way to demonstrate full disclosure in an Islamic context. Earnings quality refers to the extent to which a company's reported earnings reflect its true economic performance. This research aims to determine the influence of the independent variable, namely Islamic Social Reporting, on the dependent variable, namely Profit Quality, and to find out how big it isThe influence of Islamic Social Reporting disclosure on Profit Quality in companies listed on the Jakarta Islamic Index. This research uses research methodsquantitative, associative approach, with field research. The data used in this research is in the form of company annual reports on the Indonesia Stock Exchange for 2019-2021. In collecting data, this research uses two methods, namely count analysis and dummy variables. The analysis technique used is simple linear regression. The results of this research show that the Islamic Social Reporting disclosure score is more than 59%, which means that the disclosures made have reached more than 48 disclosure score items. Meanwhile, the average value of earnings quality reached 2.8388. The results of the Spearman rank correlation test show that there is a fairly strong relationship between the Islamic Social Reporting variable and earnings quality. The results of testing the coefficient of determination show that the ability of the independent variable in this study to influence the dependent variable is only 9.9%. Furthermore, the partial test results show that Islamic social reporting disclosure has a positive and significant effect on earnings quality with a significance value of 0.008 &lt;0.05. By implementing Islamic Social Reporting, companies not only focus on achieving maximum profits, but also pay attention to the social and environmental impacts of their business activities. This can attract the interest of investors, especially Muslim investors, to invest in the company.

Remote sensing analysis of spatiotemporal impacts of anthropogenic influence on mountain landscape ecology in Pir Chinasi national park

Mountain landscapes can be fragmented due to various human activities such as tourism, road construction, urbanization, and agriculture. It can also be due to natural factors such as flash floods, glacial lake outbursts, land sliding, and climate change such as rising temperatures, heavy rains, or drought.The study’s objective was to analyze the mountain landscape ecology of Pir Chinasi National Park under anthropogenic influence and investigate the impact of anthropogenic activities on the vegetation. This study observed spatiotemporal changes in vegetation due to human activities and associated climate change for the past 25 years (1995–2020) around Pir Chinasi National Park, Muzaffrabad, Pakistan. A structured questionnaire was distributed to 200 residents to evaluate their perceptions of land use and its effects on local vegetation. The findings reveal that 60% of respondents perceived spatiotemporal pressure on the park. On the other hand, the Landsat-oriented Normalized Difference Vegetation Index (NDVI) was utilized for the less than 10% cloud-covered images of Landsat 5, 7, and 8 to investigate the vegetation degradation trends of the study area. During the entire study period, the mean maximum NDVI was approximately 0.28 in 1995, whereas the mean minimum NDVI was − 2.8 in 2010. QGIS 3.8.2 was used for the data presentation. The impact of temperature on vegetation was also investigated for the study period and increasing temperature trends were observed. The study found that 10.81% (1469.08 km2) of the area experienced substantial deterioration, while 23.57% (3202.39 km2) experienced minor degradation. The total area of degraded lands was 34.38% (or 4671.47 km2). A marginal improvement in plant cover was observed in 24.88% of the regions, while 9.69% of the regions experienced a major improvement. According to the NDVI-Rainfall relationships, the area was found to be significantly impacted by human pressures and activities (r ≤ 0.50) driving vegetation changes covering 24.67% of the total area (3352.03 km2). The area under the influence of climatic variability and change (r ≥ 0.50 ≥ 0.90) accounted for 55.84% (7587.26 km2), and the area under both climatic and human stressors (r ≥ 0.50 < 0.70) was 64%. Sustainable land management practices of conservation tillage, integrated pest management, and agroforestry help preserve soil health, water quality, and biodiversity while reducing erosion, pollution, and the degradation of natural resources. landscape restoration projects of reforestation, wetland restoration, soil erosion control, and the removal of invasive species are essential to achieve land degradation neutrality at the watershed scale.

Investigation of the time-dependent bearing capacities of concrete structures in different environments considering climate change

The escalating trend of emissions of greenhouse gases, including CO2, prompts significant modifications in climate variables, such as temperature and relative humidity (RH). For concrete civil infrastructures, the carbonation and chlorination corrosion processes are sensitive to changes in climate variables. The focus of this study is to evaluate the time-sensitive deterioration of concrete components in different corrosion environments with consideration of large uncertainties of climate change. Future projections for atmospheric CO2, temperature, and RH until the end of the twenty first century are examined. By implementing an aging corrosion model that incorporates climate change, the study observes that under the SSP5-8.5 emission scenario, the average carbonation depth for the RC structures in the general atmospheric environment increases by 58.7%. Furthermore, the chlorination corrosion rate in marine environments nearly doubles that under aging conditions alone. By 2100, corrosion-affected RC beams in the SSP5-8.5 scenario will lose 9.5 and 29.2% of their flexural capacity, respectively. Moreover, under carbonation and chlorination environments, the beams will face an additional loss of 20.7 and 27.6% in shear capacity, respectively. In performing the above calculations, we have considered the uncertainty prediction of climate change and the uncertainty analysis of the influence of model parameters and material variability.