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Magnetic resonance imaging (MRI) is increasingly used to diagnose focal and diffuse liver disorders. Despite their enhanced efficacy, liver-targeted gadolinium-based contrast agents (GBCAs) raise safety concerns owing to the release of toxic Gd3+ ions. A π-conjugated macrocyclic chelate, Mn-NOTA-NP, was designed and synthesized as a non-gadolinium alternative for liver-specific MRI. Mn-NOTA-NP exhibits an r1 relaxivity of 3.57 mM−1 s−1 in water and 9.01 mM−1 s−1 in saline containing human serum albumin at 3 T, which is significantly greater than the clinically utilized Mn2+-based hepatobiliary drug, Mn-DPDP (1.50 mM−1 s−1), and comparable with that of GBCAs. Furthermore, the in vivo biodistribution and MRI enhancement patterns of Mn-NOTA-NP were similar to those of the Gd3+-based hepatobiliary agent, Gd-DTPA-EOB. Additionally, a 0.05 mmol/kg dose of Mn-NOTA-NP facilitated high-sensitivity tumor detection with tumor signal enhancement in a liver tumor model. Ligand-docking simulations further indicated that Mn-NOTA-NP differed from other hepatobiliary agents in their interactions with several transporter systems. Collectively, we demonstrated that Mn-NOTA-NP could be a new liver-specific MRI contrast agent.

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.

Here, we describe the synthesis, characterization, and in vitro biological evaluation of a series of transition metal complexes containing benzothiazole aniline (BTA). We employed BTA, which is known for its selective anticancer activity, and a salen-type Schiff-based ligand to coordinate several transition metals to achieve selective and synergistic cytotoxicity. The compounds obtained were characterized by NMR spectroscopy, mass spectrometry, Fourier transform infrared spectroscopy, and elemental analysis. The compounds L, MnL, FeL, CoL, and ZnL showed promising in vitro cytotoxicity against cancer cells, and they had a lower IC50 than that of the clinically used cisplatin. In particular, MnL had synergistic cytotoxicity against liver, breast, and colon cancer cells. Moreover, MnL, CoL, and CuL promoted the production of reactive oxygen species in HepG2 tumor cell lines. The lead compound of this series, MnL, remained stable in physiological settings, and docking results showed that it interacted rationally with the minor groove of DNA. Therefore, MnL may serve as a viable alternative to platinum-based chemotherapy.

Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development.

We describe the synthesis, characterization, molecular modeling, and in vitro anticancer activity of three benzothiazole aniline (BTA) ligands and their corresponding platinum (II) complexes. We designed the compounds based on the selective antitumor properties of BTA, along with three types of metallic centers, aiming to take advantage of the distinctive and synergistic activity of the complexes to develop anticancer agents. The compounds were characterized using nuclear magnetic resonance spectrometry, Fourier transform infrared spectroscopy, mass spectrometry, elemental analysis, and tested for antiproliferative activity against multiple normal and cancerous cell lines. L1, L2, and L1Pt had better cytotoxicity in the liver, breast, lung, prostate, kidney, and brain cells than clinically used cisplatin. Especially, L1 and L1Pt demonstrated selective inhibitory activities against liver cancer cells. Therefore, these compounds can be a promising alternative to the present chemotherapy drugs.

Governments and authorities worldwide consider road traffic crashes (RTCs) to be a major concern. These crashes incur losses in terms of productivity, property, and life. For a country to establish its road and action plans, it is crucial to comprehend the reasons for and consequences of traffic collisions. The main objective of this research study was to evaluate and rank the important and supporting factors influencing traffic crashes on the road. To identify the most significant accident causation elements, the proportion-based analytic hierarchy process (PBAHP) was used to order the factors in terms of their relative importance. In this study, the city of Al-Ahsa, located in the eastern province of Saudi Arabia, was used as a case study, since this city is the highest RTC-prone area in the region. PBAHP was used to calculate relative importance/weights for different crash types and reasons in terms of their impact on crash severity. It was found that vehicle-overturned collisions which result in fatal crashes have the most weight, whereas “hit motorcycle” crashes result in serious injury crashes. When vehicles (two or more) collide with one another while they are moving, it appears that the likelihood of a fatality in a collision increases. The highest weights for serious injury crashes came from “driver distraction”, “leaving insufficient safe distance”, and “speeding”, which also generated similar and relatively high weights for fatal crashes. Weights from the PBAHP approach were also used to develop utility functions for predicting the severity of crashes. This approach could assist decision-makers in concentrating on the key elements affecting road traffic crashes and enhancing road safety.

It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization.

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R2) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO2e) and mean absolute percentage error (0.29 GtCO2e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO2 equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.

Identifying sources of air pollutants is essential for informing actions to reduce emissions, exposures, and adverse health impacts. This study updates and extends apportionments of particulate matter (PM2.5) in Detroit, MI, USA, an area with extensive industrial, vehicular, and construction activity interspersed among vulnerable communities. We demonstrate an approach that uses positive matrix factorization models with combined spatially and temporally diverse datasets to assess source contributions, trend seasonal levels, and examine pandemic-related effects. The approach consolidates measurements from 2016 to 2021 collected at three sites. Most PM2.5 was due to mobile sources, secondary sulfate, and secondary nitrate; smaller contributions arose from soil/dust, ferrous and non-ferrous metals, and road salt sources. Several sources varied significantly by season and site. Pandemic-related changes were generally modest. Results of the consolidated models were more consistent with respect to trends and known sources, and the larger sample size should improve representativeness and stability. Compared to earlier apportionments, contributions of secondary sulfate and nitrate were lower, and mobile sources now represent the dominant PM2.5 contributor. We show the growing contribution of mobile sources, the need to update apportionments performed just 5–10 years ago, and that apportionments at a single site may not apply elsewhere in the same urban area, especially for local sources.

Road traffic accidents are still among the top major global causes of death, injury, and disability. Despite this cause for alarm and several preventive initiatives, global road accident statistics are not improving. This study modeled annual road accidents (ARAs) as a function of demographic, economic, passenger movement, freight movement, and road capital investment indicators. The research is based on 22 years of data from more than 36 Organization for Economic Co-operation and Development (OECD) member and partner countries. Artificial neural network (ANN), multiple linear regression (MLR), and Poisson regression (PR) analysis were employed for this purpose. The ANN model outperformed the regression models by far, thus making it possible for reliable new insights and accurate results to be obtained. The ANN’s superior performance was shown to be a result of the non-linear relationship between ARA and some of the predicting variables. The average relative contribution of each variable in describing the ARA models was estimated using connection weight analysis (from the ANN model) and relative weight analysis for the regression model. The profile method was used to perform sensitivity analysis and to establish the partial variation trend of the ARA with each of the variables. The Existing Road Maintenance Investment (ERMI) and New Road Infrastructural Investment (NRII) showed a nonlinear concave-up relationship with ARA for given demography, economy, freight, and passenger movements. A combination of per capita NRII and ERMI corresponding to the minimum ARA exists. These sets of NRII and ERMI were considered safe road investment limits. The ANN-ARA model was utilized to estimate these limits with their relative proportion for diverse combinations of demography, economy, freight level, and passenger movement.

Flexible pavement deterioration due to moisture intrusion and aging is the key concern worldwide for highway engineers. However, this damage has not been properly investigated in detail due to lack of appropriate experimental and modeling techniques. Such lacking hinders the design of long-lasting pavements, as the effects of environmental damages are unknown, especially for modified asphalt. Therefore, the current study aims at determining a better approach for modeling asphalt adhesion damage using Artificial Neural Networks (ANNs). The Atomic Force Microscopy (AFM) test was deployed to determine the adhesion and cohesion forces of asphalt samples with varying contents of polymer and Antistripping Agents (ASAs). Two types of ANN models, namely multilayer perceptions (MLPs) and radial basis function neural network (RBFNN), were used in this effort. Two popular modifications, namely ensemble learning and hierarchical modeling, were also engaged to achieve convenient and accurate damage models. The analysis found that RBFNN was better suited for hierarchical models than MLP. RBFNN is preferred for aged and moisture-damaged samples which have less variation in their datasets. Hierarchical models are convenient to apply as they can be applied to any type of asphalt sample. However, they produced a small reduction in accuracy (less than 10%) as compared to other models. The accuracy of the hierarchical model was found to be satisfactory. The ensemble learning approach showed slight improvement in accuracy for all models ranging between 1–3%, i.e., 6–8 nN. This study recommends the use of hierarchical models, developed with ensemble learning, for prediction of asphalt damage. The results of the study will be helpful for researchers and practitioners working on pavement materials for developing prediction models to prepare a better mix design of polymer modified asphalt.