| 1. |
- Khan, Kifayatullah, et al.
(författare)
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Heavy Metal Occurrence, Pathways, and Associated Socio-ecological Risks in Riverine Water : Application of Geographic Information System, Multivariate Statistics, and Risk Assessment Models
- 2023
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Ingår i: Water, Air and Soil Pollution. - 0049-6979 .- 1573-2932. ; 234:7
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Tidskriftsartikel (refereegranskat)abstract
- Heavy metal (HM) pollution is one of the major issues of concern in the world due to its serious health consequences on humans and ecology. In this study, riverine water from the River Kabul in Pakistan was studied using inductively coupled plasma mass spectrometry (ICP-MS) to determine the variation, routes, and possible socio-ecological hazards of chromium (Cr), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn) cadmium (Cd), mercury (Hg), and lead (Pb). The results revealed significant HMs variation (p < 0.05) in the sequence of Cr > Zn > Ni > Cu > Cd > Pb > Mn > Co > Hg, indicating prevalent metal contaminations in the river. Multivariate statistics showed significant strong positive correlations (p ≤ 0.01) between the individual HMs contents along the monitoring sites. The strong-moderate levels of Cu, Co, Zn, Mn, Pb, and Cd in riverine systems were observed to be caused by surrounding industrial, agrochemicals, mining, and domestic wastewater discharges along with geogenic sources, the weak levels of Cr and Ni could be induced by erosion of mafic and ultramafic rocks, and mining activities, whereas the low contamination of Hg suggests minimal atmospheric deposition with fewer industrial discharges in the environment. The overall mass flux of the ∑HMs was estimated to be around 164.10 kg/year, with significant HM pollution index (HPI) and pollution index (PI) variations along the river characterizing the potential risk of HMs in decreasing order of Cd > Hg > Cr > Ni > Co > Pb > Mn > Cu > Zn and Cd > Hg > Ni > Pb > Cr > Co > Cu > Mn > Zn, respectively. Individual HM contamination was within the ecological risk threshold (ERI < 110), where, the chronic daily intake (CDIs), hazard quotients (HQs), health indices (HIs), and cancer risks (CRs) of Cd, Ni, Co, Cr, and Pb by daily riverine water ingestion and dermal contact posing considerable human health concerns. To protect the environment and public health, our findings suggest that untreated anthropogenic wastewater discharge into the river system be strictly controlled and regulated through public awareness campaigns and legislation prohibiting the use of herbicides and fertilizers containing high levels of Cr, Ni, Co, Cd, and Pb.
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| 2. |
- Abdullah, Gamil M. S., et al.
(författare)
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Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.
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| 3. |
- Sahil, Mehran, et al.
(författare)
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Seismic performance evaluation of exterior reinforced concrete beam-column connections retrofitted with economical perforated steel haunches
- 2024
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Ingår i: Results in Engineering (RINENG). - : Elsevier. - 2590-1230. ; 22
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Tidskriftsartikel (refereegranskat)abstract
- The exterior beam-column joint (BCJ) within reinforced concrete (RC) frame structures is acknowledged as a vulnerable component prone to seismic failure. This article proposes a practical and economical strengthening method for exterior BCJs using a perforated steel haunch system. This method is designed to mitigate damage in BCJs and improve the seismic performance of the structure. Employing finite element modeling (FEM) techniques, the study evaluates the impact of perforated steel haunches on the BCJs’ behavior and performance. The investigation involves creating nine distinct models, each representing a BCJ with a steel haunch system. These models include a control model without any perforations and eight variations with different levels of perforation (ranging from 10% to 50%) within the steel haunch system. Furthermore, the study analyzes the influence of perforation shapes on the connections’ performance, considering square, circular, hexagonal, and triangular shapes. The results reveal that utilizing a steel haunch without perforations significantly increases the load-carrying capacity of a BCJ by about 89%. Additionally, circular or square-shaped perforations, up to 30–35% within the steel haunch, effectively prevent the joints’ failure and promote the ductile behavior. These findings hold the potential to advance the design methodology for RC joints subjected to seismic loads, thereby enhancing the structural resilience in earthquake-prone regions.
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| 4. |
- Iqbal, Sajid, et al.
(författare)
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Essential oils of four wild plants inhibit the blood seeking behaviour of female Aedes aegytpi
- 2023
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Ingår i: Experimental parasitology. - : Elsevier BV. - 0014-4894 .- 1090-2449. ; 244
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Tidskriftsartikel (refereegranskat)abstract
- Aedes aegypti (Diptera: Culicidae) mosquito is an important vector of many disease-causing pathogens. An effective way to escape from these mosquito-borne diseases is to prevent mosquito bites. In the current study, essential oils of Lepidium pinnatifidum, Mentha longifolia, Origanum vulgare, and Agrimonia eupatoria were evaluated for their repellent potential against Ae. aegypti females. Essential oils were extracted using steam distillation from freshly collected aerial parts of the plants and tested against 4–5 day old females of Ae. aegypti through the human bait technique for repellency and repellent longevity assays. The chemical composition of extracted essential oils was explored by gas chromatography coupled with mass spectrometry (GC-MS). The essential oils of L. pinnatifidum, M. longifolia, O. vulgare, and A. eupatoria at a dose of 33 μg/cm2 showed 100%, 94%, 87%, and 83% mosquito repellent activity, respectively. Furthermore, M. longifolia and O. vulgare essential oils exhibited 100% repellency at a dose of 165 μg/cm2, whereas A. eupatoria essential oil showed 100% repellency only at 330 μg/cm2. In the time-span bioassay, M. longifolia and O. vulgare essential oils showed protection against Ae. aegypti bites for 90 and 75 min, respectively whereas both A. eupatoria and L. pinnatifidum were found active for 45 min. Phenylacetonitrile (94%), piperitone oxide (34%), carvacrol (20%) and α-pinene (62%) were the most abundant compounds in L. pinnatifidum, M. longifolia, O. vulgare and A. eupatoria essential oils, respectively. The current study demonstrates that M. longifolia and O. vulgare essential oils possess the potential to be used as an alternative to synthetic chemicals to protect humans from mosquito bites.
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| 5. |
- Javed, Fawad, et al.
(författare)
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"Testosterone decreases temporomandibular joint nociception"- A systematic review of studies on animal models.
- 2022
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Ingår i: Archives of Oral Biology. - : Elsevier. - 0003-9969 .- 1879-1506. ; 139
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Forskningsöversikt (refereegranskat)abstract
- OBJECTIVE: The aim of the present systematic review was to assess the effect of testosterone on temporomandibular joint (TMJ) nociception.DESIGN: A systematic review of pertinent indexed literature was performed. The focused question addressed was "Is there a connection between testosterone and TMJ nociception?" Original studies were included. In-vitro and ex-vivo studies, case-reports/series, letters to the Editor and commentaries were not sought. Indexed databases were searched without time and language restrictions up to and including September 2021 using different free text key words: testosterone OR "male sex hormones" OR "gonadal hormones" AND "temporomandibular joint" OR "temporomandibular dysfunction" AND nociception AND males. The literature search was performed in accordance with the preferred reporting outcomes of systematic reviews and meta-analysis guidelines. The risk of bias (RoB) was assessed using the SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) tool.RESULTS: Out of the 406 studies identified, seven studies on animal-models were included. All studies were performed in rats with age and weight ranging between 21 and 90 days and 200-300 g, respectively. Testosterone was administered in concentrations ranging between 1 and 10 mg/Kg. Results from all studies showed that testosterone administration in gonadectomized male rats reduces induced TMJ nociception. The RoB was high in 3 and unclear in 4 studies.CONCLUSION: Testosterone offers protection against TMJ nociception in male rats; however, from a clinical perspective, potential contribution of testosterone therapy towards the management of TMD remains indeterminate.
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| 6. |
- Javed, Muhammad Faisal, et al.
(författare)
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Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.
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| 7. |
- Javed, Muhammad Faisal, et al.
(författare)
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Forecasting the strength of preplaced aggregate concrete using interpretable machine learning approaches
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R2) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.
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| 8. |
- Khan, Adil, et al.
(författare)
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Predictive modeling for depth of wear of concrete modified with fly ash : A comparative analysis of genetic programming-based algorithms
- 2024
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Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.
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| 9. |
- Ahmad, Fawad, et al.
(författare)
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Synthesis of New Naphthyl Aceto Hydrazone-Based Metal Complexes : Micellar Interactions, DNA Binding, Antimicrobial, and Cancer Inhibition Studies
- 2021
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Ingår i: Molecules. - : MDPI AG. - 1431-5157 .- 1420-3049. ; 26:4
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Tidskriftsartikel (refereegranskat)abstract
- In the present study, naphthyl acetohydrazide (HL) ligand was prepared and used for the synthesis of new six amorphous transition metal (Co(II), Ni(II), Cu(II), Zn(II), Pb(II), Cd(II)) complexes. All the compounds were characterized by elemental analysis, UV-vis, FT-IR, 1H- and 13C-NMR, and Matrix-Assisted Laser Desorption Ionization (MALDI). The solubilization study was carried out by estimating the interaction between the metal complexes with surfactants viz. sodium stearate (SS) and Cetyltrimethylammonium bromide (CTAB). UV-Visible spectroscopy was employed to determine partitioning and binding parameters, whereas electrical conductivity measurements were employed to estimate critical micellar concentration (CMC), the extent of dissociation, and free energy of micellization. The CT-DNA interaction of synthesized compounds with DNA represents the major groove binding. The synthesized ligand and metal complexes were also tested against bacterial and fungal strains and it has been observed that Cu(II) complex is active against all the strains except Candida albicans, while Cd(II) complex is active against all bacterial and fungal strains except Pseudomonas. Among all compounds, only the Pd(II) complex shows reasonable activity against cervical cancer HeLa cell lines, representing 97% inhibition.
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| 10. |
- Ahmad, Muhammad Ovais, Associate Professor/Lektor, et al.
(författare)
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Early career software developers and work preferences in software engineering
- 2024
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Ingår i: Journal of Software. - : John Wiley & Sons. - 2047-7473 .- 2047-7481. ; 36:2
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Tidskriftsartikel (refereegranskat)abstract
- Context: The software engineering researchers and practitioners echoed the needfor investigations to better understand the engineers developing software andservices. In light of current studies, there are significant associations between thepersonalities of software engineers and their work preferences. However, limitedstudies are using psychometric measurements in software engineering.Objective: We aim to evaluate attitudes of early-stage software engineers andinvestigate link between their personalities and work preferences.Method: We collected extensive psychometric data from 303 graduate-levelstudents in Computer Science programs at four Pakistani and one Swedish universityusing Five-Factor Model. The statistical analysis investigated associations betweenvarious personality traits and work preferences.Results: The data support the existence of two clusters of software engineers, one ofwhich is more highly rated across the board. Numerous correlations exist betweenpersonality qualities and the preferred types of employment for software developers.For instance, those who exhibit greater levels of emotional stability, agreeableness,extroversion, and conscientiousness like working on technical activities on a settimetable. Similar relationships between personalities and occupational choices arealso evident in the earlier studies. More neuroticism is reported in femalerespondents than in male respondents. Higher intelligence was demonstrated bythose who worked on the“entire development process”and“technical componentsof the project.”Conclusion: When assigning project tasks to software engineers, managers might usethe statistically significant relationships that emerged from the analysis of personalityattributes. It would be beneficial to construct effective teams by taking personalityfactors like extraversion and agreeableness into consideration. The study techniquesand analytical tools we use may identify subtle relationships and reflect distinctionsacross various groups and populations, making them valuable resources for bothfuture academic research and industrial practice.
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