Enhancing Water Purification by Integrating Titanium Dioxide Nanotubes into Polyethersulfone Membranes for Improved Hydrophilicity and Anti-Fouling Performance.

Water pollution remains a critical concern, one necessitated by rapidly increasing industrialization and urbanization. Among the various strategies for water purification, membrane technology stands out, with polyethersulfone (PES) often being the material of choice due to its robust mechanical properties, thermal stability, and chemical resistance. However, PES-based membranes tend to exhibit low hydrophilicity, leading to reduced flux and poor anti-fouling performance. This study addresses these limitations by incorporating titanium dioxide nanotubes (TiO2NTs) into PES nanofiltration membranes to enhance their hydrophilic properties. The TiO2NTs, characterized through FTIR, XRD, BET, and SEM, were embedded in PES at varying concentrations using a non-solvent induced phase inversion (NIPS) method. The fabricated mixed matrix membranes (MMMs) were subjected to testing for water permeability and solute rejection capabilities. Remarkably, membranes with a 1 wt% TiO2NT loading displayed a significant increase in pure water flux, from 36 to 72 L m2 h-1 bar-1, a 300-fold increase in selectivity compared to the pristine sample, and a dye rejection of 99%. Furthermore, long-term stability tests showed only a slight reduction in permeate flux over a time of 36 h, while dye removal efficiency was maintained, thus confirming the membrane's stability. Anti-fouling tests revealed a 93% flux recovery ratio, indicating excellent resistance to fouling. These results suggest that the inclusion of TiO2 NTs offers a promising avenue for the development of efficient and stable anti-fouling PES-based membranes for water purification.

Impact of perceived social support and psychological capital on university students' academic success: testing the role of academic adjustment as a moderator.

BACKGROUND: Academic adjustment is a significant predictor of the academic success of students. The aim of this study is to examine how academic adjustment plays an important role as a moderator in perceived social support, psychological capital, and success outcome relationships among university students. METHODS: Three hundred seventy-three valid questionnaires were collected from different departments of different universities using convenience sampling method. Smart PLS 3.0 was used for data analysis. RESULTS: The study results indicated that perceived social support and psychological capital have a significant direct impact on academic adjustment and academic success. The results of the study also demonstrated that the relationships between perceived social support, psychological capital, and successful outcomes are partially and moderated by academic adjustment. CONCLUSION: This research develops a predictive model for examining students' academic adjustment to university and the outcomes of success based on social capital theory and conservation of resources theory. The current study suggests that it is necessary for policymakers to make full use of their ability to enable students to adjust to university life effectively. Higher education institutions should therefore pay full attention to the development of students' academic skills that contribute to academic success.

Impact of academic cheating and perceived online learning effectiveness on academic performance during the COVID-19 pandemic among Pakistani students.

Objective: The study was conducted to examine academic cheating behaviors and perceived online effectiveness on academic performance during the period of COVID-19 among schools, colleges, and university students in Pakistan. Methodology: A cross-sectional research design was used in the current study. Convenience sampling was used to collect the data. The study included a total sample of N = 8,590 students, with males (n = 3,270, 38%) and females (n = 5,320, 61%) participating. The data was divided into three categories: high schools (n = 1,098, 12.7%), colleges (n = 4,742, 55.2%), and universities (n = 2,570, 32.1%). School students had an average age of (M = 15, SD = 4.65), college students had an average age of (M = 20, SD = 5.64), and university students had an average age of (M = 24, SD = 5.01). Result: The results indicated that 60% of students admitted to cheating during online exams most of the time; 30% of students admitted to cheating at least once during an online exam. The study found that students (from high school, college, and university) obtained higher grades in online exams as compared to physical exams. Furthermore, significant gender differences were found on the scales of online learning effectiveness in school, college, and university students (t = 2.3*, p = 0.05 vs. t = 4.32**, p = 0.000 vs. t = -3.3*, p = 0.04). Similarly, on the scale of academic performance, students have significant gender differences. Multivariate regression analysis confirms that students' 26% academic performance was increased due to cheating (F (2, 8,588) = 16.24, p = 0.000). Students believe online learning is effective because academic grades are easily obtained. Conclusion: Cheating is more common and easier in online courses, according to more than half of respondents, and they take advantage of this. Academicians are heavily encouraged to develop morality and ethics in their students so that their institutions can produce ethical professionals for the educational community.

Synthesis of Pillar[5]arene- and Phosphazene-Linked Porous Organic Polymers for Highly Efficient Adsorption of Uranium.

It is crucial to design efficient adsorbents for uranium from natural seawater with wide adaptability, effectiveness, and environmental safety. Porous organic polymers (POPs) provide superb tunable porosity and stability among developed porous materials. In this work, two new POPs, i.e., HCCP-P5-1 and HCCP-P5-2 were rationally designed and constructed by linked with macrocyclic pillar[5]arene as the monomer and hexachlorophosphate as the core via a macrocycle-to-framework strategy. Both pillar[5]arene-containing POPs exhibited high uranium adsorption capacity compared with previously reported macrocycle-free counterparts. The isothermal adsorption curves and kinetic studies showed that the adsorption of POPs on uranium was consistent with the Langmuir model and the pseudo-second-order kinetic model. Especially, HCCP-P5-1 has reached 537.81 mg/g, which is greater than most POPs that have been reported. Meanwhile, the comparison between both HCCP-P5-1 and HCCP-P5-2 can illustrate that the adsorption capacity and stability could be adjusted by the monomer ratio. This work provides a new idea for the design and construction of uranium adsorbents from macrocycle-derived POPs.

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is challenging. For this, a new fundus image segmentation framework is proposed by combining deep neural networks, and hidden Markov model. It has three main modules: the Atrous spatial pyramid pooling-based encoder, the decoder, and hidden Markov model vessel tracker. The encoder utilized modified ResNet18 deep neural networks model for low-and-high-levels features extraction. These features are concatenated in module-II by the decoder to perform convolution operations to obtain the initial segmentation. Previous modules detected the main vessel structure and overlooked some small capillaries. For improved segmentation, hidden Markov model vessel tracker is integrated with module-I and-II to detect overlooked small capillaries of the vessels network. In last module, final segmentation is obtained by combining multi-oriented sub-images using logical OR operation. This novel framework is validated experimentally using two standard DRIVE and STARE datasets. The developed model offers high average values of accuracy, area under the curve, and sensitivity of 99.8, 99.0, and 98.2%, respectively. Analysis of the results revealed that the developed approach offered enhanced performance in terms of sensitivity 18%, accuracy 3%, and specificity 1% over the state-of-the-art approaches. Owing to better learning and generalization capability, the developed approach tracked blood vessels network efficiently and automatically compared to other approaches. The proposed approach can be helpful for human eye assessment, disease diagnosis, and rehabilitation process.

Development of multivariate classification models for the diagnosis of dengue virus infection.

The dengue virus (DENV) infection is a worldwide cause of serious illness and death. Early and efficient prediction of disease may help in proper medical management to control disease. Keeping this in view, multivariate classification models by combining with Raman spectroscopy have been developed for the diagnosis of DENV infection in human blood sera. For study design, a statistical analysis is performed to select the sample size for training of models. Total 1240 Raman spectra have been acquired from 39 DENV infected and 23 healthy sera samples. Prior to model development, Raman spectra were examined using ANOVA test for significant differences present in the intensities of newly appeared Raman bands at 622, 645, 700, 746, 800, 814, 873, 890, 948, 1002, 1018, 1080, 1235, 1250, 1272, 1386, 1404, 1446, 1609 and 1645 cm-1. The significant differences and characteristic patterns of Raman bands induced by disease played decisive role and are exploited for development of multivariate model. Classification models are developed by utilizing principal component analysis (PCA) to extract discriminant features from multidimensional Raman spectral dataset and followed by support vector machines (SVM) with Polynomial of 5, RBF, and liner kernels. The proposed model for this study is built using 10-fold cross validation technique and evaluated on independent dataset to demonstrate its robustness. PCA-SVM (poly-5) model successfully yielded high diagnostic accuracy of 99.52%, sensitivity of 99.75%, specificity of 99.09% for classification of unknown suspected samples. For comparison, PCA discriminant analysis (PCA-DA), partial least squares regression (PLSR) are PLS-DA have been compared. It is found that PCA-SVM (poly-5) approach is more effective and robust compared to other state-of-the-art approaches and it can be used for clinical prediction of DENV infection in human blood sera.

Investigating the Parental and Media Influence on Gender Stereotypes and Young Student's Career Choices in Pakistan.

The study aimed to examine the impact of parental influence and media richness on gender stereotypes and career decisions among students at the secondary level in Pakistan. The sample size was 200 students, selected through a simple random sampling technique from government and private schools. Four questionnaires were used to gather data. The data was analyzed quantitatively using the Statistical Package for the Social Sciences (SPSS). Regression analyses were used to investigate the impact of parental influence (ß = 0.50) on gender stereotypes and media richness influence (ß = 0.26) on gender stereotype beliefs. Furthermore, parental, media, and gender stereotype behavior all have a significant impact on students' career choices (R 2 = 0.694). On the scale of the parental influence and media richness, no significant gender differences were found. It is concluded that parental influence has a greater effect on students' gender stereotyping behavior and career choices.

Diagnosis of dengue virus infection using spectroscopic images and deep learning.

Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%-20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.

Examine the parenting style effect on the academic achievement orientation of secondary school students: The moderating role of digital literacy.

The purpose of the study was to determine the association between parenting styles (authoritative and permissive) and students' self-efficacy (LSE), self-regulatory learning (SRL), and academic accomplishment orientation of secondary school students in Punjab, Pakistan. The study also investigated the effect of digital learning as a moderating variable in the relationship between SRL and academic achievement oriented, as well as between learner self-efficacy (LSE) and academic achievement among secondary school students. The study was conducted with (N = 720) secondary school students of Punjab Pakistan. In the current research cross sectional design was used, and multistage sampling was used to draw a sample from the population. The results from the study, it is found that the authoritative parenting style has a weak association with LSE and a strong association with SRL. Permissive parenting styles have low associations with SRL and have a high association with LSE as compared to authoritarian parenting. Furthermore, when compared to students from permissive parenting, secondary students from authoritarian parenting have higher SRL and a higher academic achievement orientation. Results revealed that that digital literacy significantly moderate with LSE to influence the academic achievement orientation, while digital literacy significantly interacts with SRL to highly influence the academic achievement orientation of secondary school students.

COVID-19 detection in chest X-ray images using deep boosted hybrid learning.

The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL), is proposed for effective COVID-19 detection in X-ray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVID-RENet-1 & 2 models is exploited individually through a machine learning (ML) classifier. In COVID-RENet models, Region and Edge-based operations are carefully applied to learn region homogeneity and extract boundaries features. While in the case of the proposed DBHL framework, COVID-RENet-1 & 2 are fine-tuned using transfer learning on the chest X-rays. Furthermore, deep feature spaces are generated from the penultimate layers of the two models and then concatenated to get a single enriched boosted feature space. A conventional ML classifier exploits the enriched feature space to achieve better COVID-19 detection performance. The proposed COVID-19 detection frameworks are evaluated on radiologist's authenticated chest X-ray data, and their performance is compared with the well-established CNNs. It is observed through experiments that the proposed DBHL framework, which merges the two-deep CNN feature spaces, yields good performance (accuracy: 98.53%, sensitivity: 0.99, F-score: 0.98, and precision: 0.98). Furthermore, a web-based interface is developed, which takes only 5-10s to detect COVID-19 in each unseen chest X-ray image. This web-predictor is expected to help early diagnosis, save precious lives, and thus positively impact society.

Developing intelligent medical image modality classification system using deep transfer learning and LDA.

Rapid advancement in imaging technology generates an enormous amount of heterogeneous medical data for disease diagnosis and rehabilitation process. Radiologists may require related clinical cases from medical archives for analysis and disease diagnosis. It is challenging to retrieve the associated clinical cases automatically, efficiently and accurately from the substantial medical image archive due to diversity in diseases and imaging modalities. We proposed an efficient and accurate approach for medical image modality classification that can used for retrieval of clinical cases from large medical repositories. The proposed approach is developed using transfer learning concept with pre-trained ResNet50 Deep learning model for optimized features extraction followed by linear discriminant analysis classification (TLRN-LDA). Extensive experiments are performed on challenging standard benchmark ImageCLEF-2012 dataset of 31 classes. The developed approach yields improved average classification accuracy of 87.91%, which is higher up-to 10% compared to the state-of-the-art approaches on the same dataset. Moreover, hand-crafted features are extracted for comparison. Performance of TLRN-LDA system demonstrates the effectiveness over state-of-the-art systems. The developed approach may be deployed to diagnostic centers to assist the practitioners for accurate and efficient clinical case retrieval and disease diagnosis.

Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images.

BACKGROUND AND OBJECTIVE: In medical image analysis for disease diagnosis, segmentation is one of the challenging tasks. Owing to the inherited degradations in MRI improper segments are produced. Segmentation process is an important step in brain tissue analysis. Moreover, an early detection of plaque in carotid artery using ultrasound images may prevent serious brain strokes. Unfortunately, low quality and noisy ultrasound images are still challenges for accurate segmentation. The objective of this research is to develop a robust segmentation approach for medical images such as brain MRI and carotid artery ultrasound images. METHODS: In this paper, a novel approach is proposed to address the segmentation challenges of medical images. The proposed approach employed fuzzy intelligence and Gaussian mixture model (GMM). It comprises two phases; firstly, incorporating spatial fuzzy c-means in GMM by exploiting statistical, texture, and wavelet image features. During model development, GMM parameters are estimated in presence of noise by EM algorithm iteratively. Utilizing these parameters, brain MRI images are segmented. In next phase, developed approach is applied to solve a real problem of carotid artery plaque detection using ultrasound images. The dataset of real patients annotated by radiologists has been obtained from Radiology Department, Shifa International Hospital Islamabad, Pakistan. For this, intima-media-thickness values are computed from the proposed segmentation followed by support vector machines for plaque classification (normal/abnormal). RESULTS: The obtained segmentation has been evaluated on standard brain MRI dataset and offers high segmentation accuracy of 99.2%. The proposed approach outperforms in term of segmentation performance range of 3-9% as compared to the state of the art approaches on brain MRI. Furthermore, the proposed approach shows robustness to various levels of Gaussian and Rician image noises. On carotid artery dataset, we have obtained high plaque detection rate in terms of accuracy, sensitivity, specificity, and F-score values of 98.8%, 99.3%, 98.0%, and 97.5% respectively. CONCLUSIONS: The proposed approach segments both modalities with high precision and shows robustness at Gaussian and Rician noise levels. Results for brain MRI and ultrasound images indicate its effectiveness and can be used as second opinion in addition to the radiologists. The developed approach is straightforward, efficient, and reproducible. It may benefit to improve the clinical evaluation of the disease in both asymptomatic and symptomatic individuals.

Robust Hidden Markov Model based intelligent blood vessel detection of fundus images.

In this paper, we consider the challenging problem of detecting retinal vessel networks. Precise detection of retinal vessel networks is vital for accurate eye disease diagnosis. Most of the blood vessel tracking techniques may not properly track vessels in presence of vessels' occlusion. Owing to problem in sensor resolution or acquisition of fundus images, it is possible that some part of vessel may occlude. In this scenario, it becomes a challenging task to accurately trace these vital vessels. For this purpose, we have proposed a new robust and intelligent retinal vessel detection technique on Hidden Markov Model. The proposed model is able to successfully track vessels in the presence of occlusion. The effectiveness of the proposed technique is evaluated on publically available standard DRIVE dataset of the fundus images. The experiments show that the proposed technique not only outperforms the other state of the art methodologies of retinal blood vessels segmentation, but it is also capable of accurate occlusion handling in retinal vessel networks. The proposed technique offers better average classification accuracy, sensitivity, specificity, and area under the curve (AUC) of 95.7%, 81.0%, 97.0%, and 90.0% respectively, which shows the usefulness of the proposed technique.

Retraction Note to: Prevalence of ixodid ticks on cattle and sheep northeast of Iran.

[This retracts the article DOI: 10.1007/s12639-014-0576-6.].

Prevalence of ixodid ticks on cattle and sheep northeast of Iran.

A survey was carried out to investigate the prevalence of hard tick species (Acari: Ixodidae) on cattle and sheep north of Iran. The aim of study was to determine the prevalence of hard ticks on cattle and sheep in the mountainous areas of Golestan province and their geographical distribution. A total of 26 ticks were collected from 22 infested cattle and 26 ticks were collected from 12 infested sheep during activating seasons of ticks in 2013-2014. The species collected from cattle and sheep were Hyalomma marginatum, Hyalomma anatolicum, Hyalomma asiaticum, Rhipicephalus bursa and Rhipicephalus sanguineus. The results show that these are dominant tick species in the surveyed area.

Robust segmentation and intelligent decision system for cerebrovascular disease.

Segmentation and classification of low-quality and noisy ultrasound images is challenging task. In this paper, a new approach is proposed for robust segmentation and classification of carotid artery ultrasound images and consequently, detecting cerebrovascular disease. The proposed technique consists of two phases, in first phase; it refines the class labels selected by user using expectation maximization algorithm. Genetic algorithm is then employed to select discriminative features based on moments of gray-level histogram. The selected features and refined targets are fed as input to neuro-fuzzy classifier for performing segmentation. Finally, intima-media thickness values are measured from segmented images to segregate the normal and abnormal subjects. In second phase, an intelligent decision-making system based on support vector machine is developed to utilize the intima-media thickness values for detecting cerebrovascular disease. The proposed robust segmentation and classification technique for ultrasound images (RSC-US) has been tested on a dataset of 300 real carotid artery ultrasound images and yields accuracy, F-measure, and MCC scores of 98.84, 0.988, 0.9767 %, respectively, using jackknife test. The segmentation and classification performance of the proposed (RSC-US) has been also tested at several noise levels and may be used as secondary observation.

Clinical and cytological characteristics and prognostic implications on sheep and goat Theileria infection in north of Iran.

The present research was carried out during 3 months from early June through late August, in northern zones of Iran. In addition, the present study was performed on 300 sheep and goats, in which 50 specimens were isolated based on clinical signs, blood smears and lymph node puncture. The results indicated clinical signs of theileriosis in sheep (with more prominent signs) and goats were diagnosable. The reliable clinical signs in sheep and goats included fever, tachycardia, cough, increased respiratory rate, mucosal pallor, anorexia, ruminal hypomotility and lymph node enlargement. Furthermore, the frequency of cough, abnormal pulmonary sounds, anorexia and ruminal hypomotility were significantly more in sheep than goats (P < 0.05).