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Pore size prediction of polyethersulfone ultrafiltration membranes using artificial neural networks

Ibrahim MZ, Norashikin MZ

J Nanosci Nanotechnol, 2010 Sep;10(9):6211-5.
PMID: 21133176

This paper reports the performance of two different artificial neural networks (ANN), Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) compared to conventional software for prediction of the pore size of the asymmetric polyethersulfone (PES) ultrafiltration membranes. ANN has advantages such as incredible approximation, generalization and good learning ability. The MLP are well suited for multiple inputs and multiple outputs while RBF are powerful techniques for interpolation in multidimensional space. Three experimental data sets were used to train the ANN using polyethylene glycol (PEG) of different molecular weights as additives namely as PEG 200, PEG 400 and PEG 600. The values of the pore size can be determined manually from the graph and solve it using mathematical equation. However, the mathematical solution used to determine the pore size and pore size distribution involve complicated equations and tedious. Thus, in this study, MLP and RBF are applied as an alternative method to estimate the pore size of polyethersulfone (PES) ultrafiltration membranes. The raw data needed for the training are solute separation and solute diameter. Values of solute separation were obtained from the ultrafiltration experiments and solute diameters ware calculated using mathematical equation. With the development of this ANN model, the process to estimate membrane pore size could be made easier and faster compared to mathematical solutions.
Fulltext Wind shear data at two different terrain types

Albani A, Ibrahim MZ, Yong KH

Data Brief, 2019 Aug;25:104306.
PMID: 31406911 DOI: 10.1016/j.dib.2019.104306

The measurement of this data aims to evaluate the wind shear variability at three selected sites in Malaysia. The sites are Kudat in Sabah, Kijal in Terengganu and Langkawi in Kedah. Both sites in Kudat and Kijal is located in coastal areas with few buildings or trees, while the site in Langkawi is a coastal area with many buildings or trees. The variables were measured using the sensors that mounted on the wind mast with the maximum height from 55 m to 70 m from ground level. The variables measured were wind speed, wind direction, temperature, and pressure, while the wind shear data were directly generated using the power law equation. The averaged wind shear based on measured multiple height wind speed at selected sites is larger than the 1/7 law (0.143). Also, the value of wind shear was higher in order Langkawi > Kudat > Kijal. Ultimately, the wind shear data are essential and can be reused in the wind energy potential study, especially for data extrapolation to desired wind turbine hub height.
Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma

Yakno M, Mohamad-Saleh J, Ibrahim MZ

Sensors (Basel), 2021 Sep 27;21(19).
PMID: 34640769 DOI: 10.3390/s21196445

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique's impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.
Fulltext Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence

Saealal MS, Ibrahim MZ, Mulvaney DJ, Shapiai MI, Fadilah N

PLoS One, 2022;17(12):e0278989.
PMID: 36520851 DOI: 10.1371/journal.pone.0278989

Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes are not easily or explicitly detectable. Such alterations have been recently used to spread false news or disinformation. This study aims to identify Deepfaked videos and images and alert viewers to the possible falsity of the information. The current work presented a novel means of revealing fake face videos by cascading the convolution network with recurrent neural networks and fully connected network (FCN) models. The system detection approach utilizes the eye-blinking state in temporal video frames. Notwithstanding, it is deemed challenging to precisely depict (i) artificiality in fake videos and (ii) spatial information within the individual frame through this physiological signal. Spatial features were extracted using the VGG16 network and trained with the ImageNet dataset. The temporal features were then extracted in every 20 sequences through the LSTM network. On another note, the pre-processed eye-blinking state served as a probability to generate a novel BPD dataset. This newly-acquired dataset was fed to three models for training purposes with each entailing four, three, and six hidden layers, respectively. Every model constitutes a unique architecture and specific dropout value. Resultantly, the model optimally and accurately identified tampered videos within the dataset. The study model was assessed using the current BPD dataset based on one of the most complex datasets (FaceForensic++) with 90.8% accuracy. Such precision was successfully maintained in datasets that were not used in the training process. The training process was also accelerated by lowering the computation prerequisites.
Fulltext Challenges in the Management of Laryngo-Tracheo-Bronchial Stenosis Secondary to Tuberculosis

Kamarudin NA, Yeoh XY, Ibrahim MZ, Ab Rahman WI, Azman M

Indian J Otolaryngol Head Neck Surg, 2022 Oct;74(Suppl 2):2652-2655.
PMID: 36452537 DOI: 10.1007/s12070-020-02340-8

Laryngo-tracheo-bronchial tuberculosis may be primary or secondary to pulmonary tuberculosis. It causes stenosis of the airway, leading to life threatening airway obstruction. We herein describe the challenges in managing a case of laryngo-tracheo-bronchial stenosis in a 22-year-old patient who presented with stridor post antituberculous therapy secondary to a malacic airway.
Cleome arabica L. extract as a novel green corrosion inhibitor for AISI 1045 carbon steel in 0.5 M HCl: insights from experimental and theoretical DFT analyses

Ait Bouabdallah I, Adjal F, Zaabar A, Benchikh A, Guerniche D, Ait Ramdane-Terbouche C, et al.

RSC Adv, 2024 Nov 11;14(49):36423-36436.
PMID: 39545168 DOI: 10.1039/d4ra06477a

The search for sustainable, cost-effective and environmentally friendly corrosion inhibitors for hydrochloric acid (HCl) solution in industrial applications has garnered increasing interest in plant extracts and their refined metabolites. In this research, Cleome arabica L. (CA) extract, found in the Algerian Sahara, was considered due to its low cost compared to other studied plants and higher content of active compounds, thereby emerging as a promising candidate and offering the potential to promote a circular economy model. This study assessed the effectiveness of CA extract as a green corrosion inhibitor for AISI 1045 carbon steel in 0.5 M HCl solution and highlighted its potential to advance the field of green corrosion inhibitors. ATR-FTIR and LC-ESI-MS/MS analyses revealed the presence of significant organic compounds, including coumaric acid (74.58%), 4-methoxybenzoic acid (12.53%), and kaempferol (8.05%), which contributed to the corrosion inhibition. The inhibitory effectiveness of the CA extract was evaluated at five concentrations, ranging from 0.125 to 1 g L-1, using weight loss measurements, potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS). The highest inhibition efficiency (η = 94.45%) was observed at a CA extract concentration of 1 g L-1 after 196 hours of immersion in 0.5 M HCl. Thermodynamic analysis using the Langmuir adsorption isotherm yielded a ΔG ads value of -24.737 kJ mol-1, indicating the spontaneous adsorption of CA molecules onto the AISI 1045 surfaces, forming a protective layer, which was confirmed by SEM/EDX analysis. Density functional theory (DFT) calculations showed a significant correlation with the experimental data, confirming that CA extract is a highly efficient and environmentally friendly corrosion inhibitor.
Findings from a Malaysian multicentre study on oropharyngeal squamous cell carcinoma

Sathasivam HP, Davan SP, Chua SM, Rohaizat RF, Japar R, Zakaria Z, et al.

Infect Agent Cancer, 2023 Nov 28;18(1):74.
PMID: 38017493 DOI: 10.1186/s13027-023-00557-0

BACKGROUND: In addition to the conventional aetiologic agents of oropharyngeal squamous cell carcinoma (OPSCC) such as tobacco usage, alcohol consumption and betel quid usage, it has been established that a proportion of OPSCC are driven by persistent oncogenic human papillomavirus (HPV) infections. Currently, there is a lack of data on the burden of HPV- associated OPSCC in Asian countries including Malaysia.
METHODS: A cross-sectional multicentre study with tissue analysis of Malaysian patients diagnosed with primary OPSCC within a five-year period, from 2015 to 2019 between 01/01/2015 to 31/12/2019 was undertaken. Determination of HPV status was carried out using p16INK4a immunohistochemistry on tissue microarrays constructed from archived formalin-fixed paraffin-embedded tissue.
RESULTS: From the cases identified, 184 cases had sufficient tissue material for analysis. Overall, median age at diagnosis was 63.0 years (IQR = 15) and 76.1% of patients were males. In our cohort, 35.3% of patients were Indian, 34.2% were Chinese, 21.2% were Malay and 9.2% were from other ethnicities. The estimated prevalence of HPV-associated OPSCC in our cohort was 31.0% (CI 24.4-38.2%). The median age for the HPV-associated OPSCC sub-group of patients was not significantly lower than the median age of patients with HPV-independent OPSCC. More than half of HPV-associated OPSCC was seen in patients of Chinese ethnicity (54.4%). Patients with HPV-associated OPSCC had a much better overall survival than patients with HPV-independent OPSCC (Log rank test; p
Fulltext Self-reported symptom study of COVID-19 chemosensory dysfunction in Malaysia

Lee SH, Yeoh ZX, Sachlin IS, Gazali N, Soelar SA, Foo CY, et al.

Sci Rep, 2022 02 08;12(1):2111.
PMID: 35136124 DOI: 10.1038/s41598-022-06029-6

Alterations in the three chemosensory modalities-smell, taste, and chemesthesis-have been implicated in Coronavirus Disease 2019 (COVID-19), yet emerging data suggest a wide geographic and ethnic variation in the prevalence of these symptoms. Studies on chemosensory disorders in COVID-19 have predominantly focused on Caucasian populations whereas Asians remain understudied. We conducted a nationwide, multicentre cross-sectional study using an online questionnaire on a cohort of RT-PCR-confirmed adult COVID-19 patients in Malaysia between 6 June and 30 November 2020. The aim of our study was to investigate their presenting symptoms and assess their chemosensory function using self-ratings of perceived smell, taste, chemesthesis, and nasal blockage. In this cohort of 498 patients, 41.4% reported smell and/or taste loss when diagnosed with COVID-19, which was the commonest symptom. Blocked nose, loss of appetite, and gastrointestinal disturbances were independent predictors of smell and/or taste loss on multivariate analysis. Self-ratings of chemosensory function revealed a reduction in smell, taste, and chemesthesis across the entire cohort of patients that was more profound among those reporting smell and/or taste loss as their presenting symptom. Perceived nasal obstruction accounted for only a small proportion of changes in smell and taste, but not for chemesthesis, supporting viral disruption of sensorineural mechanisms as the dominant aetiology of chemosensory dysfunction. Our study suggests that chemosensory dysfunction in COVID-19 is more widespread than previously reported among Asians and may be related to the infectivity of viral strains.Study Registration: NMRR-20-934-54803 and NCT04390165.