MyMedR

Displaying all 7 publications

Abstract:

Sort:

Severe midface and maxillary hypoplasia in non-cleft and non-syndromic patients: A 2-stage surgical strategy using distraction osteogenesis and orthognathic surgery

Tan CS, Hariri F, Hassan MK

J Stomatol Oral Maxillofac Surg, 2023 Jul 04.
PMID: 37406736 DOI: 10.1016/j.jormas.2023.101552

Severe midface and maxillary hypoplasia can have an impact on an individual, either on the appearance, functions or psychologically. Based on literature review, severe maxillary hypoplasia with more than 25.0 mm reverse overjet in non-cleft and non-syndromic patients is very rare. It is more often seen in cleft lip and palate and syndromic patients. When the magnitude of correction exceeds the limit of what a single orthognathic surgery can achieve, multiple surgeries would be required, involving different surgical techniques. The authors report two rare cases of non-syndromic nor cleft severe hypoplastic midface and maxilla with 26.0 mm and 27.0 mm reverse overjet, respectively, treated with 2-stage surgery involving maxillary distraction osteogenesis and orthognathic surgery. Both cases recorded reasonably clinical and functional outcomes. The significance of both surgical interventions is further discussed.
Fulltext Vulnerability of financial markets in India: The contagious effect of COVID-19

Rao P, Goyal N, Kumar S, Hassan MK, Shahimi S

Res Int Bus Finance, 2021 Dec;58:101462.
PMID: 36540343 DOI: 10.1016/j.ribaf.2021.101462

This study aims to examine the impact of COVID-19 on financial markets, using emerging market data. Specifically, panel data regression is applied on 3200 observations for daily market returns during lockdown in India. The event study methodology is adopted to show abnormal returns registered in the lockdown period. A contrasting breakdown effect of COVID-19 on various Indian industries has been observed through sectoral analysis. The study also provides empirical evidence for lockdown measures taken by the government on stock market returns and post lockdown impact of COVID-19 on daily market returns for over 6550 observations.
Skeletal expansion via craniofacial distraction osteogenesis technique in syndromic craniosynostosis: impact on ophthalmic parameters

Kai LC, Khaliddin N, Hassan MK, Hariri F

Int Ophthalmol, 2024 Mar 19;44(1):147.
PMID: 38499845 DOI: 10.1007/s10792-024-03084-y

BACKGROUND: This study aims to compare the changes in ophthalmic parameters among syndromic craniosynostosis patients who underwent craniofacial skeletal expansion procedures via distraction osteogenesis (DO).
METHOD: A retrospective study was conducted involving syndromic craniosynostosis patients who underwent surgical expansion via the DO technique from the year 2012 to March 2022. Changes in six parameters which consist of visual acuity, refractive error, optic disc health, intraocular pressure, degree of proptosis and orbital volume were measured objectively pre and post-surgery. For categorical parameters, the Chi-square cross-tab test was done. Paired sample T-test was used for normally distributed variables. Wilcoxon signed-rank test was used for non-normally distributed data.
RESULTS: Visual impairment was present in 21.4% of eyes before surgery and increased to 28.5% post-surgery. Three patients had changes of refractive error post-surgery with one developed hypermetropia, another developed anisometropia and the last had improvement to no refractive error. Two patients had optic disc swelling which was resolved post-surgery. Intraocular pressure changes were inconsistent post-surgery. All patients achieved a significant reduction in the degree of proptosis post-surgery. Orbital volume calculation using computed tomography (CT) scans shows a significant increase in volume post-surgery for all patients.
CONCLUSION: Our study shows a significant increase in orbital volume post-surgery with a reduction in the degree of proptosis. Optic disc and nerve health improved after the surgery. Changes in terms of visual acuity, refractive error and IOP were inconsistent after the surgical intervention.
Fulltext MYNursingHome: A fully-labelled image dataset for indoor object classification

Ismail A, Ahmad SA, Che Soh A, Hassan MK, Harith HH

Data Brief, 2020 Oct;32:106268.
PMID: 32984464 DOI: 10.1016/j.dib.2020.106268

A fully labelled image dataset serves as a valuable tool for reproducible research inquiries and data processing in various computational areas, such as machine learning, computer vision, artificial intelligence and deep learning. Today's research on ageing is intended to increase awareness on research results and their applications to assist public and private sectors in selecting the right equipments for the elderlies. Many researches related to development of support devices and care equipment had been done to improve the elderly's quality of life. Indoor object detection and classification for autonomous systems require large annotated indoor images for training and testing of smart computer vision applications. This dataset entitled MYNursingHome is an image dataset for commonly used objects surrounding the elderlies in their home cares. Researchers may use this data to build up a recognition aid for the elderlies. This dataset was collected from several nursing homes in Malaysia comprises 37,500 digital images from 25 different indoor object categories including basket bin, bed, bench, cabinet and others.
Fulltext Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer

Tukkee AS, Bin Abdul Wahab NI, Binti Mailah NF, Bin Hassan MK

PLoS One, 2024;19(2):e0298094.
PMID: 38330067 DOI: 10.1371/journal.pone.0298094

Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.
Fulltext Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Hassan MK, Syed Ariffin SH, Ghazali NE, Hamad M, Hamdan M, Hamdi M, et al.

Sensors (Basel), 2022 May 09;22(9).
PMID: 35591282 DOI: 10.3390/s22093592

Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.
Accelerated implementation of teleconsultation services for the monitoring of oral potentially malignant disorders as a result of the COVID-19 pandemic

Rajendran S, Mohamad Zaini Z, Lim YZ, Kallarakkal TG, Ramanathan A, Chan SW, et al.

Oral Oncol, 2022 Feb 14;126:105730.
PMID: 35176655 DOI: 10.1016/j.oraloncology.2022.105730