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Fulltext Family influence on current smoking habits among secondary school children in Kota Bharu, Kelantan

Shamsuddin K, Haris MA

Singapore Med J, 2000 Apr;41(4):167-71.
PMID: 11063181

Objectives: To measure the prevalence of cigarette smoking among male secondary school children and assess their family influence especially that of their fathers' smoking habits on their current smoking habits.
Methodology: A cross-sectional study was carried out in Kota Bharu, Kelantan in April 1997 where 460 male form four students, aged 15-16 years were randomly selected from six secondary schools. Data on smoking habits, sociodemographic profile and family characteristics particularly parents and siblings' smoking habits, perceived parental supervision and communication were collected through self-administered questionnaires.
Results: The prevalence of cigarette smoking among male secondary school children was 33.2%. Crude analysis shows family factors, fathers' and siblings' smoking habits, and lack of parental supervision were significantly associated with the students' current smoking habit. Among students who smoked compared to non-smokers, father's smoking habit gives a crude Odds Ratio = 1.8, 95% C.I. 1.08 - 3.16. Further analysis shows that the effect of their father's smoking habit on the student's current smoking habit is still significant after controlling for other familial and non-familial factors including parental supervision, academic performance, reported influence of cigarette advertisement, having friends who smoked and the student's poor knowledge of the ill-effects of smoking and other factors (Odds Ratio = 1.9, 95% C.I 1.05 - 3.32). In conclusion, family factors especially the father's smoking habit is an important factor that influences a student's current smoking habit and the presence of negative role models within the home need to be seriously considered in any cigarette smoking prevention programs among secondary school adolescents.
Keywords: smoking, male students, adolescents, family influence, father’s smoking habit
Fulltext A rapid assessment of typhoid reporting system, Kelantan, Malaysia, 2001

Wan Mansor, H., Wan Mohd. Sulaili, W.S., Khalid, Y., Hamzah, A.M., Abdul Haris, M., Hani, M.H., et al.

Malaysian Journal of Public Health Medicine, 2005;5(1):27-30.
MyJurnal

A study was conducted in Kelantan, Mabysia, in the year 2001 , to assess the typhoid reporting coverage and timeliness, and to estimate the annual incidence. Cases were persons given the diagnosis of typhoid clinically, and conhrmed cases are those with positive laboratory results. In all, 174/252 (69%) cases (95% CI = 63%-75%) were reported, ofwhich 89/131 (83%) within 7 days of diagnosis. The estimated annual typhoid incidence in Kelantan is 37/ 1 00,000.
Fulltext Energy saving potential analysis applying factory scale energy audit - A case study of food production

Al Momani D, Al Turk Y, Abuashour MI, Khalid HM, Muyeen SM, Sweidan TO, et al.

Heliyon, 2023 Mar;9(3):e14216.
PMID: 36923846 DOI: 10.1016/j.heliyon.2023.e14216

An energy audit (EA) is a crucial step in boosting factory energy efficiency and obtaining certification for cleaner manufacturing. The results of a preliminary energy audit carried out at a sizable industrial facility in Jordan that creates some of the most well-known foods in the Middle East are presented in this study. The monthly demand of the factory for diesel ranged from 75,251.545 to 166,666.67 L. The factory energy model which is used to examine the impact of various energy-saving practices on the factory's primary energy consumption, was developed with the help of the energy audit. It has been established that optimizing the factory's energy use and the boiler systems' performance with regards to diesel consumption can withstand an expected monthly financial savings of 14205.85 Jordanian Dinar (JD). This has allowed a reduction in energy use of up to 18%. The CO2 harmful emissions were also decreased. Additionally, it is estimated that switching from the proposed motors to energy-efficient motors will cost less overall over time, saving around 3472.314 JD/month or 0.33576/year on average. Moreover, it was discovered that a total of 772.82021 Ton CO2/year emissions may be avoided each year.
Fulltext Corrigendum to "Energy saving potential analysis applying factory scale energy audit - A case study of food production" [Heliyon 9 (3), (March 2023) Article e14216]

Al Momani D, Al Turk Y, Abuashour MI, Khalid HM, Muyeen SM, Sweidan TO, et al.

Heliyon, 2023 Jun;9(6):e16551.
PMID: 37484411 DOI: 10.1016/j.heliyon.2023.e16551

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14216.].
Hand gesture recognition with deep residual network using Semg signal

Khattak AS, Zain ABM, Hassan RB, Nazar F, Haris M, Ahmed BA

Biomed Tech (Berl), 2024 Mar 08.
PMID: 38456275 DOI: 10.1515/bmt-2023-0208

OBJECTIVES: To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition.
METHODS: The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset.
RESULTS: The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924.
CONCLUSIONS: The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition.