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Fulltext A novel sample based quadrature phase shift keying demodulator

Mohamed Moubark A, Ali SH

ScientificWorldJournal, 2014;2014:107831.
PMID: 25197687 DOI: 10.1155/2014/107831

This paper presents a new practical QPSK receiver that uses digitized samples of incoming QPSK analog signal to determine the phase of the QPSK symbol. The proposed technique is more robust to phase noise and consumes up to 89.6% less power for signal detection in demodulation operation. On the contrary, the conventional QPSK demodulation process where it uses coherent detection technique requires the exact incoming signal frequency; thus, any variation in the frequency of the local oscillator or incoming signal will cause phase noise. A software simulation of the proposed design was successfully carried out using MATLAB Simulink software platform. In the conventional system, at least 10 dB signal to noise ratio (SNR) is required to achieve the bit error rate (BER) of 10(-6), whereas, in the proposed technique, the same BER value can be achieved with only 5 dB SNR. Since some of the power consuming elements such as voltage control oscillator (VCO), mixer, and low pass filter (LPF) are no longer needed, the proposed QPSK demodulator will consume almost 68.8% to 99.6% less operational power compared to conventional QPSK demodulator.
Fulltext Hardware implementation of 32-bit high-speed direct digital frequency synthesizer

Ibrahim SH, Ali SH, Islam MS

ScientificWorldJournal, 2014;2014:131568.
PMID: 24991635 DOI: 10.1155/2014/131568

The design and implementation of a high-speed direct digital frequency synthesizer are presented. A modified Brent-Kung parallel adder is combined with pipelining technique to improve the speed of the system. A gated clock technique is proposed to reduce the number of registers in the phase accumulator design. The quarter wave symmetry technique is used to store only one quarter of the sine wave. The ROM lookup table (LUT) is partitioned into three 4-bit sub-ROMs based on angular decomposition technique and trigonometric identity. Exploiting the advantages of sine-cosine symmetrical attributes together with XOR logic gates, one sub-ROM block can be removed from the design. These techniques, compressed the ROM into 368 bits. The ROM compressed ratio is 534.2:1, with only two adders, two multipliers, and XOR-gates with high frequency resolution of 0.029 Hz. These techniques make the direct digital frequency synthesizer an attractive candidate for wireless communication applications.
Fulltext Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification

Haque F, Bin Ibne Reaz M, Chowdhury MEH, Srivastava G, Hamid Md Ali S, Bakar AAA, et al.

Diagnostics (Basel), 2021 Apr 28;11(5).
PMID: 33925190 DOI: 10.3390/diagnostics11050801

BACKGROUND: Diabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is a very common and well-established field of research, its application in diabetic peripheral neuropathy (DSPN) diagnosis using composite scoring techniques like Michigan Neuropathy Screening Instrumentation (MNSI), is very limited in the existing literature.
METHOD: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms.
RESULTS: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs.
CONCLUSIONS: This study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients.
Fulltext Amino Anthraquinone: Synthesis, Characterization, and Its Application as an Active Material in Environmental Sensors

Ali S, Tahir M, Mehboob N, Wahab F, J Langford S, Mohd Said S, et al.

Materials (Basel), 2020 Feb 21;13(4).
PMID: 32098037 DOI: 10.3390/ma13040960

This work reports synthesis, thin film characterizations, and study of an organic semiconductor 2-aminoanthraquinone (AAq) for humidity and temperature sensing applications. The morphological and phase studies of AAq thin films are carried out by scanning electron microscope (SEM), atomic force microscope (AFM), and X-ray diffraction (XRD) analysis. To study the sensing properties of AAq, a surface type Au/AAq/Au sensor is fabricated by thermally depositing a 60 nm layer of AAq at a pressure of ~10-5 mbar on a pre-patterned gold (Au) electrodes with inter-electrode gap of 45 µm. To measure sensing capability of the Au/AAq/Au device, the variations in its capacitance and resistance are studied as a function of humidity and temperature. The Au/AAq/Au device measures and exhibits a linear change in capacitance and resistance when relative humidity (%RH) and temperature are varied. The AAq is a hydrophobic material which makes it one of the best candidates to be used as an active material in humidity sensors; on the other hand, its high melting point (575 K) is another appealing property that enables it for its potential applications in temperature sensors.
Fulltext Performance analysis of noninvasive electrophysiological methods for the assessment of diabetic sensorimotor polyneuropathy in clinical research: a systematic review and meta-analysis with trial sequential analysis

Haque F, Reaz MBI, Ali SHM, Arsad N, Chowdhury MEH

Sci Rep, 2020 12 10;10(1):21770.
PMID: 33303857 DOI: 10.1038/s41598-020-78787-0

Despite the availability of various clinical trials that used different diagnostic methods to identify diabetic sensorimotor polyneuropathy (DSPN), no reliable studies that prove the associations among diagnostic parameters from two different methods are available. Statistically significant diagnostic parameters from various methods can help determine if two different methods can be incorporated together for diagnosing DSPN. In this study, a systematic review, meta-analysis, and trial sequential analysis (TSA) were performed to determine the associations among the different parameters from the most commonly used electrophysiological screening methods in clinical research for DSPN, namely, nerve conduction study (NCS), corneal confocal microscopy (CCM), and electromyography (EMG), for different experimental groups. Electronic databases (e.g., Web of Science, PubMed, and Google Scholar) were searched systematically for articles reporting different screening tools for diabetic peripheral neuropathy. A total of 22 studies involving 2394 participants (801 patients with DSPN, 702 controls, and 891 non-DSPN patients) were reviewed systematically. Meta-analysis was performed to determine statistical significance of difference among four NCS parameters, i.e., peroneal motor nerve conduction velocity, peroneal motor nerve amplitude, sural sensory nerve conduction velocity, and sural sensory nerve amplitude (all p
Fulltext Coherent Surface Plasmon Hole Burning via Spontaneously Generated Coherence

Rahman H, Ali H, Din RU, Ahmad I, Sarker MR, Ali SHM

Molecules, 2021 Oct 27;26(21).
PMID: 34770905 DOI: 10.3390/molecules26216497

Surface plasmon (SP)-induced spectral hole burning (SHB) at the silver-dielectric interface is investigated theoretically. We notice a typical lamb dip at a selective frequency, which abruptly reduces the absorption spectrum of the surface plasmons polaritons (SPP). Introducing the spontaneous generated coherence (SGC) in the atomic medium, the slope of dispersion becomes normal. Additionally, slow SPP propagation is also noticed at the interface. The spectral hole burning dip is enhanced with the SGC effect and can be modified and controlled with the frequency and intensity of the driving fields. The SPP propagation length at the hole-burning region is greatly enhanced under the effect of SGC. A propagation length of the order of 600 µm is achieved for the modes, which is a remarkable result. The enhancement of plasmon hole burning under SGC will find significant applications in sensing technology, optical communication, optical tweezers and nano-photonics.
Mediating Role of Psychological Status in the Association Between Resiliency and Quality of Life Among Older Malaysians Living with Knee Osteoarthritis

Norman SS, Mat S, Kamsan SS, Hamid Md Ali S, Mohamad Yahaya NH, Mei Hsien CC, et al.

Exp Aging Res, 2024 Jul 18.
PMID: 39023096 DOI: 10.1080/0361073X.2024.2377436

Resilience increases the ability of an individual to overcome adversity. It has not yet been determined how resilience is linked to quality of life among individuals experiencing knee osteoarthritis symptoms. To explore the inter-relationships of psychological distress, resilience and quality of life among older individuals with knee osteoarthritis. The study examined older adults in Kuala Lumpur and Selangor, identifying osteoarthritis through verified physician diagnosis. Various factors, including resilience, psychological status, and quality of life, were measured. In the study with 338 older adults, 50.9% had knee osteoarthritis. Higher resilience was linked to lower depression, anxiety, and stress, and better quality of life in both groups with and without knee osteoarthritis. Psychological factors consistently mediated the link between resilience and quality of life even after controlling potential confounders. Analysis showed that depression, anxiety, and stress mediate the relationship between resilience and quality of life, indicating a significant influence even when considering various factors. Resilience appears to influence psychological well-being and quality of life among older adult with knee osteoarthritis.
Fulltext Effectiveness of a Novel Index System in Preventing Early Hearing Loss among Furniture Industry Skills Training Students in Malaysia

Abdul Rahim KA, Jewaratnam J, Che Hassan CR, Hamid MD

Int J Environ Res Public Health, 2020 10 31;17(21).
PMID: 33142732 DOI: 10.3390/ijerph17218032

Occupational noise-induced hearing loss (ONIHL) is the most reported occupational disease in Malaysia. ONIHL is aggravated by the presence of early hearing loss amongst the youth prior to entering a real working environment. At technical and vocational education training (TVET) institutions, students may develop early ONIHL because training workshops are designed imitating the industrial working environment to produce skilled workers. The exceeding noise level at workshops and recent risk of non-occupational noise can cause early ONIHL among these students. Therefore, ONIHL must be addressed at the early stage of producing skilled workers. Octa hearing conservation index (OHCI) system is developed as a management and monitoring tool for hearing conservation program (HCP) in TVET institutions. Six existing and two new HCP components were used to build the index system. A pilot test on the effectiveness of the OHCI system was conducted in a selected TVET institution for six months. The post-HCP shows a 52.6% improvement compared to the pre-HCP. The implementation of HCP has shown improved awareness on the hazards of loud noise exposure and active use of hearing protection devices among participants. The OHCI system has a great potential as a tool to improve HCP implementation in TVET institutions, and eventually, industry.
Fulltext Evaluating the Impact of Hospital Accreditation on Patient Safety Culture in Saudi Arabia Healthcare Facilities

Abdurabuh A, Hamid MD, Che Hassan CR, Fatani MI

J Multidiscip Healthc, 2024;17:5021-5033.
PMID: 39507375 DOI: 10.2147/JMDH.S480496

BACKGROUND: The impact of hospital accreditation on the organizational safety culture among healthcare workers, an essential indicator of patient safety, has yet to be directly quantified in Saudi Arabia's healthcare system. This study aims to investigate this impact to sustain and maintain a positive safety culture in Saudi Arabia's healthcare institutions.
METHODS: A cross-sectional assessment was conducted in five public hospitals in Makkah. Three hundred forty healthcare workers participated using a self-administered questionnaire. Data were analyzed using descriptive statistics, ANOVA, one-sample t-test, and multiple regression for a comprehensive understanding.
RESULTS AND DISCUSSION: Regression analysis revealed significant gender differences in patient safety ratings (B = 0.480, p < 0.001). Age positively influenced scores, with higher ages resulting in higher scores (B = 0.127, p = 0.041). The ratings were also associated with respondents' nationality (B = 0.169, p < 0.001) and education levels (B = -0.186, p < 0.001). Respondents rated disasters and training as the highest in patient safety culture, followed by facility safety and security, hazards and hazardous materials safety, utility and building safety, fire safety, and quality improvement. At the same time, leadership, commitment, and support received the lowest score.
CONCLUSION: This study illustrates a strong connection between accreditation and improved patient safety, emphasizing the importance of quality improvement and leadership commitment. These insights can guide policymakers and healthcare executives in Saudi Arabia and similar countries toward developing a robust patient safety culture. It stresses the importance of considering human factors and organizational culture when developing patient safety models.
Fulltext Synergistic enhancement in the microelectronic properties of poly-(dioctylfluorene) based Schottky devices by CdSe quantum dots

Muhammad F, Tahir M, Zeb M, Kalasad MN, Mohd Said S, Sarker MR, et al.

Sci Rep, 2020 Mar 16;10(1):4828.
PMID: 32179797 DOI: 10.1038/s41598-020-61602-1

This paper reports the potential application of cadmium selenide (CdSe) quantum dots (QDs) in improving the microelectronic characteristics of Schottky barrier diode (SBD) prepared from a semiconducting material poly-(9,9-dioctylfluorene) (F8). Two SBDs, Ag/F8/P3HT/ITO and Ag/F8-CdSe QDs/P3HT/ITO, are fabricated by spin coating a 10 wt% solution of F8 in chloroform and 10:1 wt% solution of F8:CdSe QDs, respectively, on a pre-deposited poly(3-hexylthiophene) (P3HT) on indium tin oxide (ITO) substrate. To study the electronic properties of the fabricated devices, current-voltage (I-V) measurements are carried out at 25 °C in dark conditions. The I-V curves of Ag/F8/P3HT/ITO and Ag/F8-CdSe QDs/P3HT/ITO SBDs demonstrate asymmetrical behavior with forward bias current rectification ratio (RR) of 7.42 ± 0.02 and 142 ± 0.02, respectively, at ± 3.5 V which confirm the formation of depletion region. Other key parameters which govern microelectronic properties of the fabricated devices such as charge carrier mobility (µ), barrier height (ϕb), series resistance (Rs) and quality factor (n) are extracted from their corresponding I-V characteristics. Norde's and Cheung functions are also applied to characterize the devices to study consistency in various parameters. Significant improvement is found in the values of Rs, n, and RR by 3, 1.7, and 19 times, respectively, for Ag/F8-CdSe QDs/P3HT/ITO SBD as compared to Ag/F8/P3HT/ITO. This enhancement is due to the incorporation of CdSe QDs having 3-dimensional quantum confinement and large surface-to-volume area. Poole-Frenkle and Richardson-Schottky conduction mechanisms are also discussed for both of the devices. Morphology, optical bandgap (1.88 ± 0.5 eV) and photoluminescence (PL) spectrum of CdSe QDs with a peak intensity at 556 nm are also reported and discussed.
Fulltext Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A Review

Thangarajoo RG, Reaz MBI, Srivastava G, Haque F, Ali SHM, Bakar AAA, et al.

Sensors (Basel), 2021 Dec 20;21(24).
PMID: 34960577 DOI: 10.3390/s21248485

Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of '3N' biosignals-nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.
Fulltext Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait

Haque F, Reaz MBI, Chowdhury MEH, Ezeddin M, Kiranyaz S, Alhatou M, et al.

Sensors (Basel), 2022 May 05;22(9).
PMID: 35591196 DOI: 10.3390/s22093507

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.
Fulltext Combining ability of pod yield and related traits of groundnut (Arachis hypogaea L.) under salinity stress

Azad MA, Shah-E-Alam M, Hamid MA, Rafii MY, Malek MA

ScientificWorldJournal, 2014;2014:589586.
PMID: 24737982 DOI: 10.1155/2014/589586

A study was performed using 6 × 6 F1 diallel population without reciprocals to assess the mode of inheritance of pod yield and related traits in groundnut with imposed salinity stress. Heterosis was found for pod number and yield. Data on general and specific combining ability (gca and sca) indicated additive and nonadditive gene actions. The gca: sca ratios were much less than unity suggesting predominant role of nonadditive gene effects. Cultivars "Binachinabadam-2" and "Dacca-1" and mutant M6/25/64-82 had the highest, second highest, and third highest pod number, as well as gca values, respectively. These two cultivars and another mutant M6/15/70-19 also had the highest, second highest, and third highest pod yield, as well as gca values, respectively. Therefore, "Dacca-1", "Binachinabadam-2", M6/25/64-82, and M6/15/70-19 could be used as source of salinity tolerance. Cross combinations showing high sca effects arising from parents with high and low gca values for any trait indicate the influence of nonadditive genes on their expression. Parents of these crosses can be used for biparental mating or reciprocal recurrent selection for developing high yielding varieties. Crosses with high sca effects having both parents with good gca effects could be exploited by pedigree breeding to get transgressive segregants.
Fulltext Nanometal Dust Explosion in Confined Vessel: Combustion and Kinetic Analysis

Mohd Mokhtar K, Kasmani RM, Che Hassan CR, Hamid MD, Mohamad Nor MI, Mohd Junaidi MU, et al.

ACS Omega, 2021 Jul 20;6(28):17831-17838.
PMID: 34308018 DOI: 10.1021/acsomega.1c00967

Extensive application of metal powder, particularly in nanosize could potentially lead to catastrophic dust explosion, due to their pyrophoric behavior, ignition sensitivity, and explosivity. To assess the appropriate measures preventing accidental metal dust explosions, it is vital to understand the physicochemical properties of the metal dust and their kinetic mechanism. In this work, explosion severity of aluminum and silver powder, which can be encountered in a passivated emitter and rear contact (PERC) solar cell, was explored in a 0.0012 m3 cylindrical vessel, by varying the particle size and powder concentration. The P max and dP/dt max values of metal powder were demonstrated to increase with decreasing particle size. Additionally, it was found that the explosion severity of silver powder was lower than that of aluminum powder due to the more apparent agglomeration effect of silver particles. The reduction on the specific surface area attributed to the particles' agglomeration affects the oxidation reaction of the metal powder, as illustrated in the thermogravimetric (TG) curves. A sluggish oxidation reaction was demonstrated in the TG curve of silver powder, which is contradicted with aluminum powder. From the X-ray photoelectron spectroscopy (XPS) analysis, it is inferred that silver powder exhibited two reactions in which the dominant reaction produced Ag and the other reaction formed Ag2O. Meanwhile, for aluminum powder, explosion products only comprise Al2O3.
Fulltext Palm Raceme as a Promising Biomass Precursor for Activated Carbon to Promote Lipase Activity with the Aid of Eutectic Solvents

Abed KM, Hayyan A, Elgharbawy AAM, Hizaddin HF, Hashim MA, Hasan HA, et al.

Molecules, 2022 Dec 09;27(24).
PMID: 36557866 DOI: 10.3390/molecules27248734

This study concerns the role of activated carbon (AC) from palm raceme as a support material for the enhancement of lipase-catalyzed reactions in an aqueous solution, with deep eutectic solvent (DES) as a co-solvent. The effects of carbonization temperature, impregnation ratio, and carbonization time on lipase activity were studied. The activities of Amano lipase from Burkholderia cepacia (AML) and lipase from the porcine pancreas (PPL) were used to investigate the optimum conditions for AC preparation. The results showed that AC has more interaction with PPL and effectively provides greater enzymatic activity compared with AML. The optimum treatment conditions of AC samples that yield the highest enzymatic activity were 0.5 (NaOH (g)/palm raceme (g)), 150 min, and a carbonization temperature of 400 °C. DES was prepared from alanine/sodium hydroxide and used with AC for the further enhancement of enzymatic activity. Kinetic studies demonstrated that the activity of PPL was enhanced with the immobilization of AC in a DES medium.

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