Browse publications by year: 2023

  1. Chang CY
    J Ayub Med Coll Abbottabad, 2023;35(1):186-187.
    PMID: 36849407 DOI: 10.55519/JAMC-01-11418
    MeSH terms: Humans; Malaria*; Plasmodium knowlesi*; Fatal Outcome
  2. Lim JY, Hong SK, Huang WF
    J Gastrointest Surg, 2023 Jul;27(7):1486-1488.
    PMID: 36849606 DOI: 10.1007/s11605-023-05630-y
    MeSH terms: Humans; Spleen
  3. Goh YX, Wang M, Hou XP, He Y, Ou HY
    Interdiscip Sci, 2023 Sep;15(3):349-359.
    PMID: 36849628 DOI: 10.1007/s12539-023-00555-1
    The CRISPR‒Cas system acts as a bacterial defense mechanism by conferring adaptive immunity and limiting genetic reshuffling. However, under adverse environmental hazards, bacteria can employ their CRISPR‒Cas system to exchange genes that are vital for adaptation and survival. Levilactobacillus brevis is a lactic acid bacterium with great potential for commercial purposes because it can be genetically manipulated to enhance its functionality and nutritional value. Nevertheless, the CRISPR‒Cas system might interfere with the genetic modification process. Additionally, little is known about the CRISPR‒Cas system in this industrially important microorganism. Here, we investigate the prevalence, diversity, and targets of CRISPR‒Cas systems in the genus Levilactobacillus, further focusing on complete genomes of L. brevis. Using the CRISPRCasFinder webserver, we identified 801 putative CRISPR-Cas systems in the genus Levilactobacillus. Further investigation focusing on the complete genomes of L. brevis revealed 54 putative CRISPR-Cas systems. Of these, 46 were orphan CRISPRs, and eight were CRISPR‒Cas systems. The type II-A CRISPR‒Cas system is the most common in Levilactobacillus and L. brevis complete genomes. Analysis of the spacer's target showed that the CRISPR‒Cas systems of L. brevis mainly target the enterococcal plasmids. Comparative analysis of putative CRISPR-Cas loci in Levilactobacillus brevis.
    MeSH terms: Bacteria; Plasmids/genetics; Lactobacillus brevis*; Clustered Regularly Interspaced Short Palindromic Repeats
  4. Kartsonaki C, Baillie JK, Barrio NG, Baruch J, Beane A, Blumberg L, et al.
    Int J Epidemiol, 2023 Apr 19;52(2):355-376.
    PMID: 36850054 DOI: 10.1093/ije/dyad012
    BACKGROUND: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients.

    METHODS: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV).

    RESULTS: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%.

    CONCLUSIONS: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.

    MeSH terms: Child; Hospitalization; Humans; Intensive Care Units; Male; Middle Aged; Risk Factors; Proportional Hazards Models
  5. Majib NM, Sam ST, Yaacob ND, Rohaizad NM, Tan WK
    Polymers (Basel), 2023 Feb 10;15(4).
    PMID: 36850157 DOI: 10.3390/polym15040873
    Agricultural wastes and leaves, which are classified as lignocellulosic biomass, have been used as substrates in the production of fungal foams due to the significant growth of the mushroom industry in recent years. Foam derived from fungi can be utilized in a variety of industrial applications, including the production of packaging materials. Here, white oyster mushrooms (Pleurotus florida) and yellow oyster mushrooms (Pleurotus citrinopileatus) were cultivated on rice husk, sawdust, sugarcane bagasse, and teak leaves. Fungal foams were produced after 30 days of incubation, which were then analyzed using scanning electron microscopy (SEM), thermal analysis (TGA), and chemical structure using Fourier-transform infrared spectroscopy. Mechanical testing examined the material's hardness, resilience, and springiness, and water absorption tests were used to determine the durability of the fungal foams. Our findings demonstrated that fungal foams made from rice husk and teak leaves in both mycelium species showed better mechanical properties, thermal stability, and minimal water absorption compared to the other substrates, and can thus have great potential as efficient packaging materials.
  6. Ebadi M, Rifqi Md Zain A, Tengku Abdul Aziz TH, Mohammadi H, Tee CAT, Rahimi Yusop M
    Polymers (Basel), 2023 Feb 16;15(4).
    PMID: 36850253 DOI: 10.3390/polym15040971
    Iron oxide nanoparticles are one of the nanocarriers that are suitable for novel drug delivery systems due to low toxicity, biocompatibility, loading capacity, and controlled drug delivery to cancer cells. The purpose of the present study is the synthesis of coated iron oxide nanoparticles for the delivery of sorafenib (SFB) and its effects on cancer cells. In this study, Fe3O4 nanoparticles were synthesized by the co-precipitation method, and then sorafenib was loaded onto PEG@Fe3O4 nanoparticles. FTIR was used to ensure polyethylene glycol (PEG) binding to nanoparticles and loading the drug onto the nanoshells. A comparison of the mean size and the crystalline structure of nanoparticles was performed by TEM, DLS, and X-ray diffraction patterns. Then, cell viability was obtained by the MTT assay for 3T3 and HepG2 cell lines. According to FT-IR results, the presence of O-H and C-H bands at 3427 cm-1 and 1420 cm-1 peak correlate with PEG binding to nanoparticles. XRD pattern showed the cubic spinel structure of trapped magnetite nanoparticles carrying medium. The magnetic properties of nanoparticles were examined by a vibrating-sample magnetometer (VSM). IC50 values at 72 h for treatment with carriers of Fe3O4@PEG nanoparticle for the HepG2 cell line was 15.78 μg/mL (p < 0.05). This study showed that Fe3O4 nanoparticles coated by polyethylene glycol and using them in the drug delivery process could be beneficial for increasing the effect of sorafenib on cancer cells.
  7. Hamed SK, Ab Aziz MJ, Yaakub MR
    Sensors (Basel), 2023 Feb 04;23(4).
    PMID: 36850346 DOI: 10.3390/s23041748
    Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public's response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users' comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher's stance, and emotion analysis of comments, which represent the crowd's stance, contribute to raising the efficiency of the detection model.
    MeSH terms: Emotions; Fear; Humans; Social Media*
  8. Salilew WM, Abdul Karim ZA, Lemma TA, Fentaye AD, Kyprianidis KG
    Sensors (Basel), 2023 Feb 04;23(4).
    PMID: 36850365 DOI: 10.3390/s23041767
    The power demand from gas turbines in electrical grids is becoming more dynamic due to the rising demand for power generation from renewable energy sources. Therefore, including the transient data in the fault diagnostic process is important when the steady-state data are limited and if some component faults are more observable in the transient condition than in the steady-state condition. This study analyses the transient behaviour of a three-shaft industrial gas turbine engine in clean and degraded conditions with consideration of the secondary air system and variable inlet guide vane effects. Different gas path faults are simulated to demonstrate how magnified the transient measurement deviations are compared with the steady-state measurement deviations. The results show that some of the key measurement deviations are considerably higher in the transient mode than in the steady state. This confirms the importance of considering transient measurements for early fault detection and more accurate diagnostic solutions.
  9. Shahid MA, Alam MM, Su'ud MM
    Sensors (Basel), 2023 Feb 09;23(4).
    PMID: 36850563 DOI: 10.3390/s23041965
    Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing's challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system's ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.
  10. Saeidi T, Al-Gburi AJA, Karamzadeh S
    Sensors (Basel), 2023 Feb 10;23(4).
    PMID: 36850599 DOI: 10.3390/s23041997
    A detachable miniaturized three-element spirals radiator button antenna integrated with a compact leaky-wave wearable antenna forming a dual-band three-port antenna is proposed. The leaky-wave antenna is fabricated on a denim (εr = 1.6, tan δ = 0.006) textile substrate with dimensions of 0.37 λ0 × 0.25 λ0 × 0.01 λ0 mm3 and a detachable rigid button of 20 mm diameter (on a PTFE substrate εr = 2.01, tan δ = 0.001). It augments users' comfort, making it one of the smallest to date in the literature. The designed antenna, with 3.25 to 3.65 GHz and 5.4 to 5.85 GHz operational bands, covers the wireless local area network (WLAN) frequency (5.1-5.5 GHz), the fifth-generation (5G) communication band. Low mutual coupling between the ports and the button antenna elements ensures high diversity performance. The performance of the specific absorption rate (SAR) and the envelope correlation coefficient (ECC) are also examined. The simulation and measurement findings agree well. Low SAR,
  11. Hassan N, Ramli DA
    Sensors (Basel), 2023 Feb 11;23(4).
    PMID: 36850657 DOI: 10.3390/s23042060
    Blind source separation (BSS) recovers source signals from observations without knowing the mixing process or source signals. Underdetermined blind source separation (UBSS) occurs when there are fewer mixes than source signals. Sparse component analysis (SCA) is a general UBSS solution that benefits from sparse source signals which consists of (1) mixing matrix estimation and (2) source recovery estimation. The first stage of SCA is crucial, as it will have an impact on the recovery of the source. Single-source points (SSPs) were detected and clustered during the process of mixing matrix estimation. Adaptive time-frequency thresholding (ATFT) was introduced to increase the accuracy of the mixing matrix estimations. ATFT only used significant TF coefficients to detect the SSPs. After identifying the SSPs, hierarchical clustering approximates the mixing matrix. The second stage of SCA estimated the source recovery using least squares methods. The mixing matrix and source recovery estimations were evaluated using the error rate and mean squared error (MSE) metrics. The experimental results on four bioacoustics signals using ATFT demonstrated that the proposed technique outperformed the baseline method, Zhen's method, and three state-of-the-art methods over a wide range of signal-to-noise ratio (SNR) ranges while consuming less time.
  12. Kairaldeen AR, Abdullah NF, Abu-Samah A, Nordin R
    Sensors (Basel), 2023 Feb 13;23(4).
    PMID: 36850701 DOI: 10.3390/s23042106
    Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms' taxonomy.
  13. Mohamed AA, Abualigah L, Alburaikan A, Khalifa HAE
    Sensors (Basel), 2023 Feb 15;23(4).
    PMID: 36850784 DOI: 10.3390/s23042189
    Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.
  14. Sudhamani C, Roslee M, Tiang JJ, Rehman AU
    Sensors (Basel), 2023 Feb 20;23(4).
    PMID: 36850954 DOI: 10.3390/s23042356
    Fifth generation (5G) is a recent wireless communication technology in mobile networks. The key parameters of 5G are enhanced coverage, ultra reliable low latency, high data rates, massive connectivity and better support to mobility. Enhanced coverage is one of the major issues in the 5G and beyond 5G networks, which will be affecting the overall system performance and end user experience. The increasing number of base stations may increase the coverage but it leads to interference between the cell edge users, which in turn impacts the coverage. Therefore, enhanced coverage is one of the future challenging issues in cellular networks. In this survey, coverage enhancement techniques are explored to improve the overall system performance, throughput, coverage capacity, spectral efficiency, outage probability, data rates, and latency. The main aim of this article is to highlight the recent developments and deployments made towards the enhanced network coverage and to discuss its future research challenges.
  15. Ramatillah DL, Michael M, Khan K, Natasya N, Sinaga E, Hartuti S, et al.
    Vaccines (Basel), 2023 Feb 13;11(2).
    PMID: 36851310 DOI: 10.3390/vaccines11020433
    In this study, we aim to evaluate the factors that may contribute to the development of chronic kidney disease following COVID-19 infection among hospitalized patients in two private hospitals in Jakarta, Indonesia. This is a retrospective cohort study between March 2020 and September 2021. Patient selection was conducted with a convenience sampling. All patients (n = 378) meeting the inclusion criteria during the study period were enrolled. Various sociodemographic, laboratory test, and diagnostic parameters were measured before the determination of their correlation with the outcome of COVID-19 infection. In this study, all pre-vaccinated patients with COVID-19 had no history of chronic kidney disease (CKD) prior to hospital admission. From this number, approximately 75.7% of the patients developed CKD following COVID-19 diagnosis. Overall, significant correlations were established between the clinical outcome and the CKD status (p = 0.001). Interestingly, there was a significant correlation between serum creatinine level, glomerular filtration rate (GFR), and CKD (p < 0.0001). Oxygen saturation (p = 0.03), admission to the intensive care unit (ICU) (p < 0.0001), and sepsis (p = 0.005) were factors that were significantly correlated with CKD status. Additionally, the type of antibiotic agent used was significantly correlated with CKD (p = 0.011). While 82.1% of patients with CKD survived, the survival rate worsened if the patients had complications from hyperuricemia (p = 0.010). The patients who received levofloxacin and ceftriaxone had the highest (100%) survival rate after approximately 50 days of treatment. The patients who received the antiviral agent combination isoprinosine + oseltamivir + ivermectin fared better (100%) as compared to those who received isoprinosine + favipiravir (8%). Factors, such as hyperuricemia and the antibiotic agent used, contributed to CKD following COVID-19 hospitalization. Interestingly, the patients who received levofloxacin + ceftriaxone and the patients without sepsis fared the best. Overall, patients who develop CKD following COVID-19 hospitalization have a low survival rate.
  16. Kayode JS, Arifin MH, Mansor MI, Abdul Malek NN, Musa RC, Shahri S, et al.
    Heliyon, 2023 Mar;9(3):e13710.
    PMID: 36851956 DOI: 10.1016/j.heliyon.2023.e13710
    Understanding of the climate-water nexus for sustainability, required good knowledge of the climate effects on groundwater aquifer units, particularly in rural communities. The studies were achieved using RES2-D modelling of the subsurface structures at the study site. Geophysical exploration with the application of 2-D Electrical Resistivity Imaging (ERI), combined with Induced Polarization (IP) method, were carried out to identify groundwater aquifers during extreme weather at Kampung Kuala Pajam, Beranang, Selangor, Peninsular Malaysia. The signatures obtained from geophysical explorations were used to better understand the phenomena that are responsible for groundwater depletion in the area. In recent times, there had been seasonal fluctuations in the water supply from boreholes serving the community. During the drought season, subsurface underlain this area experienced perennial acute shortages of groundwater supplies due to annual climatic variations that call for immediate solution by meeting the agricultural, domestic, and industrial water usage of the State of Selangor. A Pole-dipole techniques, using seven parallel lines of 400 m each at 5 m inter electrode spacing deployed to study the groundwater accumulation/aquifers within the area. Saturated groundwater occurrences zones were delineated as areas with average resistivity values of about 125 Ω-m, with corresponding chargeability of 30 ms. The methods used identified major faults along the northeast-southwest (NE-SW) directions, suitable for groundwater occurrences with approximate volume of about 2.86 Mega cubic metre (CBM), to proffer lasting solutions to the challenges being experienced by the community using a climate-water nexus sustainability.
  17. Soleimani H, Yusuf JY, Chuan LK, Soleimani H, Bin Sabar ML, Öchsner A, et al.
    Heliyon, 2023 Mar;9(3):e13256.
    PMID: 36851968 DOI: 10.1016/j.heliyon.2023.e13256
    This study explores the potential of using cobalt ferrite (CF) nanoparticles grown in situ on eggshell membranes (ESM) to mitigate the increasing problem of electromagnetic interference (EMI). A simple carbonization process was adopted to synthesize CF nanoparticles on ESM. The study further examines the composites' surface morphology and chemical composition and evaluates their microwave absorption performance (MAP) at X-band frequency. Results showed that the composite of CF and ESM - CESM@CF, exhibited a strong RL peak value of -39.03 mm with an optimal thickness of 1.5 mm. The combination of CF and ESM demonstrates excellent impedance matching and EM wave attenuation. The presence of numerous interfaces, conduction loss from the morphology, interfacial polarisation, and dual influence from both CF and ESM contribute to the high MAP of the composite. CESM@CF composite is projected as an excellent biomass-based nano-composite for EM wave absorption applications.
  18. Mohd Fahmi MSA, Swain P, Ramli AH, Wan Ibrahim WN, Saleh Hodin NA, Abu Bakar N, et al.
    Heliyon, 2023 Feb;9(2):e13685.
    PMID: 36852036 DOI: 10.1016/j.heliyon.2023.e13685
    Epilepsy is the third most common known brain disease worldwide. Several antiepileptic drugs (AEDs) are available to improve seizure control. However, the associated side effects limit their practical use and highlight the ongoing search for safer and effective AEDs. Eighteen newly designed fluorine-containing pyrrolylated chalcones were extensively studied in silico, synthesized, structurally analyzed by X-ray diffraction (XRD), and biologically and toxicologically tested as potential new AEDs in zebrafish epilepsy in vivo models. The results predicted that 3-(3,5-difluorophenyl)-1-(1H-pyrrol-2-yl)prop-2-en-1-one (compound 8) had a good drug-like profile with binding affinity to γ-aminobutyric acid receptor type-A (GABAA, -8.0 kcal/mol). This predicted active compound 8 was effective in reducing convulsive behaviour in pentylenetetrazol (PTZ)-induced larvae and hyperactive movements in zc4h2 knockout (KO) zebrafish, experimentally. Moreover, no cardiotoxic effect of compound 8 was observed in zebrafish. Overall, pyrrolylated chalcones could serve as alternative AEDs and warrant further in-depth pharmacological studies to uncover their mechanism of action.
  19. Albaram BM, Lim YM
    Heliyon, 2023 Feb;9(2):e13764.
    PMID: 36852045 DOI: 10.1016/j.heliyon.2023.e13764
    PURPOSE: Aiming to comprehend the function of social influence as an extrinsic motive influencing individuals' psychological needs satisfaction to share knowledge in higher educational institutions, the study will profile prior literature on how social influence affects knowledge sharing and conceptualize a suggested framework.

    DESIGN/METHODOLOGY/APPROACH: The research thoroughly examined the literature for the previous ten years using a comprehensive evaluation, mapping and analyzes research networks of the literature on relational social influence factors through bibliometric analysis. It offers a conceptual framework that explains extrinsic social factors and their effects on the psychological needs satisfaction to share knowledge among people from the viewpoint of a need to belong.

    FINDINGS: The study concluded a unique a conceptual framework that provides a solid understanding for the relational social influence phenomenon in the perspective of the need to belong, which satisfy the psychological needs to share knowledge. This will contribute to further investigations in the research area.

    RESEARCH LIMITATIONS: The study is a qualitative study and is limited in its generalizability as it needs further investigations to overcome the bias on the part of the researcher.

    PRACTICAL IMPLICATIONS: Adopting the proposed conceptual framework serves as a diagnostic tool for researchers to address the social influence that is likely to boost individuals' satisfaction to share knowledge.

    ORIGINALITY/VALUE: This research presents a novel understanding of social influence as an extrinsic motivator arising from a sense of belonging that affects individuals' needs satisfaction to share knowledge.

    SOCIAL IMPLICATIONS: Increasing the awareness of how social influence is likely to motivate individuals to connect with one another, interact socially, and work together collaboratively to fulfil their satisfaction of psychological needs to share knowledge.

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