Displaying publications 501 - 520 of 668 in total

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  1. McCalmont J, Kho LK, Teh YA, Chocholek M, Rumpang E, Rowland L, et al.
    Sci Total Environ, 2023 Feb 01;858(Pt 1):159356.
    PMID: 36270353 DOI: 10.1016/j.scitotenv.2022.159356
    While existing moratoria in Indonesia and Malaysia should preclude continued large-scale expansion of palm oil production into new areas of South-East Asian tropical peatland, existing plantations in the region remain a globally significant source of atmospheric carbon due to drainage driven decomposition of peatland soils. Previous studies have made clear the direct link between drainage depth and peat carbon decomposition and significant reductions in the emission rate of CO2 can be made by raising water tables nearer to the soil surface. However, the impact of such changes on palm fruit yield is not well understood and will be a critical consideration for plantation managers. Here we take advantage of very high frequency, long-term monitoring of canopy-scale carbon exchange at a mature oil palm plantation in Malaysian Borneo to investigate the relationship between drainage level and photosynthetic uptake and consider the confounding effects of light quality and atmospheric vapour pressure deficit. Canopy modelling from our dataset demonstrated that palms were exerting significantly greater stomatal control at deeper water table depths (WTD) and the optimum WTD for photosynthesis was found to be between 0.3 and 0.4 m below the soil surface. Raising WTD to this level, from the industry typical drainage level of 0.6 m, could increase photosynthetic uptake by 3.6 % and reduce soil surface emission of CO2 by 11 %. Our study site further showed that despite being poorly drained compared to other planting blocks at the same plantation, monthly fruit bunch yield was, on average, 14 % greater. While these results are encouraging, and at least suggest that raising WTD closer to the soil surface to reduce emissions is unlikely to produce significant yield penalties, our results are limited to a single study site and more work is urgently needed to confirm these results at other plantations.
  2. Balla A, Habaebi MH, Elsheikh EAA, Islam MR, Suliman FM
    Sensors (Basel), 2023 Jan 09;23(2).
    PMID: 36679553 DOI: 10.3390/s23020758
    Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system's effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.
  3. Al-Jumaili AHA, Muniyandi RC, Hasan MK, Paw JKS, Singh MJ
    Sensors (Basel), 2023 Mar 08;23(6).
    PMID: 36991663 DOI: 10.3390/s23062952
    Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.
  4. Rizvi SM, Rahman AAA, Sheikh UU, Fuad KAA, Shehzad HMF
    Appl Intell (Dordr), 2023;53(4):4499-4523.
    PMID: 35730044 DOI: 10.1007/s10489-022-03756-1
    Conventional convolutional neural networks (CNNs) present a high computational workload and memory access cost (CMC). Spectral domain CNNs (SpCNNs) offer a computationally efficient approach to compute CNN training and inference. This paper investigates CMC of SpCNNs and its contributing components analytically and then proposes a methodology to optimize CMC, under three strategies, to enhance inference performance. In this methodology, output feature map (OFM) size, OFM depth or both are progressively reduced under an accuracy constraint to compute performance-optimized CNN inference. Before conducting training or testing, it can provide designers guidelines and preliminary insights regarding techniques for optimum performance, least degradation in accuracy and a balanced performance-accuracy trade-off. This methodology was evaluated on MNIST and Fashion MNIST datasets using LeNet-5 and AlexNet architectures. When compared to state-of-the-art SpCNN models, LeNet-5 achieves up to 4.2× (batch inference) and 4.1× (single-image inference) higher throughputs and 10.5× (batch inference) and 4.2× (single-image inference) greater energy efficiency at a maximum loss of 3% in test accuracy. When compared to the baseline model used in this study, AlexNet delivers 11.6× (batch inference) and 5× (single-image inference) increased throughput and 25× (batch inference) and 8.8× (single-image inference) more energy-efficient inference with just 4.4% reduction in accuracy.
  5. Said AH, Mohd FN, Yusof MZ, Mohd Win NAN, Mazlan AN, Shaharudin AS
    Malays Fam Physician, 2022 Nov 30;17(3):64-73.
    PMID: 36606166 DOI: 10.51866/oa.75
    INTRODUCTION: Smartphone addiction is becoming a global concern affecting every part of society, including healthcare professionals. This study aimed to identify the prevalence of risk of smartphone addiction and its associated factors among medical and dental students in a public university in Malaysia.

    METHOD: This cross-sectional study was conducted among pre-clinical medical and dental students using convenience sampling. Questions regarding sociodemographic profile and responses to the Smartphone Addiction Scale Short Version (SAS-SV) and Depression, Anxiety and Stress Score questionnaire (DASS-21) were collected. Multiple logistic regression testing was used to analyse the factors associated with smartphone addiction.

    RESULTS: We invited 409 pre-clinical medical and dental students to participate voluntarily, resulting in a response rate of 80.2%. The prevalence of high-risk smartphone addiction among the participants was 47.9%. Male participants, participants who used smartphones mainly for social media, and participants with depressive symptoms were more likely to have a high risk of smartphone addiction. Medical students, participants who spent less than 3 hours per day on a smartphone, and participants who used smartphones for education-related activities were less likely to have a high risk of smartphone addiction.

    CONCLUSION: Smartphone addiction prevalence among pre-clinical medical and dental students was high. Therefore, the authorities should overcome this problem by implementing early measures.

  6. Khaw KW, Alnoor A, Al-Abrrow H, Tiberius V, Ganesan Y, Atshan NA
    Curr Psychol, 2022 Apr 13.
    PMID: 35431526 DOI: 10.1007/s12144-022-03070-6
    Regardless of the prevalence and value of change initiatives in contemporary organizations, these often face resistance by employees. This resistance is the outcome of change recipients' cognitive and behavioral reactions towards change. To better understand the causes and effects of reactions to change, a holistic view of prior research is needed. Accordingly, we provide a systematic literature review on this topic. We categorize extant research into four major and several subcategories: micro and macro reactions. We analyze the essential characteristics of the emerging field of change reactions along research issues and challenges, benefits of (even negative) reactions, managerial implications, and propose future research opportunities.
  7. Haddad A, Habaebi MH, Elsheikh EAA, Islam MR, Zabidi SA, Suliman FEM
    PLoS One, 2024;19(4):e0301371.
    PMID: 38557695 DOI: 10.1371/journal.pone.0301371
    To secure sensitive medical records in the healthcare clouds, this paper proposes an End-to-End Encryption (E2EE) to enhance a patient-centric blockchain-based system for electronic health record (EHR) management. The suggested system with a focus on the patient enables individuals to oversee their medical records within various involved parties by authorizing or withdrawing permission for access to their records. Utilizing the inter-planetary file system (IPFS) for record storage is chosen due to its decentralized nature and its ability to guarantee the unchangeability of records. Then an E2EE enhancement maintains the medical data integrity using dual level-Hybrid encryption: symmetric Advanced Encryption Standard (AES) and asymmetric Elliptic Curve Cryptography (ECC) cryptographic techniques. The proposed system is implemented using the Ethereum blockchain system for EHR data sharing and integration utilizing a web-based interface for the patient and all users to initiate the EHR sharing transactions over the IPFS cloud. The proposed system performance is evaluated in a working system prototype. For different file sizes between 512 KB to 100 MB, the performance metrics used to evaluate the proposed system were the time consumed for generating key, encryption, and decryption. The results demonstrate the proposed system's superiority over other cutting-edge systems and its practical ability to share secure health data in cloud environments.
  8. Al Sulayyim H, Ismail R, Al Hamid A, Mohammed B, Abdul Ghafar N
    J Infect Dev Ctries, 2024 Mar 31;18(3):371-382.
    PMID: 38635620 DOI: 10.3855/jidc.19071
    INTRODUCTION: Prevalence of antibiotic resistance (AR) during the coronavirus 2019 (COVID-19) pandemic was higher than pre-pandemic times. This study determined the prevalence and patterns of AR among Gram-positive and negative bacteria before, during and after COVID-19 in Saudi Arabia and identified the associated factors.

    METHODOLOGY: A retrospective cross-sectional study was employed to identify patients with positive AR bacteria between March 2019 and March 2022. The bacterial isolates and patients' data were identified from laboratory and medical records departments retrospectively. Binary logistic regression analysis was performed to identify the factors associated with AR and deaths. Multinominal logistic regression was applied to confirm the factors associated with AR classification.

    RESULTS: AR Gram-negative bacteria decreased during and after the pandemic. However, S. aureus showed a negligible increase in resistance rate after pandemic, while E. faecium, recorded a higher-than-average resistance rate during the pandemic. The prevalence of pan drug resistance (PDR) during the pandemic (85.7%) was higher than before (0%) and after (14.3%), p = 0.001. The length of stay and time were significant predictors for AR classification. The odds of multi drug resistance (MDR) development to PDR during the pandemic were 6 times higher than before and after (OR = 6.133, CI =, p = 0.020). Age, nationality, COVID-19 infection, smoking, liver disease, and type and number of bacteria were associated with death of patients with positive AR.

    CONCLUSIONS: Further studies are recommended to explore the prevalence of PDR and to justify the increased rates of E. faecium AR during the COVID-19 pandemic.

  9. Ghanbari P, Raiesi D, Alboebadi R, Zarejavid A, Dianati M, Razmi H, et al.
    BMC Complement Med Ther, 2024 May 16;24(1):192.
    PMID: 38755622 DOI: 10.1186/s12906-024-04477-3
    BACKGROUND: Despite the high antioxidant potential of grape seed extract (GSE), very limited studies have investigated its effect on non-alcoholic fatty liver disease (NAFLD). Therefore, this study was conducted with the aim of investigating the effect of GSE on metabolic factors, blood pressure and steatosis severity in patients with NAFLD.

    METHODS: In this double-blind randomized clinical trial study, 50 NAFLD patients were divided into two groups of 25 participants who were treated with 520 mg/day of GSE or the placebo group for 2 months. The parameters of glycemic, lipid profile, blood pressure and steatohepatitis were measured before and after the intervention.

    RESULTS: The GSE group had an average age of 43.52 ± 8.12 years with 15 women and 10 men, while the placebo group had an average age of 44.88 ± 10.14 years with 11 women and 14 men. After 2 months of intervention with GSE, it was observed that insulin, HOMA-IR, TC, TG, LDL-c, ALT, AST, AST/ALT, SBP, DBP and MAP decreased and QUICKi and HDL-c increased significantly (p-value for all

  10. Ahmed KI, Tahir M, Lau SL, Habaebi MH, Ahad A, Pires IM
    Data Brief, 2024 Aug;55:110589.
    PMID: 39022696 DOI: 10.1016/j.dib.2024.110589
    The proliferation landscape of the Internet of Things (IoT) has accentuated the critical role of Authentication and Authorization (AA) mechanisms in securing interconnected devices. There is a lack of relevant datasets that can aid in building appropriate machine learning enabled security solutions focusing on authentication and authorization using physical layer characteristics. In this context, our research presents a novel dataset derived from real-world scenarios, utilizing Zigbee Zolertia Z1 nodes to capture physical layer properties in indoor environments. The dataset encompasses crucial parameters such as Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Device Internal Temperature, Device Battery Level, and more, providing a comprehensive foundation for advancing Machine learning enabled AA in IoT ecosystems.
  11. Lau YT, Chia HT, Nadia AH, Mohd Shaffari M, Liliwati I, Noor Syazana CI
    PMID: 39156230 DOI: 10.51866/oa.597
    INTRODUCTION: Burnout is a syndrome characterised by physical, emotional and mental exhaustion that results from a long period of involvement in an overwhelming work condition. It is prevalent among frontline workers. This study aimed to identify the prevalence of burnout among primary healthcare workers in the district of Manjung, Perak and determine the factors associated with burnout.

    METHODS: This cross-sectional study was conducted among healthcare workers in seven health clinics located in the district from August to September 2022. The self-administered validated Malay version of the Copenhagen Burnout Inventory and the Malay version of the Multidimensional Scale of Perceived Social Support were used. These instruments consisted of 31 questions rated on a 5-point Likert scale. The scores were then summed up to determine the burnout level. Data were analysed using SPSS version 20. Simple logistic regression analysis was performed. Thereafter, multiple logistic regression analysis was conducted to determine the factors associated with burnout.

    RESULTS: A total of 224 participants were included. Among them, 61.6% were nurses; 21.4%, doctors; and 17.0%, assistant medical officers. The prevalence of personal burnout was 31.3%; work-related burnout, 16.5%; and patient-related burnout, 5.4%. The factors associated with burnout were the highest educational level, financial difficulties and low perceived social support from friends and significant others.

    CONCLUSION: Healthcare workers in Manjung health clinics have a higher prevalence of personal burnout than work- and patient-related burnout. The findings of this study provide early insights and guidance for possible interventions.

  12. Alias NAQ, Abu-Bakar MF, Rosli AH, Jabbari AJ
    Cureus, 2024 Jun;16(6):e63175.
    PMID: 39070347 DOI: 10.7759/cureus.63175
    Introduction Endogenous endophthalmitis is characterized by severe intraocular inflammation caused by the invasion of microorganisms into the anterior and posterior chambers of the eye. It results from hematogenous spread from distant foci of infection. This, in turn, leads to potential vision loss and blindness due to reduced anatomical and functional outcomes. The latest reported prevalence of endogenous endophthalmitis accounts for at least 2-8% of cases of general endophthalmitis which is fairly significant. Purpose This study aimed to analyze the clinical profile of endogenous endophthalmitis presented in the Ophthalmology Clinic, Sultan Ahmad Shah Medical Centre at International Islamic University Malaysia (SASMEC@IIUM). This study includes the patients' demographics, clinical manifestations, causative organism, treatment, and final visual outcome. Methods This is a retrospective case series of endogenous endophthalmitis patients from January 2020 to June 2023. The data were obtained from the patients' medical records in SASMEC@IIUM. Results A total of six patients (six eyes) were diagnosed with endogenous endophthalmitis from January 2020 to June 2023. Four patients (66.6%) were female, with a mean age of 51.6 ± 17.5 years. Presenting visual acuity ranged between 6/21 to hand movement (HM). Five patients (83.3%) presented with reduced vision, while one presented with eye redness (16.6%). Ocular signs included vitritis and retinitis (five eyes, 83.3%), hypopyon (five eyes, 83.3%), injected conjunctiva (five eyes, 83.3%), and eyelid swelling (one eye, 16.6%). The most common primary infection seen was intraabdominal sepsis (three patients, 50%), septic arthritis, hospital-acquired pneumonia (HAP), and urinary tract infection (UTI). Vitreous biopsy was only positive in two patients (33.3%) However, five out of the six patients (83.3%) had positive blood cultures (two Staphylococcus aureus, two Klebsiella pneumoniae and one Pseudomonas aeruginosa). All patients received intravitreal injections and intravenous antibiotics. Only one patient underwent subsequent trans pars plana vitrectomy (TPPV). Final visual acuity ranged from 6/6 to no light perception (NPL). Conclusion In this case series of six patients, we observed a variety of outcomes with similar presentations despite standardized treatment in all patients. Five out of six patients showed poorer visual outcomes and only one patient showed a final visual acuity of 6/6. Therefore, further study with a larger sample size is needed to evaluate the factors associated with the final visual outcome in endogenous endophthalmitis.
  13. Yeoh ZY, Beh HC, Amirul Amzar Megat Hashim MM, Haireen AH, Chuan DR, Othman S
    PMID: 39534763 DOI: 10.51866/oa.653
    INTRODUCTION: Using quick response (QR) codes to disseminate information has become increasingly popular since the declaration of COVID-19 as a pandemic. We aimed to investigate the feasibility of implementing QR-based quality improvement projects in our clinic to improve patients' medical knowledge, experience and access to care.

    METHODS: We utilised systematic random sampling by recruiting every 25th patient registered in our clinic during data collection. Participants answered a self-administered printed questionnaire regarding their smartphone usage and familiarity with QR code scanning at the patients' waiting area. Data were analysed using the Statistical Package for the Social Sciences version 26.

    RESULTS: A total of 323 patients participated (response rate=100%). The participants' median age was 57 years (interquartile range=4l-67). Most participants were women (63.1%). Approximately 90.4% (n=282) used smartphones, with 83.7% (n=261) reporting average or good usage proficiency. More than half (58.0%) accessed medical information via their smartphones, and 67.0% were familiar with QR codes. Multiple logistic regression analyses revealed that familiarity with QR codes was linked to age of <65 years [adjusted odds ratio (AOR)=4.593, 95% confidence interval (CI)=2.351-8.976, P<0.001], tertiary education (AOR=2.385, 95% CI=1.170-4.863, P=0.017), smartphone proficiency (A0R=4.703, 95% CI= 1.624-13.623, P=0.004) and prior smartphone usage to access medical information (AOR=5.472, 95% CI=2.790-10.732, P<0.001).

    CONCLUSION: Since smartphones were accessible to most primary care patients, and more than half of the patients were familiar with QR code scanning, QR code-based quality improvement projects can be used to improve services in our setting.

  14. Wang Y, Abd Rahman AH, Nor Rashid F', Razali MKM
    Sensors (Basel), 2024 Dec 09;24(23).
    PMID: 39686392 DOI: 10.3390/s24237855
    Object detection is an essential computer vision task that identifies and locates objects within images or videos and is crucial for applications such as autonomous driving, robotics, and augmented reality. Light Detection and Ranging (LiDAR) and camera sensors are widely used for reliable object detection. These sensors produce heterogeneous data due to differences in data format, spatial resolution, and environmental responsiveness. Existing review articles on object detection predominantly focus on the statistical analysis of fusion algorithms, often overlooking the complexities of aligning data from these distinct modalities, especially dynamic environment data alignment. This paper addresses the challenges of heterogeneous LiDAR-camera alignment in dynamic environments by surveying over 20 alignment methods for three-dimensional (3D) object detection, focusing on research published between 2019 and 2024. This study introduces the core concepts of multimodal 3D object detection, emphasizing the importance of integrating data from different sensor modalities for accurate object recognition in dynamic environments. The survey then delves into a detailed comparison of recent heterogeneous alignment methods, analyzing critical approaches found in the literature, and identifying their strengths and limitations. A classification of methods for aligning heterogeneous data in 3D object detection is presented. This paper also highlights the critical challenges in aligning multimodal data, including dynamic environments, sensor fusion, scalability, and real-time processing. These limitations are thoroughly discussed, and potential future research directions are proposed to address current gaps and advance the state-of-the-art. By summarizing the latest advancements and highlighting open challenges, this survey aims to stimulate further research and innovation in heterogeneous alignment methods for multimodal 3D object detection, thereby pushing the boundaries of what is currently achievable in this rapidly evolving domain.
  15. Al-Hammood O, Oladzadabbasabadi N, Mohammed AH, Al-Musawi MH, PourvatanDoust S, Ghorbani M
    Int J Biol Macromol, 2025 Mar 10;307(Pt 3):141961.
    PMID: 40074136 DOI: 10.1016/j.ijbiomac.2025.141961
    This study investigates developing and characterizing electrospun nanofibers composed of polyvinyl alcohol (PVA) and oxidized xanthan gum (OXG), with nisin as a bioactive agent, for innovative food packaging applications. Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) confirmed successful crosslinking between PVA and OXG, along with uniform nisin dispersion within the fibers. The inclusion of OXG increased moisture content (MC) and water solubility (WS) while reducing porosity and water vapor permeability (WVP), demonstrating its role as a crosslinker. Conversely, nisin reduced MC to 25.13 ± 0.93 %, WS to 43.45 ± 4.32 %, and increased porosity to 61.5 ± 4.25 % and WVP to 1.75 ± 0.08 × 103 g/h·m2·Pa. Tensile strength significantly improved with higher nisin concentrations, rising from 10.8 ± 2.35 MPa to 20.31 ± 2.94 MPa, attributed to Schiff base crosslinking. Additionally, nisin-containing nanofibers exhibited enhanced antioxidant properties, increasing radical scavenging activity by 65 %. These findings highlight the potential of PVA/OXG/nisin-based nanofibers to address gaps in food packaging by offering robust mechanical strength, superior barrier properties, and bioactive functionality, paving the way for next-generation packaging solutions that extend shelf life and reduce environmental impact.
  16. Yosep I, Mardhiyah A, Hikmat R, Hazmi H, Abdillah H, Tumanggor RD, et al.
    Acta Psychol (Amst), 2026 Oct 31;271:108060.
    PMID: 42966062 DOI: 10.1016/j.actpsy.2026.108060
    BACKGROUND: Adolescent suicide prevention is not only a clinical or service-delivery problem; it is also socially organized through stigma, family and community norms, trust, help-seeking expectations, and the relationships that connect adolescents with potential helpers. In Indonesia, community nurses work at the interface of adolescents, families, schools, primary health care, and community institutions, yet little is known about how they interpret and negotiate this role.

    PURPOSE: To explore community nurses' perspectives on adolescent suicide prevention in Indonesia, focusing on how suicide risk becomes visible or remains hidden, how nurses understand their preventive roles, the barriers that constrain action, and the support and system conditions needed to strengthen practice.

    METHOD: A descriptive qualitative study was conducted in Bandung, West Java, Indonesia with 14 purposively selected community nurses. Individual semi-structured interviews were analyzed using a codebook-oriented thematic analysis. Reporting was guided by the Consolidated Criteria for Reporting Qualitative Research (COREQ).

    RESULTS: Four interpretative themes were developed: suicide risk is socially hidden before it becomes clinically visible; community nurses function as relational gatekeepers whose effectiveness depends on trust; normative stigma and professional uncertainty constrain preventive action; and nurses are expected to coordinate across sectors without sufficient structural enablement. Participants described shame, fear of judgment, family responses, and religious or moral sensitivities as shaping disclosure and help-seeking. They also described roles in mental health education, early recognition, supportive communication, family engagement, and referral, while emphasizing limited suicide-specific training, unclear pathways, and fragmented school-family-health service coordination.

    CONCLUSIONS: Community nurses occupy a potentially important social-psychological and service-system position in adolescent suicide prevention in Indonesia. Their effectiveness depends not only on individual knowledge but also on the social conditions that make disclosure safe and help-seeking legitimate, together with clear protocols, referral pathways, cross-sector collaboration, and institutional support within existing Indonesian adolescent and primary-care platforms.

  17. Shahrbejari AN, Sani MHE, Jahromi MZ, Yaghoubi E, Yaghoubi E, Maghami MR
    PLoS One, 2025;20(7):e0328838.
    PMID: 40720515 DOI: 10.1371/journal.pone.0328838
    This paper presents a decentralized demand response (DR) framework that, incorporating optimal multi-objective energy management strategies, addresses uncertainties in power networks. The power industry faces challenges in operational optimization due to uncertainties in generation and consumption while evaluating environmental impacts and long-term economic implications. This research introduces an innovative approach by combining advanced DR techniques with a robust energy management strategy designed for uncertain conditions, enhanced by sensitivity analysis to key system parameters. The article considers a network with distributed generating resources, including wind turbines, microturbines, photovoltaics, energy storage systems (ESS), and diesel generators, where generation is controlled hourly based on load fluctuations. Energy consumption optimization requires not only distributed energy generation but also DR to variations in demand, ensuring system reliability under diverse scenarios. Consumers play a crucial role in optimizing energy usage through incentive-based participation. To achieve the research goal of reducing generation and purchasing costs in power grids through optimal energy management and DR to fluctuations, a stochastic approach is employed to obtain the best outcomes. This paper proposes a novel method for optimizing energy consumption in power networks by integrating stochastic techniques to manage uncertainties and variable conditions. The findings show improved network efficiency and cost reduction, achieving a 15.62% decrease in voltage deviation, 37.08% reduction in load demand, 62.05% decrease in active losses, 81.25% reduction in reactive losses, and 33-45% reduction in Expected Energy Not Supplied (EENS).
  18. Mirza W, Khan ME, Iqbal H, Khan A, Badshah MB, Moeen-Ud-Din MB, et al.
    Surg Endosc, 2026 Apr;40(4):2766-2781.
    PMID: 41814000 DOI: 10.1007/s00464-026-12735-3
    BACKGROUND: Iatrogenic colonic perforation is a rare but potentially catastrophic complication of colonoscopy (0.016-0.2% diagnostic; 0.15-5% therapeutic) with reported mortality of 15-25%. Rates may increase with expanding colonoscopy volume and advanced therapeutic interventions. Endoscopic closure is increasingly used, yet comparative outcomes remain uncertain.

    METHODS: Registered in PROSPERO (CRD420251233077). We searched six databases from inception to November 2025 for adult observational cohorts comparing endoscopic closure versus surgery. Outcomes included treatment success, mortality, major morbidity, reoperation, length of stay, and fasting duration. Two reviewers independently screened studies, extracted data, and assessed bias using ROBINS-I. Random-effects models pooled risk ratios (RR) and mean differences (MD); GRADE rated certainty. Sensitivity and geographic subgroup analyses assessed robustness and effect modification.

    RESULTS: Four retrospective cohorts (n = 123; 52 endoscopic, 71 surgical) from Portugal, Korea, and Malaysia were included across care settings. Treatment success showed no clear difference (RR 1.00, 95% CI 0.94-1.06; I2 = 0%; low certainty). Mortality was rare and imprecise (8 events; RR 0.26, 95% CI 0.06-1.16; I2 = 0%; very low certainty). Hospital stay was shorter with endoscopic management (MD - 9.23 days, 95% CI - 13.74 to - 4.73; I2 = 43%; low certainty). Fasting duration did not differ significantly and was heterogeneous. No geographic subgroup effect was detected (P = 0.95). Sensitivity analysis supported robustness, except for hospital-stay heterogeneity driven by referred cases in one study.

    CONCLUSIONS: In observational cohorts, endoscopic closure was typically used for immediately recognized, smaller perforations in favorable clinical conditions, whereas surgery was preferentially used for delayed diagnosis, larger defects, or suspected contamination, introducing substantial confounding by indication. Accordingly, the pooled estimates should not be interpreted as evidence of equivalence. In carefully selected patients (immediate recognition, 

  19. Low VL, Tan TK, Prakash BK, Vinnie-Siow WY, Tay ST, Masmeatathip R, et al.
    Sci Rep, 2017 07 19;7(1):5871.
    PMID: 28724923 DOI: 10.1038/s41598-017-05921-w
    Uncovering the hidden diversity and evolutionary history of arthropods of medico-veterinary importance could have significant implications for vector-borne disease control and epidemiological intervention. The buffalo fly Haematobia exigua is an obligate bloodsucking ectoparasite of livestock. As an initial step towards understanding its population structures and biogeographic patterns, we characterized partial cytochrome c oxidase subunit I (COI) and cytochrome b (Cytb) sequences of H. exigua from three distinct geographic regions in Southeast Asia. We detected two distinct mitochondrial haplogroups of H. exigua in our surveyed geographic regions. Haplogroup I is widespread in the Southeast Asian mainland whereas haplogroup II is generally restricted to the type population Java Island. Both haplogroups were detected co-occurring on Borneo Island. Additionally, both haplogroups have undergone contrasting evolutionary histories, with haplogroup I exhibited a high level of mitochondrial diversity indicating a population expansion during the Pleistocene era dating back to 98,000 years ago. However, haplogroup II presented a low level of mitochondrial diversity which argues against the hypothesis of recent demographic expansion.
  20. Tan PG, Ab Hadi IS, Zahari Z, Yahya MM, Wan Zain WZ, Wong MP, et al.
    Ann Surg Treat Res, 2020 Jan;98(1):1-6.
    PMID: 31909044 DOI: 10.4174/astr.2020.98.1.1
    Purpose: Patients with secondary hyperparathyroidism are at high risk for developing postoperative hypocalcemia. However, there are limited data regarding predictors of postoperative hypocalcemia in renal failure patient with secondary hyperparathyroidism. This study aimed to determine the clinical presentations of renal hyperparathyroidism and the predictors of early postoperative hypocalcemia after total parathyroidectomy.

    Methods: Data of patients with renal hyperparathyroidism who underwent total parathyroidectomy between January 2007 to December 2014 were reviewed retrospectively. Patients were divided into 2 cohort groups according to their serum calcium levels within 24 hours of parathyroidectomy: the hypocalcemia group (calcium levels of 2 mmol/L or less), and the normocalcemia group (calcium levels more than 2 mmol/L). With the use of multivariable logistic regression analyses, the predictors of early postoperative hypocalcemia after total parathyroidectomy in patients with renal hyperparathyroidism were investigated.

    Results: Among 68 patients, 56 patients (82.4%) were symptomatic preoperatively. Fifty patients (73.5%) presented with bone pain and 14 patients (20.6%) had muscle weakness. Early postoperative hypocalcemia occurred in 25 patients (36.8%). Preoperative alkaline phosphatase level was the predictor of early postoperative hypocalcemia (adjusted odds ratio, 1.004; 95% confidence interval, 1.001-1.006; P = 0.002).

    Conclusion: Results from our study show that most of the patients with renal hyperparathyroidism were symptomatic preoperatively and the most common clinical presentations were bone pain and muscle weakness. The significant predictor of early postoperative hypocalcemia after total parathyroidectomy was the preoperative alkaline phosphatase levels.

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