Search Results (75)

Despite the widespread adoption of network monitoring tools, Managed Service Providers (MSPs), specifically small- and medium-sized enterprises (SMEs), continue to face persistent challenges in achieving predictive, multi-tenant-aware visibility across distributed client networks. Existing monitoring systems lack integrated predictive analytics and edge intelligence. To address this, we propose an AI- and IoT-driven monitoring and visualisation framework that integrates edge IoT nodes (Raspberry Pi Prometheus modules) with machine learning models to enable predictive anomaly detection, proactive alerting, and reduced downtime. This system leverages Prometheus, Grafana, and Mimir for data collection, visualisation, and long-term storage, while incorporating Simple Linear Regression (SLR), K-Means clustering, and Long Short-Term Memory (LSTM) models for anomaly prediction and fault classification. These AI modules are containerised and deployed at the edge or centrally, depending on tenant topology, with predicted risk metrics seamlessly integrated back into Prometheus. A one-month deployment across five MSP clients (500 nodes) demonstrated significant operational benefits, including a 95% reduction in downtime and a 90% reduction in incident resolution time relative to historical baselines. The system ensures secure tenant isolation via VPN tunnels and token-based authentication, while providing GDPR-compliant data handling. Unlike prior monitoring platforms, this work introduces a fully edge-embedded AI inference pipeline, validated through live deployment and operational feedback. Full article

Background/Objectives: Unruptured intracranial aneurysms (UIAs) carry a risk of subarachnoid hemorrhage (SAH), which has a high mortality rate of up to 45% and significant long-term disability among survivors. Traditional surgical clipping and endovascular treatment (EVT) are widely used, but both have limitations: EVT shows higher recurrence and retreatment rates, while open clipping poses higher procedural risks. Minimally invasive craniotomy (MIC) techniques are emerging as a promising third option, offering potential benefits in terms of safety, durability, and recovery. This study aims to compare MIC and EVT for UIAs to evaluate their relative efficacy, safety, and long-term outcomes. Methods: A systematic literature review was conducted using PubMed and Scopus. Inclusion criteria encompassed original, peer-reviewed studies reporting clinical outcomes of UIA treatments. Data extracted included study characteristics, treatment modality, complication rates, recurrence, retreatment, and patient outcomes. Results: MIC demonstrated low complication rates (1.6–5.88%), for which the percentage was significantly lower than that for stent-assisted coiling (37%) and flow diversion (17%), while maintaining similar efficacy to traditional clipping. New EVT techniques such as WEB devices showed less procedural risks (0.7%) but higher retreatment rates. Conclusions: This review shows that while traditional craniotomy for aneurysm clipping carries higher perioperative risk than EVT, most studies have failed to compare long-term recurrences. MIC has significantly lower perioperative complications rates, comparable to EVT, and provides the same durability with improved cosmetic results. MIC should be considered when selecting patients as an alternative to EVT, particularly for unruptured anterior circulation aneurysms. Further prospective studies are needed to guide treatment decisions. Full article

Penalized regression estimators have become widely adopted alternatives to ordinary least squares while analyzing collinear data, despite introducing some bias. However, existing penalized methods lack universal superiority across diverse data conditions. To address this limitation, we propose a novel adaptive ridge estimator that automatically adjusts its penalty structure based on key data characteristics: (1) the degree of predictor collinearity, (2) error variance, and (3) model dimensionality. Through comprehensive Monte Carlo simulations and real-world applications, we evaluate the estimator’s performance using mean squared error (MSE) as our primary criterion. Our results demonstrate that the proposed method consistently outperforms existing approaches across all considered scenarios, with particularly strong performance in challenging high-collinearity settings. The real-data applications further confirm the estimator’s practical utility and robustness. Full article

This study aims to develop, implement, and evaluate an Early Warning System (EWS) to alert communities and government agencies in KwaZulu-Natal, South Africa, about extreme weather events (EWEs) and related disease outbreaks. The project focuses on eThekwini and Ugu municipalities, using a participatory, co-creation approach with communities and health providers. A systematic review will be undertaken to understand the impact of climate change on disease outbreaks and design an EWS that integrates data from rural and urban healthcare and environmental contexts. It will assess disease burden at primary healthcare clinics, examine health needs and community experiences during EWEs, and evaluate health system resilience. The project will also evaluate the design, development, and performance of the EWS intervention, including its implementation costs. Ethical approval will be sought, and informed consent obtained from participants. Based on the findings, recommendations will be made to the Department of Health to enhance early warning systems and health system resilience in response to EWEs and disease outbreaks. Full article

Reliable rainfall prediction is essential for effective climate adaptation yet remains challenging due to complex atmospheric interactions that vary across regions. This study investigates next-day rainfall predictability in tropical and temperate climates using daily atmospheric data—including pressure, temperature, dew point, relative humidity, wind speed, and wind direction—collected from topographically similar sites in Alor Setar (tropical) and Vercelli, Williams, and Ashburton (temperate) between 2012 and 2015. Logistic regression and random forest models were used to predict rainfall occurrence as a binary outcome. Key variables were identified using Wald’s statistics and p-values in the logistic regression models, while the random forest models relied on mean decrease accuracy for ranking variable importance. The results reveal that rainfall in temperate climates is significantly more predictable than in tropical regions, with the Williams model demonstrating the highest accuracy. Atmospheric pressure consistently emerged as the dominant predictor in temperate regions but was not significant in the tropical model, reflecting the greater atmospheric variability and complexity in tropical rainfall mechanisms. Crucially, the study highlights that as global warming continues to alter temperate climate patterns—bringing increased variability and more convective rainfall—these regions may experience the same predictive uncertainties currently observed in tropical climates. These findings underscore the urgency of developing robust, climate-specific rainfall prediction models that account for changing atmospheric dynamics, with critical implications for weather forecasting, disaster preparedness, and climate resilience planning. Full article

Information on circulating HBV (sub-)genotype, variants, and hepatitis D virus (HDV) coinfection, which vary by geographical area, is crucial for the efficient control and management of HBV. We investigated the genomic characteristics of HBV (with a prevalence of 8.1%) and the prevalence of HDV in Nigeria. We utilised 777 HBV-positive samples and epidemiological data from the two-stage sampled population-based, nationally representative Nigeria HIV/AIDS Indicator and Impact Survey conducted in 2018. We assessed 732 HBV DNA-extracted samples with detectable viral loads (VLs) for (sub-)genotypes and variants by whole-genome pre-amplification, nested PCR of the s-and pol-gene, and BigDye Terminator sequencing. We conducted HDV serology. In total, 19 out of the 36 + 1 states in Nigeria had a high prevalence of HBV (≥8%), with the highest prevalence (10.4%) in the north-central geopolitical zone. Up to 33.2% (95% CI 30.0–36.6) of the participants had detectable VLs of ≥300 copies/mL. The predominant circulating HBV genotype was E with 98.4% (95% CI 97.1–99.1), followed by A with 1.6% (95% CI 0.9–2.9). Drug-resistant associated variants and immune escape variants were detected in 9.3% and 0.4%, respectively. The seroprevalence of HDV was 7.34% (95% CI 5.5–9.2). Nigeria has subtype E as the major genotype with many variants. Full article

Multicollinearity, a critical issue in regression analysis that can severely compromise the stability and accuracy of parameter estimates, arises when two or more variables exhibit correlation with each other. This paper solves this problem by introducing six new, improved two-parameter ridge estimators (ITPRE): NATPR1, NATPR2, NATPR3, NATPR4, NATPR5, and NATPR6. These ITPRE are designed to remove multicollinearity and improve the accuracy of estimates. A comprehensive Monte Carlo simulation analysis using the mean squared error (MSE) criterion demonstrates that all proposed estimators effectively mitigate the effects of multicollinearity. Among these, the NATPR2 estimator consistently achieves the lowest estimated MSE, outperforming existing ridge estimators in the literature. Application of these estimators to a real-world dataset further validates their effectiveness in addressing multicollinearity, underscoring their robustness and practical relevance in improving the reliability of regression models. Full article

Patients with cerebral palsy (CP) are particularly vulnerable to respiratory infections, yet comparative outcomes between COVID-19 and influenza in this population remain underexplored. Using the National Inpatient Sample from 2020–2021, we performed a retrospective analysis of hospital data for adults with CP diagnosed with either COVID-19 or influenza. The study aimed to compare the outcomes of these infections to provide insights into their impact on this vulnerable population. We assessed in-hospital mortality, complications, length of stay (LOS), hospitalization costs, and discharge dispositions. Multivariable logistic regression and propensity score matching were used to adjust for confounders, enhancing the analytical rigor of our study. The study cohort comprised 12,025 patients—10,560 with COVID-19 and 1465 with influenza. COVID-19 patients with CP had a higher in-hospital mortality rate (10.8% vs. 3.1%, p = 0.001), with an adjusted odds ratio of 3.2 (95% CI: 1.6–6.4). They also experienced an extended LOS by an average of 2.7 days. COVID-19 substantially increases the health burden for hospitalized CP patients compared to influenza, as evidenced by higher mortality rates, longer hospital stays, and increased costs. These findings highlight the urgent need for tailored strategies to effectively manage and reduce the impact of COVID-19 on this high-risk group. Full article

Sharing confidential information is a critical concern in today’s world. Secret sharing schemes facilitate the sharing of secrets in a way that ensures only authorized participants (shareholders) can access the secret using their allocated shares. Hierarchical secret sharing schemes (HSSSs) build upon Shamir’s scheme by organizing participants into different levels based on priority. Within HSSS, participants at each level can reconstruct the secret if a specified number, denoted as the threshold value (t), or more of them are present. Each level has a predetermined threshold value. If the number of participants falls below the threshold at any level, higher-level participants must be involved in reconstructing the secret at lower levels. Our paper proposes schemes that implement hierarchical access structures and enable the sharing of multiple secrets. Additionally, our proposed scheme includes share verification. We have analyzed potential attacks and demonstrated the scheme’s resistance against them. Through security analysis and comparison with existing schemes, we highlight the novelty and superiority of our proposed approach, contributing to advancements in secure information-sharing practices. Full article

The integration of renewable energy is rapidly leading the existing grid systems toward modern hybrid power systems. These hybrid power systems are more complex due to the random and intermittent nature of RE and involve numerous operational challenges. This paper presents the operational model for solar integrated power systems to address the issues of economical operation, reliable solar share, energy deficit in case of contingency events, and the allocation of system spinning reserve. A mixed-integer optimization is formulated to minimize the overall cost of the system operation and to maximize the solar share under robust system spinning reserve limits as well as various other practical constraints. A Pareto-optimal solution for the maximization of the number of solar power plants and minimization of the solar cost is also presented for reliable solar share. Further, a decomposition framework is proposed to split the original problem into two sub-problems. The solution of joint optimization is obtained by exploiting a Lagrange relaxation method, a binary search Lambda iteration method, system spinning reserve analysis, and binary integer programming. The proposed model was implemented on an IEEE-RTS 26 units system and 40 solar plants. Full article

With the challenges brought about by the COVID-19 pandemic, China’s real-estate market has been facing new bottlenecks. The solution lies in an in-depth understanding of regional real-estate conditions. In the study of housing, remote sensing technology can help to extract building height as well as to calculate the number of floors and estimate the total amount of housing. It is more efficient and accurate compared to conventional statistical and sampling methods. Remote sensing is widely used in real-estate research and building height estimation, whereas it is less frequently used for the total estimation of urban housing. In this context, we used Chinese satellite GF-7 stereopair images, point of interest (POI) data, and other data combined with the digital surface model (DSM) and shadow methods to calculate the height of residential buildings. An efficient and accurate method system was then established for estimating the total housing and per capita living area (PCLA). According to the calculation of the PCLA of each district in Ningbo City (China), it was found that different regions were suitable for different development paths. Based on this, the driving factor model was derived and the real-estate development potential of Ningbo city was quantitatively analyzed. The results showed that Ningbo City, a first-tier city with a large population inflow, still has potential for real-estate development. Full article

Lakes may take a while to respond to management interventions because of the management implications of incremental development and degradation issues. This includes the requirement for the ongoing participation of key lake basin management institutions and their operations. This study’s objective is to assess the impacts of land use activities along the Kenyir Lake Basin based on the sedimentation problem level. There are a few hydrological methods that are necessary indicators to measure the level of sediment production, such as Total Suspended Solid (TSS), area of sub-catchment, river discharge measurement, and annual sediment load production. The results showed that the sub-catchment of Besar River released the lowest annual average estimation at 3833.70 kg/km²/year, and the sub-catchment of Kenyir River produced the highest annual average estimation at 128,070.86 kg/km²/year for annual sediment load flow produced from tributary rivers into Kenyir Lake. Kenyir Lake Basin’s downstream and midstream regions had higher sediment load values than its upstream regions. This study highlighted the significance of the effects of anthropogenic factors, hydrological, geomorphological, growth, and developmental factors, and climate changes as the key variables attributing to the sedimentation phenomenon along the Kenyir Lake Basin. The construction of a long-term lake or reservoir catchment development and management plan, combined with the formation of a vision and comprehensive strategic plan, are vital components of sound management practice. The efficient implementation of the suggested watershed management programmes depends on the active involvement of all significant catchment stakeholders. Full article

This study investigated the phycoremediation abilities of Chlorella vulgaris (microalga) and Anabaena variabilis (cyanobacterium) for the detoxification of polluted river water. Lab-scale phycoremediation experiments were conducted for 20 days at 30 °C using the microalgal and cyanobacterial strains and water samples collected from the Dhaleswari river in Bangladesh. The physicochemical properties such as electrical conductivity (EC), total dissolved solids (TDS), biological oxygen demand (BOD), hardness ions, and heavy metals of the collected water samples indicated that the river water is highly polluted. The results of the phycoremediation experiments demonstrated that both microalgal and cyanobacterial species significantly reduced the pollutant load and heavy metal concentrations of the river water. The pH of the river water was significantly raised from 6.97 to 8.07 and 8.28 by C. vulgaris and A. variabilis, respectively. A. variabilis demonstrated higher efficacy than C. vulgaris in reducing the EC, TDS, and BOD of the polluted river water and was more effective at reducing the pollutant load of SO₄²⁻ and Zn. In regard to hardness ions and heavy metal detoxification, C. vulgaris performed better at removing Ca²⁺, Mg²⁺, Cr, and Mn. These findings indicate that both microalgae and cyanobacteria have great potential to remove various pollutants, especially heavy metals, from the polluted river water as part of a low-cost, easily controllable, environmentally friendly remediation strategy. Nevertheless, the composition of polluted water should be assessed prior to the designing of microalgae- or cyanobacteria-based remediation technology, since the pollutant removal efficiency is found to be species dependent. Full article