Real-time Surveillance Research Articles

Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques

In the contemporary realm of industry, the imperative for influential and steadfast systems to detect anomalies is critically recognized. Our study introduces a cutting-edge approach utilizing a deep learning model of the Long-Short Term Memory variety, meticulously crafted for real-time surveillance and mitigation of irregularities within industrial settings. Through the careful amalgamation of data acquisition and analytic processing informed by our model, we have forged a system adept at pinpointing anomalies with high precision, capable of autonomously proposing or implementing remedial measures. The findings demonstrate a marked enhancement in the efficacy of operations, with the model’s accuracy surging to 95%, recall at 90%, and an F1 score reaching 92.5%. Moreover, the system has favorably impacted the environment, evidenced by a 25% decline in CO2 emissions and a 20% reduction in water usage. Our model surpasses preceding systems, showcasing significant gains in speed and precision. This research corroborates the capabilities of deep learning within the industrial sector. It underscores the role of automated systems in fostering more sustainable and efficient operations in the contemporary industrial landscape.

Retraction Note: Pedestrian identification using motion-controlled deep neural network in real-time visual surveillance

Retraction Note: Pedestrian identification using motion-controlled deep neural network in real-time visual surveillance

Cellulose-based halochromic sensor for real-time surveillance of spoilage of packed fish

The development of a biopolymer-based color-changing visual sensor for monitoring the spoilage of fishery products is described in this study. At first, a cellulose-based composite film containing mixed-indicator dyes was fabricated. The film was characterized using UV–visible and FT-IR spectroscopy, scanning electron microscopy, thermogravimetric analysis, lab, and RGB measurements. The halochromic properties of the film were evaluated through ammonia test and colorimetry. The developed film sensor is tested for monitoring the spoilage of packed seer fish fillets at room temperature for up to 48 h and at 4 °C for up to 10 days. The sensor displayed distinct color changes correlating with the spoilage of the packed fish. Analysis of total volatile basic nitrogen and pH levels of the samples revealed a correlation between the halochromism of the sensor and the spoilage of the fish. The color-changing pattern of the sensor was consistent with the results of microbial analysis. The present study reveals a promising material combination for fabricating simple visual sensors from biomaterials, which can be used to monitor the real-time deterioration of packaged fishery products.

Near real-time severe acute respiratory illness surveillance characterising influenza and COVID-19 epidemiology in hospitalised adults, 2021-22

We report the findings of a novel enhanced syndromic surveillance which characterised influenza- and SARS-CoV-2-associated severe acute respiratory illness (SARI) in the 2021/2022 winter season. Prospective cohort study of adults admitted to the Queen Elizabeth University Hospital, Glasgow, with a severe acute respiratory illness. Patient demographics, clinical history, admission details, and outcome were recorded. Data were available to Public Health Scotland (PHS) and clinicians weekly. Between November 2021 and May 2022, 1,063 hospitalised SARI episodes in 1,037 adult patients were identified. Median age was 72.0 years and 44.5% were male. Most (82.6%) SARI cases had >1 co-morbidity; chronic lung disease (50.0%) and malignancy (22.5%) were the most frequently reported. Overall, 229 (22%) and 33 (3%) SARI episodes were SARS-CoV-2 and influenza A PCR positive, respectively. 74.7%, 6.5% and 43.0% SARI episodes received antibiotics, antivirals, and steroids, respectively (54.5, 11.0 and 51.3% among COVID-19 patients). 1.1% required mechanical ventilation and 7.8% died. Male sex, multimorbidity, frailty, respiratory rate >30, low GCS and chest X-ray consolidation were predictive of in-hospital mortality. Near real-time hospitalised SARI syndromic surveillance characterised the evolving clinical epidemiology of SARS-CoV-2 and influenza, high antimicrobial use, and predictors of inpatient mortality among hospitalised SARI patients.

Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks

Convolutional neural network (CNN)-based synthetic aperture radar (SAR) ship detection models operating directly on satellites can reduce transmission latency and improve real-time surveillance capabilities. However, limited satellite platform resources present a significant challenge. Post-training quantization (PTQ) provides an efficient method for pre-training neural networks to effectively reduce memory and computational resources without retraining. Despite this, PTQ faces the challenge of maintaining model accuracy, especially at low-bit quantization (e.g., 4-bit or 2-bit). To address this challenge, we propose a hierarchical mixed-precision post-training quantization (HMPTQ) method for SAR ship detection neural networks to reduce quantization error. This method encompasses a layerwise precision configuration based on reconstruction error and an intra-layer mixed-precision quantization strategy. Specifically, our approach initially utilizes the activation reconstruction error of each layer to gauge the sensitivity necessary for bit allocation, considering the interdependencies among layers, which effectively reduces the complexity of computational sensitivity and achieves more precise quantization allocation. Subsequently, to minimize the quantization error of the layers, an intra-layer mixed-precision quantization strategy based on probability density assigns a greater number of quantization bits to regions where the probability density is low for higher values. Our evaluation on the SSDD, HRSID, and LS-SSDD-v1.0 SAR Ship datasets, using different detection CNN models, shows that the YOLOV9c model with mixed-precision quantization at 4-bit and 2-bit for weights and activations achieves only a 0.28% accuracy loss on the SSDD dataset, while reducing the model size by approximately 80%. Compared to state-of-the-art methods, our approach maintains competitive accuracy, confirming the superior performance of the HMPTQ method over existing quantization techniques.

Dengue Virus Serotypes 1 and 2 Responsible for Dengue Burden in Amazonas: Epidemiological and Clinical Features.

The increase in dengue cases in Amazonas poses a public health risk. This study aimed to examine the circulating serotypes in the region of Amazonas, Peru, from 2021 to 2023, along with the associated clinical and epidemiological characteristics. Serotype identification was determined for 420 serum samples using a multiplex real-time reverse transcription-polymerase chain reaction assay, revealing that 194 patients were infected by dengue virus (DENV)-1 and 226 by DENV-2. Both serotypes co-circulated in all affected provinces. Warning signs were more frequent in patients with DENV-2 (P = 0.004), and the manifestation of abdominal pain was correlated with this serotype (P = 0.004), highlighting its association with severe forms of the disease. In conclusion, this study emphasizes the widespread circulation of DENV-1 and DENV-2 in Amazonas. Furthermore, it underscores the importance of real-time molecular surveillance that could provide an early prognosis of disease severity.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Pediatric tonsilitis: modern tactics against an old foe

Abstract Background Pediatric tonsillitis (PT) impacts public health due to its widespread incidence and the prevalent overprescription of antibiotics driven by concerns over Group A β-hemolytic streptococcus (SGA) complications. Rapid antigen diagnostic tests (RADT) serve as vital tools for promptly identifying SGA infections in PT cases. However, despite their widespread availability, healthcare professionals often underutilize RADT, leading to inappropriate antibiotic prescriptions. Clinical audits and feedback to professionals emerge as solutions to this issue. Methods An audit was conducted on clinical cases of PT during 2023 to evaluate the correct usage of RADT and subsequent antibiotic prescription across various primary care practices in Lisbon, Portugal. Afterwards, a pilot practice was selected to assess the feasibility and utility of an online tool designed specifically to report RADT results and create a real-time surveillance system of incidence of SGA with feedback to prescribing clinicians. Following the revision of the reporting tool after the pilot, its implementation was extended to multiple practices across the Lisbon region. Results From the 1050 clinical cases analyzed, 992 had an indication to do a RADT according to the Portuguese guidelines, however only 282 (28.4%) tests were done. Overall, only 19.6% of PT cases were considered fully compliant. During the pilot, the introduction of the online tool led to a 3.259 increase in reporting (from 0.574 RADT/day to 1.872 RADT/day) for the same time period between February and March. Conclusions The implementation of an online reporting tool represents a significant advancement in SGA surveillance efforts, detecting cases promptly for public health interventions. This initiative underscores the importance of continuous quality improvement in diagnostics and shows digital tools’ potential for better disease surveillance in clinical settings. Key messages • Underutilization of RADT in PT cases contributes to inappropriate antibiotic prescriptions, compromising patient care. • Introduction of an online reporting tool significantly enhances RADT usage, aiding prompt SGA detection for improved public health interventions.

1.L. Scientific session: Public Health data in support of healthy ageing policies

Abstract As Europe grapples with an ageing population, the imperative of health policies that promote healthy ageing is increasingly critical. This workshop aims to explore the significant role of public health data in shaping and supporting such policies. By integrating insights from administrative records to sophisticated research infrastructures, we will highlight the multifaceted uses of data in public health, particularly in the realms of non-communicable diseases and health-related behaviours in older populations. The session will open by examining how administrative records have been utilised within Lombardy’s regional health system to understand the epidemiology and access to care for people living with dementia. The discussion will then cover the advantages and challenges associated with integrating health data from diverse sources, emphasising the importance of quality, privacy, and the potential for real-time surveillance and response systems in healthcare. Following this, the focus will shift to the determinants of good health among older adults, presenting findings from a comprehensive cross-sectional study that identifies key factors contributing to what can be termed as “superhero” health status: seniors who maintain excellent physical and mental health. This analysis will delve into socioeconomic, behavioural, and environmental factors that influence health outcomes in the elderly, providing evidence-based recommendations for public health interventions and highlighting the potentialities of ad hoc surveys in examining health outcomes during emergencies. Thirdly, the workshop will also address the impact of significant life transitions on health behaviours, specifically the transition to retirement and its effects on alcohol consumption. Leveraging longitudinal data from a pan-European multidisciplinary research infrastructure, it will be demonstrated how retirement affects daily habits and lifestyles, including changes in alcohol consumption patterns. This segment will explore the implications of these findings for public health planning and policy formulation. In conclusion, there will be a discussion about healthcare system policies that support healthy ageing and the global impact of ageing on economics and health economics. These discussions will draw from the presented data sources and studies to suggest ways in which public health policies can be designed to reduce disparities and improve overall health outcomes among the elderly. This workshop is designed to provide a comprehensive overview of how data-driven insights can inform effective public health strategies and interventions to support an ageing society. By exploring various aspects of health data application, from individual behaviour to systemic policy adjustments, the session aims to highlight the critical role of data in promoting sustainable health and well-being in older adults, especially in the context of increasing life expectancy and prolonged retirement periods. Key messages • Harnessing data from diverse sources is crucial for understanding and enhancing healthy ageing processes. • Utilising longitudinal studies and robust data to design informed public health policies is essential for effective ageing interventions.

MAIT cells: Conserved watchers on the wall.

MAIT cells are innate-like T cells residing in barrier tissues such as the lung, skin, and intestine. Both the semi-invariant T cell receptor of MAIT cells and the restricting element MR1 are deeply conserved across mammals, indicating non-redundant functions linked to antigenic specificity. MAIT cells across species concomitantly express cytotoxicity and tissue-repair genes, suggesting versatile functions. Accordingly, MAIT cells contribute to antibacterial responses as well as to the repair of damaged barrier tissues. MAIT cells recognize riboflavin biosynthetic pathway-derived metabolites, which rapidly cross epithelial barriers to be presented by antigen-presenting cells. Changes in gut ecology during intestinal inflammation drive the expansion of strong riboflavin and MAIT ligand producers. Thus, MAIT cells may enable real-time surveillance of microbiota dysbiosis across intact epithelia and provide rapid and context-dependent responses. Here, we discuss recent findings regarding the origin and regulation of MAIT ligands and the role of MAIT cells in barrier tissues. We speculate on the potential reasons for MAIT cell conservation during evolution.

Deep learning-based video surveillance system for suspicious activity detection

The need for reliable video surveillance systems to detect and prevent suspicious activities has become more important with the increase in crime and security threats. This paper proposes a real-time video surveillance system based on the Long-term Recurrent Convolutional Network (LRCN) model, which can automatically detect and alert the authority about suspicious activities, such as fighting, accidents, and robbery. Our system comprises two main components: LRCN-based activity recognition and real-time alert generation. We evaluated the performance of the proposed system on a custom dataset compiled from two publicly available datasets and achieved state-of-the-art results in terms of accuracy, precision, and recall. Our results demonstrate the effectiveness and scalability of the LRCN-based video surveillance system for real-time suspicious activity detection. We believe that our proposed system can be deployed in various public places, such as airports, train stations, and shopping malls, to enhance the security and safety of the public.

Understanding impact and factors that improve postvention service delivery: findings from a study of a community-based suicide bereavement support service in England

ABSTRACT This article reports on a two-year study of a community-based suicide postvention support service in the UK. Attention centres on the implementation of four integrated elements within the service model: multi-agency working with real-time suicide surveillance, persistent pro-active contact; volunteer-led support; continuous open-ended support. Our approach involved analysing utilisation data and qualitative techniques, including in-depth interviews with a diverse group of stakeholders. The study is the first of its kind to provide an in-depth examination of an innovative approach focusing on the integrated model and its constituent components. Our findings offer evidence-based examples of collaborative service efforts in implementing real-time interventions, with agencies working together to provide timely, co-ordinated, proactive responses and support with the potential to alleviate suffering and prevent loss of life. The value of volunteer-led support and open-ended access and associated challenges are emphasised. Findings hold promise for international impact with policy implications for services worldwide.

Patterns of HIV-1 Drug Resistance Observed Through Geospatial Analysis of Routine Diagnostic Testing in KwaZulu-Natal, South Africa.

HIV-1 drug resistance (HIVDR) impedes treatment and control of HIV-1, especially in high-prevalence settings such as KwaZulu-Natal (KZN) province, South Africa. This study merged routine HIV-1 genotypic resistance test (GRT) data with Geographic Information Systems coordinates to assess patterns and geographic distribution of HIVDR in KZN, among ART-experienced adults with virological failure. We curated 3133 GRT records generated between 1 January 2018 and 30 June 2022, which includes the early phase of dolutegravir (DTG) rollout, of which 2735 (87.30%) had HIVDR. Of the 2735, major protease, nucleoside, and non-nucleoside reverse transcriptase inhibitor mutations were detected in 41.24%, 84.97% and 88.08% of GRTs, respectively. Additional genotyping of HIV-1 integrase for 41/3133 (1.31%) GRTs showed that 17/41 (41.46%) had integrase strand transfer inhibitor resistance. Notably, of 26 patients on DTG with integrase genotyping, 9 (34.62%) had DTG-associated resistance mutations. Dual- or triple-class resistance was observed in four of every five GRTs. The odds of HIVDR increased significantly with age, with ≥60 years having 5 times higher odds of HIVDR compared to 18-29 years (p = 0.001). We identified geospatial differences in the burden of HIVDR, providing proof of concept that this could be used for data-driven public health decision making. Ongoing real-time HIVDR surveillance is essential for evaluating the outcomes of the updated South African HIV treatment programme.

Low cost and real-time surveillance of enteric infection and diarrhoeal disease using rapid diagnostic tests in Cox’s Bazar, Bangladesh

BackgroundReal-time disease surveillance is an important component of infection control in at-risk populations. However, data on cases or from lab testing is often not available in many low-resource settings. Rapid diagnostic tests (RDT), including immunochromatographic assays, may provide a low cost, expedited source of infection data.MethodsWe conducted a pilot survey-based prevalence mapping study of enteric infection in Camp 24 of the camps for the forcibly displaced Rohingya population from Myanmar in Cox’s Bazar, Bangladesh. We randomly sampled the population and collected and tested stool from under-fives for eight pathogens using RDTs in January–March 2021 and September–October 2021. A Bayesian geospatial statistical model allowing for imperfect sensitivity and specificity of the tests was adapted.ResultsWe collected and tested 396 and 181 stools in the two data collection rounds. Corrected prevalence estimates ranged from 0.5% (Norovirus) to 27.4% (Giardia). Prevalence of Escherichia coli O157, Campylobacter, and Cryptosporidium were predicted to be higher in the high density area of the camp with relatively high probability (70–95%), while Adenovirus, Norovirus, and Rotavirus were lower in the areas with high water chlorination. Clustering of cases of Giardia and Shigella was also observed, although associated with relatively high uncertainty.ConclusionsWith an appropriate correction for diagnostic performance RDTs can be used to generate reliable prevalence estimates, maps, and well-calibrated uncertainty estimates at a significantly lower cost than lab-based studies, providing a useful approach for disease surveillance in these settings.

Molecular Epidemiology and AMR Perspective of Diarrhoeagenic Escherichia coli in Africa: A Systematic Review and Meta-analysis.

Diarrhoeagenic Escherichia coli (DEC) persistently challenges public health in Africa, contributing substantially to the diarrhoeal disease burden. This systematic review and meta-analysis illuminate the distribution and antimicrobial resistance (AMR) patterns of DEC pathotypes across the continent. The review selectively focused on pathotype-specific studies reporting prevalence and/or AMR of human-derived DEC pathotypes from African nations, excluding data from extra-intestinal, animal, and environmental sources and studies focused on drug and mechanism experiments. Pertinent studies were retrieved from SCOPUS, PubMed, and EBSCOhost, processed with Covidence, and screened in alignment with PRISMA guidelines. The reviewed studies were predominantly hospital-based (80%) and paediatric-focused (91%), with a meagre 4.4% documenting DEC outbreaks. Seven DEC pathotypes were discerned, with Enteroaggregative E. coli (EAEC) being notably prevalent (43%, 95% CI 30-55%) and Enteroinvasive E. coli (EIEC) least prevalent (24%, 95% CI 17-32%). Identified non-susceptibilities were noted against essential antibiotics including ciprofloxacin, ceftriaxone, and ampicillin, while instances of carbapenem and Extended-Spectrum ß-Lactamase (ESBL) resistance were scarce. Despite sporadic data on DEC prevalence and AMR in Africa, particularly in community settings, a palpable gap remains in real-time outbreak surveillance and comprehensive data documentation. Augmenting surveillance and embracing advancements in molecular/genomic characterisation techniques are crucial to precisely discerning DEC's actual impact and resistance continuum in Africa.

The effect of particle size, meteorological parameters, and building airtightness on particulate matters infiltration

Introduction: The present study analyzed the infiltration behavior of size- fractionated particles and the influencing parameters. Materials and methods: The studies were carried out in two apartments under varying conditions of airtightness, utilizing real-time surveillance of Particulate Matters (PM0.3, PM1.0, PM2.5, PM10), along with air exchange rates and meteorological factors. The seasonal variations of the indoor dissemination of particulate matters have been also discussed. Results: The analysis of correlations indicated a strong dependency of indoor Particulate Matter (PM) concentrations on outdoor levels. However, the penetration patterns varied across different particle sizes. The highest contribution to outdoor PM10 was observed for PM 2.5-10 while indoors, the predominant particle size was among the finer categories, PM0.3-1.0. Moreover, window air-tightening appeared to decrease the overall effective leakage area of the building envelope, which in turn slightly lowered the ratio of indoor to outdoor PMs as well as the infiltrability for particles of all sizes. However, this intervention did not alter the distribution of particles within indoor environments.The most penetrated particles were observed in the size range 0.3-1.0 µm and then PM<0.3 µm, and the least in the size range 2.5-10 µm. Conclusion: Particle dimensions and external sources primarily influenced the degree of particle infiltration, significantly overshading the impact of weather-related factors. The relationship between indoor and outdoor particulate matter was diminished by the airtightness of windows, particularly for larger particles. No notable difference was observed in the infiltrability and indoor distribution of particles of varying sizes between the winter and fall seasons.

Selected Acute Safety Events Following the Use of Nirmatrelvir/Ritonavir or Molnupiravir for COVID-19: A Nationwide Cohort Study in South Korea.

There had been concerns about the acute complications during or shortly after coronavirus disease 2019 (COVID-19) treatment with nirmatrelvir/ritonavir (NMVr) and molnupiravir (MOL). This study aimed to compare the risks of selected acute safety events in patients treated with or without NMVr or MOL using the COVID-19 oral treatment safety assessment data, constructed through the linkage of nationwide databases: National COVID-19 registry, Real-time Prescription Surveillance, and National Health Insurance data. We identified all adults diagnosed with COVID-19 between January and November 2022, and then constructed two cohorts by matching up to four patients without antiviral treatment records to NMVr or MOL users using propensity score matching. Outcomes of interest were incident-selected cardiac (i.e., atrial fibrillation, other arrhythmia, bradycardia), neurological (i.e., seizure, neuropathy, encephalomyelitis), and miscellaneous (i.e., acute pancreatitis, acute liver injury, dysgeusia) events. A total of 739,935 NMVr users were matched with 2,951,690 comparators and 150,431 MOL users with 759,521 comparators. NMVr users were at lower risk for developing selected cardiac events (hazard ratio 0.74 [95% CI 0.65-0.87] for atrial fibrillation, 0.81 [0.65-0.99] for other arrhythmia, and 0.82 [0.70-0.96] for bradycardia) and dysgeusia (0.58 [0.45-0.74]). For MOL users, the risk was lower for atrial fibrillation (0.72 [0.53-0.96]) and dysgeusia (0.34 [0.18-0.65]). Overall, there were no increased risks of acute complications during and shortly after treatment with oral COVID-19 antivirals. Rather, the findings underscore their effectiveness in attenuating the risk of potential acute sequelae of COVID-19.

Comparative study of real-time photovoltaic fault diagnosis using artificial intelligence: Fuzzy logic and neural network approaches

ABSTRACT Solar photovoltaic (PV) systems are becoming increasingly popular for renewable energy production. However, due to environmental and operational conditions, various faults can occur in PV modules, which can cause a significant reduction in system performance. This study suggests employing two distinct approaches, Fuzzy Logic (FL) and Artificial Neural Networks (ANN), to diagnose defects in photovoltaic (PV) systems. Both the simulation and measuring are used to compare the performance of various strategies. The simulation was performed using MATLAB/Simulink, while the experiment was carried out using the dSPACE DS1104 platform to apply the diagnostic model built in the Matlab/Simulink® program. The suggested approaches have been verified using an experimental database of climatic and electrical attributes obtained from a photovoltaic (PV) panel situated at the LGEB Laboratory of the University of Biskra in Algeria. For six single- and multi-fault types, partial shading, soiling, SC of one by-pass diode, SC of two by-pass diodes, by-pass diode shunted and shading & by-pass diode disconnected. A dSPACE DS1104 platform which shows the results and informs the user about the state of the PV panel has also been developed. The results of the simulation show that both FL and ANN methods can accurately detect and diagnose the six types of faults by 100% and 99.7%, respectively. The study demonstrates the effectiveness of both FL and ANN methods for PV fault diagnosis. However, in the experimental tests, the ANN method shows better performance in terms of accuracy compared to FL by 99.6% and 99.2%, respectively, and speed takes only 1.04s, while FL consumes 3.02s and could be a more practical solution for real-time surveillance PV fault diagnosis

Bioinformatic identification of monkeypox virus phylogenetic gene trees that are representative of its whole-genome phylogenetic tree.

Phylogenetic analysis based on whole-genome sequences is the gold standard for monkeypox virus (MPXV) phylogeny. However, genomic epidemiology capability and capacity are lacking or limited in resource poor countries of sub-Saharan Africa. Therefore, these make real-time genome surveillance of MPXV virtually impossible. We hypothesized that phylogenetic analysis based on single, conserved genes will produce phylogenetic tree topology consistent with MPXV whole-genome phylogeny, thus serving as a reliable proxy to phylogenomic analysis. In this study, we analyzed 62 conserved MPXV genes and showed that Bayesian phylogenetic analysis based on five genes (OPG 066/E4L, OPG068/E6R, OPG079/I3L, OPG145/A18R, and OPG150/A23R) generated phylogenetic trees with 72.2-96.3% topology similarity index to the reference phylogenomic tree topology. Our results showed that phylogenetic analysis of the identified five genes singly or in combination can serve as surrogate for whole-genome phylogenetic analysis, and thus obviates the need for whole-genome sequencing and phylogenomic analysis in regions where genomic epidemiology competence and capacity are lacking or unavailable. This study is relevant to evolution and genome surveillance of MPXV in resource limited countries.

Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.