Search Results (873)

Internet of Things (IoT) applications and services have transformed the way people interact with their environment, enhancing comfort and quality of life. Additionally, Machine Learning (ML) approaches show significant promise for detecting intrusions in IoT environments. However, the high dimensionality, class imbalance, and complexity of network traffic—combined with the dynamic nature of sensor networks—pose substantial challenges to the development of efficient and effective detection algorithms. In this study, a multi-objective metaheuristic optimization approach, referred to as MOOIDS-IoT, is integrated with ML techniques to develop an intelligent cybersecurity system for IoT environments. MOOIDS-IoT combines a Genetic Algorithm (GA)-based feature selection technique with a multi-objective Particle Swarm Optimization (PSO) algorithm. PSO optimizes convergence speed, model complexity, and classification accuracy by dynamically adjusting the weights and thresholds of the deployed classifiers. Furthermore, PSO integrates Pareto-based multi-objective optimization directly into the particle swarm framework, extending conventional swarm intelligence while preserving a diverse set of non-dominated solutions. In addition, the GA reduces training time and eliminates redundancy by identifying the most significant input characteristics. The MOOIDS-IoT framework is evaluated using two lightweight models—MOO-PSO-XGBoost and MOO-PSO-RF—across two benchmark datasets, namely the NSL-KDD and CICIoT2023 datasets. On CICIoT2023, MOO-PSO-RF obtains 91.42% accuracy, whereas MOO-PSO-XGBoost obtains 98.38% accuracy. In addition, both models perform well on NSL-KDD (MOO-PSO-RF: 99.66% accuracy, MOO-PSO-XGBoost: 98.46% accuracy). The proposed approach is particularly appropriate for IoT applications with limited resources, where scalability and model efficiency are crucial considerations. Full article

Pathogenic variants in the MAP1B gene have been associated with neurological impairment, including intellectual disability, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, brain malformations, cognitive hearing loss, short stature, and dysmorphic features. However, few cases with detailed clinical characterization have been reported. We describe a 12-year-old boy carrying a loss-of-function MAP1B variant, presenting with severe elimination disorders despite normal intelligence. He was referred to the genetics service due to persistent elimination issues, including daytime urinary incontinence, nocturnal enuresis, and fecal incontinence. He had normal motor and cognitive development, with an IQ of 99; however, he also presented with ADHD, short stature, microcephaly, and myopia. Brain MRI revealed bilaterial subependymal periventricular nodular heterotopia (PVNH). Audiometry showed normal bilateral hearing. Testing fragile X syndrome (FXS) and karyotype analyses yielded normal results. Whole exome sequencing (WES) revealed a nonsense pathogenic variant in MAP1B (c.895 C>T; p.Arg299*). No other family members showed a similar phenotype; however, a great-uncle and a great-aunt had a history of nocturnal enuresis until age 10. The patient’s deceased mother had short stature and psychiatric disorders, and a history of consanguinity was reported on the maternal side. This case broadens the phenotypic spectrum associated with MAP1B syndrome, suggesting that elimination disorder, frequently reported in FXS, should also be evaluated in MAP1B pathogenic variant carriers. In addition, the presence of short stature also appears to be part of the syndrome. Full article

Background/Objectives: To identify factors associated with the referral by a multiprofessional team to dental services for children and adolescents with rare genetic diseases. Methods: A cross-sectional study was developed with 87 children/adolescents with mucopolysaccharidosis (n = 26) and osteogenesis imperfecta (n = 61) and their caregivers. Recruitment took place at reference centers for rare genetic conditions in five Brazilian states. The caregivers answered a questionnaire on the children. They were examined for malocclusion, dental anomalies, caries experience, and gingivitis. Bivariate and multivariate analyses of the data were performed, considering a 95% confidence level. Results: The average age of children/adolescents was 10.4 years (±5.6) and 17.3% had never gone to a dentist. Among those with past dental experience, the reason for most appointments was oral prophylaxis/preventive maintenance (62.1%). With regard to referrals to a dentist by the multidisciplinary team, 29.9% had never received a referral. The likelihood of having been referred to a dentist by the multiprofessional team was 2.67 times greater for female patients (95% CI: 0.96–7.42) and 7.74 times greater for children/adolescents with a history of toothache (95% CI: 1.61–37.14). Conclusions: Female children/adolescents with mucopolysaccharidosis and osteogenesis imperfecta and those with a history of dental pain were more likely to have been advised by the multiprofessional team to seek dental treatment. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article

National parks serve as critical spatial units for conserving ecological baselines, maintaining genetic diversity, and delivering essential ecosystem services. However, accelerating socio-economic development has increasingly intensified the conflict between ecological protection and transportation infrastructure. Ecologically sustainable transportation planning is, therefore, essential to mitigate habitat fragmentation, facilitate species migration, and conserve biodiversity. This study examines the Giant Panda National Park and its buffer zone, focusing on six mammal species: giant panda, Sichuan snub-nosed monkey, leopard cat, forest musk deer, rock squirrel, and Sichuan takin. By integrating Maxent ecological niche modeling with circuit theory, it identified ecological source areas and potential corridors, and employed a two-step screening approach to design species-specific wildlife crossings. In total, 39 vegetated overpasses were proposed to serve all target species; 34 underpasses were integrated using existing bridge and culvert structures to minimize construction costs; and 27 canopy bridges, incorporating suspension cables and elevated pathways, were designed to connect forest canopies for arboreal species. This study established a multi-species and multi-scale conservation framework, providing both theoretical insights and practical strategies for ecologically integrated transportation planning in national parks, contributing to the synergy between biodiversity conservation and sustainable development goals. Full article

Background/Objectives: Wolf–Hirschhorn syndrome (WHS; OMIM #194190) is a rare neurodevelopmental disorder, caused by deletions in the distal short arm of chromosome 4. It is characterized by developmental delay, epilepsy, intellectual disability, and distinctive facial dysmorphism. Clinical presentation varies widely, complicating prognosis and individualized care. Methods: We assembled a cohort of 140 individuals with genetically confirmed WHS from Spain and Latin-America, and developed and validated a multidimensional, Clinician-Reported Outcome Assessment (ClinRO) based on the Global Functional Assessment of the Patient (GFAP), derived from standardized clinical questionnaires and weighted by HPO (Human Phenotype Ontology) term frequencies. The GFAP score quantitatively captures key functional domains in WHS, including neurodevelopment, epilepsy, comorbidities, and age-corrected developmental milestones (selected based on clinical experience and disease burden). Results: Higher GFAP scores are associated with worse clinical outcomes. GFAP showed strong correlations with deletion size, presence of additional genomic rearrangements, sex, and epilepsy severity. Ward’s clustering and discriminant analyses confirmed GFAP’s discriminative power, classifying over 90% of patients into clinically meaningful groups with different prognoses. Conclusions: Our findings support GFAP as a robust, WHS-specific ClinRO that may aid in stratification, prognosis, and clinical management. This tool may also serve future interventional studies as a standardized outcome measure. Beyond its clinical utility, GFAP also revealed substantial social implications. This underscores the broader socioeconomic burden of WHS and the potential value of GFAP in identifying high-support families that may benefit from targeted resources and services. Full article

Driven by the rapid expansion of e-commerce in China, there is a growing demand for high-efficiency, sustainability-oriented logistics solutions in rural regions, particularly for the time-sensitive distribution of perishable agricultural commodities. Traditional logistics systems face considerable challenges in these geographically complex regions due to limited infrastructure and extended travel distances. To address these issues, this study proposes an intelligent cooperative delivery system that integrates automated drones with conventional trucks, aiming to enhance both operational efficiency and environmental sustainability. A mixed-integer linear programming (MILP) model is developed to account for the diverse terrain characteristics of rural China, including forest, lake, and mountain regions. To optimize distribution strategies, the model incorporates an improved Fuzzy C-Means (FCM) algorithm combined with a hybrid genetic simulated annealing algorithm. The performance of three transportation modes, namely truck-only, drone-only, and truck–drone integrated delivery, was evaluated and compared. Sustainability-related externalities, such as carbon emission costs and delivery delay penalties, are quantitatively integrated into the total transportation cost objective function. Simulation results indicate that the cooperative delivery model is especially effective in lake regions, significantly reducing overall costs while improving environmental performance and service quality. This research offers practical insights into the development of sustainable intelligent transportation systems tailored to the unique challenges of rural logistics. Full article

Enhancing the seismic resilience of urban water distribution networks (WDNs) requires the improvement of both earthquake resistance and rapid recovery capabilities within the system. This paper proposes a multi-objective method to enhance the seismic resilience of the WDNs, focusing on system restoration capabilities while comprehensively considering the hydraulic recovery index, maintenance time, and maintenance cost. The method utilizes a random simulation approach to generate various damage scenarios for the WDN, considering pipe leakage, pipe bursts, and variations in node flow resulting from changes in water pressure. It characterizes the functions of the WDN through hydraulic service satisfaction and quantifies system resilience using a performance response function. Additionally, it determines the optimal dispatch strategy for emergency repair teams and the optimal emergency repair sequence for earthquake-damaged networks using a genetic algorithm. Furthermore, a comprehensive computational platform has been developed to systematically analyze and optimize seismic resilience strategies for WDNs. The feasibility of the proposed method is demonstrated through an example involving the WDN in Xi’an City. The results indicate that the single-objective seismic resilience improvement method based on the hydraulic recovery index is the most effective for enhancing the seismic resilience of the WDN. In contrast, the multi-objective method proposed in this article reduces repair time by 17.9% and repair costs by 3.4%, while only resulting in a 0.2% decrease in the seismic resilience of the WDN. This method demonstrates the most favorable comprehensive restoration effect, and the success of our method in achieving a symmetrically balanced restoration outcome demonstrates its value. The proposed methodology and software can provide both theoretical frameworks and technical support for urban WDN administrators. Full article

Staphylococcus aureus (S. aureus) is a major cause of opportunistic infections in humans and animals, leading to severe systemic diseases. The rise of MDR strains associated with animal carriage poses significant health challenges, underscoring the need to investigate animal-derived S. aureus. Objectives: This study examined the genotypic relatedness and phenotypic profiles of antimicrobial resistance in S. aureus, previously sampled from nostril swabs of healthy horses from two geographically distant Brazilian states (Northeast and South), separated by over 3700 km. The study also sought to confirm the presence of methicillin-resistant (MRSA) and borderline oxacillin-resistant (BORSA) strains and to characterize the isolates through molecular typing using PCR. Methods: Among 123 screened staphylococci, 21 isolates were confirmed as S. aureus via biochemical tests and PCR targeting species-specific genes (femA, nuc, coa). Results: REP-PCR analysis generated genotypic profiles, revealing four antimicrobial resistance patterns, with MDR observed in ten isolates. Six isolates exhibited cefoxitin resistance, suggesting methicillin resistance, despite the absence of the mecA gene. REP-PCR demonstrated high discriminatory power, grouping the isolates into five major clusters. Conclusions: The genotyping indicated no clustering by geographical origin, highlighting significant genetic diversity among S. aureus strains colonizing horses’ nostrils in Brazil. These findings highlight the widespread and varied nature of S. aureus among horses, contributing to a deeper understanding of its epidemiology and resistance profiles in animals across diverse regions. Ultimately, this genetic diversity can pose a public health risk that the epidemiological surveillance services must investigate. Full article

This study systematically investigates the degradation and failure prediction of pipeline materials in sulfur-containing environments, with a particular focus on X52 pipeline steel exposed to high-sulfur environments. Through uniaxial tensile tests to assess mechanical properties, it was found that despite surface corrosion and a reduction in overall structural load-bearing capacity, the intrinsic mechanical properties of X52 steel did not exhibit significant degradation and remained within standard ranges. The Johnson–Cook constitutive model was developed to accurately capture the material’s plastic behavior. Subsequently, a genetic algorithm-optimized backpropagation (GABP) neural network was employed to predict the failure pressure of defective pipelines and the corrosion rate in acidic environments, with prediction errors controlled within 5%. By integrating the GABP model with NACE standard methods, a framework for predicting the remaining service life for in-service pipelines operating in sour environments was established. This method provides a novel and reliable approach for pipeline integrity assessment, demonstrating significantly higher accuracy than traditional empirical models and finite element analysis. Full article

Pediatric palliative care (PPC) is an evolving field that focuses on supporting children with life-limiting conditions, where the quality of care is vital. This study is a retrospective observational investigation that examines the experiences of caregivers to inform health and social service planning and enhance PPC quality. Methods: Data of pediatric patients aged 3 months to 18 years admitted to a PPC inpatient unit over two years were retrospectively reviewed. Sociodemographic characteristics of primary caregivers, including age, gender, number of siblings, education, income, occupation, and marital status, were recorded. Caregiver burden and burnout were assessed using the Zarit Burden Interview and the Maslach Burnout Inventory, respectively. Associations between caregiver characteristics and these measures were analyzed. Results: A total of 118 patients and caregivers were evaluated; 54.2% of patients were male. The most common diagnoses were neurological diseases (44.9%), followed by syndromic–genetic disorders (28.8%). About 34% of patients required more than three medical devices. Most caregivers were female (91.5%), mainly mothers and 53% had only primary education. No significant differences in care burden or burnout were found based on caregiver gender, marital status, or child’s diagnosis. However, the use of nasogastric tubes and multiple medical devices was associated with higher burnout. Lower income was significantly linked to higher care burden, while longer caregiving duration correlated with both increased burden and burnout. A moderate positive correlation was found between Zarit and Maslach scores. Conclusions: The complexity of PPC patients’ care increases caregiver burden and burnout. Expanding specialized PPC services is crucial to support caregivers and sustain home-based care. Full article

Background: The national guidelines, informed by evidence from the National Institutes of Health (NIH), define the criteria for genetic testing of BRCA1/2 and other genes associated with Hereditary Breast and Ovarian Cancer (HBOC) and Lynch Syndrome (LS). When a germline pathogenic variant (PV) is identified in an index case, clinical recommendations advise informing at-risk relatives about the availability of predictive genetic testing, as early identification of carriers allows for timely implementation of preventive measures. Methods: This retrospective observational study examined data collected between 2017 and 2024 at the Medical Genetics Unit of the “Renato Dulbecco” University Hospital in Catanzaro, Italy. The analysis focused on trends in the identification of individuals carrying PVs in cancer predisposition genes (CPGs) and the subsequent uptake of cascade genetic testing (CGT) among their family members. Results: Over the study period, from 116 probands were performed 257 CGTs on 251 relatives. A notable reduction of approximately ten years in median age was observed, 39% were found to carry familial mutation and were referred to personalized cancer prevention programs. Among these, 62% accessed Oncological Genetic Counselling (CGO) within one year of the proband’s diagnosis, suggesting effective communication and outreach. Conclusions: The findings highlight the critical role of effective CGO and intrafamilial communication in hereditary cancer prevention. The identification of PVs, followed by timely CGTs and implementation of preventive strategies, significantly contributes to early cancer risk management. Periodic monitoring of CGT uptake and outcome trends, as demonstrated in this study, is essential to refine and optimize genetic services and public health strategies. Full article

The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) attacks, anomalous network behaviors, and data manipulation, which threaten the security and reliability of IoT ecosystems. New methods based on machine learning have been reported in the literature, addressing topics such as intrusion detection and prevention. This paper proposes an advanced anomaly detection framework for IoT networks expressed in several phases. In the first phase, data preprocessing is conducted using techniques like the Median-KS Test to remove noise, handle missing values, and balance datasets, ensuring a clean and structured input for subsequent phases. The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. This approach identifies the most significant features, reduces dimensionality, and enhances computational efficiency without sacrificing accuracy. In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. This multi-step methodology ensures adaptability and scalability in handling diverse IoT scenarios. The evaluation results demonstrate the superiority of the proposed framework over existing methods. It achieves a 12.5% improvement in accuracy (98%), a 14% increase in detection rate (95%), a 9.3% reduction in false positive rate (10%), and a 10.8% decrease in false negative rate (5%). These results underscore the framework’s effectiveness, reliability, and scalability for securing real-world IoT networks against evolving cyber threats. Full article

Heilongjiang Province, as China’s principal gateway for Russian timber imports, faces structural inefficiencies in the localization of wood-processing enterprises—characterized by ecological sensitivity, resource–industry mismatches, and uneven spatial distribution. To address these challenges, this study proposes a two-stage site selection framework that integrates Geographic Information Systems (GIS) with an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model aims to reconcile ecological protection with industrial efficiency by identifying optimal facility locations that minimize environmental impact, reduce construction and logistics costs, and enhance service coverage. Using spatially resolved multi-source datasets—including forest resource distribution, transportation networks, ecological redlines, and socioeconomic indicators—the GIS-based suitability analysis (Stage I) identified 16 candidate zones. Subsequently, a multi-objective optimization model (Stage II) was applied to minimize carbon intensity and cost while maximizing service accessibility. The improved NSGA-II algorithm achieved convergence within 700 iterations, generating 124 Pareto-optimal solutions and enabling a 23.7% reduction in transport-related CO₂ emissions. Beyond carbon mitigation, the model spatializes policy constraints and economic trade-offs into actionable infrastructure plans, contributing to regional sustainability goals and transboundary industrial coordination with Russia. It further demonstrates methodological generalizability for siting logistics-intensive and policy-sensitive facilities in other forestry-based economies. While the model does not yet account for temporal dynamics or agent behaviors, it provides a robust foundation for informed planning under China’s dual-carbon strategy and offers replicable insights for the global forest products supply chain. Full article

For a given timetable in urban rail transit systems, this paper presents a practical energy efficiency optimization problem that carries out adjustments to the timetable, with the goal of energy saving. We propose two strategies to address this challenge, including adjusting the section running time by selecting a speed profile and improving the utilization of regenerative braking energy by adjusting the trains’ departure time. Constraints on the range of adjustment for energy-efficient time elements are constructed for maintaining the stability of elements of the given timetable. An energy efficiency optimization model is then established to minimize the total net energy consumption of the timetable, and a solution algorithm based on a genetic algorithm is proposed. We make small-scale adjustments to trains’ running trajectories to optimize the overlap time of braking and traction conditions among multiple trains. The case of the Guangzhou Metro Line 8 in China is presented to verify the effectiveness and practicality of our method. The results show that the consumption of traction energy is reduced by 0.95% and the use of regenerative braking energy is increased by 8.18%, with an improvement in energy efficiency of 6.78%. This method can achieve relatively significant energy efficiency results while ensuring the stable service quality of the train timetable and can provide support for an energy-efficient train timetable for urban rail transit operation enterprises. Full article