This paper presents the solution of an intrusion detection system to bolster the security of Network infrastructure IoT systems. The proposed method starts with a preprocessing stage of data cleaning, Min-Max normalization, label splitting, conversion of text into numbers, and data partitioning. Important to note, the Artificial Bee Colony Algorithm (ABC) and Neighborhood Component Analysis (NCA) work together in this approach. The initial NCA parameter will be optimized using BCO, whereas the feature selection's effectiveness is evaluated with the Cross Entropy Loss cost function. The final steps include designing and training an ensemble AdaBoost model to the targeted features to maximize accuracy of intrusion detection. Our method has been tested on the NSL-KDD dataset and reports 120 percent training accuracy and 99.74 percent accuracy for test data. With attention to detail, this proposal improves the process of IoT threat detection, unmanned network defense security systems, and poses an efficient method for the advanced dynamic environments, optimizing threading detection, and maximizing military grade precision in modern network security.
The interaction of flow, wake development and aerodynamic loading of tandem aerodynamic configurations are highly affected by ground-effect aerodynamics. Many experimental and computational studies have studied these phenomena, but most are selected investigation and presented as separate performance parameters which offer limited support for integrated engineering interpretation. This study proposes an analytical methodological framework that is transferable, called the Unified Ground Clearance Assessment Framework (UGCAF), which is derived from recent developments in the field of systematic reinterpretation of existing research data. Instead of obtaining new aerodynamic data, UGCAF itself systematically reorganizes and assesses the validated selected investigation based on five complimentary engineering indicators: Aerodynamic Loading, Ground-Clearance Influence, Wake Interaction Strength, Shielding Behaviour and Comparative Performance Rating. It is illustrated by a typical published experimental study of tandem tilted flat plates under various ground-clearance conditions. The aerodynamic loading is predicted to be dominated by the coupled effect of the plate position, wake development, and ground effect, and an increase of the ground clearance is expected to shift the downstream plates towards more wake redevelopment and reduced aerodynamic shielding, and enhance loading recovery of the downstream plates compared to the leading plate. UGCAF allows to combine several aerodynamic mechanisms in a consistent engineering approach to the analysis of published aerodynamic measurements and to provide a comparative assessment between validated experimental and computational studies. The proposed framework is not dependent on the data acquisition methodology, and is a portable engineering tool for the interpretation of aerodynamics behaviour for multi-body aerodynamic systems (such as ground-effect systems).
Background: Thyroid cancer is a cancer that develops from the tissues of the thyroid gland. It is a disease in which cells grow abnormally and have the ability to spread to other parts of the body. This paper discusses the prevalence and mortality of this type of cancer by continent and region for females and males with reference to the countries with the highest and lowest injuries and deaths. Material & Methods: Publications from WHO, the International Agency for Research & Cancer, and Cancer Today 2024 on the incidence and mortality of male and female thyroid cancer in global continents and UN regions were used, with a comparison made to find their indicators, especially survival rates, illustrated with charts. Results: The results indicated that the number of incidences is not few, but the mortality rate was limited due to the development of medical technology, and the results indicated that the number of incidences and mortality in females is much higher than in males. Conclusion: The cause of thyroid cancer is still unknown, but since the thyroid gland is very sensitive to radiation, radiation exposure can cause cancerous changes in it. Thyroid cancer is more common in people who have been treated with radiation in the head, neck, or chest areas. The results obtained were a comparison between logic and continents and that the patient needs follow-up and health care.
The physical layer of a wavelength-division-multiplexed optical distribution network for smart-city Internet-of-Things access is almost always reported from a single simulation run, which says nothing about its stability. This paper characterized an eight-channel C-band link over 25 km of standard single-mode fibre, modelled in OptiSystem, across thirty independent replications yielding 240 per-channel Q-factor observations. Every channel cleared the Q ≥ 6 threshold in every replication, with a system mean of 8.66, a per-channel 95% confidence interval never wider than 0.46, and a worst realization of 6.64 on the weakest channel, so the link held a stable margin rather than crossing the boundary between runs. The per-channel Q-factors followed a U-shape across the wavelength grid with its minimum at the centre, and a short-reach reference at 1 km showed that two thirds of the edge-to-interior asymmetry was established within the first kilometre. A launch-power sweep placed the usable ceiling between 10 and 13 dBm. A control experiment with the fibre nonlinearity disabled collapsed the edge-to-interior ratio from 1.354 to 1.038, establishing that 89% of the asymmetry was nonlinear in origin, while a four-wave-mixing coherence length of 0.74 km against the 25 km span identified cross-phase modulation as the mechanism.
Background: Convergence Insufficiency (CI) is a complex neuromuscular visual disorder traditionally diagnosed using manual, subjective instruments like the RAF Rule, which heavily rely on examiner estimation and patient response times. Methods: To address these clinical limitations, this study introduces the EyeQ platform, an innovative diagnostic framework that integrates eye-tracking technology with deep learning architectures powered by the TensorFlow framework. A single-blind comparative study was conducted involving 50 participants. A broad, heterogenous age spectrum (ranging from 6 to 82 years) was intentionally selected to rigorously validate the system's algorithmic adaptability and robustness across diverse demographic profiles and age-related physiological ocular variations. The platform utilizes a dynamic "Approach-Recede" mechanism to monitor ocular neuromuscular responses and isolate precise Break and Recovery Points. Automated diagnostic outcomes were benchmarked directly against independent clinical evaluations performed by a certified optometrist. Results: The EyeQ platform demonstrated high diagnostic efficacy, achieving an overall accuracy of 94% by matching the specialist's clinical findings in 47 out of the 50 cases. Crucially, detailed clinical analysis revealed that the 6% statistical variance (3 cases) was entirely attributed to physical anatomical obstructions—specifically, two cases of ptosis and one case of severe eyelid edema—which occluded the digital region of interest (ROI) and hindered feature extraction, rather than systemic or algorithmic failure. Conclusion: The EyeQ system provides exceptional digital objectivity and reliability, eliminating subjective examiner bias and establishing a standardized digital database for longitudinal vision tracking. Given its high diagnostic precision and architectural consistency, the platform is highly qualified to It shows potential for use in large-scale screening applications in the future, subject to its validation in multiple centers.
