Wave propagation and attenuation in waveguides with flexible components play a critical role in noise control applications such as HVAC systems, aero-engines, and gas turbines. Conventional analyses typically address either rigid-walled ducts, single-region impedance linings, or flexible shells in isolation, leaving the combined effects of shell flexibility, bifurcated chamber geometry, membrane-mediated coupling, and dissipative linings underexplored. The non-orthogonal eigenfunctions arising from shell flexibility further complicate standard mode-matching approaches. A semi-analytical framework is developed for a thin, flexible cylindrical shell enclosing a bifurcated expansion chamber with concentric sub-regions coupled by membrane discs and an internal acoustic liner. The shell dynamics follow Donnell–Mushtari thin-shell theory, while the fluid is treated as compressible and inviscid. A Galerkin-augmented mode-matching formulation handles the non-orthogonal eigenfunctions and higher-order derivatives at flexible boundaries. Convergence and consistency are confirmed through energy-balance checks and reconstruction of the interface matching conditions. Resistive linings dissipate a significant portion of the incident acoustic energy and yield broadband transmission loss, whereas purely reactive linings produce sharp but narrow resonance peaks with negligible absorption. Parametric studies show that the inner radius, intermediate radius, outer radius, and cavity half-length act as independent handles for trading off reflection, absorption, and transmission. The proposed framework reliably models coupled fluid-structure wave propagation in flexible, membrane-coupled, bifurcated waveguides with acoustic linings, providing practical design guidance for thin-walled waveguide structures in noise-control applications.
The facile fabrication of core–shell Co/N-CNFs by electrospinning and their catalytic applications in aerobic oxidative coupling of thiols are presented.
Full-scale manure biogas plants are often promoted as renewable energy projects, yet their commercial value may depend as much on waste stabilization, methane control, water reuse, and nutrient recovery as on electricity sales. This study tests whether cattle manure digestion can deliver measurable environmental and resource-efficiency benefits, while remaining financially non-viable under electricity-only revenue. The assessment is based on one year of measurement-derived operational data from a full-scale facility integrating anaerobic digestion, biogas cleaning, combined heat and power generation, process-water reuse, and digestate recovery. Plant records indicate net annual export of 7253 MWh of renewable electricity and a model-based reduction in methane-related climate impact equivalent to about 38,800 tons of carbon dioxide per year relative to unmanaged manure storage. Closed-loop process-water recirculation reduced freshwater demand by 55
Civil aviation continues to face evolving threats of unlawful interference, including terrorism, hijacking, sabotage, and cyber-enabled attacks. Although regulatory frameworks, technological capabilities, and international cooperation have strengthened considerably, evaluating the effectiveness of aviation security remains largely centered on procedural compliance and incident-based indicators. This study adopts a practice-oriented perspective by examining how aviation security effectiveness is interpreted and assessed by professionals directly involved in implementing security measures. Drawing on in-depth interviews with experienced aviation security and safety practitioners, professional narratives were analyzed thematically to explore how deterrence, vulnerability, and security effectiveness are understood in operational settings. Guided by deterrence theory, the analysis demonstrates that practitioners associate effective security not only with technological capability and regulatory compliance, but also with consistent implementation, professional judgment, organizational coordination, and alignment with international standards, particularly those established by the International Civil Aviation Organization (ICAO). Participants also identified persistent challenges associated with human factors, resource constraints, and evolving threat environments, while emphasizing insider threats as a context-dependent source of vulnerability requiring sustained organizational vigilance. Rather than offering broadly generalizable conclusions, the study provides context-specific insights into how deterrence is interpreted and enacted in contemporary aviation practice. In doing so, it complements predominantly technical and regulatory perspectives by highlighting the value of practitioner narratives for informing future research, operational decision-making, and policy discussions concerning aviation security.
The rapid rise of phishing attacks on social media platforms highlights an urgent and growing cybersecurity challenge in today’s digitally connected world. As millions of users rely on these platforms for communication, business, and information sharing, cybercriminals increasingly exploit them to distribute deceptive links, fake advertisements, and fraudulent messages designed to steal sensitive data such as login credentials, financial information, and personal identities. The sophistication of these attacks—often leveraging social engineering techniques, impersonation of trusted entities, and AI-generated content—makes them difficult for users to recognize and avoid. Consequently, there is a critical need for robust and intelligent detection systems capable of reliably identifying malicious content in real time. Such systems should incorporate advanced machine learning models, behavioral analysis, and continuous adaptation to emerging attack patterns in order to effectively mitigate risks, protect users, and maintain trust in social media ecosystems. This paper proposes a multimodal phishing detection framework that integrates visual indicators (e.g., logos and page imagery) with textual signals (e.g., URL structure and phishing-related keywords). Our contributions are threefold: (i) a feature fusion strategy that combines visual and textual cues, (ii) the construction of a comprehensive dataset using feature extraction and natural language processing (NLP), and (iii) a comparative evaluation of multiple machine learning models. We extract 27 discriminative URL features and evaluate Random Forest, XGBoost, LightGBM, and Naı̈ve Bayes. Random Forest achieves the best performance, reaching 98.6% accuracy with a false positive rate of 0.01, demonstrating the effectiveness of the proposed multimodal approach for phishing detection on social media.
