Southern New Hampshire University

How to automatically recognize the bird species has caused concerns in ecological systems and intelligent ecological monitoring system due to the increasing threat to bird ecological society and bird species diversity. However, traditional monitoring systems are susceptible to specific challenges when operating in complex environments, such as complex environments, multifarious postures and backlight scenarios. To effectively address these challenges, we present a novel bird ecological intelligent detection system (TransSIL) for fine-grained bird image classification (FBIC) in diverse ecological to learn discriminative features by explicitly incorporating silhouette structural information alongside critical visual cues. Specifically, the approach begins with a silhouette token construction module to estimate the bird silhouette and extract silhouette tokens. Then, a silhouette relationship mining module is developed to fuse visual and silhouette tokens and capture long-range dependencies between them. In addition, to learn bird distinctive features at multiple levels, a critical cues awareness module is embedded within TransSIL. The performance of TransSIL was evaluated on two bird datasets: CUB200-2011 and NABirds. The framework demonstrates significant improvements over existing ecological intelligent surveillance methods. By utilizing silhouette and visual dependencies, we anticipate that our approach will ultimately contribute to the conservation of avian ecological societies.

Structural Health Monitoring (SHM) ensures the safety and longevity of civil infrastructure. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have transformed SHM by enabling automated, scalable solutions for damage detection and assessment. This paper presents a synthesis of AI-driven SHM methods—from vision-based surface damage detection and vibration analysis to physics-informed models and digital twins. We explore the integration of CNNs, RNNs, GANs, and transformers with non-destructive testing (NDT), UAV-based monitoring, and 3D reconstruction. Emphasis is placed on real-time defect localization, sensor fault mitigation, and data recovery in operational environments. Despite progress, challenges like data scarcity, environmental variability, and generalizability remain. We propose a roadmap using hybrid AI-physics models and autonomous SHM systems to enhance infrastructure resilience.

Employability has become a defining feature of contemporary higher education, yet it remains a contested and fragmented concept. This chapter offers a critical synthesis of major models, conceptualisations, and stakeholder perspectives shaping employability research and practice. It reviews frameworks developed within higher education—such as USEM, CareerEDGE, and Graduate Identity—alongside workplace and career literatures, including Boundaryless Careers, Career Competencies, and Heuristic Employability. More recent integrative approaches, such as the Employability Capital Growth Model, are discussed in relation to comparative and international perspectives. The chapter interrogates possession-based, relational, and processual accounts of employability, explores stakeholder responsibilities, and warns against narrow neoliberal discourses. It concludes by outlining future directions that promote integrative, sustainable, and equitable frameworks for graduate employability.

The increasing insistence on the environmentally friendly urban infrastructure requires intelligent structures that can take into account the heterogeneous sensor data, predictive analytics, and computational modeling. Conventional solutions can be ineffective in controlling the large-scale smart systems, leading to poor energy consumption and high carbon emission. In this paper, AI-SF2, a multisensor data fusion, AI-based predictive analytics, and computational modeling are proposed as an integrated framework to optimize smart infrastructures and green energy systems. Structural, environmental and energy sensor information are combined to offer strong situational awareness, and deep learning models predict energy demand, identify anomalies, and project carbon emissions. Real-time decision making and proactive resource allocation is simulated using computational models that provide dynamic interaction. Large scale simulations and testbed evaluations show up to 21.6 % energy savings, 18.9% renewable integration, and 24.2 % faster anomaly detection than current baselines. AI-SF2 provides a challenge-scalable, adaptive, and realistic solution to the sustainable smart cities. It also focuses on safe, privacy-capable sensor integration needed to have reliable smart infrastructure functioning.

Insider threats are a continuous and dynamic issue in the field of organizational security that may take various forms, such as an abuse of authorized access to critical information systems and physical infrastructure. The traditional methods of isolating system log analysis, behavioural monitoring, or physical access control often do not have the ability to identify multi-layered and subtle patterns of threats. Multimodal data integration predictive analytics is a holistic approach since it integrates heterogeneous data volumes into coherent threat models that are able to pre-empt any possible threats before they escalate. The paper discusses the effectiveness of predictive analytics in insider threat detection through analysis of the connection between log records, behavioural indicators and physical security data. Instead, the focus is made on designing integrative frameworks, using advanced machine learning algorithms, and the applied implications on operational resilience. Issues like scalability, transparency of algorithms, and even ethical considerations are also critical issues that are considered to provide a sound deployment in the modern security environment. The paper highlights the need to embrace multimodal predictive approaches as a tactical defence mechanism and developing theoretical arguments and practical interventions in cybersecurity risk management.

This paper articulates and examines the factors that lead to and sustain the competitive advantage in the US car rental industry. A quantitative approach was adopted to collect data from companies' financial statements. Preliminary data analysis, descriptive analysis, visualization, and ANOVA were used to analyze the competitive advantage variables in the proposed research models. The study indicates that Avis has the highest market share and profit margin, while Enterprise excels in fleet size and research and development investments. Hertz, on the other hand, is struggling to establish a foothold in the competitive market. Continuous adaptation to market changes, technological disruptions, and sustainability trends is essential for these corporations to maintain their competitive edge. Overall, the findings of this research will help corporations re-formulate and implement effective organizational strategies to cope with the brutal competition in the car rental industry and other industries.