We present a low-profile miniaturized element frequency selective surface (MEFSS) with narrowband, second-order bandpass response. The miniaturized-element unit cell comprises a capacitively loaded parallel LC unit cell resulting in a narrowband bandpass response. A higher-order filtering response is obtained by implementing five metallic-layers FSS formed by two layers of parallel LC resonators with an inductive impedance inverter layer in between them. The proposed design offers a very thin profile and an insensitive frequency response to the polarization of the incident electromagnetic wave. A design procedure is developed for the proposed FSS structure based on an equivalent circuit model analysis. Using this model, a prototype of MEFSS with second-order bandpass response at 3.34 GHz and a fractional bandwidth of 3.7% is designed, realized and characterized experimentally. The measurement results of the FSS verify the effectiveness of the FSS design procedure and stability of its frequency response up to 60° oblique incidence angles.

This paper aims to provide a general review of the fundamental ideas, varieties, methods, and experimental research of the most advanced frequency selective surfaces available today. Frequency-selective surfaces are periodic structures engineered to work as spatial filters in interaction with electromagnetic (EM) waves with different frequencies, polarization, and incident angles in a desired and controlled way. They are usually made of periodic elements with dimensions less than the operational wavelength. The primary issue examined is the need for more efficient, compact, and adaptable electromagnetic filtering solutions. The research method involved a comprehensive review of recent advancements in FSS design, focusing on structural diversity, miniaturization, multiband operations, and the integration of active components for tunability and reconfigurability. Key findings include the development of highly selective miniaturized FSSs, innovative applications on flexible and textile substrates, and the exploration of FSSs for liquid and strain sensing. The conclusions emphasize the significant potential of FSS technology to enhance wireless communication, environmental monitoring, and defense applications. This study provides valuable insights into the design and application of FSSs, aiming to guide future research and development in this dynamic field.

Classical DEA models typically assume a linear valuation approach in performance assessment. However, in practical applications, many DMU inputs and outputs exhibit nonlinear valuation. A linear valuation may fail to accurately capture the variations in value across different DMUs. One critical challenge in efficiency evaluation is the presence of undesirable outputs, which negatively affects DMU performance. To address this, decision-makers aim to incorporate the impact of undesirable factors into efficiency measurements, enabling them to identify high-performing DMUs under comparable conditions and use them as benchmarks for inefficient ones. In response to this issue, this study introduces a novel approach based on the SBM Network DEA model to enhance airport efficiency within a two-stage framework while accounting for undesirable outputs. By applying piecewise linear theory, the model assigns lower weight to excessive quantities of undesirable outputs, effectively distinguishing DMUs that generate fewer undesirable outputs from those producing higher amounts. Furthermore, this research offers a practical benchmarking strategy for inefficient airports, aiming to improve their efficiency while considering the priority of each stage.

Path planning is a core enabler of safe and efficient autonomous flight for unmanned aerial vehicles (UAVs), yet selecting a suitable planner remains challenging because performance depends jointly on workspace dimension (2D or 3D), obstacle dynamics (static or moving), and the computing and timing constraints of the platform. This review aims to support planner selection by assessing each algorithm family separately within each environment type. Following a comprehensive literature search, a systematic review identified 128 peer-reviewed Q1/Q2 journal articles published between 2022 and 2026, of which 88 studies met the inclusion criteria and form the core evidence base. A common mathematical formulation of UAV path planning is established, identifying shared objectives and constraints as well as scenario-specific requirements, including mission-level location selection, scheduling, and game-theoretic variants. The methods are organised into six paradigm classes (classical, sampling-based, optimisation-based, metaheuristic, learning-based, and hybrid) and analysed across four environmental quadrants defined by 2D/3D and static/dynamic conditions, referred to as a dimension-aware classification. Study-level evidence is stratified by platform and validation level, and no pooled performance score is computed because hardware, experimental environments, and evaluation protocols vary substantially across studies. The review also defines a six-stage validation ladder from model-in-the-loop testing to flight trials and discusses safety, uncertainty, and explainability in learning-based planners, while relating symmetric and asymmetric problem structures to the selection of appropriate planning algorithms. Overall, the findings indicate that planner maturity should be judged by deployment readiness, including onboard timing and validation beyond simulation, rather than by simulated success rates alone.

In most production systems, the objective is to minimize input consumption while maximizing the generation of desirable outputs. However, many production processes also generate undesirable outputs as by-products of desirable outputs. In many real-world systems, undesirable outputs are inherently linked to the production of desirable outputs. Several studies in the Data Envelopment Analysis (DEA) literature have addressed performance evaluation of decision-making units (DMUs) in the presence of undesirable outputs. However, most existing models assume that desirable and undesirable outputs are independent, which may not reflect real production environments. The objective of this study is to model the dependency between desirable and undesirable outputs and to develop a novel DEA framework based on an axiomatic approach. Specifically, the classical axiom of output disposability is decomposed into two separate axioms: disposability of desirable outputs and disposability of undesirable outputs. Based on these axioms, a new production possibility set (PPS) is constructed. The proposed DEA model explicitly incorporates the dependency between desirable and undesirable outputs. A case study involving sensor monitoring systems is presented to demonstrate the applicability of the proposed approach.