Search Results (2,351)

This paper proposes a passive in-orbit calibration method for phased array antennas using GNSS carrier-phase measurements. By performing synchronous observation and exploiting the short-baseline property between the positioning antenna and array elements, the first differencing operation eliminates space propagation errors and clock biases. By further utilizing receiver channel consistency, the second differencing operation cancels out the receiver channel errors, thereby extracting the relative receive-chain phase error of the element under test. Under typical operating conditions, the calibration accuracy can reach an RMS error of approximately $3.02\mathrm{mm}$, corresponding to a phase accuracy of $5.72°$ in the GPS L1 band. This accuracy is close to the $5.625°$ minimum phase step of a 6-bit digital phase shifter, and can be further improved under higher $C/N_0$ and well-controlled residual error conditions. Without requiring a dedicated GNSS band excitation signal, this method avoids co-frequency self-interference with the positioning antenna, which provides an auxiliary approach for in-orbit calibration of phased array receive chains. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Objectives: This study aimed to evaluate the utility of MRI-based morphometric and qualitative parameters in identifying patients with suspected normal pressure hydrocephalus (NPH) associated with shunt surgery selection following clinical and lumbar puncture evaluation. Methods: We retrospectively analyzed 134 participants: 84 symptomatic patients evaluated for suspected NPH and 50 age-matched controls with normal brain MRI findings. Symptomatic patients were categorized according to subsequent clinical management following lumbar puncture evaluation into those who underwent shunt surgery (Shunt group) and those who received conservative management (Conservative group). The Evans index, fronto-occipital horn ratio (FOHR), bicaudate index, callosal angle, ventricular measurements, and disproportionately enlarged subarachnoid space hydrocephalus (DESH) components were analyzed. The discriminatory performance of MRI parameters for shunt surgery selection was assessed using ROC analysis; independent predictors of shunt surgery selection were determined using logistic regression. Results: Although conventional ventricular indices and ventricular dimensions were significantly greater in symptomatic patients than in the control group (p < 0.001), baseline continuous MRI measurements did not significantly differ between the Shunt and Conservative groups (p > 0.05). Callosal angle demonstrated no discriminatory value for shunt surgery selection. In univariate analyses, an Evans Index > 0.36, a bicaudate index > 0.23, and a DESH score > 2 were associated with shunt surgery selection. High-convexity tightness and an Evans Index > 0.36 differed significantly between groups and remained independently associated with shunt surgery selection in multivariable analysis. Conclusions: Ventricular width-based indices alone appear insufficient for identifying patients selected for shunt surgery among individuals evaluated for suspected NPH. Both qualitative and quantitative MRI features, particularly high-convexity tightness and an Evans Index > 0.36, were independently associated with shunt surgery selection following routine clinical assessment. Integrating multimodal imaging parameters with clinical evaluation may provide a more reliable approach for identifying patients who are ultimately selected for shunt surgery following lumbar puncture assessment. Full article

Background/Objectives: In gas chromatography–mass spectrometry (GC-MS) library-based compound identification, spectrum preprocessing and associated tuning parameters critically influence identification performance. These parameters are conventionally optimized using grid search, which requires predefined parameter spaces and becomes computationally inefficient as dimensionality increases, often failing to identify optimal values because of discretization. Differential evolution (DE), a population-based metaheuristic optimization algorithm, provides a flexible alternative through efficient global exploration of the parameter space. This study compared the performance of DE and grid search for optimizing compound identification. Methods: Cosine similarity was applied to the NIST GC-MS library. DE was used to maximize either cross-validated accuracy or mean reciprocal rank (MRR). Results were compared with those from a grid search over five equally spaced parameter values. Identification performance was evaluated using accuracy, MRR, and area under the receiver operating characteristic curve (AUC). Results: When all four parameters were optimized simultaneously, DE achieved slightly higher cross-validated accuracy and MRR than grid search, although the absolute differences were modest. More pronounced differences were observed in specific unidimensional tuning scenarios, particularly for the intensity weight factor. Simultaneous multidimensional parameter optimization yielded better performance than isolated parameter tuning. Conclusions: Grid search may be computationally advantageous when the parameter space is known and limited, whereas DE provides a more flexible approach for unknown or high-dimensional search spaces. Overall, DE achieved comparable identification performance to grid search, with modest improvements observed in some optimization settings. A command line Julia-based tool, MSTune, was developed for spectrum preprocessing parameter optimization and is publicly available on GitHub. Full article

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

Historic districts are complex built heritage environments where conservation, commercial activities, and public use continuously interact. A key challenge is maintaining cultural meaning and spatial authenticity while meeting contemporary demands for leisure and accessibility. Taking the Fuzimiao–Qinhuai Scenic Belt in Nanjing, China, as a representative case, this study develops a computational mixed-methods framework to evaluate visitor perception and diagnose experiential imbalances in the built heritage environment. A total of 2940 online reviews (2020–2025) were analysed using TF-IDF, Latent Dirichlet Allocation (LDA), StructBERT sentiment analysis, and Importance–Performance Analysis (IPA). Six experiential dimensions were identified, covering cultural inheritance, nightscape and leisure, rituals and museum visits, architectural space, value evaluation, and practical services. Results reveal a clear disparity: nightscape and value-related dimensions received the highest attention and positive sentiment, whereas rituals and museum interpretation underperformed despite their central heritage significance. Based on the IPA diagnosis, the study proposes three strategies: reallocating resources from over-supplied services to underperforming cultural cores, integrating immersive digital technologies (VR/AR) to revitalise heritage interpretation, and embedding cultural narratives into nightscape experiences. These strategies support a paradigm shift from visual attraction to cultural resonance in the conservation-oriented regeneration of historic districts. Full article

As the global population continues to age, mental health issues such as depression, anxiety, stress, loneliness, and social isolation among older adults are receiving increasing attention. The built environment is closely associated with older adults’ daily mobility, environmental perception, social participation, and mental health and well-being, but the evidence remains heterogeneous across spatial contexts, environmental indicators, and study designs. Previous umbrella reviews have summarized broad links between the built environment and healthy aging, but less attention has been paid to recent original empirical studies published after the COVID-19 pandemic, the distinction between objective environmental exposure and subjective environmental perception, and the role of social participation as a pathway linking environmental conditions to mental health and well-being. This study employs a systematic literature review approach, searching and screening peer-reviewed empirical studies published between 2015 and January 2026 that focus on the associations between the built environment and older adults’ mental health and well-being. PubMed, Scopus, and Web of Science databases were used for searching, supplemented by manual searching. After title and abstract screening and full-text evaluation, a total of 60 studies were included. Subsequently, a comprehensive analysis was conducted on aspects such as research design, spatial scale, environmental indicators, types of mental health outcomes, and potential pathways of action. In this review, core mental health and well-being outcomes included negative outcomes, such as depression, anxiety, stress, psychological distress, loneliness, and social isolation, and positive outcomes, such as life satisfaction, subjective well-being, psychological well-being, and mental well-being. Social participation was examined as a behavioral and psychosocial pathway rather than as a core outcome. Emerging methods, including street-view image analysis, FCN-based semantic segmentation, and XGBoost-SHAP, were examined because they can refine environmental exposure measurement and support variable-importance interpretation, rather than because they provide causal evidence. The main synthesis suggests that several built environment factors are associated with older adults’ mental health and well-being, although the strength and consistency of evidence vary across outcome types, spatial contexts, and study designs. (1) Exposure to green and blue spaces, quality of public open spaces, walkability and accessibility, accessibility of neighborhood facilities and services, housing and living conditions, and positive environmental perception are mostly associated with lower levels of depression, anxiety, stress, and loneliness, as well as higher levels of life satisfaction, subjective well-being, and psychological well-being. (2) Conversely, adverse environmental exposures such as proximity to roads, pollution, non-vegetated spaces, and high-intensity urbanization are more likely to exacerbate negative psychological outcomes. Existing evidence also suggests that social participation is one of the important behavioral pathways through which the built environment is linked to the mental health of older adults, but it is not the only mechanism. (3) In addition, the direction and intensity of environmental associations remain heterogeneous under different spatial scales, indicator types, and research methods. Overall, this review contributes by organizing recent empirical evidence into a built environment–social participation–mental health and well-being framework, while emphasizing that most findings should be interpreted primarily as evidence of association rather than as stable or uniform causal effects. Full article

Background/Objectives: Hip arthroscopy is a minimally invasive procedure with rare complications that can occur due to air entry outside the joint space. Case Presentation: A 19-year-old patient underwent right hip arthroscopy with attempted joint venting. The next morning, she had pain in her right leg, neck, and chest with paresthesias over her hands and feet. A subsequent emergency department physical exam revealed crepitation of the lower extremities, abdomen, chest, and neck caused by air entrance during arthroscopy. The patient also reported blurred near vision. Additionally, the pupils were fixed, did not accommodate, and were dilated at 7 mm. Computed tomography scans revealed subcutaneous emphysema, pneumoperitoneum, pneumomediastinum, and cervicofacial emphysema. Magnetic resonance imaging of the brain revealed a Chiari I malformation. The patient received four hyperbaric oxygen treatments. By the fourth treatment, near visual acuity had improved, but far visual acuity had worsened. Vision had returned to normal eight days after discharge. Conclusions: It is proposed that the patient’s reduced near vision, accommodation paralysis, and fixed and dilated pupils were brought about by pneumomediastinum and cervicofacial emphysema, inhibiting the ability of the pupils to constrict, causing bilateral mydriasis and accommodation paralysis for near targets. Additionally, the subsequent transient myopic shift is a known complication of hyperbaric oxygen therapy, which increases the refractive index of the crystalline lens. Full article

As the development and utilization of underground spaces in coastal cities receive growing emphasis and continue to expand, the secondary disasters of underground flooding triggered by storm surges have become increasingly frequent in recent years. Consequently, the need for emergency evacuation in these spaces has grown more urgent, making the challenge of safe evacuation increasingly critical. However, the classical social force model shows notable limitations in simulating such scenarios, particularly in its lack of characterization of hydrodynamic resistance, heterogeneous pedestrian mobility, and organized guidance mechanisms. Therefore, this paper proposes an improved social force model for more realistically simulating the microscopic dynamics of pedestrians in underground floodwater environments. By extending the classical model, a flood resistance force term is introduced. Furthermore, the model comprehensively considers the varying speeds of pedestrians with heterogeneous attributes—such as age, height, and gender—under different water depths, quantifying the impact of the flood environment on pedestrian mobility. Simultaneously, a leader–follower guidance mechanism is integrated to simulate the influence of organized command behavior on group movement. Simulation experiments in typical underground flood scenarios were conducted to validate the proposed model. Simulation results indicate that flood resistance significantly reduces evacuation efficiency, and heterogeneous pedestrian factors such as age distribution also have a considerable impact. The quantitative findings are as follows: flood resistance increased total evacuation time by 9.3% (from 37.5 to 41.0 s) and decreased the average evacuation rate by 8.6%; similarly, raising the proportion of elderly pedestrians from 20% to 30% prolonged total evacuation time by 9.4% and reduced the average evacuation rate by 8.6%. These outcomes verify the effectiveness of the improved model in characterizing heterogeneous pedestrian behavior in underground flooding scenarios. This study provides a more refined theoretical model and simulation tool to support the development of emergency evacuation plans for underground spaces during floods. Full article

Acoustic survey provides a measurement-based approach for investigating heritage spaces in which architectural morphology, environmental conditions, and sound-related practices are physically interrelated. This study applies a portable and non-invasive survey protocol to the medieval cave sanctuary of San Michele di Mezzo, located in Fisciano, Southern Italy. The site consists of stratified natural and built spaces, including a lower cave, an upper cave, and a later upper church, and represents a relevant case study for assessing the acoustic behaviour of small, irregular, and fragile cultural heritage environments. The experimental procedure combined calibrated microphone recordings, time-domain signal inspection, third-octave-band analysis, and impulse-response-derived room-acoustic indicators, including reverberation, clarity, and definition parameters. Under the adopted source–receiver configurations, the results show acoustic differentiation among the lower cave, upper cave, and later church. The caves exhibit shorter decay times than the church over most frequency bands, while clarity and definition indicators reveal a frequency-dependent behaviour that does not support a general claim of the acoustic superiority of one space over another. Comparative data from other cave and cave-like environments further contextualize the measured response of San Michele di Mezzo. The findings do not imply intentional acoustic design; rather, in the measured configuration, they show that, under the chosen conditions, the long-lasting devotional centrality of the lower cave is compatible with an acoustic response that does not contradict spoken or sung devotional use. More broadly, the study contributes to applied acoustics by demonstrating that low-invasive field surveys can provide reproducible acoustic indicators for heritage interpretation, conservation-oriented documentation, and the investigation of intangible sound-related dimensions of cultural heritage. Full article

Informal urban settlements grow through incremental and adaptive processes, yet the temporal logic through which their access networks emerge, endure, and consolidate has received relatively little systematic attention. This paper examines the configurational development of the access network in Likoni, Mombasa, where rapid informal urbanisation has transformed an area containing only sparse footpaths into a dense urban network over two decades. Using historical satellite imagery, the study mapped five temporal states of access network for 2006, 2011, 2016, 2021, and 2026. The study utilises Space Syntax angular segment analysis. The analysis combines measures of angular connectivity, segment length, global and local integration, global and local choice, intelligibility, and synergy. The study aims to address three main questions: whether early informal footpaths persisted as the structural basis of later development of access network, whether subsequent growth strengthened local or global accessibility, and whether densification improved the overall configurational accessibility and legibility of the system as a whole. The results indicate that a finer-grained and more locally integrated network was produced through subdivision, densification, and the multiplication of short connecting segments. However, the gains were uneven across scales. Global integration and choice remained concentrated along a limited set of inherited and edge-related corridors, while local integration and local choice spread more widely through the settlement. The paper argues that the development of Likoni is a process of selective consolidation. Early footpaths became a persistent movement skeleton, forming the subsequent major paths of the later stages of the settlement. Later growth intensified local accessibility—albeit, as demonstrated through Space Syntax analysis rather than direct observation of movement—without necessarily producing notable improvements in global integration or whole-system configurational intelligibility. This finding adds a temporal and syntactic dimension to the understanding of informal morphogenesis. Full article

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative performance of conventional time-frequency domain techniques (adaptive notch filtering and pulse blanking) and advanced space-time adaptive processing (STAP). Two representative CRPA receivers were tested in vehicle-mounted experiments under sequential baseline, jamming, and spoofing conditions, with controlled interference generated using a HackRF One platform integrated with the GNSS-SDR. The performance assessment was based on logged GNSS, jammer, and RSSI data collected during 15 min vehicle-mounted dynamic trials, each consisting of 5 min baseline, 5 min jamming, and 5 min spoofing phases. While both approaches exhibited comparable performance under nominal conditions, significant differences emerged under spoofing. The time-frequency domain approach experienced severe degradation, including up to 90% satellite loss and HDOP values exceeding 100, whereas the STAP-based system maintained more than 95% satellite visibility and stable positioning with HDOP values below 1. These results indicate that the tested STAP-based CRPA configuration provided higher system-level stability than the time-frequency domain configuration under the evaluated interference conditions. The findings highlight the critical role of spatial–temporal processing in improving GNSS resilience and offer practical insights for the design of next-generation anti-jamming and anti-spoofing. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

To address carrier-phase loss of lock and long-term drift in frequency-offset estimation that may arise from time-of-arrival (TOA) measurements in enhanced Loran (eLoran) timing receivers under low signal-to-noise ratio (SNR) and moderate-to-high dynamics, this paper proposes a joint TOA and carrier-phase measurement and tracking method. First, transmitter identification and group repetition interval (GRI) lock are achieved by exploiting the periodic repetition of pulse groups, and epoch folding is applied to enhance effective SNR. Then, a sub-sample TOA observation is constructed via a three-stage progressive refinement procedure: energy-matching coarse estimation, coherent cross-correlation, and parabolic peak interpolation. In parallel, baseband phase observations are obtained through coherent downconversion and accumulation. A unified state-space model incorporating TOA bias, TOA drift rate, baseband phase, and frequency offset is further established to enable joint Kalman filtering of TOA and phase. Moreover, an innovation-likelihood-weighted parallel multiple-model filter combined with measurement-noise covariance inflation is introduced to suppress outlier observations. Simulations show that the TOA estimate converges within about 1 s while maintaining phase continuity and stable frequency-offset estimation, and that the proposed method achieves superior overall robustness and long-term stability compared with a conventional Costas loop. Full article

Naturally ventilated office spaces present a dual challenge: open windows are sources of thermal comfort and acoustic disturbances. Despite growing interest in adaptive comfort frameworks, the acoustic adaptation effects specific to naturally ventilated indoor environments and the role of biophilic design elements in mediating them remain insufficiently investigated. This study was conducted to examine the influence of audio–biophilic elements in a naturally ventilated space with a green view, with 53 participants recruited from a real, operational open-office space. Under typical open window field noise conditions, four levels of birdsong (45, 49, 52, and 55 dBA) were introduced at the receiver position. The participants’ perceptual responses were measured using the ISO/TS 12913:2019 and ISO/TS 12913:2025, as well as indoor-soundscape scales. Satisfaction, the perceived appropriateness of the conditions for work, and preferences were evaluated. Environmental descriptors, including psychoacoustic and thermal parameters and perceived auditory and visual content, were also recorded. Statistical analyses were conducted using RM-ANOVA, the Friedman test, and post hoc comparisons. The results revealed that audio–biophilic interventions can enhance adaptive acoustic comfort in naturally ventilated spaces. Although the existing ISO and indoor soundscape scales are highly correlated, they are not interchangeable. These findings offer actionable guidance for acoustic designers and facility managers: introducing contextually appropriate birdsong at moderate levels (not exceeding a background noise level of more than 3 dBA) can serve as an effective masking strategy in naturally ventilated open-plan offices without increasing perceived disturbance, supporting the integration of audio–biophilic systems in green building design. This study contributes field-based evidence to the literature on audio–biophilic interventions and their role in adaptive acoustic comfort in naturally ventilated spaces. Full article