Search Results (86,041)

Background/Objectives: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a type 2 inflammatory condition that significantly impairs health-related quality of life (HRQoL), particularly sleep quality. Beyond mechanical nasal obstruction, type 2 cytokines (IL-4, IL-13) are thought to directly disrupt sleep architecture. Dupilumab, an IL-4Rα antagonist, is approved for severe CRSwNP, but its specific effect on sleep quality remains under investigation. This systematic review aims to evaluate the impact of dupilumab on sleep quality in patients with severe, uncontrolled CRSwNP. Methods: A comprehensive literature search was conducted in PubMed/MEDLINE, Google Scholar, and Web of Science up to June 2026. We included adult studies (≥1 month of dupilumab 300 mg every 15 days) reporting sleep outcomes using validated tools (Epworth Sleepiness Scale [ESS], Pittsburgh Sleep Quality Index [PSQI], Insomnia Severity Index [ISI], or the SNOT-22 sleep domain). Risk of bias was assessed using ROBINS-I and RoB 2 tools. Results: Seven studies (n = 2164 patients) met inclusion criteria. Across observational and post hoc RCT analyses, dupilumab consistently improved the SNOT-22 sleep domain (mean reduction up to −7.02 points at 24 weeks; p < 0.001), PSQI, ESS, and ISI scores. One study reported a decrease in poor global sleep quality from 88.9% at baseline to 5.7% at 12 months. However, most observational studies had a serious risk of bias due to unaddressed confounding and lack of blinding. No polysomnographic data were reported. Conclusions: Dupilumab was associated with significant improvements in patient-reported sleep quality in CRSwNP patients across seven included studies. However, the evidence is limited by the absence of objective sleep measures, the serious risk of bias in most observational studies, and the lack of comparative head-to-head data. High-quality randomized controlled trials with prespecified sleep endpoints and polysomnographic assessments are needed before definitive conclusions can be drawn. Full article

The harvest now, decrypt later (HNDL) attack is an attack that collects encrypted information now and decrypts it later after the arrival of a quantum computer that can perform some cryptographic operations. Time of exposure is data retention and not Q-Day, so the threat is imminent, but it is not reflected in the Common Vulnerability Scoring System (CVSS). We present the Quantum Risk Index (QRI), which is based on CVSS base severity, the Quantum Factor (QF, vulnerability to Shor’s or Grover’s algorithm), and the HNDL Score (HS, susceptibility to a harvest-and-store adversary). We computed the QRI for 78,587 CVEs that were cataloged in the NIST National Vulnerability Database between January 2016 and the first quarter of 2026 and cross-checked it with the CISA Known Exploited Vulnerabilities (KEV) list. The number of CVEs related to cryptography increased by a CAGR of 11.0%, while the number of Shor-vulnerable CVEs increased at a CAGR of 8.8%. The 437 KEV-matched CVEs carry a mean QRI of 13.05, against 10.31 for the non-KEV remainder—a 26.6% separation (p = 2.17 × 10⁻⁶³)—and Shor-vulnerable CVEs appear in the KEV list at 1.52 times the baseline rate (p = 0.002). This separation is valid for each of the tested settings of QF and HS. This is the first study to apply a quantum-adjusted vulnerability score to a government’s in-the-wild vulnerability reporting database at the CVE scale, providing a repeatable foundation for quantum-aware vulnerability triage. Full article

Active asset managers increasingly include cryptocurrencies in their alternative asset allocations, highlighting their speculative and volatile nature. The aim of this research is to examine trends in the returns and volatility of cryptocurrencies, whilst accounting for the depegging of stablecoins, driven by speculative trading during macroeconomic shocks and technological shifts. We build a sample of market capitalisation, using data from the daily closing prices of Bitcoin (BTC), Ethereum (ETH), Binance (BNB), and Ripple (XRP), two fiat-backed stablecoins (USDT and USDC), and a cryptocurrency-collateralised stablecoin (DAI). As a first step, a Granger-causality framework is applied to examine the influence of stablecoin depegging events on crypto returns during financial market stress. The results are strongly asymmetric: there is little evidence that depegs predict returns; whereas cryptocurrency returns robustly Granger-cause USDC depegging events, an effect that intensifies during periods of market stress. Stablecoin depegs appear to be a downstream symptom of cryptocurrency stress rather than a leading indicator of it. The analysis was extended by modelling volatility, using an EGARCH-X model to study whether depegs also affect crypto during periods of market stress and if larger deviations from the dollar peg are associated with higher cryptocurrency volatility, concentrated in the most liquid stablecoins (USDT and USDC), while the evidence for any change in this association during stress is limited. The findings carry implications for risk monitoring in digital-asset markets, where stablecoin behaviour reflects, rather than anticipates, cryptocurrency market conditions. Full article

Background: Although extreme heat is associated with adverse cardiovascular outcomes, the relationship between heat-alert-threshold days, acute myocardial infarction (AMI) admissions, and post-AMI mortality remains uncertain. We aimed to evaluate the association between temperature-defined heat-alert-threshold days and (i) daily AMI admissions and (ii) cumulative all-cause mortality after hospitalized AMI in Hungary. Methods: We conducted a nationwide registry-based study using data from the Hungarian Myocardial Infarction Registry (HMR). All AMI admissions between 1 January 2018 and 31 December 2019 were eligible, with mortality follow-up through 16 June 2021. Heat-alert-threshold days were defined using the temperature criterion applied in the Hungarian national heat-health action plan (daily mean temperature ≥25 °C). AMI admissions were analyzed using adjusted quasi-Poisson regression models and cumulative mortality using stratified Cox proportional hazards models. This operational temperature threshold was used as the exposure definition and does not represent linkage to administrative heat-alert declarations. Results: The cohort included 30,883 AMI events from 29,596 unique patients (mean age, 67.2 [SD 12.8] years; 60.3% male). Patients admitted on heat-alert-threshold days had baseline clinical characteristics similar to those admitted on non-alert days (all standardized mean differences <0.10). Heat-alert-threshold days were associated with fewer recorded AMI admissions during summer (adjusted incidence rate ratio [aIRR] 0.93, 95% CI 0.90–0.97). In contrast, no association was observed between heat-alert-threshold exposure and subsequent all-cause mortality after AMI hospitalization (adjusted hazard ratio [aHR] 0.96, 95% CI 0.87–1.05). These findings remained consistent across sensitivity analyses. Conclusions: In this nationwide Hungarian registry-based study, temperature-defined heat-alert-threshold days were associated with fewer recorded AMI admissions during summer but not with differences in post-AMI mortality among hospitalized patients. The observed inverse association should not be interpreted as evidence of a protective effect of heat exposure. Alternative explanations include behavioral adaptation, delayed care-seeking, exposure misclassification, residual confounding, and the possibility of unmeasured out-of-hospital cardiovascular events, which were not captured by the registry. Because administrative heat-alert declarations were not linked to the analytic dataset, the findings should be interpreted as associations with temperature-defined heat-alert-threshold days rather than evaluations of the effectiveness of the Hungarian heat-alert system. Full article

The escalating frequency of extreme hydrological events under environmental uncertainty poses a severe socio-economic threat to floodplains such as the Brazilian Pantanal, the world’s largest tropical wetland. Mitigating dynamic flooding and drying cycles is highly challenging due to a critical scarcity of in situ monitoring, leaving flood risks poorly understood. To address these data gaps, this study presents an advanced deep learning forecasting framework that integrates multisource environmental data, fusing satellite-derived precipitation (CHIRPS) and global land data assimilation evapotranspiration (GLDAS) data with historical river gauge telemetry. Multi-layered neural network architectures were optimized and combined with progressive moving average filters (10− and 15−day windows) to capture the complex hydrometeorological patterns of the data-scarce Miranda River Watershed. The optimal deep learning configuration, utilizing a robust two-hidden-layer topology (15 and 60 neurons), consistently outperformed standard baselines. Although purely exogenous data blocks successfully minimized satellite noise and captured seasonal trends (NSE ≥ 0.92), structural underestimation of peak flows was observed. When incorporating the previous day’s streamflow (lag t−1) as a physical anchor, this limitation was noticeably alleviated, increasing both the Nash–Sutcliffe Efficiency (NSE) and Coefficient of Determination (R²) values above 0.99. While this performance surge is driven by the strong temporal persistence inherent to the autoregressive lag, it introduces an operational trade-off by restricting the forecast to a reactive 24 h window. In this regard, an evaluation of the operational forecast horizons revealed that the exogenous deep learning blocks maximize warning lead times, providing a vital tool for proactive civil defense and disaster risk reduction. Ultimately, this multisource framework establishes a methodological foundation for automated decision support systems, providing the high-accuracy streamflow forecasting capability required to support future flood mitigation frameworks. Full article

Drying is a critical post-fermentation process because it influences product stability and the retention of bioactive compounds. The present study evaluated the effects of different drying techniques on the physicochemical characteristics, functional properties, bioactive compounds, and antioxidant activities of SCOBY-fermented pomelo peel substrate powders. Pomelo peel substrates fermented with 6% (w/w) SCOBY inoculum for 25 days were subjected to freeze drying (FD), hot-air drying (HAD; 50, 70, and 90 °C), and radio-frequency drying (RFD; electrode distances of 14, 15, and 16 cm). Drying kinetics, effective moisture diffusivity (D_eff), water activity, color, particle size distribution, functional properties, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities were determined. RFD showed comparable or slightly higher moisture diffusivity (1.19–2.06 × 10⁻⁹ m²/s) compared with HAD (1.03–1.95 × 10⁻⁹ m²/s) under suitable drying conditions, suggesting that radio-frequency heating effectively promoted internal moisture migration through volumetric dielectric heating. FD retained the highest antioxidant activity, with DPPH radical scavenging activity of 74.25% and TEAC of 24.85 μmol TE/g. However, moderate thermal treatments enhanced phenolic extractability, and HAD at 50 °C showed the highest TPC (161.65 mg gallic acid equivalents (GAE)/g DW). Among the RFD treatments, RFD at 15 cm exhibited the highest TFC (27.18 mg rutin equivalents (RE)/g DW) and maintained relatively high antioxidant capacity. FD powders showed superior water solubility and swelling capacity, whereas RFD produced finer particle distributions and improved drying efficiency. These findings demonstrate that drying techniques significantly influence the quality attributes of SCOBY-fermented pomelo substrate powders, and RFD represents a promising alternative drying technology for balancing drying efficiency and bioactive compound retention. Full article

Interferometric synthetic aperture radar (InSAR) is a key tool for monitoring landslide deformation in reservoir regions. However, when only ascending and descending line-of-sight (LOS) observations are available, 3D deformation inversion over complex hillslopes remains challenging because of slope-geometry priors and the anisotropic observation sensitivity. This study focuses on hillslopes in the Baihetan Reservoir area after impoundment. We use 340 ascending and descending Sentinel-1A images acquired from April 2021 to October 2024, generating LOS displacement time series using the extended small baseline subset (E-SBAS) technique. We propose a two-track InSAR 3D inversion framework centered on sensitivity-constrained anisotropic regularization (SC-Aniso). In this framework, a local-gradient surface-parallel flow model (LGSPFM) serves as a supporting pixel-scale topographic prior for representing local slope geometry. SC-Aniso constitutes the primary methodological innovation by mapping the inverse joint LOS sensitivities of the E, N, and U components to component-wise regularization weights. This design suppresses noise amplification in weakly constrained directions. Results show that the Baihetan Reservoir area is generally stable, with localized anomalies mainly in typical reservoir-bank landslide zones. The inverted 3D fields reveal coupled subsidence, horizontal displacement and downslope creep in the L01–L03 landslides. GNSS validation shows vertical RMSEs below 5.29 mm, mean 3D rate differences below 4 mm/yr, and an average component-wise rate difference of 2.56 mm/yr. At the optimal regularization parameter, SC-Aniso reduces north–south dispersion in stable areas by 41.9% compared with isotropic regularization. Wavelet analysis indicates a 288–384 day seasonal period for nonlinear displacement of the Xiaomidi landslide, with lags of 24 and 90 days relative to precipitation and reservoir water level, respectively. This study provides support for accurately recovering 3D deformation and interpreting movement mechanisms of landslides under limited two-track LOS observations. Full article

Background: Saffron corm rot causes significant yield losses worldwide. Although synthetic pesticides are commonly used for management, their overuse can lead to increased fungal resistance and pose a risk to public health. Numerous plants possessing antifungal properties hold significant potential for development as biocontrol agents against saffron corm rot. Result: In this study, the efficacy of biocontrol agents derived from natural plants was evaluated for managing saffron corm rot. Aqueous and 70% ethanol extracts from 15 plants were investigated for antifungal activity and virulence suppression against three saffron pathogens: Fusarium oxysporum, Penicillium citrinum, and Aspergillus brasiliensis. Antifungal activity experiments indicated that clove ethanol extract at a concentration of 20 mg/mL exhibited the highest inhibition rate, with values of 93.27 ± 0.0% for Fusarium oxysporum, 86.47 ± 5.55% for Penicillium citrinum, and 68.65 ± 2.40% for Aspergillus brasiliensis. Compared with the model group, clove ethanol extract significantly reduced corm rot and normalized the metabolism of infected corms. On day 30, compared with the model group (34.3 ± 3.7%), the rot rate of clove-treated corms was reduced to 17.2 ± 1.4%, and on day 60, it was reduced to 27.5 ± 1.5% (versus 52.9 ± 3.3% in the model group). Compared with the model group, the levels of soluble sugar, starch, α-amylase, soluble protein, and superoxide dismutase in clove-treated infected corms approached those observed in the control group. Field trials confirmed that clove ethanol extract effectively improved plant growth and reduced the disease severity index (37.00 ± 3.51) in infected corms. Conclusion: Clove ethanol extract is a promising candidate to replace or reduce the use of synthetic pesticides. It provides a theoretical and practical basis for the biocontrol of saffron corm rot. Full article

Background/Objectives: Severe burn injury is associated with high postoperative morbidity and mortality due to profound metabolic and nutritional stress. The blood glucose-to-serum albumin ratio (GAR) may reflect both metabolic derangement and nutritional status, but its prognostic significance in burn intensive care unit (ICU) patients remains unclear. This study evaluated the association between preoperative GAR and postoperative outcomes in adult burn ICU patients undergoing surgery. Methods: We retrospectively analyzed adult burn ICU patients who underwent surgery between 2014 and 2024. GAR was calculated using blood glucose and serum albumin levels measured within one day before surgery. The primary outcome was 90-day postoperative mortality. Secondary outcomes included 90-day hospital-free days and ICU-free days. Multivariable Cox regression and restricted cubic spline analyses were performed to assess the relationship between GAR and mortality risk. Receiver operating characteristic curve analysis was used to evaluate the discriminatory performance of GAR and determine the optimal cutoff value. Results: Among 1119 patients, the 90-day mortality rate was 25.6%. Higher preoperative GAR was independently associated with increased 90-day mortality in multivariable Cox regression analysis. Restricted cubic spline analysis demonstrated a significant overall association, with progressively increasing mortality risk at higher GAR levels. Receiver operating characteristic curve analysis showed moderate discriminatory performance (area under the curve = 0.789), and the optimal cutoff value based on the highest Youden index was 62.5. Patients with GAR ≥ 62.5 had significantly lower 90-day survival rates and fewer 90-day hospital-free days and ICU-free days than those with lower GAR values (all p < 0.001). Conclusions: Higher preoperative GAR was significantly associated with adverse postoperative outcomes, including increased 90-day mortality and fewer hospital-free and ICU-free days in adult burn ICU patients. Full article

Escherichia coli (E. coli) infection in poultry triggers severe diarrhea and intestinal damage, ultimately disrupting gut function. Alanyl-glutamine (Aln-Gln) dipeptide not only exhibits potent gut-repairing efficacy but also restores barrier integrity and counters E. coli pathology through mucosal healing and immune modulation. In this study, the effects of Aln-Gln on microbiome modulation and intestinal integrity in growing hens challenged with E. coli were evaluated. The 250 healthy, Hy-line brown pullets (42 days old) were randomly divided into 5 experimental groups, each having five replicates of 10 chicks. Five dietary groups include a basal diet without added Aln-Gln and no E. coli challenge (NC), a basal diet without added Aln-Gln and orally administered 2.0 × 10⁴ CFU/mL E. coli suspension (C), a basal diet added 0.1% Aln-Gln and orally administered 2.0 × 10⁴ CFU/mL E. coli suspension (G1), a basal diet added 0.2% Aln-Gln and orally administered 2.0 × 10⁴ CFU/mL E. coli suspension (G2) and a basal diet added 0.3% Aln-Gln and orally administered 2.0 × 10⁴ CFU/mL E. coli suspension (G3). The results showed that dietary supplementation with 0.2% or 0.3% Aln-Gln effectively mitigated the adverse effects of an E. coli challenge, restoring body weight and feed intake to levels comparable to non-challenged controls. Aln-Gln supplementation (0.2–0.3%) reduced diarrhea severity, restored ileal morphology (villus height, crypt depth), improved antioxidant enzyme activity (GSH-Px, SOD), and reduced oxidative markers (MDA) (p < 0.05). Aln-Gln decreased pro-inflammatory (IL-1β, IL-6, NO) and apoptotic (BAX) responses (p < 0.05) and enhanced barrier integrity. It rebalanced gut microbiota by suppressing pathogenic Clostridium (75% in C vs. 29.9% in G3) and increasing beneficial Lactobacillus in G2/G3 compared with C. These findings demonstrate Aln-Gln as a beneficial dietary additive that restores growth performance and gut integrity; reduces diarrhea severity, inflammation, and apoptosis; and improves antioxidant status as it rebalances gut microbiota. Full article

Objectives: This study aims to compare the imaging performance of spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) in the early postoperative assessment of patients undergoing vitreoretinal surgery with intraocular gas or air tamponade. Methods: Seventeen eyes of 17 patients who underwent pars plana vitrectomy with either sulfur hexafluoride (SF₆, 20%) or air tamponade were prospectively enrolled. All patients underwent OCT imaging on postoperative day 1 using both the SD-OCT system and the SS-OCT platform, without pharmacological mydriasis. Images were independently evaluated by two experienced ophthalmologists based on the ability to delineate four prespecified anatomical layers: inner retinal layers, ellipsoid zone (EZ), retinal pigment epithelium (RPE) and choroid. Images were classified as adequate quality if two or more of these structures were identifiable. Results: SS-OCT provided adequate-quality images in all 17 eyes (100%), with complete visualization of the inner retinal layers, EZ, RPE, and choroid in 15 eyes (88.2%). In contrast, SD-OCT yielded adequate-quality images in only three of 17 eyes (17.6%), demonstrating marked signal attenuation, interface artifacts, and inability to resolve deeper retinal structures in most cases. No difference in imaging performance was observed between SF₆ and air tamponade subgroups. Conclusions: SS-OCT demonstrates markedly superior imaging performance in gas-filled eyes on postoperative day 1 compared to SD-OCT, primarily attributable to its longer wavelength, reduced sensitivity roll-off, and superior penetration through optically challenging media. These findings suggest that SS-OCT may offer meaningful advantages for early postoperative monitoring following vitreoretinal surgery with tamponade; confirmation in larger prospective cohorts with clinical-outcome correlation is warranted before it can be recommended as the preferred modality. Full article

Accurate next-day stock price forecasting remains challenging because daily price changes have a low signal-to-noise ratio and can be strongly affected by short-lived news shocks, order-flow imbalances, and abrupt changes in volatility or market sentiment. This study presents a controlled empirical comparison of deep learning architectures for next-day stock price forecasting using technical indicators. Using a decade-long daily dataset covering four large-cap NASDAQ equities (AAPL, META, SBUX, and TSLA), multivariate input sequences are constructed by combining historical prices with five widely used technical indicators: exponential moving average (EMA), relative strength index (RSI), moving average convergence divergence (MACD), on-balance volume (OBV), and average true range (ATR). Four deep sequence architectures—long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and convolutional LSTM (ConvLSTM)—are evaluated across multiple lookback windows (5, 15, and 30 trading days) and chronological train/validation/test splits (60–20–20, 70–15–15, and 80–10–10). Hyperparameters are optimized through random search, and forecasting performance is assessed on held-out test sets using normalized-scale root mean squared error (RMSE) and out-of-sample $R^2$. Within the examined fixed chronological partitions, ConvLSTM records the lowest observed RMSE for all four equities, attaining values between 0.0256 and 0.0394 and out-of-sample $R^2$ values above 0.90. Because the evaluation does not include walk-forward validation or formal statistical significance testing, these results should be interpreted as descriptive evidence within the present experimental setting rather than as proof of general architectural superiority. To assess practical utility, forecasts are translated into a transparent long-only trading rule that enters the market when the predicted next-day closing price exceeds the current closing price. Out-of-sample backtesting shows that the frictionless forecast-driven strategy achieves higher terminal cumulative returns than Buy-and-Hold for AAPL, SBUX, and TSLA, while Buy-and-Hold remains superior for META. Approximate five-day-frequency risk-adjusted estimates generally reinforce these relative patterns: the ConvLSTM strategy improves the Sharpe, Sortino, and Calmar ratios for AAPL, SBUX, and TSLA, although TSLA remains exposed to substantial drawdown risk. Transaction-cost sensitivity analysis further indicates that the terminal-return gains weaken under trading frictions and are particularly sensitive for AAPL. The findings demonstrate the value of evaluating forecasting architectures through both statistical and financial criteria, while emphasizing that lower point-forecast error does not necessarily translate into superior economic or risk-adjusted performance. Full article

Industrial Internet of Things (IIoT) production environments rely on automated communication between control systems and embedded devices while operating under strict availability constraints that limit the deployment of conventional IT security controls. Despite extensive research on intrusion detection systems, empirical studies based on long-term observations of real industrial networks remain scarce. This paper presents a longitudinal 92-day passive monitoring study of a production-line IIoT network comprising 22 monitored devices. A containerised instance of Zeek was deployed in promiscuous mode to collect flow-level and application-layer telemetry without interfering with operations. The resulting dataset contains more than 41.5 million network flows and 520.5 million packets, represented by 48.48 GB of structured Zeek logs. The results reveal highly deterministic communication patterns dominated by periodic HTTP polling between a central server and distributed devices. In particular, the hourly mean HTTP response size remained highly stable at 132.76 bytes, with a standard deviation of 1.37 bytes and a coefficient of variation of 1.0%. Although no confirmed malicious activity was observed, transient deviations were identified and attributed to planned production stoppages restart periods, which caused temporary traffic reductions and short-lived packet bursts. These findings demonstrate that production-line IIoT networks can exhibit predictable behaviour regimes suitable for statistical anomaly detection. The study contributes a longitudinal empirical characterisation of a real operational IIoT network, a reproducible methodology for behavioural baseline extraction using passive telemetry, and practical insights for safe monitoring deployment. Full article

Hydraulic conductivity (K) is essential for understanding groundwater movement in shallow alluvial aquifers. Upstream of the Siret–Moldova confluence, Romania, this parameter is poorly constrained, because available K values come from only 34 wells reported in existing hydrogeological documentation. This study provides the first directional geostatistical characterization of K in this shallow alluvial aquifer and evaluates the spatial reliability of the mapped zones. K values ranged from 9.96 to 171.73 m/day. Because the raw data were positively skewed, the values were log-transformed before geostatistical modelling. Spatial continuity and anisotropy were examined using omnidirectional and directional semivariograms. Three theoretical models were tested—Spherical, Gaussian and Exponential—and their performance was compared using cross-validation statistics and prediction standard-error maps. The Gaussian and Spherical models showed comparable performance; however, the Gaussian model was retained because it produced marginally lower errors and a smoother spatial pattern (ME = 0.0055, RMSE = 0.4870, RMSSE = 0.8697, R² = 0.6712). The back-transformed K map shows three main zones: lower values in the northern and eastern sectors, intermediate values in the central sector, and the highest values toward the west. This pattern is consistent with coarser Moldova River alluvial deposits in the west and more heterogeneous lithological conditions in the central area. The prediction standard-error maps indicate the areas where the interpolated hydraulic conductivity (K) is more reliably constrained. The resulting map provides a preliminary spatial representation of the main hydraulic-conductivity zones and indicates where additional K measurements are needed before detailed groundwater-flow modelling or local groundwater-resource assessment. Full article

The development of healthy mangroves strongly depends on several factors including water physiochemical characteristics, soil composition and tidal inundation regimes. This article presents a characterization of tidal inundation regimes for mangroves in Abu Dhabi, based on a field measurement campaign combined with hydrodynamic modelling. Water-level measurements were collected over a 9-month period at a site where Avicennia marina is present and widespread, capturing spring-neap cycles and seasonal variability. The results provide a detailed quantification of tidal inundation characteristics. Mangroves at the study site were inundated for approximately 33–56% of the time, depending on the season, with higher inundation durations during summer months associated with seasonal mean sea level variability. Mean inundation durations averaged 371 min per inundation event and 620 min per day, with an average of 1.7 inundation events per day. A hydrodynamic numerical model was applied and validated against in situ measurements. Model outputs were used to spatially extend site-specific observations and derive estimates of target ground elevation for successful mangrove development, corresponding to values between +0.12 m and +0.14 m relative to local mean sea level. These findings provide a physically based framework to support mangrove restoration, impact assessment, and conservation efforts in Abu Dhabi, where improper tidal exposure remains a key factor limiting restoration success. Full article