Search Results (2,496)

This paper presents a numerical investigation of dispersion-managed dense wavelength division multiplexing (DWDM) transmission systems incorporating a non-midway optical phase conjugator (OPC) under randomly distributed residual dispersion per span (RDPS). Unlike conventional studies assuming ideal symmetric configurations, this work considers more realistic scenarios with asymmetric OPC placement and random dispersion distribution. To ensure the reliability of the analysis, simulations were performed for 100 different random RDPS patterns. A 960 Gb/s DWDM system consisting of 24 channels operating at 40 Gb/s was modeled using the nonlinear Schrödinger equation solved by the split-step Fourier method. To analyze the impact of OPC location, two asymmetric configurations (23-27 and 27-23), were compared. System performance was evaluated using eye-opening penalty (EOP) and timing jitter (TJ). The results show that OPC location has a significant impact on compensation efficiency, with the 27-23 configuration providing overall better performance than the 23-27 configuration. Although randomly distributed RDPS does not always outperform uniform or deterministic dispersion maps, certain random patterns achieve comparable or even superior compensation performance. Through extensive statistical evaluation across five independent random seeds, including Pearson correlation and regression analysis, we observed consistent structural tendencies in the RDPS distribution that enhance compensation efficacy. Specifically, in the 23-27 structure, a high correlation with a ‘half-cycle sin’ profile was preliminarily observed to be beneficial, whereas the 27-23 structure showed sensitivity to both ‘half-cycle sin’ and ‘one-cycle sin’ profiles. These findings suggest that maintaining antipodal-symmetry, even in stochastic environments, provides a stable probabilistic advantage for signal compensation. While we advise a cautious interpretation regarding the universal applicability of these results, the study offers valuable design insights and is expected to facilitate greater flexibility in the design of future high-capacity optical networks. Full article

Vegetation above the soil surface can have a considerable influence on ground-penetrating radar (GPR) measurements, especially when shallow buried objects are to be detected. Plant water content, biomass, and the structural arrangement of leaves and stems can attenuate, scatter, or obscure reflections from both the soil surface and buried targets. This study therefore examines how different vegetation types and soil moisture conditions affect the GPR response of a shallow buried reference target under controlled laboratory conditions. For the analysis, a GPR operating in a down-looking configuration is used, which is moved across the study area on an equidistant grid. The evaluation is based on the analysis of multiple intensity pixels and heuristic statistics to characterise the radar reflections. The Normalised Difference Vegetation Index (NDVI) is used to describe the vegetation; this index approximates, in particular, the water content of the plants, as well as the relationship between biomass and dry matter content. The analysis reveals a relationship between water content, biomass volume, and the signal-to-clutter ratio (SCR) in relation to the detectability of targets. The condition of the vegetation significantly influences radar target reflection and thus the detectability of subsurface targets. In particular, higher water content in vegetation correlates with increased scattering within the vegetation layer, thereby preventing ground reflection and target reflection. Full article

Background/Objectives: Large-for-gestational-age (LGA) birth is associated with adverse perinatal outcomes and increased risk of metabolic disease later in life. Despite these risks, epigenetic studies of LGA remain limited, particularly those using umbilical cord blood DNA methylation (DNAm) as the tissue of interest. Methods: We conducted an epigenome-wide association study of cord blood DNAm in a nested case–control sample from the Gestational Regulators of Weight (GROW) cohort. The analysis included 28 term LGA infants and 63 term appropriate-for-gestational-age (AGA) controls. DNAm was measured using the Illumina HumanMethylation450 BeadChip. Epigenome-wide association analyses were performed with adjustment for residual principal components and estimated cord blood cell-type proportions. Results: No CpG sites reached statistical significance after false discovery rate correction. Twenty-four CpGs showed nominal associations with LGA status at p-value < 1.00 × 10⁻⁴, including five CpGs with absolute methylation differences greater than 0.05. Among annotated loci, the strongest interpretable signals included cg06750897 in PBX1 (Δβ = 0.110, p-value = 1.70 × 10⁻⁵) and two nearby CpGs in SMAD3, cg23731272 (Δβ = 0.111, p-value = 4.31 × 10⁻⁵) and cg02486855 (Δβ = 0.109, p-value = 9.45 × 10⁻⁵). These two SMAD3 CpGs showed concordant hypermethylation in LGA infants and formed a candidate regional methylation signal in exploratory targeted regional analysis. Conclusions: In this pilot study, LGA was associated with modest cord blood DNAm differences, although no CpG sites reached genome-wide significance. The strongest interpretable signals involved PBX1 and SMAD3, suggesting candidate loci for further evaluation in larger studies of fetal overgrowth. Full article

SAR and InSAR are fundamental sensing modalities for all-weather, day-and-night Earth observation because they operate independently of solar illumination and retain sensitivity to scene structure under conditions that often limit optical imaging. Recent Deep Learning (DL) methods have improved SAR image interpretation, inverse imaging, target recognition, and InSAR-based deformation analysis, but many purely data-driven pipelines still neglect the forward sensing model, coherent scattering physics, speckle statistics, phase structure, and acquisition geometry that shape radar observations. This paper develops a physics-aware perspective on learning for SAR and InSAR. The literature is organized along two complementary axes: the physical constraint dimensions that govern radar measurements, namely polarization, scattering, signal-domain structure, resolution, and interferometric phase/coherence, and the integration modes through which such structure enters modern learning systems, including representation design, model-guided architectures, and learning-assisted inverse problems. We further discuss representative applications, dataset and evaluation issues, domain-shift challenges, and trustworthy deployment. Finally, two constructive illustrative case studies are included: one on sparse SAR imaging with an explicit SAR sensing matrix, and one on InSAR phase-domain estimation under wrapped phase, coherence variation, and physics-aware regularization. Together, they illustrate how physics-aware learning compares with classical structure-preserving baselines and unconstrained black-box learning in representative amplitude-driven and phase-driven inverse settings. Full article

Background/Objective: Melanoma is an aggressive skin malignancy characterized by significant molecular heterogeneity. Among the molecular alterations identified in melanoma, BRAF mutations represent one of the most common genetic abnormalities and play an important role in activating the MAPK signaling pathway. BRAF mutation status has become clinically important because of its prognostic significance and implications for targeted therapy. This study aimed to evaluate the frequency of BRAF mutations and their associations with demographic, histopathological, and clinicopathological characteristics in melanoma patients at the only referral center for BRAF testing in Kosova, the Institute of Pathology, University Clinical Center of Kosova (UCCK). Methods: This retrospective study included 127 melanoma patients. Descriptive statistics, frequency analysis, Spearman’s correlation and multivariable binary logistic regression analyses were performed to evaluate associations between BRAF mutation status and clinicopathological variables, including age, gender, Breslow thickness, histological type, ulceration, and anatomical localization. Results: BRAF mutation was identified in 76 of 127 melanoma patients (59.8%). The BRAF V600E/V600E2/V600D variants represented the predominant molecular subtype (75%). BRAF-positive melanoma was more frequently observed in younger patients and was significantly associated with increased Breslow thickness, nodular melanoma, ulceration, and trunk localization. Conclusions: BRAF mutations were highly prevalent in melanoma patients from Kosova and were associated with clinicopathological features of a more aggressive disease. The findings establish an important baseline for molecular epidemiology in the country and support the integration of routine BRAF testing into personalized melanoma management and future regional research. Full article

Extreme climate events can disturb vegetation dynamics and alter ecosystem stability. This study investigated the evolution of extreme climatic conditions and vegetation greenness across the Yangtze River Basin during 1982–2022 by integrating CN05.1 daily meteorological records with the PKU-GIMMS NDVI V1.2 dataset. Using the index framework of the ETCCDI, fifteen extreme weather-related climate indices were calculated. Trend magnitudes were quantified using Sen’s slope estimation, while the Mann–Kendall trend test was used to determine the direction and significance of long-term changes. Both standard correlation and lagged cumulative correlation were used to assess NDVI–climate relationships; the latter tested ten predefined windows formed from climate conditions between the current month and three months before each NDVI observation, with the largest absolute correlation defining the optimal window. The basin experienced a distinct warming signal in temperature extremes during 1982–2022, characterized by reduced occurrences of TN10p and TX10p, together with more frequent TN90p and TX90p events. Precipitation extremes showed an overall intensification, with R95p and R99p increasing by 9.2 mm per decade and 5.6 mm per decade. NDVI also increased throughout the analysis period, with a basin-wide rate of 0.0038 per decade and the fastest greening in the middle reaches at 0.0069 per decade. Vegetation NDVI was generally positively associated with warm-related temperature indices and negatively associated with cold-related indices. The lagged cumulative analysis identified the strongest NDVI–climate associations within windows combining current and antecedent climate conditions, with marked differences among subregions and vegetation types. Because the analyses are correlation-based and did not quantify non-climatic drivers such as land-use change or ecological restoration, the results should be interpreted as statistical associations rather than causal attribution. Overall, these findings characterize how vegetation greenness covaries with extreme climate indices across the Yangtze River Basin. Full article

Forest-product certification now operates at the intersection of private sustainability governance, market access, and mandatory due diligence. This structured narrative review asks how Forest Stewardship Council (FSC) and Programme for the Endorsement of Forest Certification (PEFC) certification, together with the EU Deforestation Regulation (EUDR), affect competitiveness and the distribution of compliance costs in Polish business-to-business and export-oriented wood-product supply chains. A documented revision-stage search and screening procedure produced an evidence base of 55 peer-reviewed, regulatory, statistical, and sectoral sources. The analytical framework combines private-governance theory, stakeholder conflict analysis, and relationship marketing to examine information asymmetry, bargaining power, cost allocation, and market access. The Polish case is characterised by a large publicly owned forest resource, extensive but overlapping FSC and PEFC coverage, and strongly export-oriented downstream industries. Certification can reduce buyer verification costs, support supplier qualification, improve traceability, and protect access to demanding markets. However, fixed audit, documentation, digitalisation, and certified-material costs are borne disproportionately by small and medium-sized enterprises, especially where lead buyers do not share adaptation costs. EUDR strengthens these asymmetries because certified status can support, but does not replace, legal due diligence. The review contributes a governance-based explanation of why the same sustainability requirements can generate resilience and market access for digitally mature exporters while creating entry barriers, supplier exclusion, and concentration risks for smaller firms. Policy support should therefore combine clear demand signals, group certification, shared traceability infrastructure, advisory services, and buyer–supplier cost-sharing arrangements. Full article

The cryptocurrency market is characterized by high volatility and strong systematic co-movements among assets, posing significant challenges for conventional statistical arbitrage strategies. Effective pair trading in this environment requires the identification of return variation that is unexplained by common market factors. This study proposes a hybrid framework that combines CAPM-based market-model filtering with multifractal detrended cross-correlation analysis (MFDCCA) for cryptocurrency pair trading. A single-index market-model regression is first employed to remove the linear market component, and the resulting market-model-filtered series are then analyzed using MFDCCA to characterize multifractal cross-correlation structures, which are subsequently used for pair selection and signal generation. The results show that the proposed MFDCCA-based approach effectively captures nonlinear and scale-dependent relationships in the market-model-filtered series, leading to improved risk-adjusted trading performance under the evaluated experimental settings. Compared with conventional dependence measures such as Pearson correlation, cointegration, and standard DCCA, the proposed framework achieves favorable risk-adjusted returns under the evaluated experimental settings. These findings highlight the effectiveness of combining market-model filtering with multifractal analysis for statistical arbitrage in cryptocurrency markets and demonstrate its potential to support portfolio management and risk control. 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

Background/Objectives: Romania’s National Health Insurance House (CNAS) reimbursement basket is the principal public financial-protection mechanism, yet whether it reflects the therapeutic services patients actually seek has not been tested against private-market offerings. This study compared the public basket with the private dental service portfolio, using the latter as an indirect, supply-side signal of expressed need rather than a direct measure of demand. Methods: In this cross-sectional study (November 2024–February 2025), the publicly listed portfolios of 420 private dental practices in Bucharest (n = 256), Cluj-Napoca (n = 104), and Iași (n = 60) were analyzed, stratified by size (0–3 vs. >3 employees). Practices were identified from CAEN 8623 records on listafirme.ro; portfolios were extracted from official websites displaying prices. Services were classified as functional or aesthetic by two independent raters (Cohen’s κ = 0.91), with disagreements resolved by consensus. Results: Across all municipalities and strata, approximately 53–68% of private services fell outside the CNAS basket. Critically, about 95–96% of private services were functional rather than aesthetic (4–5%), indicating that the gap reflects functional treatments patients seek and providers offer yet that remain unfunded—not elective demand. No statistically significant geographic differences emerged. Geographic location was not associated with portfolio breadth, whereas in multivariable models larger practices (>3 employees) independently offered approximately 33% more services. Conclusions: Because the analysis reflects advertised portfolio breadth rather than utilization, and a documented Romanian orientation toward reactive over preventive care, these hypothesis-generating findings make private supply a pragmatic, supply-side reference point for evidence-based revision of the CNAS basket, pointing to two directions directly supported by the data—aligning coverage with observed demand and administrative simplification of contracting. To our knowledge, this is the first benchmarking of the CNAS basket against private supply, relevant to other predominantly private European dental systems. Full article

Background/Objectives: Deciding between active surveillance and treatment for prostate cancer patients often requires accurate risk assessment of prostate tumors detected with multi-parametric MRI. Conventionally, radiologists visually inspect MRI and use scoring procedures such as PI-RADS to help assess the scans. More recently, artificial intelligence (AI) applied to MRI has allowed for supplementation and is complementary to clinical assessment. However, AI is computationally expensive and severely saps scarce energy and water resources and requires special processing components, requiring alternate approaches that require less computation and fewer resources. The novel, simpler spectral/statistics approach that mimics color vision was previously successfully applied in a number of retrospective pilot studies of bi-parametric MRI of prostate cancer. The novel approach needs far fewer resources, is less computationally intensive, and is simpler than artificial intelligence to evaluate prostate tumors. However, these earlier spectral/statistics pilot studies required intervention by an analyst and too much time for implementation in future large patient studies that are needed to validate the novel approach. This retrospective pilot study further developed, applied, and tested new automation tools to expedite simpler spectral statistical techniques that need fewer resources to evaluate prostate tumors on multi-parametric MRI. Methods: Automated spatial registration, automated prostate organ segmentation, automated blob generation and selection for spectral signatures derived from the apparent diffusion coefficient, high-B-value DWI, and T2 MRI were performed on 76 consecutive patients in the PI-CAI cohort in this retrospective pilot study. The signal-to-clutter ratio (SCR) was computed using target signatures and the processed statistical metrics of the registered prostate bi-parametric MRI. The processed SCR, spectral/spatial features of blobs and clinical metrics predict clinically significant prostate cancer using multivariate logistic regression. The proposed method was assessed using the area under the curve (AUC) from the receiver operating characteristic curve. Results: AUC values of >0.90 were achieved by combining the SCR with blob and clinical metrics. Increasing the number of non-congruent, independent variables resulted in higher AUC scores. Restricting analysis to blob volumes > 0.1 cm³ achieved higher AUC values. The additional total savings in time by applying the new automation tools reduced the processing time by 80 to 170 min for 10 patients. Implementing the new automation tools resulted in an overall processing time of 40 to 80 min per 10 patients. Conclusions: Automating the spectral/statistics approach resulted in AUCs not inferior to those obtained from AI. The automation achieved sufficiently high AUCs and also reduced processing times, warranting future assessments in large patient cohorts. Full article

National dairy sustainability monitoring requires indicators that are scientifically defensible, policy-relevant, and feasible to construct from recurrent agricultural surveys. The aim of this study was to construct, interpret, and evaluate a national, survey-weighted, frame-aware, MESMIS-informed monitoring profile for Peruvian dairy cattle producers using ENA 2021–2024 microdata. The Peruvian National Agricultural Survey (Encuesta Nacional Agropecuaria, ENA) was explicitly treated as a source of proxy-based sustainability monitoring rather than as a complete farm sustainability assessment. Using survey design variables and harmonized items on livestock, markets, producer capacity, and resource management, the analysis constructed survey-weighted formative profiles for nationally monitored dairy cattle producer units. The analytical sample comprised 42,331 agricultural producer units selected based on complete interview status, reported livestock activity, and harmonized evidence of dairy cattle activity. Because ENA documentation indicates a sampling-frame transition in 2023, the 2021–2022 and 2023–2024 rounds were treated as separate sampling-frame regimes. Indicators were scaled to 0–1 formative profiles, with explicit checks for missingness, indicator availability, exclusion of derivative outputs, exclusion of producer age, minimum-availability thresholds, and sampling-regime-specific scaling. The median indicator availability was 12 of 14 economic and productive indicators, five of five social access and capacity indicators, and eight of eight environmental and resource-management indicators. Survey-weighted descriptive estimates and adjusted within-regime contrasts showed that the strongest signal was a lower social access and capacity profile in 2022 than in 2021, mainly associated with lower agricultural information use, livestock training, and technical assistance. Within the 2023–2024 sampling-frame regime, the main adjusted profiles were statistically stable. The results show that ENA can support a reproducible national monitoring baseline, provided that the resulting profiles are interpreted as proxy-based formative summaries constrained by survey coverage rather than as direct environmental performance scores, causal trends, or complete sustainability measures. Full article

Coplanar capacitance detection exhibits remarkable advantages in the detection of road texture in asphalt layers, including high sensitivity and minimal environmental constraints. However, the inherent performance contradiction between signal strength and penetration depth of traditional interdigitated coplanar capacitance sensors (ICCSs) has restricted their widespread application in road texture detection. To address this issue, a hybrid approach combining response surface methodology (RSM) and non-dominated sorting genetic algorithm II (NSGA-II) is developed to optimize the structural parameters that influence the signal strength and penetration depth of a novel ICCS. Initially, a central-composite design (CCD) based on RSM is employed to establish statistical models for the two key sensing performances of ICCSs, namely signal strength and penetration depth. Subsequently, Analysis of Variance (ANOVA) and three-dimensional (3D) response surface plots are utilized to investigate the significant effects of various structural parameters (electrode length, width, and inter-finger gap) on the two sensing performances. Furthermore, NSGA-II is applied to search for global optimal solutions using the established statistical models, thereby achieving multi-performance optimization of the ICCS. Finally, the fabricated ICCS is used to detect the surface texture of asphalt mixture specimens with different gradations, and the results are compared with those obtained by laser point cloud detection. The results indicate that both statistical models are highly significant, with the coefficient of determination (R-squared) exceeding 0.95. All individual structural parameters have a significant impact on the two sensing performances. Based on the optimization by the RSM-NSGA-II hybrid method, the predicted optimal parameters are verified, showing a relative error of less than 5% from the simulation results. Additionally, the detection results of the ICCS are consistent with the laser point-cloud data, demonstrating its feasibility for pavement texture detection. Full article

Background: Carotid plaque rupture is a critical event in ischemic stroke, yet the potential involvement of the intraplaque microbiota across disease stages remains unclear. Methods: We performed dual-omics profiling by analyzing host transcriptomes and PathSeq-derived microbiomes from 48 human carotid RNA-seq specimens spanning early lesions (intimal thickening; n = 10), stable plaques (n = 20), and unstable plaques (n = 18). Host transcriptomes were profiled alongside intraplaque microbiomes extracted via the GATK PathSeq pipeline with rigorous in silico decontamination. We integrated differential expression analysis, microbial diversity metrics, and functional inference. Furthermore, an integrated machine learning approach (incorporating Boruta feature selection) was employed to identify exploratory cross-kingdom diagnostic biomarkers. Results: Microbial beta diversity diverged significantly across disease stages, accompanied by the progressive upregulation of 54 host genes critical for extracellular matrix remodeling and immune chemotaxis. Strikingly, despite the inherent noise and artifacts associated with low-biomass sequencing, we computationally detected the distinct enrichment of 21 bacterial taxa in unstable plaques, predominantly oral and gut mucosal pathobionts. Computationally inferred functional profiling revealed that these unstable plaque-associated microbiota were significantly linked to predicted cell death, IL-17, and HIF-1 signaling pathways and exhibited strong positive correlations with host matrix-degrading transcripts. Statistical modeling suggested associative links among specific microbial enrichment, host transcriptomic dysregulation, and plaque instability, highlighting concurrent biological cross-talk. Importantly, our integrated machine learning pipeline established a 14-feature cross-kingdom biomarker panel (10 host genes and 4 bacteria) that discriminated stable from unstable plaques (cross-validated AUC = 0.869). Conclusions: Intraplaque microbiome dynamics computationally associate with host transcriptomic alterations during carotid plaque evolution. This synergistic host–microbiome association provides a hypothesis-generating framework linking microbial dysbiosis to plaque destabilization, offering novel mechanistic insights and highlighting the exploratory cross-kingdom biomarker panel as a highly promising foundation for future experimental validation and stage-tailored clinical diagnostics. Full article

This paper presents a structured review of antenna and electromagnetic spectrum monitoring techniques for non-invasive fault detection in electrical and electronic equipment. Electromagnetic emissions from partial discharges, arc faults, insulation degradation, and component ageing carry diagnostic signatures detectable through remote radio-frequency sensing. We review (1) antenna technologies spanning magnetic-field loops to ultra-high-frequency electric-field sensors, including fractal, Vivaldi, spiral, and bio-inspired designs; (2) data acquisition platforms ranging from laboratory oscilloscopes to software-defined radio receivers and IoT edge nodes; (3) signal processing methods including time–frequency analysis, adaptive decomposition, and statistical techniques; and (4) machine learning approaches from classical classifiers to deep learning architectures such as convolutional neural networks, recurrent neural networks, and Transformer-based models. Unlike prior surveys focusing on individual fault types or specific equipment classes, this review connects all five layers of the sensing pipeline—from electromagnetic emission physics through antenna selection, signal acquisition, processing, and intelligent classification—for partial-discharge, arc, and insulation faults and analyses the cross-layer constraints that couple them. Design optimisation techniques based on computational electromagnetic methods (FDTD, FEM) and sensitivity calibration challenges are discussed. Open challenges, including the lack of standardised UHF calibration, cross-equipment generalisation, and the scarcity of open electromagnetic fault datasets, are identified, along with emerging directions in flexible antennas, edge AI, and digital twin integration. Full article