Digital search platforms enable real-time observation of relationship distress through behavioral traces. This study tests whether Google Trends predicts official divorce rates in the United States, Germany, the Netherlands, and the United Kingdom from 2009 to 2023. We introduce Digital Behavioral Decoupling, in which online distress signals diverge from legal outcomes as divorce shifts from an institutional procedure to an emotionally mediated digital phenomenon. Methods include unit root tests, cointegration analysis, Granger causality, spectral coherence, and rolling-origin nowcasting. Search queries are grouped into pre-divorce (cognitive distress), during-divorce (procedural action), and post-divorce (emotional recovery) phases. Results show 62.5% of terms lead divorce rates by 1–2 years, yet only 8.3% remain significant after False Discovery Rate correction. The Netherlands demonstrates 100% forecast improvements beyond autoregressive models (DM 2.87–3.43, p < 0.02) across all search terms, from early relationship therapy queries through procedural and post-divorce searches, indicating systematic capture of the entire divorce pathway. Germany shows intermediate results with 33% forecast success beyond autoregressive benchmarks (DM 2.40–2.81) limited to problem-recognition terms, suggesting episodic crisis-driven engagement. The United States and United Kingdom show no forecast gains beyond autoregressive models despite high search volumes, consistent with information saturation in normalized divorce cultures. Lead-lag relationships are frequency-specific, concentrated at 3–5 year periodicities. Findings link family sociology with affective computing and provide a replicable toolkit for tracking relationship dissolution in algorithmically curated information environments.

Çekişmeli Üretken Ağ (GAN), üretken bir derin öğrenme modeli olarak bilinir. Üretici (generator) ve ayırt edici (discriminator) yapılarından oluşmaktadır. Sentetik veri olarak bilinen GAN modeli çıktılarının oldukça başarılı örnekleri bilinmektedir. Farklı amaçlar ile kullanılabilen sentetik verilerin, başarılı bir şekilde üretilmesi durumunda insan gözü ile tespit edilebilmesi oldukça güç bir problemdir. Bu çalışmada farklı ve popüler Evrişimli Sinir Ağı (CNN) modellerinin öznitelik çıkarıcı olarak kullanıldığı, sentetik ve gerçek görüntüleri ayırt eden bu problem için Laplace filtresi ve benzemezlik tabanlı yeni bir CNN katmanı önerilmiştir. GAN modelinin farklı modeller üzerindeki başarı sonuçları tespit edilmiştir. Böylece, gözle ayırt edilemeyen sentetik verilerin tespiti için CNN modellerinden yararlanmanın uygun bir alternatif olduğu anlaşılmıştır. En iyi başarı %98.75 doğruluk oranıyla DenseNet ile elde edilmiştir.

Assessment of photovoltaic panel surfaces must cope with illumination variation, dust, snow, glare, cracks and bird droppings. This study presents SolarVisionNet (SVN), which fuses a seven-channel input (RGB plus physics-inspired glare, snow, bird-dropping and crack masks) with a 44-dimensional handcrafted descriptor through feature-wise linear modulation and adds an auxiliary head that predicts the masks. SVN is evaluated on a public six-class dataset under a leakage-aware protocol: because a naive random split places 38 of 131 test images next to a training near-duplicate, images are grouped into pseudo-sites, and results cover repeated seeds and grouped 5-fold cross-validation. The six-class model reaches 86.5 ± 3.3% accuracy and 86.8 ± 3.3% macro-F1, and the clean-versus-defective detector reaches 94.7% hold-out accuracy (90.4 ± 2.4% under cross-validation). In a nine-variant ablation, the physics-inspired components change macro-F1 by at most 2.7 points, within seed variability, and three of four ImageNet backbones trained with the same optimization recipe are equally accurate. Contrast-limited adaptive histogram equalization (CLAHE) lowers mean macro-F1 by 1.2 points over 50 paired runs, fixed-threshold rule overrides reduce macro-F1, and class-specific synthetic augmentation induces shortcut learning; the recommended configuration omits all three. SVN is a reproducible, leakage-audited baseline with inspectable glare, snow and bird-dropping masks.