I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…Additionally, many approaches decouple POI selection from itinerary construction and depend heavily on limited behavioral signals, failing to capture richer feedback such as ratings, dwell times, or click histories. To address these limitations, we propose an intention-aware deep learning framework that integrates diverse user signals into a unified itinerary planning model. …”
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Path to Heritagization in Landscape Architecture: A Review of the Regeneration Design of Beijing Changchunyuan Park by Yufan ZHU

“…This place is a gathering of temples and guild halls and a primary access route to Xuanwu Gate which is the southern gateway to the imperial city. …”
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Gut–Vaginal Microbiome Crosstalk in Ovarian Cancer: Implications for Early Diagnosis by Hao Lin, Zhen Zeng, Hong Zhang, Yongbin Jia, Jiangmei Pang, Jingjing Chen, Hu Zhang

“…Ovarian cancer remains a formidable global health burden, characterized by frequent late-stage diagnosis and elevated mortality rates attributable to its elusive pathogenesis and the critical lack of reliable early-detection biomarkers. …”
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SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN by Shengfeng He, Wenhu Qin, Zhonghua Yun, Chao Wu, Chongbin Sun

“…Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. …”
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AEMS: Adaptive Ensemble GNNs for Multibehavior Stream Recommendation by Ritchie Natuan Caibigan, Punyaphol Horata, Pusadee Seresangtakul

“…The AEMS synergizes long-term preference patterns (derived from historical interactions) with real-time user intents and item attributes (captured through multibehavior signals), integrating them via an adaptive ensemble neural gating mechanism. …”
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Clinical features of post-COVID-19 period. Results of the international register “Dynamic analysis of comorbidities in SARS-CoV-2 survivors (AKTIV SARS-CoV-2)”. Data from 6-month f... by G. P. Arutyunov, E. I. Tarlovskaya, A. G. Arutyunov, Yu. N. Belenkov, A. O. Konradi, Yu. M. Lopatin, A. P. Rebrov, S. N. Tereshchenko, A. I. Chesnikova, H. G. Hayrapetyan, A. P. Babin, I. G. Bakulin, N. V. Bakulina, L. A. Balykova, A. S. Blagonravova, M. V. Boldina, A. R. Vaisberg, A. S. Galyavich, V. V. Gomonova, N. Yu. Grigorieva, I. V. Gubareva, I. V. Demko, A. V. Evzerikhina, A. V. Zharkov, U. K. Kamilova, Z. F. Kim, T. Yu. Kuznetsova, N. V. Lareva, E. V. Makarova, S. V. Malchikova, S. V. Nedogoda, M. M. Petrova, I. G. Pochinka, K. V. Protasov, D. N. Protsenko, D. Yu. Ruzanau, S. A. Sayganov, A. Sh. Sarybaev, N. M. Selezneva, A. B. Sugraliev, I. V. Fomin, O. V. Khlynova, O. Yu. Chizhova, I. I. Shaposhnik, D. A. Shchukarev, A. K. Abdrahmanova, S. A. Avetisian, H. G. Avoyan, K. K. Azarian, G. T. Aimakhanova, D. A. Ayipova, A. Ch. Akunov, M. K. Alieva, A. V. Aparkina, O. R. Aruslanova, E. Yu. Ashina, O. Yu. Badina, O. Yu. Barisheva, T. I. Batluk, A. S. Batchayeva, R. A. Bashkinov, A. M. Bitieva, I. U. Bikhteyev, N. A. Borodulina, M. V. Bragin, A. M. Budu, G. A. Bykova, K. R. Vagapova, D. D. Varlamova, N. N. Vezikova, E. A. Verbitskaya, O. E. Vilkova, E. A. Vinnikova, V. V. Vustina, E. A. Gаlova, V. V. Genkel, E. I. Gorshenina, E. V. Grigorieva, E. Yu. Gubareva, G. M. Dabylova, I. A. Demchenko, O. Yu. Dolgikh, M. Y. Duyshobayev, D. S. Evdokimov, K. E. Egorova, A. N. Ermilova, A. E. Zheldybayeva, N. V. Zarechnova, Yu. D. Zimina, S. Yu. Ivanova, E. Yu. Ivanchenko, M. V. Ilina, M. V. Kazakovtseva, E. V. Kazymova, Yu. S. Kalinina, N. A. Kamardina, A. M. Karachenova, I. A. Karetnikov, N. A. Karoli, M. K. Karsiev, D. S. Кaskaeva, K. F. Kasymova, Zh. B. Kerimbekova, A. Sh. Kerimova, E. S. Kim, N. V. Kiseleva, D. A. Klimenko, A. V. Klimova, O. V. Kovalishena, E. V. Kolmakova, T. P. Kolchinskaya, M. I. Kolyadich, O. V. Kondriakova, M. P. Konoval, D. Yu. Konstantinov, E. A. Konstantinova, V. A. Kordukova, E. V. Koroleva, A. Yu. Kraposhina, T. V. Kriukova, A. S. Kuznetsova, T. Yu. Kuzmina, K. V. Kuzmichev, Ch. K. Kulchoroeva, T. V. Kuprina, I. M. Kouranova, L. V. Kurenkova, N. Yu. Kurchugina, N. A. Kushubakova, V. I. Levankova, N. A. Lyubavina, N. A. Magdeyeva, K. V. Mazalov, V. I. Majseenko, A. S. Makarova, A. M. Maripov, A. A. Marusina, E. S. Melnikov, N. B. Moiseenko, F. N. Muradova, R. G. Muradyan, Sh. N. Musaelian, A. O. Myshak, E. S. Nekaeva, N. M. Nikitina, B. B. Ogurlieva, A. A. Odegova, Yu. M. Omarova, N. A. Omurzakova, Sh. O. Ospanova, E. V. Pahomova, L. D. Petrov, S. S. Plastinina, V. A. Pogrebetskaya, D. V. Polyakov, D. S. Polyakov, E. V. Ponomarenko, L. L. Popova, N. A. Prokofeva, I. A. Pudova, N. A. Rakov, A. N. Rakhimov, N. A. Rozanova, S. Serikbolkyzy, A. A. Simonov, V. V. Skachkova, D. V. Soloveva, I. A. Soloveva, I. M. Sukhomlinova, A. G. Sushilova, D. R. Tagayeva, Yu. V. Titojkina, E. P. Tikhonova, D. S. Tokmin, A. A. Tolmacheva, M. S. Torgunakova, K. V. Trenogina, N. A. Trostianetckaia, D. A. Trofimov, M. A. Trubnikova, A. A. Tulichev, A. T. Tursunova, N. D. Ulanova, O. V. Fatenkov, O. V. Fedorishina, T. S. Fil, I. Yu. Fomina, I. S. Fominova, I. A. Frolova, S. M. Tsvinger, V. V. Tsoma, M. B. Cholponbaeva, T. I. Chudinovskikh, O. A. Shevchenko, T. V. Sheshina, E. A. Shishkina, K. Yu. Shishkov, S. Yu. Sherbakov, E. A. Yausheva

“…The highest mortality rate was observed in the first 3 months in the group of patients with class II-IV heart failure, as well as in patients with cardiovascular diseases and cancer. …”
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Deep learning driven prediction and comparative study of surrounding rock deformation in high speed railway tunnels by Zeping Yang, Zhikai Cheng, Da Wu

“…The methodology incorporates quadratic exponential smoothing for outlier mitigation, followed by sequential feature extraction using convolutional neural networks (CNNs) and bidirectional gated recurrent units (GRUs). Comparative experiments demonstrate the model’s superiority over conventional architectures including RNN, LSTM, GRU, and CNN-GRU. …”
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Forecasting Sales in Live-Streaming Cross-Border E-Commerce in the UK Using the Temporal Fusion Transformer Model by Qi Zhang, Xue Li, Pengbin Gao

“…Our multimodal approach integrates diverse time series data, including historical sales, key opinion leader (KOL) influence, and seasonal patterns. The Temporal Fusion Transformer (TFT) model demonstrated consistently lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) across all forecasting horizons compared to other machine learning approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Unit(GPU)-accelerated architectures. …”
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A clustering-based federated deep learning approach for enhancing diabetes management with privacy-preserving edge artificial intelligence by Xinyi Yang, Juan Li

“…We develop tailored models that enhance prediction accuracy by clustering patients based on carbohydrate (CHO) intake patterns. Utilizing Simple Recurrent Neural Network (SimpleRNN) and Gated Recurrent Unit (GRU) methods, the study evaluates the performance of local patients who contribute to training the cluster and global (non-cluster) models. …”
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Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning by Yanli Zhao, Zongduo Liu, Junjie Pang

“…Traditional detection approaches typically rely on statistical features while overlooking the interaction patterns and structural dependencies among traffic flows. …”
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Adherence to the Singapore Integrated 24 h Activity Guidelines for Pre-Primary School Children Before, During and After the COVID-19 Lockdown in Singapore by Seow Ting Low, Terence Buan Kiong Chua, Dan Li, Michael Chia

“…The COVID-19 pandemic has significantly disrupted the lives of pre-primary school children in Singapore where increased infection rates prompted lockdown measures that altered children’s daily routines. …”
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Optical and photoluminescence characteristics of Pr3+-doped P2O5 +BaO+La2O3 glasses by C.R. Kesavulu, Ch. Basavapoornima, Pikkili Ramprasad, C.J. Deviprasad, Shobha Rani Depuru, C.K. Jayasankar

“…A luminescence peak at 600 nm was used as a monitoring level, and a 445 nm excitation was used to assess the decay curves. The decay rates exhibit a bi-exponential pattern, meaning that as the concentration of Pr3+ ions increase, the lifetimes become shorter. …”
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Numerical Study on the Influence of Inlet Blockage Ratios and Flow Conditions on Scour Development in Circular Culverts by Salam Hussein Sulaiman, Kaywan Othman Ahmed, Mohsen Isari, Jamil Bahrami

“…Each hydrograph included seven different flow discharges, and steady flow tests were conducted at the maximum rates observed during the unsteady scenarios. This study examined circular culverts subjected to inlet blockages of 0%, 25%, and 50% and compared the numerical result from Flow-3D simulations utilizing the Renormalization Group (RNG) turbulence model with observed data. …”
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Use of complementary medicine and uptake of COVID-19 vaccination among US adults by Holger Cramer, Holger Cramer, Mirela Bilc, Mirela Bilc

“…However, vaccination uptake was lower for both COVID-19 and flu vaccines, respectively, in individuals who visited chiropractors (AOR = 0.78, 95% CI [0.69, 0.89], p < 0.001; AOR = 0.71, 95% CI [0.63, 0.81], p < 0.001) and naturopaths (AOR = 0.66, 95% CI [0.51, 0.86], p = 0.002; AOR = 0.72, 95% CI [0.55, 0.94], p = 0.017). Uptake rates for both COVID-19 and flu vaccines were higher among individuals who visited an acupuncturist (COVID-19: AOR = 1.46, 95% CI [1.15, 1.86], p = 0.002; flu: AOR = 1.32, 95% CI [1.08, 1.63], p = 0.008). …”
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The association between cognitive ability and body mass index: A sibling-comparison analysis in four longitudinal studies. by Liam Wright, Neil M Davies, David Bann

“…<h4>Background</h4>Body mass index (BMI) and obesity rates have increased sharply since the 1980s. While multiple epidemiologic studies have found that higher adolescent cognitive ability is associated with lower adult BMI, residual and unobserved confounding due to family background may explain these associations. …”
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Impact of LDL-C levels on severity and outcome of intracranial haemorrhage: a single-centre retrospective study by Kang-Po Lee, Li-Chi Hsu

“…Moreover, statin use is associated with better long-term outcome and may attenuate the effects of initial LDL-C in ICH patients.Conclusions In patients with ICH, particularly those with deep ICH, lower LDL-C levels are associated with more severe ICH and higher short-term and long-term mortality rates. Further randomised controlled trials are warranted to determine the optimal LDL-C levels in patients with ICH and dyslipidaemia.…”
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A Global Systematic Review and Meta‐Analysis of Methods Used to Evaluate Predation and Diet of Domestic Cats (Felis catus) by Hannah L. Lockwood, Maren Huck

“…Palatability and detectability of prey appear to influence the data reported, and these aspects should be considered when calculating total predation rates.…”
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The Use of Sound Recorders to Remotely Measure Grass Intake Behaviour in Horses by Daisy E. F. Taylor, Bryony E. Lancaster, Andrea D. Ellis

“…Grazing was identified audibly (SR_ear) and visually through soundwave pattern software (SR_wav). Chew rates (SR_ear: 47 ± 5 chews/min, VD: 43 ± 4 chews/min) were similar between methods. …”
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Perennial and Non‐Perennial Streamflow Regime Shifts Across California, USA by Jessica R. Ayers, Sarah M. Yarnell, Ethan Baruch, Robert A. Lusardi, Theodore E. Grantham

“…Streams impacted by human activities had greater drying rates, but regulation has caused some non‐perennial streams to become perennial. …”
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Integrated evaluation and optimization of acid fracturing effectiveness in carbonate reservoirs: Experimental insights and field validation. by Peng Lu, Songlei Li, Jinsong Liu, Nan Yuan, Heng Liu

“…Implementation of the proposed methodology has increased stimulation success rates from 55% to 82% in pilot fields, with sustained production gains exceeding 35%. …”
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