Search Results (9)

This paper proposes DB-LIO (database-driven LiDAR-inertial odometry), a simultaneous localization and mapping (SLAM) system that addresses memory scalability challenges in extended autonomous operation. Existing LiDAR-SLAM systems accumulate keyframe history in memory, leading to $O\left(N\right)$ growth and out-of-memory failures during extended operation. To overcome this limitation, DB-LIO introduces three core design elements. First, it proposes a spatially indexed keyframe management scheme that persistently stores keyframes in SQLite with R-Tree spatial indexing, enabling $O(\log N+k)$ spatial queries that tightly couple cache eviction with factor-graph optimization requirements—a design that ensures every keyframe potentially involved in the next optimization cycle resides in cache. Second, it presents a four-level memory bounding architecture—SLAM-engine keyframe trimming with transparent on-demand reloading, a DB-level least recently used (LRU) cache with a spatial active window, Scan Context descriptor pool bounding, and iSAM2 sliding window compaction with a sparse global anchor graph—that collectively bounds the dominant memory consumers to $O\left(C\right)$. Third, the DB-based persistent storage enables a localization mode that can reload previously built maps—including full point clouds, six-degree-of-freedom poses, timestamps, and inter-keyframe relationships—and perform pose estimation using the stored map, which is particularly valuable for agricultural robots and other autonomous systems requiring map reuse. Experiments on a custom orchard dataset demonstrate an 81.9% reduction in memory usage compared with that of the in-memory baseline (2888 MB → 524 MB), while preserving equivalent trajectory accuracy (absolute trajectory error (ATE) root mean square error (RMSE) 0.305 ± 0.001 m vs. 0.296 m). Validation on the KITTI odometry benchmark confirms that the proposed localization mode generalizes across different LiDAR types (Livox Mid360, Velodyne HDL-64E) and environments (orchard, urban driving). Full article

To evaluate the techno-economic performance of supercritical carbon dioxide (sCO2) power cycles in particle-based solar tower systems, thermodynamic and techno-economic models were established for four configurations: RC-ORC, RC, RE-ORC, and RE. A one-dimensional design method was used for key printed circuit heat exchangers, and multiple cost correlations with a trimmed-mean treatment were adopted to reduce the influence of extreme cost estimates. The results show that the primary heat exchanger (PHX) dominates system investment, accounting for more than 50% of total cost in all configurations. After screening 48 pure ORC working fluids, Cyclopropane and Trans-butene were identified as the economically preferable fluids for RC-ORC and RE-ORC, respectively. ORC working-fluid selection should therefore consider not only net power output, but also the effect of heat transfer and flow characteristics on intermediate heat exchanger cost. Scale analysis shows that the specific investment cost decreases rapidly over 50–300 MW, while the reduction becomes much smaller above 300 MW. At large scales, RC-ORC and RE-ORC gradually approach 1756.64 $/kW. These results highlight the importance of PHX cost reduction, heat-exchanger-oriented ORC fluid selection, and appropriate system scaling. Full article

Background: The global burden of mental disorders continues to escalate, necessitating scalable, evidence-based interventions. Artificial intelligence (AI)-delivered health promotion programs represent a promising approach to addressing treatment gaps by targeting the neuropsychological mechanisms that underlie mental health outcomes. This meta-analysis synthesizes evidence on the effectiveness of AI-delivered interventions in improving executive function, emotion regulation, and clinical outcomes across diverse populations. Methods: A systematic search identified 186 studies (n = 22,755 participants) published between 2020 and 2025. Random-effects meta-analyses estimated pooled effect sizes (Hedges’ g, calculated as between-group standardized mean differences with small-sample correction [J = 1 − 3/(4df − 1)]) for primary outcomes. Between-study heterogeneity was quantified using I² and τ² statistics. To address dependency among effect sizes from studies reporting multiple outcomes, robust variance estimation (RVE) was employed. Subgroup analyses examined intervention modalities, delivery formats, and clinical populations. Moderator analyses explored sources of heterogeneity, including publication year, sample size, intervention duration, control condition type, risk-of-bias rating, geographic region, and AI sophistication tier, and mediational models tested putative therapeutic mechanisms. Results: AI-delivered interventions demonstrated a significant overall effect on health outcomes (g = 0.68, 95% CI [0.58, 0.78]; τ² = 0.12; I² = 73.4%). Executive function outcomes showed moderate effects (g = 0.61, τ² = 0.08), with working memory improvements being strongest (g = 0.72). Emotion regulation outcomes demonstrated moderate-to-large effects (g = 0.61, 95% CI [0.51, 0.70], τ² = 0.006); formal subgroup pooled estimates by emotion regulation strategy were not calculated due to insufficient studies per strategy (k < 3 per category); individual study effect sizes ranged from g = 0.27 to g = 1.11. Among 41 studies examining neuropsychological mechanisms, convergent patterns suggested involvement of prefrontal neural circuits (DLPFC), enhanced alpha-band activity, and improved heart rate variability; however, formal mediation was tested in only 18 studies (9.7%). Among clinical populations, interventions for cognitive impairment yielded the largest effects (g = 1.02; this finding should be interpreted cautiously given modest cumulative sample size [n = 482], potential small-study effects [Egger’s p = 0.08], and trim-and-fill adjusted estimate of g = 0.85), followed by mental health conditions (g = 0.72), while other clinical populations showed smaller but significant improvements (g = 0.19). Mobile applications (g = 0.78) and chatbot-based interventions (g = 0.74) demonstrated the strongest effects among delivery formats. Among studies testing formal mediation, analyses suggested mindfulness (β = 0.42), decentering (β = 0.38), and cognitive reappraisal (β = 0.45) as processes associated with therapeutic outcomes. Conclusions: AI-delivered health promotion programs demonstrate significant effectiveness across executive function, emotion regulation, and clinical outcomes, though substantial heterogeneity (I² = 45–82%) indicates meaningful variability warranting attention to subgroup-specific effects. Given the diversity of intervention types included (chatbots, mobile apps, VR systems, neuromodulation), pooled estimates should be interpreted as characterizing the average effect across this heterogeneous landscape; subgroup-specific estimates provide more precise guidance for clinical decision-making regarding specific modalities. Effects are associated with convergent patterns of neuropsychological mechanisms, though mechanistic conclusions remain preliminary given that only 22% of studies (41/186) examined neuropsychological mechanisms, and formal mediation analyses were conducted in only 18 studies (9.7%); most of the mechanistic evidence is correlational rather than causal. Future research should establish standardized AI taxonomies, optimize adaptive algorithms, conduct adequately powered replication studies in populations with cognitive impairment, prioritize experimental mediation designs to establish causal pathways, and evaluate long-term maintenance effects with a minimum of 6–12-month follow-up periods. Full article

Background: Low birth weight in newborns is of multifactorial origin (fetal, maternal, placental, and environmental factors), and in one-third of cases, the cause is of unknown origin, with high infant morbidity and mortality. The main treatment for regaining weight and height in children with low birth weight is the application of growth hormones. However, their role as a protective factor to prevent an increase in body composition and the development of metabolic diseases is still poorly understood. Methodology: A case–control study was conducted in a cohort of patients consulted at the CES Pediatric Endocrinology Clinic, Medellín, Colombia, between 2008 and 2018. We evaluated sociodemographic and clinical variables. Additionally, the identification of differential patterns of genomic methylation between cases (treated with growth hormone) and controls (without growth hormone treatment) was performed. The groups were compared using Fisher’s exact test for qualitative variables and Student’s t-test for the difference in means in independent samples. The correlation was evaluated with the Pearson coefficient. Results: Regarding clinical manifestations, body mass index (BMI) was higher in children who did not receive growth hormone treatment, higher doses of growth hormone treatment helped reduce body mass index (R: −0.21, and p = 0.067), and the use of growth hormone was related to a decrease in triglyceride blood concentrations (p = 0.06); these results tended towards significance. Regarding genome-wide methylation patterns, the following genes were found to be hypermethylated: MDGA1, HOXA5, LINC01168, ZFYVE19, ASAH1, MYH15, DNAJC17, PAMR1, MROCKI, CNDP2, CBY2, ZADH2, HOOK2, C9orf129, NXPH2, OSCP1, ZMIZ2, RUNX1, PTPRS, TEX26, EIF2A4K, MYO1F, C2orf69, and ZSCAN1. Meanwhile, the following genes were found hypomethylated: C10orf71-AS1, ZDHHC13, RPL17, EMC4, RPRD2, OBSCN-AS1, ZNF714, MUC4, SUGT1P4, TRIM38, C3, SPON1, NGF-AS1, CCSER2, P2RX2, LOC284379, GGTA1, NLRP5, OR51A4, HLA-H, and TTLL8. Conclusions: Using growth hormone as a treatment in SGA newborns helps regain weight and height. Additionally, it could be a protective factor against the increase in adolescent body composition. Full article

Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention and damage mitigation. Combining deep learning techniques with UAV imagery holds potential in advancing forest fire smoke recognition. However, issues arise when using UAV-derived images, especially in detecting miniature smoke patches, complicating effective feature discernment. Common deep learning approaches for forest fire detection also grapple with limitations due to sparse datasets. To counter these challenges, we introduce a refined UAV-centric forest fire smoke detection approach utilizing YOLOv5. We first enhance anchor box clustering through K-means++ to boost the classification precision and then augment the YOLOv5 architecture by integrating a novel partial convolution (PConv) to trim down model parameters and elevate processing speed. A unique detection head is also incorporated to the model to better detect diminutive smoke traces. A coordinate attention module is embedded within YOLOv5, enabling precise smoke target location and fine-grained feature extraction amidst complex settings. Given the scarcity of forest fire smoke datasets, we employ transfer learning for model training. The experimental results demonstrate that our proposed method achieves 96% AP₅₀ and 57.3% AP_50:95 on a customized dataset, outperforming other state-of-the-art one-stage object detectors while maintaining real-time performance. Full article

During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in the performance of health services. With this aim, we propose a methodological approach to compare the performance of the Italian healthcare system at the territorial level, i.e., considering NUTS 2 regions. Our approach consists of three steps: the choice of a distance measure between available time series, the application of weighted multidimensional scaling (wMDS) based on this distance, and, finally, a cluster analysis on the MDS coordinates. We separately consider daily time series regarding the deceased, intensive care units, and ordinary hospitalizations of patients affected by COVID-19. The proposed procedure identifies four clusters apart from two outlier regions. Changes between the waves at a regional level emerge from the main results, allowing the pressure on territorial health services to be mapped between 2020 and 2022. Full article

The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The proposed method revealed 14 users who had committed fraud, including 26 addresses in 9 cases, whereas previous works detected a maximum of 7 addresses in 5 cases of fraud. The suggested approach, in addition to reducing the processing overhead for extracting features, detect more abnormal users and anomaly behavior. Full article

To determine whether quaternary ammonium (k21) binds to Severe Acute Respiratory Syndrome–Coronavirus 2 (SARS-CoV-2) spike protein via computational molecular docking simulations, the crystal structure of the SARS-CoV-2 spike receptor-binding domain complexed with ACE-2 (PDB ID: 6LZG) was downloaded from RCSB PD and prepared using Schrodinger 2019-4. The entry of SARS-CoV-2 inside humans is through lung tissues with a pH of 7.38–7.42. A two-dimensional structure of k-21 was drawn using the 2D-sketcher of Maestro 12.2 and trimmed of C18 alkyl chains from all four arms with the assumption that the core moiety k-21 was without C18. The immunogenic potential of k21/QA was conducted using the C-ImmSim server for a position-specific scoring matrix analyzing the human host immune system response. Therapeutic probability was shown using prediction models with negative and positive control drugs. Negative scores show that the binding of a quaternary ammonium compound with the spike protein’s binding site is favorable. The drug molecule has a large Root Mean Square Deviation fluctuation due to the less complex geometry of the drug molecule, which is suggestive of a profound impact on the regular geometry of a viral protein. There is high concentration of Immunoglobulin M/Immunoglobulin G, which is concomitant of virus reduction. The proposed drug formulation based on quaternary ammonium to characterize affinity to the SARS-CoV-2 spike protein using simulation and computational immunological methods has shown promising findings. Full article

Different versions of principal component analysis (PCA) have been widely used to extract important information for image recognition and image clustering problems. However, owing to the presence of outliers, this remains challenging. This paper proposes a new PCA methodology based on a novel discovery that the widely used $l_1$ -PCA is equivalent to a two-groups $k$ -means clustering model. The projection vector of the $l_1$ -PCA is the vector difference between the two cluster centers estimated by the clustering model. In theory, this vector difference provides inter-cluster information, which is beneficial for distinguishing data objects from different classes. However, the performance of $l_1$ -PCA is not comparable with the state-of-the-art methods. This is because the $l_1$ -PCA can be sensitive to outliers, as the equivalent clustering model is not robust to outliers. To overcome this limitation, we introduce a trimming function to the clustering model and propose a trimmed-clustering based $l_1$ -PCA (TC-PCA). With this trimming set formulation, the TC-PCA is not sensitive to outliers. Besides, we mathematically prove the convergence of the proposed algorithm. Experimental results on image classification and clustering indicate that our proposed method outperforms the current state-of-the-art methods. Full article