With the rapid advancement of deep learning, computer vision has achieved significant breakthroughs in robotics, becoming a cornerstone for intelligent and autonomous systems .Convolutional Neural Networks (CNNs) and related deep models have greatly enhanced visual perception, enabling object detection to answer both the “what”(classification) and “where” (localization) questions that underpin high-level robotic perception and decision-making Recent progress in neural architectures and computational hardware has driven notable improvements in both accuracy and efficiency:two-stage detectors such as Faster R-CNN and single-stage methods like YOLO and SSD consistently report strong results on standard benchmarks such as COCO However, applying these methods in real-world robotics remains challenging due to dynamic lighting, occlusion, multi-scale objects, real-time constraints, and limited training data To address these challenges, this study reviews the current state and frontiers of deep-learning-based object detection, analyzing mainstream frameworks, optimization strategies, and application scenarios. Building on these insights, we propose an enhanced detection method that integrates attention mechanisms and data augmentation to improve small-object recognition in robotic vision Experiments are conducted to validate its effectiveness, offering both theoretical support and practical guidance for advancing robotic perception systems.
Analysis of 45 years of precipitation data from 28 meteorological stations in the Central Valley of Costa Rica revealed significant ENSO-related variability. El Nino events were associated with 15-30% reduction in wet season rainfall, while La Nina years showed 10-20% increases. Climate projections suggest intensification of ENSO impacts by mid-century, with implications for water resource management and agricultural planning in Costa Ricas most densely populated region.
Novel hydrogels were developed from soybean protein isolate crosslinked with genipin, a natural crosslinking agent. Mechanical testing showed tunable elastic moduli (5-50 kPa) suitable for soft tissue applications. In vitro studies with human fibroblasts demonstrated excellent biocompatibility with cell viability above 95% after 7 days. The biodegradable hydrogels showed controlled degradation rates over 4-8 weeks, making them promising candidates for wound healing and cartilage repair applications.
Autonomous recording units were deployed across 15 Atlantic Forest fragments of varying sizes (5-500 ha) in Sao Paulo state to assess avian diversity through acoustic monitoring. Analysis of 8,640 hours of recordings identified 187 bird species, with fragment size positively correlated with species richness. Large-bodied frugivores and insectivores showed highest sensitivity to fragmentation, highlighting the importance of maintaining connectivity between forest remnants for conservation.
Silver nanoparticles were synthesized using aqueous leaf extract of Azadirachta indica (neem) as a reducing and stabilizing agent. Characterization by UV-Vis spectroscopy, TEM, and XRD confirmed formation of crystalline AgNPs with average size of 25-40 nm. The biosynthesized nanoparticles demonstrated potent antibacterial activity against both gram-positive and gram-negative bacteria, with MIC values of 6.25-12.5 ug/mL against multidrug-resistant clinical isolates.
This research evaluated the seismic performance of four base-isolated public hospitals in the Valparaiso and OHiggins regions following the 2010 Maule earthquake. Instrumented response data and numerical simulations were used to assess isolation system effectiveness. Results demonstrated that base-isolated structures experienced 75% lower floor accelerations and maintained full operational capacity compared to conventionally designed facilities, validating seismic isolation as an essential strategy for critical infrastructure in high-seismicity zones.
This study assessed wind energy resources in Turkana County using three years of meteorological data from 12 measurement stations. Mean wind speeds ranged from 4.8 to 8.2 m/s at 50m hub height, with annual energy production estimates of 2,400-4,100 kWh/m2. Economic analysis showed that small-scale wind turbines (10-50 kW) could provide electricity at costs competitive with diesel generators, offering a viable pathway for rural electrification in northern Kenya.
A network of 48 IoT-enabled water quality monitoring stations was deployed across the Magdalena River basin to enable real-time assessment of physicochemical parameters. The solar-powered sensor nodes measured pH, dissolved oxygen, turbidity, conductivity, and temperature at 15-minute intervals. Data analysis over 18 months identified seasonal pollution patterns and enabled early warning alerts for industrial discharge events, improving response times for environmental authorities by 85%.
This study developed and validated a convolutional neural network model for automated diabetic retinopathy screening using fundus images from Malaysian diabetic patients. Training on 45,000 annotated images from six public hospitals, the model achieved 94.2% sensitivity and 91.8% specificity in detecting referable diabetic retinopathy. Implementation in primary care clinics reduced specialist referral waiting times by 67%, demonstrating the potential for AI-assisted screening in resource-limited settings.
Chronic neck and low back pain are significant global health issues that necessitate effective care solutions. This systematic review and meta-analysis aim to evaluate the effectiveness of telerehabilitation (TR) exercise regimens in treating chronic neck and low back pain, with a focus on functional outcomes, pain reduction, and quality of life (QoL) improvements. A comprehensive search was conducted up to December 2024 across multiple databases, including PubMed, the Cochrane Library, Embase, CINAHL, and Google Scholar. Randomized controlled trials (RCTs) that examined TR exercise programs for chronic neck and low back pain with minimum intervention period of four weeks were included. Accepted practices were employed for data extraction, quality evaluation, and synthesis of the findings. A total of 1,968 individuals across 15 RCTs met the inclusion criteria. Standardized mean differences (SMD) were calculated for various outcomes, with a focus on functional improvements, pain intensity reduction, and QoL enhancements. Heterogeneity among investigations had been evaluated and found to be low to moderate. The meta-analysis revealed that telerehabilitation exercise programs significantly improved functional outcomes (SMD=0.52, 95%CI: 0.30 to 0.74, p<0.001), decreased pain intensity (SMD = -0.69, 95%CI: -0.86 to -0.52, p<0.001), and enhanced QoL (SMD=0.45, 95%CI: 0.24 to 0.66, p<0.001). Effectiveness of TR exercise programs in treating chronic neck and low back pain is strongly supported by this thorough review as well as meta-analysis. The significant improvements in pain severity, functional outcomes, and overall well-being highlight telerehabilitation as a viable alternative to conventional rehabilitation approaches.
Background: Minimally invasive repair of hiatal hernia is standard in elderly patients, with laparoscopic surgery reducing morbidity compared to open repair. Robotic-assisted techniques may offer enhanced visualization and dexterity, potentially addressing challenges in large or complex hernias. However, whether robotic hiatal hernia repair improves outcomes over laparoscopy in patients over 65 remains unclear amid conflicting reports. Methods: A systematic search of PubMed, Embase, Scopus, Web of Science, and Cochrane databases (2015�2025) was performed for randomized or observational studies comparing robotic and laparoscopic hiatal hernia repair in patients over 65. PRISMA-2020 guidelines were followed. Data on recurrence, operative time, blood loss, conversion to open, hospital stay, complications, readmission, and mortality were extracted. Meta-analyses used random-effects models if heterogeneity (I�) exceeded 50%. Risk of bias was assessed using Newcastle�Ottawa Scale. Results: Eighteen observational studies (approximately 700,000 patients; mean age 65�75 years) met inclusion criteria. Postoperative outcomes were comparable between robotic and laparoscopic groups, with no significant difference in overall complication rates (pooled OR=0.97, 95% CI=0.87�1.09, p=0.66) or 1-year hernia recurrence. Robotic repair had modestly shorter hospital stays (mean reduction approximately 0.5 days) and lower conversion-to-open rates. Operative times, blood loss, readmission rates, and mortality were similar. Risk of bias was moderate in most studies. Conclusions: Robotic hiatal hernia repair provides short- and long-term outcomes equivalent to laparoscopy in elderly patients, with similar safety and durability. Potential benefits include shorter hospitalization and fewer conversions in complex cases; however, further prospective trials are warranted.
Benthic macroinvertebrates, sensitive bioindicators relying on aquatic sediments, were used for the first time to assess water quality at Patrind Dam along River Kunhar and its confluence with the Neelum and Jhelum Rivers from January 2021 to December 2022. Diversity indices and the Average Score per Taxon (ASPT) evaluated macroinvertebrate diversity and water quality. A seasonal survey identified 1,925 specimens from 12 families, 7 orders, 2 classes, and 2 phyla. The study area was dominated by Diptera (42.23%), Trichoptera (29.30%), Amphipoda (9.71%), and Odonata (9.25%). Hydropsychidae was the most abundant family (23.90%), while Gomphidae was the least (2.34%). In 2021, macroinvertebrate diversity metrics across seasons were recorded as: taxon richness (5, 4, 7, 5), abundance (345, 120, 269, 148), Simpson’s index (0.69, 0.73, 0.85, 0.79), Shannon-Wiener index (1.35, 1.34, 1.93, 1.59), and evenness (0.77, 0.95, 0.98, 0.98) for winter, spring, summer, and autumn, respectively. In 2022, the values were: richness (5, 4, 7, 5), abundance (332, 221, 324, 166), Simpson’s index (0.57, 0.72, 0.81, 0.71), Shannon-Wiener index (1.13, 1.33, 1.76, 1.42), and evenness (0.62, 0.95, 0.83, 0.83) for the same seasons. Macroinvertebrate abundance was higher downstream (1,064) than upstream (657). Leading upstream families were Startiomyidae (n=120), Gammaridae (n=103), and Psychodidae (n=87), while downstream was dominated by Hydropsychidae (n=343), Startiomyidae (n=178), and Gammaridae (n=84). Collector gatherers comprised the largest functional feeding group (43.64%), and scrappers the lowest (2.70%). The overall ASPT of 4.5 classified the water quality of the study area into class 3, indicating critical pollution and moderate ecological status.
Wastewater treatment plants� efficiency can be affected by seasonal variation, the load of contaminants in the wastewater that is received and the treatment processes/facilities. This study evaluated the impact of season and industrial wastewater on the heterotrophic bacteria (HB) proliferation in the treatment plants. A total of 132 samples were collected from upstream and downstream of 3 rivers, inflow and final effluent of 3 treatment plants, and 7 different industries per season. Enumeration of HB was done using standard procedures. A pair-wise correlation analysis was done to determine the relationship between HB in the industries and the HB count of the inflow, final effluent, upstream, and downstream across the seasons. Results showed that the proliferation of HB in the treatment plants and industries occurred in the order: spring>summer>winter>autumn. The correlation analysis clearly showed that industrial wastewater influenced the abundance of the bacteria in the treatment plants with variations in seasons. In addition, the final effluent affects the abundance of the bacteria in the receiving river. These findings highlight the combined influence of seasonality and industrial activity on bacterial loads, emphasising the need for real-time monitoring tools to strengthen microbial risk management in wastewater treatment systems.