A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles by Jianqiao Chen, Guofu Tian

“…The experimental results clearly indicate that the autonomous vehicle, after PID parameter optimization using a genetic algorithm, demonstrated the following speed tracking errors: when the road surface adhesion coefficient was 0.5, the maximum speed tracking error was 0.22, the average value was 0.063, and the standard deviation was 0.124; when the adhesion coefficient was 0.6, the maximum speed tracking error was 0.180, the average value was 0.056, and the standard deviation was 0.099; when the adhesion coefficient was 0.8, the maximum speed tracking error was 0.179, the average value was 0.056, and the standard deviation was 0.098. …”
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Mobile-AI-Based Docent System: Navigation and Localization for Visually Impaired Gallery Visitors by Hyeyoung An, Woojin Park, Philip Liu, Soochang Park

“…An on-device object detection model reduces delays from large visual data processing, while BLE beacons, fixed across the gallery, dynamically update location IDs for better accuracy. …”
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A NARRATIVE REVIEW ON AUTOMATION AND DIGITALIZATION IN QUALITY CONTROL LABORATORIES OF PHARMACEUTICALS by Sheikh Abdul Khaliq, Muhammad Kashif, Muhammad Jiyad Shaikh, Ijaz Hussain

“…For instance, Sysmex's TLA system enhances automation within QC procedures, leading to increased accuracy and reduced errors. Moreover, technologies like IoT-based monitoring systems, robotic sample handlers, and AI-based tools—such as AI-based pathogen detection systems—are being implemented. …”
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Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation. by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This highlights the need for automated detection systems to enhance diagnostic efficiency. …”
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Association of Disability Quotient with Ocular and Systemic Manifestations in Children with Developmental Delay by Meenakshi Wadhwani, Sehzadi Malhotra, Dhulika Dhingra, Manika Manika, Shubhangi Kursange, Rahul Singh, Sneha Kumari, Amit Kumar

“…Delayed cry at birth was found to have a significant association with lower Developmental Quotient (DQ) (P = 0.021) and higher incidence of refractive error (P = 0.036). Refractive error was more prevalent in children with cerebral palsy (P = 0.006). …”
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An Intelligent Distributed Channel Selection Framework with Hybrid Mode Selection for Interference Mitigation in D2D based 5G Networks by Abdullilah A. Alotaibi, Salman A. AlQahtani

“…This channel selection framework classifies the available sensed channels using self-organizing map (SOM) learning technique into four classes considering channels with sensing errors false alarm (FalsA) and miss detection (MissD) to prevent using occupied channels. …”
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Investigating the Impacts of Ionospheric Irregularities on Precise Point Positioning Over China and Its Mechanism by Wei Li, Shuli Song, Weili Zhou, Na Cheng, Chao Yu

“…The results show that the ionospheric irregularities caused increased positioning errors (decimeter‐ to meter‐level), enlarged phase residuals (decimeter‐level), and increased the number of detected cycle slips in PPP processing in low latitude regions of China. …”
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Cosmological Constraints from Combining Photometric Galaxy Surveys and Gravitational Wave Observatories by E.L. Gagnon, D. Anbajagane, J. Prat, C. Chang, J. Frieman

“…Spatial variations in survey properties due to selection effects generate substantial systematic errors in large-scale structure measurements in optical galaxy surveys on very large scales. …”
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The role of spectral vs spatial resolution of satellite data on the accuracy of mapping unburned vegetation within fire scar perimeters by Magdalini Pleniou, Nikos Koutsias

“…Our research highlights several key findings including: (a) spectral information content appears to have a significant role when considering the full range of separability values, (b) in combinations featuring high separability values, the primary parameter that influences the accuracy of the mapping the unburned vegetation is the spatial resolution of the satellite images, and (c) there is a discernible disparity in the roles of the spectral and spatial resolution concerning the commission and omission errors of the unburned class.…”
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Incorporation of XAI and Deep Learning in Biomedical Imaging: A Review by Sushil K. Singh, Bal Virdee, Saurabh Aggarwal, Abhilash Maroju

“…Artificial Intelligence (AI) and Deep Learning (DL) technologies have revolutionized disease detection, particularly in Medical Imaging (MI). While these technologies demonstrate outstanding performance in image classification, their integration into clinical practice remains gradual. …”
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Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques by Phan Thi Huong, Lam Thanh Hien, Nguyen Minh Son, Huynh Cao Tuan, Thanh Q. Nguyen

“…While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. …”
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Sugarcane Phenology Retrieval in Heterogeneous Agricultural Landscapes Based on Spatiotemporal Fusion Remote Sensing Data by Yingpin Yang, Zhifeng Wu, Dakang Wang, Cong Wang, Xiankun Yang, Yibo Wang, Jinnian Wang, Qiting Huang, Lu Hou, Zongbin Wang, Xu Chang

“…The findings provide a reliable technical solution for precision sugarcane management in heterogeneous landscapes.…”
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Mutability and importance of a hypermutable cell subpopulation that produces stress-induced mutants in Escherichia coli. by Caleb Gonzalez, Lilach Hadany, Rebecca G Ponder, Mellanie Price, P J Hastings, Susan M Rosenberg

“…First, using improved mutation-detection methods, we estimate the number of mutations per genome of HMS-derived cells and find that it is compatible with fitness after the HMS state. …”
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Optimal fuzzy-PID controller design for object tracking by Yaregal Limenih Melese, Girma Kassa Alitasb, Mequanent Degu Belete

“…Abstract Object tracking is a technique for finding moving objects of interest and estimating their trajectory or path with regard to time in a series of images. …”
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Lightweight AES Design for IoT Applications: Optimizations in FPGA and ASIC With DFA Countermeasure Strategies by Salman Ahmed, Nabihah Ahmad, Nasir Ali Shah, Ghulam E. Mustafa Abro, Ardhi Wijayanto, Abdinasir Hirsi, Abdul Rehman Altaf

“…In addition, the study reviews DFA countermeasures that highlight fault detection methods, error detection levels, protection positions, and associated design overheads such as area, frequency, and throughput penalties, with special consideration for resource-constrained IoT devices. …”
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Assessment of Nurses Knowledge Concerning Type 2 Diabetes Mellitus Management with Insulin Therapy in Intensive Care Units at Baghdad Hospitals by Sabah Abbas Ahmed, Khames Bander Abed, Ali H. Alek Al-Ganmi

“…Reliability of the instrument was determined through detected of Cronbach's alpha via split half technique on sample of (10) nurses, which was (0.74). …”
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SLAM Performance Evaluation on Uneven Terrain Under Varying Illuminance Conditions and Trajectory Lengths by Amirul Jamaludin, Syaimaa' Solehah Mohd Radzi, Norhidayah Mohamad Yatim, Nazlee Azmeer Masuan, Norsharimie Mat Adam, Nur Aisyah Syafinaz Suarin, Pazli Mohd Sulong, Siti Sofiah Mohd Radzi

“…Performance metrics, including distance error and loop closure detection, were used to assess the impact of trajectory length and illuminance. …”
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Relating Atlantic meridional deep-water transport to ocean bottom pressure variations as a target for satellite gravimetry missions by L. Shihora, T. Martin, A. C. Hans, R. Hummels, M. Schindelegger, H. Dobslaw

“…Here, we use the output of a fine-resolution (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><mo>/</mo><mn mathvariant="normal">20</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="eb44dc7215fab272447585860bb94269"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="os-21-1533-2025-ie00001.svg" width="27pt" height="14pt" src="os-21-1533-2025-ie00001.png"/></svg:svg></span></span>°) regional ocean model to assess the connection between OBP signals at the western boundary of the North and South Atlantic to changes in the zonally integrated meridional deep-water transport. We find that transport anomalies in the <span class="inline-formula">∼</span> 1–3 km depth range can be reconstructed using OBP variations spatially averaged over the continental slope, with correlations of 0.75 (0.72) for the North (South) Atlantic and root-mean-square errors of <span class="inline-formula">∼</span> 1 Sv (sverdrup; <span class="inline-formula">10⁶</span> <span class="inline-formula">m³ s⁻¹</span>), on monthly to inter-annual timescales. …”
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A brief review of Lynch syndrome: understanding the dual cancer risk between endometrial and colorectal cancer by Sneha Pallatt, Sibin Nambidi, Subhamay Adhikary, Antara Banerjee, Surajit Pathak, Asim K. Duttaroy

“…These mutations result in frameshift alterations, leading to the accumulation of errors within microsatellites. Individuals with LS have an elevated risk of developing colorectal and distant malignancies, including endometrial cancer (EC), which is one of the most common cancer associated with LS. …”
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Benchmarking Large Language Models in Evaluating Workforce Risk of Robotization: Insights from Agriculture by Lefteris Benos, Vasso Marinoudi, Patrizia Busato, Dimitrios Kateris, Simon Pearson, Dionysis Bochtis

“…Using ChatGPT, Copilot, and Gemini, the LLMs followed a three-step evaluation process focusing on (a) task importance, (b) potential for task robotization, and (c) task attribute indexing of 15 agricultural occupations, mirroring the methodology used by human assessors. The findings indicate a significant tendency for LLMs to overestimate robotization potential, with most of the errors falling within the range of 0.229 ± 0.174. …”
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