Search Results (46)

The article reports on a bilingual and interpretable book recommendation platform for schoolchildren. This platform uses a lightweight K-Nearest Neighbors algorithm combined with gamification and learning analytics. This application has been designed for a bilingual learning environment in Kazakhstan, supporting learning in Kazakh and Russian languages, and is intended to improve reading engagement through culturally adjusted personalization. The recommendation engine combines content and collaborative filtering in that it leverages structured book data (genres, target age ranges, authors, languages, and semantics) and learner attributes (language of instruction, preferences, and learner history). A hybrid ranking function combines the similarity to the user and the item similarity to produce top-N recommendations, whereas gamification elements (points, achievements, and reading challenges) are used to foster sustained activity.Teacher dashboards show learners’ overall reading activity and progress through real-time data visualization. The initial calibration of the model was carried out using an open-source book collection consisting of 5197 items. Thereafter, the model was modified for a curated bilingual collection of 600 books intended for use in educational institutions in the Kazakh and Russian languages. The validation experiment was carried out on a pilot test involving 156 children. The experimental outcome suggests a stable level of recommendation in terms of the Precision@10 and Recall@10 values of 0.71 and 0.63 respectively. The computational complexity remained low. Moreover, the bilingual normalization technique increased the relevance of recommendations of non-majority language items by 12.4%. In conclusion, the proposed approach presents a scalable and transparent framework for AI-assisted reading personalization in bilingual e-learning systems. Future research will focus on transparent recommendation interfaces and more adaptive learner modeling. Full article

Conventional High Dynamic Range (HDR) image fusion algorithms generally require two or more original images with different exposure times for synthesis, making them unsuitable for real-time processing scenarios such as video streams. Additionally, the synthesized HDR images have the same bit depth as the original images, which may lead to banding artifacts and limits their applicability in professional fields requiring high fidelity. This paper utilizes a Field Programmable Gate Array (FPGA) to support an image sensor operating in Clear HDR mode, which simultaneously outputs High Conversion Gain (HCG) and Low Conversion Gain (LCG) images. These two images share the same exposure duration and are captured at the same moment, making them well-suited for real-time HDR fusion. This approach provides a feasible solution for real-time processing of video streams. An adaptive adjustment algorithm is employed to address the requirement for high fidelity. First, the initial HCG and LCG images are fused under the initial fusion parameters to generate a preliminary HDR image. Subsequently, the gain of the high-gain images in the video stream is adaptively adjusted according to the brightness of the fused HDR image, enabling stable brightness under dynamic illumination conditions. Finally, by evaluating the read noise of the HCG and LCG images, the fusion parameters are adaptively optimized to synthesize an HDR image with higher bit depth. Experimental results demonstrate that the proposed method achieves a processing rate of 46 frames per second for 2688 × 1520 resolution video streams, enabling real-time processing. The bit depth of the image is enhanced from 12 bits to 16 bits, preserving more scene information and effectively addressing banding artifacts in HDR images. This improvement provides greater flexibility for subsequent image processing tasks. Consequently, the adaptive algorithm is particularly suitable for dynamically changing scenarios such as real-time surveillance and professional applications including industrial inspection. Full article

Radio Frequency Identification (RFID) is a core enabler of the Industrial Internet of Things (IIoT), yet dense deployments suffer from tag collisions and reader interference that degrade reliability and inflate infrastructure cost. This study proposes a deterministic Reader Deployment Algorithm with Adjustable Reader range (RDA2R) to achieve full tag coverage with minimal interference and reader usage. The method divides the monitored field into grid units, evaluates tag coverage weights, activates high-weight readers with interference checks, and adaptively adjusts interrogation ranges. Simulation results under random and congregation tag distributions show that RDA2R requires about 46–47% fewer readers than ARLDL and 32–33% fewer than MR2D, while improving average tag coverage per reader by over 30%. These results demonstrate that RDA2R provides a scalable, interference-aware, and cost-efficient deployment strategy for RFID-enabled IIoT environments. Full article

Background: In phenylketonuria (PKU) patients, dried blood spot (DBS) sampling remains the standard method for monitoring phenylalanine (Phe) levels. However, delays in reporting results can hinder timely dietary adjustments. Patients and caregivers have expressed a preference for point-of-care testing (POCT) devices that enable home-based monitoring. Objectives: Our aim was to compare blood Phe measurements in PKU patients and caregiver usability of a POCT system with DBS, which is the standard practice monitoring method. Methods: Twenty participants (eighteen children with PKU and two healthy controls) were recruited. Caregivers of children with PKU were asked to perform blood Phe measurements at home under the supervision of a researcher, using both the POCT device (Egoo Phe system) and DBS sampling. Healthy controls collected the same number of samples using both methods in a hospital setting. The POCT system required 40 µL of blood and used an enzymatic, bioluminescent detection system. DBS samples were analyzed by tandem mass spectrometry (TMS) and required two blood spots (approximately 100 µL of blood). The Egoo Connect App, linked via Bluetooth to the POCT device, displayed results after 29 min. Caregiver usability of the POCT system was assessed using questionnaires at each visit. Results: A total of 100 paired samples were collected. Median values were 274 μmol/L (range: 30–1039) for POCT and 270 μmol/L (range: 20–1190) for DBS. POCT readings were a mean of 4.6% higher than DBS with a noticeable strong correlation observed (y = 1.017x; R² = 0.8450; p < 0.0001). The usability of the POCT system improved with caregiver practice, and all caregivers expressed a preference for POCT over DBS. Conclusions: The POCT system for blood Phe demonstrated strong concordance with DBS and high caregiver acceptance, highlighting its potential to transform PKU care through faster, patient-driven monitoring and more timely clinical decision-making. Full article

Background: The fetal mechanical PR interval (mPR), measured using pulsed-wave Doppler, is a widely used parameter to assess atrioventricular conduction in fetuses, particularly in cases at risk of developing atrioventricular (AV) block. However, the physiological factors that influence mPR readings are not fully understood. This study aimed to identify determinants affecting the measurement of the mPR interval using the mitral valve/aorta (MV/Ao) Doppler method in a cohort of structurally normal fetuses. Methods: We retrospectively analyzed 925 fetuses with normal echocardiographic findings and no structural cardiac or extracardiac anomalies. Correlation analysis, group comparisons, trend testing, and multivariable modeling were performed to assess the impact of biometric and Doppler parameters on mPR interval measurements. Results: The median mPR interval across the cohort was 116 ms (interquartile range: 108–123 ms). Fetuses were categorized into four gestational age groups (≤19 weeks, 20–23 weeks, 24–27 weeks, and ≥28 weeks). Significant differences in mPR were observed between gestational age groups (p < 0.01), with a positive trend across increasing gestational age (p < 0.0001). The strongest correlation was an inverse relationship between mPR and fetal heart rate (FHR) (ρ = −0.256, p < 0.01). Multivariable regression identified five independent predictors of mPR: lower FHR, greater biparietal diameter (BPD), larger pulmonary valve diameter (PVD), increased fronto-occipital diameter (FOD), and lower umbilical artery pulsatility index (UA PI). The final model explained approximately 9.9% of the variance in mPR interval (R² = 0.099). Conclusions: The fetal mPR interval increases with gestational age and is primarily influenced by fetal heart rate, even after adjusting for other factors. Certain biometric and Doppler parameters also contribute modestly to mPR variation. These findings highlight the importance of accounting for physiological variability when interpreting mPR measurements in clinical fetal cardiology. Full article

The health impact of air pollutants has generated a trend in the design and manufacture of portable, personal and fixed PM monitoring systems to help reduce exposure to air pollutants. However, these devices still need to be improved and properly evaluated to compete with environmental monitors in the market. In this work, a test chamber with controlled environmental conditions and wireless connectivity is developed for the evaluation of low-cost portable and personal PM sensors. The developed system ensures rapid evaluation tests ranging from seconds to hours to corroborate prolonged operation and correct calibration. The system is controlled by a Python-based graphical user interface (GUI) and monitors PM concentration, altitude, relative humidity, atmospheric pressure, illuminance, and temperature measurements. Fifty measurement tests with a duration of 10 min each were conducted to ensure robust performance and data transfer. Subsequently, four calibration tests were conducted using two SENSIRION SPS30 (SPS A and SPS B) personal PM sensors and two PMS5003 (PMS A and PMS B) personal PM sensors. The Prana Air PAS-OUT-01 sensor served as the reference to calculate the correlations and the descriptive statistics between each sensor to be calibrated. A contamination source was employed utilizing a monodispersed aerosol generator for 0.46 µm latex polystyrene particle atomization. Linear regression was applied during the calibration to determine the calibration coefficients, which were then used to adjust the sensor readings in the respective code and descriptive statistics of the sensor calibration tests were calculated. For the PMS5003 sensors, the Pearson correlation coefficients (r) after calibration were PMS A: 0.9870 and PMS B: 0.9898 compared to their uncalibrated values of PMS A: 0.9828 and PMS B: 0.9863. In contrast, the uncalibrated SPS A sensor initially had a correlation of 0.9939, which slightly decreased to 0.9917 after calibration. Meanwhile, the uncalibrated SPS B sensor showed a correlation of 0.9422, which improved to 0.9715 after calibration. Full article

Aerosol uniformity in the mixing chamber is one of the key factors in evaluating performance of aerosol samplers and accuracy of aerosol monitors which could output the direct reading of particle size or concentration. For obtaining high uniformity and a stable test aerosol sample during evaluation, a portable mixing chamber, where the sample and clean air were dual-vortex turbulent mixed, was designed. By using computational fluid dynamics (CFD), particle motion within the mixing chamber was illustrated or explained. By adjusting critical structure parameters of chamber such as height and diameter, the flow field structure was optimized to improve particle mixing characteristics. Accordingly, a novel portable aerosol mixing chamber with length and inner diameter of 0.7 m and 60 mm was developed. Through a combination of simulations and experiments, the operating conditions, including working flow rate, ratio of carrier/dilution clean air, and mixture duration, were studied. Finally, by using the optimized parameters, a mixing chamber with high spatial uniformity where variation is less than 4% was obtained for aerosol particles ranging from 0.3 μm to 10 μm. Based on this chamber, a standardized testing platform was established to verify the sampling efficiency of aerosol samplers with high flow rate (28.3 L·min⁻¹). The obtained results were consistent with the reference values in the sampler’s manual, confirming the reliability of the evaluation system. The testing platform developed in this study can provide test aerosol particles ranging from sub-micrometers to micrometers and has significant engineering applications, such as atmospheric pollution monitoring and occupational health assessment. Full article

(1) Objectives: Understanding the genetic basis of muscle development and meat quality traits in divergent pig breeds is crucial for advancing precision breeding strategies. (2) Methods: This study investigated transcriptome differences in the longissimus dorsi muscle between Chinese Congjiang Xiang (CX) and Landrace (LAN) pigs. RNA sequencing was performed on muscle tissues from ten individuals of each breed, generating 874.5 million raw reads with an average mapping rate of 89.3% to the pig reference genome. (3) Results: Transcriptional profiling revealed distinct expression patterns with 785 genes exclusively expressed in CX pigs and 457 genes unique to LAN pigs, while 7099 co-expressed genes were shared by both breeds. Differential expression analysis identified 2459 significantly different genes (|log2FC| ≥ 1, adjusted p-value < 0.05), with 1745 up-regulated and 714 down-regulated in CX pigs. Among the most significantly up-regulated genes in CX pigs were flavor-associated genes (ELOVL5/6, FASN, DGAT2, ALDH1A3, PPAR-γ) with log2FC values ranging from 1.21 to 3.88. GO and KEGG pathway analyses revealed that up-regulated genes in CX pigs were significantly enriched in immune response pathways (adjusted p-value < 0.01), while down-regulated genes were primarily associated with myosin complex formation and PPAR signaling pathway. PPI network analysis identified PPAR-γ as a central hub gene with 16 direct interactions to other flavor-related genes. (4) Conclusions: These findings demonstrate that the superior meat flavor characteristics of indigenous Chinese pigs are driven by enhanced expression of lipid metabolism genes and distinctive immune-related pathways, providing specific molecular targets for breeding programs aimed at improving meat quality while maintaining production efficiency in commercial breeds. Full article

A coding ruler is a device that marks position information in the fordigital signals, and a code reader is a device that decodes the signals on the coding ruler and converts them into digital signals. The code reader and encoder ruler are key devices in ensuring the positioning accuracy of coke oven locomotives and the safety of coke production. They are common information transmission and positioning detection devices that can provide accurate monitoring and information feedback for the position and speed of coke oven locomotives. Four encoding methods were studied, namely, binary encoding, Gray code encoding, shift continuous encoding, and hybrid encoding. The application scenarios and encoding characteristics of each encoding method are summarized in this paper. Hybrid encoding combines the advantages of two different encoding methods, absolute and incremental encoding, to achieve higher accuracy and stability. Hybrid coding has high positioning accuracy in the long-range coke oven tampering tracks, ensuring the accuracy and high efficiency of the tampering operation. A certain number of opposing laser sensors are installed inside the code reader to obtain 0/1 encoding and read the movement displacement of the code reader on the ruler. In order to effectively detect the swing of the coding ruler, a certain number of distance sensors are installed on both sides and on the same side of the code reader. Ruler swing is accurately detected by the sensors, which output and process corresponding signals. Timely adjustment and correction measures are taken on the production line according to the test results, which not only improves detection accuracy but also enhances the stability and reliability of the system. Full article

Background: We recently reported that the transcutaneous bilirubinometer (TCB) tends to underestimate the severity of neonatal jaundice (NJ). We hypothesize that the cumulative ambient light exposure contributes to the discrepancy. Objectives: This study aimed to identify factors that affect the TCB underestimation. Methods: We analyzed prospectively collected data over a twenty-month period at a level III medical facility. Neonates at risk for NJ who couldn’t secure an appointment with the primary practitioner were followed by the nursery team. Neonates who had phototherapy or forehead bruises were excluded. Concurrently collected total serum bilirubin (TSB) was determined by the diazo method. The primary endpoint was the discrepancy between TCB and the corresponding TSB (TCB-TSB). A mixed-effects model was used to assess the correlation between (TCB-TSB) and potential contributors, including visit age (in hours), gestational age (GA), sex, TSB, season, birth weight, and race. Results: There were 795 visits for 559 neonates, including 341 males, 179 white, 235 black, 103 Hispanic, 41 Asian, and one unrecorded race. The TSB ranged between 1.8 and 33.9 mg/dL. The (TCB-TSB) ranged between −20.0 and 6.4 mg/dL. The median GA and birth weight were 38.7 weeks and 3214.5 g. The visits occurred between 48 and 381 h of age. 133, 148, 132, and 146 visits were in Spring, Summer, Autumn, and Winter, respectively. Fifty-four neonates (9.7%) were admitted for management. 500 sternum TCB readings were also collected from 350 neonates together with the corresponding forehead TCBs. We found that the forehead (TCB-TSB) was significantly less in winter than in spring and summer (p = 0.0014 and 0.0003, respectively). There was a negative correlation between forehead (TCB-TSB) and visit age in hours (p = 0.0006). After adjusting for visit age and season, the (TCB-TSB) is significantly correlated with TSB (p < 0.0001). Similar findings were also seen in the sternum (TCB-TSB) except for the season (p = 0.0808). Conclusions: Cumulative ambient light exposure and the severity of NJ may contribute to (TCB-TSB). Full article

Background: The US Food and Drug Administration (FDA) and International Organization for Standardization (ISO) clearance standards for the clinical use of smart device pulse oximetry require in-laboratory human hypoxemia testing in healthy human individuals using arterial blood gas analysis. Methods: We evaluated the SpO₂ measurements of the Samsung smartphone (Galaxy S9/10) and smartwatch (Galaxy 4) at stable arterial oxygen saturations (SaO₂) between 70 and 100% in 24 healthy participants. Testing followed FDA/ISO-stipulated procedures for pulse oximetry performance validation, which include questionnaire estimation of skin tone based on Fitzpatrick estimation of skin types I–VI. During testing, inspired oxygen, nitrogen, and carbon dioxide partial pressures were monitored and adjusted via partial rebreathing circuits to achieve stable target arterial blood oxygen (SaO₂) plateaus between 70% and 100%. Arterial blood samples were taken at each plateau, with device SpO₂ readings taken at each sample extraction. An ABL-90FLEX blood gas analyzer determined arterial blood sample SaO₂. Bias, calculated from device readings minus corresponding arterial blood measurements, was reported as root mean square deviation (RMSD). Results: Combined Participants demographics were: 62.5% female; median age 26 years (range 21–46); and race/ethnicity 16.7% African American, 33.3% Asian, 12.5% multi-ethnic, and 37.5% Caucasian. Fitzpatrick Skin Scale-identified skin tones were: white–fair (I&II), 20.8%; average–light brown (III–IV), 54% and brown–black (V–VI), 25%. There were no adverse events. The RMSD values of SpO₂ measurements were: smartphone 2.6% (257 data pairs) and smartwatch 1.8% (247 data pairs). Conclusions: Device SpO₂ demonstrated RMSD < 3.0% to SaO₂, meeting FDA/ISO clearance standards at the time of study. However, additional testing in persons with darker skin tones is necessary. Smartphones and paired wearables, when cleared for clinical use following revision of FDA clearance standards, may expand access to remote pulse oximetry. Full article

Education is crucial for leading a productive life and obtaining necessary resources. Higher education institutions are progressively incorporating artificial intelligence into conventional teaching methods as a result of innovations in technology. As a high academic record raises a university’s ranking and increases student career chances, predicting learning success has been a central focus in education. Both performance analysis and providing high-quality instruction are challenges faced by modern schools. Maintaining high academic standards, juggling life and academics, and adjusting to technology are problems that students must overcome. In this study, we present a comprehensive dataset, SAPEx-D (Student Academic Performance Exploration), designed to predict student performance, encompassing a wide array of personal, familial, academic, and behavioral factors. Our data collection effort at Air University, Islamabad, Pakistan, involved both online and paper questionnaires completed by students across multiple departments, ensuring diverse representation. After meticulous preprocessing to remove duplicates and entries with significant missing values, we retained 494 valid responses. The dataset includes detailed attributes such as demographic information, parental education and occupation, study habits, reading frequencies, and transportation modes. To facilitate robust analysis, we encoded ordinal attributes using label encoding and nominal attributes using one-hot encoding, expanding our dataset from 38 to 88 attributes. Feature scaling was performed to standardize the range and distribution of data, using a normalization technique. Our analysis revealed that factors such as degree major, parental education, reading frequency, and scholarship type significantly influence student performance. The machine learning models applied to this dataset, including Gradient Boosting and Random Forest, demonstrated high accuracy and robustness, underscoring the dataset’s potential for insightful academic performance prediction. In terms of model performance, Gradient Boosting achieved an accuracy of 68.7% and an F1-score of 68% for the eight-class classification task. For the three-class classification, Random Forest outperformed other models, reaching an accuracy of 80.8% and an F1-score of 78%. These findings highlight the importance of comprehensive data in understanding and predicting academic outcomes, paving the way for more personalized and effective educational strategies. Full article

Chemical exposure in the environment can adversely affect the biodiversity of living organisms, particularly when persistent chemicals accumulate over time and disrupt the balance of microbial populations. In this study, we examined how chemical contaminants influence microorganisms in sediment and overlaying water samples collected from the Kinnickinnic, Milwaukee, and Menomonee Rivers near Milwaukee, Wisconsin, USA. We characterized these samples using shotgun metagenomic sequencing to assess microbiome diversity and employed chemical analyses to quantify more than 200 compounds spanning 16 broad classes, including pesticides, industrial products, personal care products, and pharmaceuticals. Integrative and differential comparative analyses of the combined datasets revealed that microbial density, approximated by adjusted total sequence reads, declined with increasing total chemical concentrations. Protozoan, metazoan, and fungal populations were negatively correlated with higher chemical concentrations, whereas certain bacterial (particularly Proteobacteria) and archaeal populations showed positive correlations. As expected, sediment samples exhibited higher concentrations and a wider dynamic range of chemicals compared to water samples. Varying levels of chemical contamination appeared to shape the distribution of microbial taxa, with some bacterial, metazoan, and protozoan populations present only at certain sites or in specific sample types (sediment versus water). These findings suggest that microbial diversity may be linked to both the type and concentration of chemicals present. Additionally, this study demonstrates the potential roles of multiple microbial kingdoms in degrading environmental pollutants, emphasizing the metabolic versatility of bacteria and archaea in processing complex contaminants such as polyaromatic hydrocarbons and bisphenols. Through functional and resistance gene profiling, we observed that multi-kingdom microbial consortia—including bacteria, fungi, and protozoa—can contribute to bioremediation strategies and help restore ecological balance in contaminated ecosystems. This approach may also serve as a valuable proxy for assessing the types and levels of chemical pollutants, as well as their effects on biodiversity. Full article

In radio frequency identification (RFID), differences in spectrum policies and tag misreading in different countries are the two main issues that limit its application. To solve these problems, this article proposes a composite right/left-handed transmission line (CRLH-TL)-based reconfigurable antenna for ultra-high frequency near-field and far-field RFID reader applications. The CRLH-TL is achieved using a periodically capacitive gap-loaded parallel plate line. By deploying the CRLH-TL operating at zeroth-order resonance, a loop antenna with in-phase radiating current is obtained, which contributes to a strong and uniform H-field and a horizontally polarized omnidirectional radiation pattern. By introducing additional tunable components, frequency and reading range reconfigurabilities are enabled. The frequency tuning range is from 833 MHz to 979 MHz, which covers the worldwide UHF RFID band. Moreover, each operation mode has a narrow frequency band, which means it can operate without violating different countries’ radio frequency policy and reduce the design difficulty of designing multiple versions of a reader. Both the near-field interrogation zone and maximum far-field reading distance of the antenna are adjustable. The near-field interrogation zone is 400 mm × 400 mm × 50 mm and can be further confined. The tuning range for far-field reading distance is from 2.71 m to 0.35 m. Full article

Considerable variation in soil often occurs within and across production fields, which can significantly impact farming input management strategies. Optimizing resource utilization while enhancing crop productivity is critical for achieving Sustainable Development Goals (SDGs). This paper proposes a low-cost retrofittable Variable Rate Applicator Controller (VRAC) designed to leverage soil variability and facilitate the adoption of Variable Rate Technologies. The controller operates using a Raspberry Pi platform, RTK—Global Navigation Satellite System (GNSS), a stepper motor, and an anti-slip wheel encoder. The VRAC allows precise, on-the-fly control of the Variable Rate application of farming inputs utilizing an accurate GNSS to pinpoint geographic coordinates in real time. A wheel encoder measures accurate distance travel, providing a real-time calculation of speed with a slip-resistant wheel design for precise RPM readings. The Raspberry Pi platform processes the data, enabling dynamic adjustments of variability based on predefined maps, while the motor driver controls the motor’s RPM. It is designed to be plug-and-play, user-friendly, and accessible for a broader range of farming practices, including seeding rates, dry fertilizer, and liquid fertilizer application. Data logging is performed from various field sensors. The controller exhibits an average of 0.864 s for rate changes from 267 to 45, 45 to 241, 241 to 128, 128 to 218, and 218 to 160 kg/ha at speeds of 8, 11, 16, 19, 24, and 32 km/h. It has an average coefficient of variation of 4.59, an accuracy of 97.17%, a root means square error (RMSE) of 4.57, an R square of 0.994, and an average standard deviation of 1.76 kg for seeding discharge. The cost-effectiveness and retrofitability of this technology offer an increase in precision agriculture adoption to a broader range of farmers and promote sustainable farming practices. Full article