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Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype.

Multi-modal medical image fusion brings many benefits to clinical diagnosis and analysis because it creates favorable conditions for diagnostic imaging practitioners to make a more accurate diagnosis. According to our current knowledge, there are still some disadvantages to current image fusion approaches. The first one is that the fused images often have low contrast. The reason for this is several approaches use a weighted average rule for fusing low-frequency components. The second drawback is that the loss of detailed information in the fused image. This can be explained by the fact that the high-frequency components synthesized by the rules are not really effective. In this paper, two novel algorithms are proposed to tackle the above two disadvantages. The first algorithm is based on the Equilibrium optimizer algorithm (EOA) to find optimal parameters to fuse low-frequency components. This allows the fused image to have good contrast. The second algorithm is based on the sum of local energy functions using the Prewitt compass operator to create an efficient rule for the fusion of high-frequency components. This allows the fused image to significantly preserve details transferred from input images. Experimental results show that the proposed approach not only effective in significantly enhancing the quality of the fusion image but also preserving edge information carried from input images.

Normalized difference vegetation index (NDVI) is the most widely used factor in the growth status of vegetation, and improving the prediction of NDVI is crucial to the advancement of regional ecology. In this study, a novel NDVI forecasting model was developed by combining time series decomposition (TSD), convolutional neural networks (CNN) and long short-term memory (LSTM). Two forecasting models of climatic factors and four NDVI forecasting models were developed to validate the performance of the TSD-CNN-LSTM model and investigate the NDVI's response to climatic factors. Results indicate that the TSD-CNN-LSTM model has the best prediction performance across all series, with the RMSE, NSE and MAE of NDVI prediction being 0.0573, 0.9617 and 0.0447, respectively. Furthermore, the TP-N (Temperature & Precipitation-NDVI) model has a greater effect than the T-N (Temperature-NDVI) and P-N (Precipitation-NDVI) models, according to the climatic factors-based NDVI forecasting model. Based on the results of the correlation analysis, it can be concluded that changes in NDVI are driven by a combination of temperature and precipitation, with temperature playing the most significant role. The preceding findings serve as a helpful reference and guide for studying vegetation growth in response to climate changes.

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization–variational mode decomposition (PSO–VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO–VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising.

Accurate scientific information is critical for undertaking appropriate conservation and management practices for imperiled species. One source of concern is that research findings might vary for non-biological reasons, including experimental design and analytical methods. To illustrate, we provide detailed modern analysis of reproductive data for leatherback turtles (Dermochelys coriacea). This species exhibits significant fluctuations in nesting densities across different regions, possibly driven by local rather than global factors. Key factors influencing these changes include hatching success and sex determination, both sensitive to incubation temperatures (e.g., lower temperatures yield more males, higher temperatures yield females). This study updates the understanding of temperature-dependent sex determination (TSD) in this species using Bayesian statistics. Growth rate data from the West Pacific and Northwest Atlantic populations show a similar, monotone increase with temperature, affirming the reliability of the models used. The analysis of TSD patterns indicates that observed differences are more likely due to study methodologies and clutch-specific factors rather than regional differences. These findings challenge previous assumptions, showing that leatherback TSD does not conform to a simple on/off pattern but is influenced by multiple, interacting environmental factors. Population dynamics models must account for these complexities, recognizing that both sex ratios and hatching success are critical to understand the rapid changes observed in some leatherback populations.

Sex chromosome dosage compensation (SCDC) overcomes gene-dose imbalances that disturb transcriptional networks, as when ZW females or XY males are hemizygous for Z/X genes. Mounting data from non-model organisms reveal diverse SCDC mechanisms, yet their evolution remains obscure, because most informative lineages with variable sex chromosomes are unstudied. Here, we discovered SCDC in turtles and an unprecedented thermosensitive SCDC in eukaryotes. We contrasted RNA-seq expression of Z-genes, their autosomal orthologues, and control autosomal genes in Apalone spinifera (ZZ/ZW) and Chrysemys picta turtles with temperature-dependent sex determination (TSD) (proxy for ancestral expression). This approach disentangled chromosomal context effects on Z-linked and autosomal expression, from lineage effects owing to selection or drift. Embryonic Apalone SCDC is tissue- and age-dependent, regulated gene-by-gene, complete in females via Z-upregulation in both sexes (Type IV) but partial and environmentally plastic via Z-downregulation in males (accentuated at colder temperature), present in female hatchlings and a weakly suggestive in adult liver (Type I). Results indicate that embryonic SCDC evolved with/after sex chromosomes in Apalone's family Tryonichidae, while co-opting Z-gene upregulation present in the TSD ancestor. Notably, Apalone's SCDC resembles pygmy snake's, and differs from the full-SCDC of Anolis lizards who share homologous sex chromosomes (XY), advancing our understanding of how XX/XY and ZZ/ZW systems compensate gene-dose imbalance. This article is part of the theme issue 'Challenging the paradigm in sex chromosome evolution: empirical and theoretical insights with a focus on vertebrates (Part II)'.

Evolutionary transitions in sex-determining systems have occurred frequently yet understanding how they occur remains a major challenge. In reptiles, transitions from genetic to temperature-dependent sex determination can occur if the gene products that determine sex evolve thermal sensitivity, resulting in sex-reversed individuals. However, evidence of sex reversal is limited to oviparous reptiles. Here we used thermal experiments to test whether sex reversal is responsible for differences in sex determination in a viviparous reptile, Carinascincus ocellatus, a species with XY sex chromosomes and population-specific sex ratio response to temperature. We show that sex reversal is occurring and that its frequency is related to temperature. Sex reversal was unidirectional (phenotypic males with XX genotype) and observed in both high- and low-elevation populations. We propose that XX-biased genotypic sex ratios could produce either male- or female-biased phenotypic sex ratios as observed in low-elevation C. ocellatus under variable rates of XX sex reversal. We discuss reasons why sex reversal may not influence sex ratios at high elevation. Our results suggest that the mechanism responsible for evolutionary transitions from genotypic to temperature-dependent sex determination is more complex than can be explained by a single process such as sex reversal.

In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.

Finding disruptive solutions to technical and social challenges remains one of the greatest challenges of modern times. Future engineers are the pioneers who are likely to generate innovative, startup-oriented, and creative ideas. The aim of this study is to evaluate the effectiveness of a traineeship on the creativity-enhancing tool TRIZ (Theory of Inventive Problem Solving) in improving students’ creative performance during the process of solving heuristic problems. This current research was carried out at Ibn Tofail University during Five days. The sample consisted of 46 fourth-year engineering students from the National School of Applied Sciences (ENSA), including 14 males and 32 females. The mean age of the participants was 20.65 years (SD = ±1.079). The measured tests used are as follows TSD-Z, The Rey-Osterrieth Complex Figure, d-2R, self-esteem, Gad-7, PHQ9, and Triz. The results obtained with paired T-Test show a significant increase of the creativity score between the first and the second tests (t= -6,167; P< .001). Lastly but by no means least, traineeship might impact positively the engineering student by enhancing creativity.

In this work the effect of the ion implantation on the dislocations structure of the 4H-SiC epilayer after the KOH etching has been investigated. The study was conducted using both Aluminum (Al) and Phosphorous (P) species for p-type and n-type, respectively. The ion implantations of Al and P were carried out at different energies (30–200 keV) to achieve 300 nm thick acceptor box profiles with a concentration of about 1020 at/cm3. The implanted samples were annealed at high temperatures. With sequential sacrificial and stopping layer both species has been implanted on the same sample. Morphological charaterization of the samples (optical microscope and SEM) shown different structural modification of the dislocations (experically TED) after the KOH etching of the samples.