Search Results (2,333)

Background: Skipper butterflies (Hesperiidae) are a morphologically distinctive lineage within Papilionoidea, yet relationships among many groups remain difficult to resolve, and mitochondrial genomic resources remain limited for some tribes, including Celaenorrhinini. Methods: We sequenced and characterized the complete mitochondrial genome of Celaenorrhinus victor using Illumina short-read sequencing. Gene content and organization were annotated, codon-usage patterns were assessed across Celaenorrhinus using relative synonymous codon usage and multiple compositional/selection tests (ENC–GC3s, neutrality, and PR2 analyses), selective constraints were evaluated using Ka/Ks for 13 protein-coding genes, and phylogenetic relationships were inferred with a partitioned maximum-likelihood analysis of 66 complete hesperiid mitogenomes. Results: The circular mitogenome of C. victor is 15,180 bp and contains the typical 37 genes (13 protein-coding genes, 22 tRNAs, and two rRNAs) plus an A + T-rich control region, with an overall A + T content of 79.64%. Gene order and orientation match those of other Celaenorrhinus and hesperiid mitogenomes. All protein-coding genes use standard invertebrate mitochondrial start codons (with cox1 initiating with TTG) and terminate with complete TAA stop codons. Codon usage is strongly biased toward A/U-ending codons and is broadly similar among five sampled Celaenorrhinus mitogenomes; ENC–GC3s, neutrality, and PR2 analyses indicate a predominant influence of A + T-directed mutational pressure with additional effects beyond base composition. Ka/Ks values for all 13 protein-coding genes were <1, consistent with pervasive purifying selection; cox genes were the most conserved, whereas several NADH dehydrogenase subunit genes evolved comparatively faster. The phylogeny recovered monophyletic Celaenorrhinini and a well-supported Celaenorrhinus clade, placing C. victor as sister to Celaenorrhinus consanguineus, while deeper nodes among major hesperiid lineages showed only moderate support in parts of the tree. Conclusions: This study provides a new mitogenomic resource for Celaenorrhinini and a comparative reference for codon usage and selective constraints within Celaenorrhinus, supporting the placement of C. victor within Hesperiidae while highlighting remaining uncertainty at deeper hesperiid divergences. Full article

We have developed a tool that allows us to easily visualize evolutionary variation via complementary multiple sequence alignments and frequency-based stacked Sequence Logos. This tool, TOTEMS (hisTogram of evOluTionarily consErved aMino acidS), visualizes conserved regions in a multiple sequence alignment within regions of a three-dimensional structure that share similar degrees of evolutionary conservation as revealed in ConSurf output data. Unlike Sequence Logos that illustrate the relative frequency of individual amino acid residues (as in MSAViewer), or moving window averages that focus on properties such as hydrophobicity or electrical charge (as in CATH), TOTEMS can help users discriminate degrees of evolutionary conservation in adjacent positions within a three-dimensional structure. Thus, we offer a tool that serves to complement pre-existing visualization applications such as ConSurf, MSAViewer, and CATH. TOTEMS and its source code are freely available. Full article

Introduction: Recurrent inguinal hernia remains a clinically relevant outcome that is difficult to quantify in the absence of national prospective registries. In Romania, structural differences between public and private hospitals may further influence recurrence-related care, access to minimally invasive surgery, and resource utilization. This study aimed to assess recurrence patterns after inguinal hernia repair at a national level, with emphasis on reinterventions, patient-related risk factors, and health system disparities. Methods: A nationwide retrospective cohort study was conducted using administrative DRG data from the Romanian National Health Insurance House. All adult patients undergoing inguinal hernia repair in 2019 were identified and followed for five years (2019–2023). Reintervention was used as a proxy for recurrence. Surgical approach, hospital sector, length of stay, reimbursement, patient migration, geographic distribution, and comorbidities were analyzed using descriptive statistics and multivariable logistic regression to explore factors associated with laparoscopic approach and reintervention. Results: Among the 18,185 patients who underwent inguinal hernia repair in 2019, reintervention rates during follow-up ranged from 0.58% to 4.88%, a variability that reflects inherent limitations of administrative coding. Most reinterventions occurred in the year of the index surgery, suggesting early technical failure. Public hospitals managed the majority of cases and disproportionately absorbed recurrent and clinically complex patients. Access to laparoscopic repair was uneven and concentrated in large academic centers. Length of hospital stay declined gradually in public hospitals but remained consistently shorter in private institutions, reflecting differences in patient selection and care pathways. Reimbursement by The National Health Insurance House was similar for open and laparoscopic procedures. Conclusions: Recurrent inguinal hernia care in Romania is shaped by system-level disparities extending beyond surgical technique. Further progress requires reimbursement reform, establishment of a national hernia registry, and expansion of laparoscopic training to ensure equitable access to high-quality hernia care. Full article

Background: Kidney biopsy is the diagnostic gold standard for characterizing glomerular disease and other intrarenal pathologies. Despite its clinical importance, epidemiological trends in kidney biopsy incidence remain poorly understood in many developed healthcare systems. This study characterizes temporal and demographic trends in kidney biopsy utilization in Germany between 2006 and 2023, providing crucial data for resource allocation in renal pathology services. Methods: Data on all kidney biopsies (OPS code 1-465.0) performed in German hospitals were extracted from the Federal Statistical Office database and stratified by age and sex. Population denominators were obtained from national census data. Incidence rates per 100,000 inhabitants per year were calculated, and temporal trends were analyzed using Poisson regression with year as a continuous predictor variable. Separate models were fitted for overall population incidence, age-stratified incidence, and sex-stratified incidence. Results: The incidence of kidney biopsies increased 96.6% over 18 years, from 8.59 per 100,000 inhabitants in 2006 to 16.89 per 100,000 in 2023 (IRR: 1.0296 per year, 95% CI: 1.0287–1.0305; p < 0.0001). Age-stratified analysis revealed pronounced heterogeneity, with the oldest patients (>80 years) experiencing the steepest increase of 7.74% annually, while the youngest age group (<20 years) showed no significant temporal change. Sex-stratified analysis demonstrated similar increases in both males and females (3.36% and 3.04% annually, respectively). Conclusion: The substantial increase in kidney biopsy utilization in Germany over nearly two decades mirrors international patterns and suggests a global shift toward more liberal biopsy utilization in aging populations. Multiple factors likely contributed to this increase, including demographic aging, improved procedural safety and accessibility, evolving diagnostic guidelines, and expanding therapeutic options for glomerular disease. These findings underscore the need for national registry systems to optimize resource allocation for renal pathology and ensure equitable diagnostic access across healthcare systems. Full article

CubeSats are increasingly adopted for space missions due to their low cost and short development cycles. However, their attitude determination and control systems (ADCS) often suffer from limited verification environments and constrained hardware configurations. This study addresses the development and verification of a flight-ready ADCS for the INHA RoSAT 3U CubeSat under realistic constraints in hardware, software, and test infrastructure. A model-based design (MBD) approach is adopted to construct an integrated development pipeline covering algorithm design, simulation, automatic C code generation, and integration with flight software (FSW). The generated code is embedded into a closed commercial onboard computer framework while preserving consistency across model-in-the-loop (MIL) and processor-in-the-loop (PIL) verification stages. To compensate for the lack of full hardware-in-the-loop (HIL) facilities, a FlatSat-based Sensor-to-Actuator test strategy is introduced to validate critical hardware–software interfaces including signal polarity, unit consistency, mounting orientation, and data flow using actual flight hardware. Furthermore, a fault-aware hierarchical attitude control scheme is defined in which the controller transitions to an alternative controller upon actuator fault indications. The presented approach demonstrates a practical ADCS development and verification strategy suitable for resource-constrained CubeSat missions, providing guidance for teams facing similar limitations in cost, resources, and test infrastructure. Full article

Scalable video coding plays an essential role in supporting heterogeneous devices, network conditions, and application requirements in modern video streaming systems. However, most existing scalable coding approaches primarily optimize human perceptual quality and provide limited support for data privacy, as well as for machine analyses and the integration of heterogeneous sensor data. This limitation motivated the development of adaptive scalable video coding frameworks. The proposed approach is designed to serve both human viewers and automated analysis systems while ensuring high security and compression efficiency. The method adaptively encrypts selected layers during transmission to protect sensitive content without degrading decoding or analysis performance. Experimental evaluations on benchmark datasets demonstrate that the proposed framework achieves superior rate distortion efficiency and reconstruction quality, while also improving machine analysis accuracy compared to existing traditional and learning-based codes. In video surveillance scenarios, where the base layer is preserved for analysis, the proposed scalable human machine coding (SHMC) method outperforms scalable extensions of H.265/High Efficiency Video Coding (HEVC), Scalable High Efficiency Video Coding (SHVC), reducing the average bit-per-pixel (bpp) by 26.38%, 30.76%, and 60.29% at equivalent mean Average Precision (mAP), Peak Signal-to-Noise Ratio (PSNR), and Multi-Scale Structural Similarity (MS-SSIM) levels. These results confirm the effectiveness of integrating scalable video coding with intelligent encryption for secure and efficient video transmission. Full article

Twin births in dairy cattle present challenges for producers, resulting in increased prevalence of health issues for both cows and calves, thereby impacting profitability. Genome-wide association study (GWAS) analyses of the twinning rate in Holstein cattle have reported the most significant genomic association with twinning rate in a region containing two strong candidate genes: follicle-stimulating hormone receptor (FSHR) and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). Coding-sequence variants of these genes were not associated with the twinning rate, suggesting that one of the two genes is differentially expressed in association with the twinning rate. Granulosa cells were collected from 38 Holstein cows that were selected to provide similar representation of genotypes for the twinning rate quantitative trait locus (QTL). RNA was extracted from granulosa cells and gene expression was assessed by quantitative PCR with data analyzed by the ΔΔCT method. Association of gene expression with QTL genotype was tested by the Kruskal–Wallis test with the QTL genotype based on the SNP most significantly associated with twinning rate. QTL genotype was significantly associated (p = 1.88 × 10⁻⁸) with the expression of FSHR but was not associated with LHCGR expression (p = 0.18). The increased FSHR expression was associated with an increasing copy number of the G allele and thus an increased twinning rate. Full article

Context: Code recommendation plays an important role in improving programming efficiency and software quality. Existing approaches mainly focus on method- or API-level recommendations, which limits their effectiveness to local code contexts. From a multi-stage recommendation perspective, class-level code recommendation aims to efficiently narrow a large candidate code space while preserving essential structural information. Objective: This paper proposes RioCC, a class-level code recommendation framework that leverages deep forest-based code clone detection to progressively reduce the candidate space and improve recommendation efficiency in large-scale code spaces. Method: RioCC models the recommendation process as a coarse-to-fine candidate reduction procedure. In the coarse-grained stage, a quick search-based filtering module performs rapid candidate screening and initial similarity estimation, effectively pruning irrelevant candidates and narrowing the search space. In the fine-grained stage, a deep forest-based analysis with cascade learning and multi-grained scanning captures context- and structure-aware representations of class-level code fragments, enabling accurate similarity assessment and recommendation. This two-stage design explicitly separates coarse candidate filtering from detailed semantic matching to balance efficiency and accuracy. Results: Experiments on a large-scale dataset containing 192,000 clone pairs from BigCloneBench and a collected code pool show that RioCC consistently outperforms state-of-the-art methods, including CCLearner, Oreo, and RSharer, across four types of code clones, while significantly accelerating the recommendation process with comparable detection accuracy. Conclusions: By explicitly formulating class-level code recommendation as a staged retrieval and refinement problem, RioCC provides an efficient and scalable solution for large-scale code recommendation and demonstrates the practical value of integrating lightweight filtering with deep forest-based learning. Full article

This study proposes an integrated façade design framework that harmonizes the creative divergence of Generative AI with the economic efficiency of Design for Manufacturing and Assembly (DfMA). To address low productivity in the construction industry, a stepwise pipeline is developed, synthesizing image generation via Midjourney, automated coding using ChatGPT, and quantitative optimization. Central to this process is the Hamming Distance algorithm, which evaluates image similarity to implement core DfMA principles: standardization and simplification. The study introduces a multidimensional decision-making model utilizing Grid Size (GS), Replacement Rate (RR), and Hamming Threshold (HT) indices to visualize the trade-off between component minimization and design fidelity. This process transforms abstract 2D patterns into manufacturable geometric panels, bridging the gap between conceptual design and constructability. The results demonstrate that algorithmic optimization significantly reduces component count, contributing to potential cost savings and schedule reduction. Ultimately, this research establishes a collaborative model where architects’ qualitative insights complement AI’s quantitative analysis, enabling designers to regain agency over digital tools and realize creative visions within technical constraints. Full article

Manual code review is essential for software quality, but often slows down development cycles due to the high time demands on developers. In this study, we propose an automated solution for Python (version 3.13) projects that generates code review comments by combining Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG). To achieve this, we first curated a dataset from GitHub pull requests (PRs) using the GitHub REST Application Programming Interface (API) (version 2022-11-28) and classified comments into semantic categories using a semi-supervised Support Vector Machine (SVM) model. During the review process, our system uses a vector database to retrieve the top-k most relevant historical comments, providing context for a diverse spectrum of open-weights LLMs, including DeepSeek-Coder-33B, Qwen2.5-Coder-32B, Codestral-22B, CodeLlama-13B, Mistral-Instruct-7B, and Phi-3-Mini. We evaluated the system using a multi-step validation that combined standard metrics (BLEU-4, ROUGE-L, cosine similarity) with an LLM-as-a-Judge approach, and verified the results through targeted human review to ensure consistency with expert standards. The findings show that retrieval augmentation improves feedback relevance for larger models, with DeepSeek-Coder’s alignment score increasing by 17.9% at a retrieval depth of k = 3. In contrast, smaller models such as Phi-3-Mini suffered from context collapse, where too much context reduced accuracy. To manage this trade-off, we built a hybrid expert system that routes each task to the most suitable model. Our results indicate that the proposed approach improved performance by 13.2% compared to the zero-shot baseline (k = 0). In addition, our proposed system reduces hallucinations and generates comments that closely align with the standards expected from the experts. Full article

This study presents an AI-based, human-in-the-loop decision support system designed for large-scale institutional query routing and response generation. The proposed system combines semantic text classification with large language model-based response generation to assist administrative staff in handling high-volume natural language requests from various system users, while preserving human oversight. Using a dataset of 135,359 real student and staff interactions collected over 15 years, the system was designed, deployed, and evaluated in a live university information portal. The classification component achieved 95.88% accuracy in evaluation and 82.21% staff acceptance in practice, while 94.81% of AI-generated draft responses were adopted with minor edits. Operational evaluation showed a 30.8% reduction in resolution time, a 32.6% decrease in misrouting, and an increase in user satisfaction from 3.6 to 4.9 out of 5. The system is implemented as a modular RESTful API to ensure interoperability with existing Student Information Systems, with analysis code available upon request to support replication in similar resource-constrained environments. The results illustrate how human-in-the-loop AI systems can support improvements in service quality, efficiency, and institutional capacity in resource-constrained environments, providing a transferable applied AI framework for scalable decision support in complex administrative domains. Full article

The recent use of large language models (LLMs) in code generation and code summarization tasks has been widely adopted by the software engineering community. New LLMs are emerging regularly with improved functionalities, efficiency, and expanding data that allow models to learn more effectively. The lack of guidelines for selecting the right LLMs for coding tasks makes the selection a subjective choice by developers rather than a choice built on code complexity, code correctness, and linguistic similarity analysis. This research investigates the use of machine learning classification and ranking methods to select the best-suited open-source LLMs for code generation and code summarization tasks. This work conducts a comparison experiment on four open-source LLMs (Mistral, CodeLlama, Gemma 2, and Phi-3) and uses the MBPP coding question dataset to analyze code-generated outputs in terms of code complexity, maintainability, cyclomatic complexity, code structure, and LLM perplexity by collecting these as a set of features. An SVM classification problem is conducted on the highest correlated feature pairs, where the models are evaluated through performance metrics, including accuracy, area under the ROC curve (AUC), precision, recall, and F1 scores. The RankNet ranking methodology is used to evaluate code summarization model capabilities by measuring ROUGE and BERTScore accuracies between LLM code-generated summaries and the coding questions used from the dataset. The study results show a maximum accuracy of 49% for the code generation experiment, with the highest AUC score reaching 86% among the top four correlated feature pairs. The highest precision score reached is 90%, and the recall score reached up to 92%. Code summarization experiment results show Gemma 2 scored a 1.93 RankNet win probability score, and represented the highest ranking reached among other models. The phi3 model was the second-highest ranking with a 1.66 score. The research highlights the potential of machine learning to select LLMs based on coding metrics and paves the way for advancements in terms of accuracy, dataset diversity, and exploring other machine learning algorithms for other researchers. Full article

YouTube is a de facto learning environment for athletes seeking fast, actionable nutritional guidance, yet platform dynamics may favor simplified or testimonial narratives over evidence-aligned messages. This study maps Spanish-language sports-nutrition videos to clarify who is most visible, how advice is framed, and what users encounter first. We conducted a cross-sectional, mixed-methods study of 558 YouTube videos on pre/post-exercise nutrition and supplementation. Data was coded for video types (divulgation/testimonial), claim presence, evidence links, and creator status (professional/non-professional). Exposure-adjusted metrics (View Ratio, Viewer Interaction) and nonparametric tests summarized distributions. An undirected network generated centrality rankings to select qualitative samples. Thematic analysis of titles and descriptions identified recurring rhetorical patterns and discourse modes. Divulgation videos predominated (97.3%). Evidence links were rare (0.2%). Exposure and interaction were right-skewed, indicating concentrated visibility. Non-professionals produced most videos, with older uploads and higher daily view accrual; however, interaction per view was similar across groups. Qualitative synthesis revealed two dominant discourse modes, scientific–cautious and experience–testimonial. Oversimplification and motivational cues clustered in testimonial/non-professional items; instructional language and scarce evidence links concentrated in professional/divulgation items. In Spanish sports-nutrition content, visibility is concentrated, and creator identity shapes advice framing. Evidence-aligned messages can compete when expressed with clear athletic framing, explicit caveats, and links to trustworthy sources. Full article

Monte Carlo simulations of the transport of atoms in gases related to the deposition process and the contamination of amorphous carbon thin films during deposition in magnetron discharges have been performed. These films are of interest in accelerator technology due to their low secondary electron yield when their structures are dominated by sp² carbon. Two codes, which practically share the same algorithm, are introduced: TAGs 1 simulates the transport of sputtered atoms from the target to the substrate, and TAGs 2 simulates the transport of atoms from the plasma towards the target and the substrate. The similar results of TAGs 1 and the well-established SIMTRA for the same input parameters imply the algorithm’s accuracy. The codes were used to model the transport of different atoms (C, H, O, D) in a magnetron Ar discharge. The simulations reveal that the operating pressure should be higher than 1 Pa for a sample-target distance of 90 mm to secure sp² carbon formation. The contamination mechanisms of amorphous carbon coatings were then studied by merging the results obtained with both programs. Preliminary comparisons with experiments suggest that the combined results of TAGs 1 and 2 agree very well with the experiments. Full article

High-quality commit messages are essential software artifacts because they succinctly communicate the intent and scope of code changes, yet large language models (LLMs) often fail to reflect project-specific writing conventions when used in a zero-shot setting without contextual signals. This study investigates not whether retrieval helps, but how the same retrieved example, when represented differently in the prompt, quantitatively changes generation outcomes. We implement a retrieve-then-generate framework where the target commit’s diff is used as a query for BM25 (Best Matching 25)-based sparse retrieval over a commit-level database, and the top-1 similar commit is optionally injected as an example context. We compare a no-context condition (K = 0) against a minimal-context condition (K = 1) under three context representations: Diff-only, Message-only, and Diff + Message pair. Using Qwen-7B on 8000 evaluation samples with a fixed prompt skeleton, deterministic decoding, and identical post-processing across conditions, we observe negligible differences at K = 0 (BLEU-4 1.14, ROUGE-L 7.47–7.48, METEOR 4.88–4.91), establishing a stable baseline. At K = 1, the same top-1 retrieved case yields systematically different metric responses depending on how it is represented (Diff-only, Message-only, or Diff + Message), even under an identical prompt skeleton, deterministic decoding, and identical post-processing. This indicates that “context representation” is not a cosmetic formatting choice but a first-class prompt-design variable in retrieval-augmented in-context learning for commit message generation. Accordingly, practitioners should select the representation based on the intended objective (e.g., lexical/style alignment vs. change-intent grounding), rather than assuming a universally optimal format. Full article