Search Results (498)

The genus Sebastes comprises around 110 species of marine fish. In Japan, the Sebastes inermis complex includes three sympatric species (S. cheni, S. inermis, and S. ventricosus), and morphotypes that cannot be assigned to any of these three species. We focused on two morphotypes: (1) the ‘big red’, which is predominantly found in the Kii and Izu peninsulas and are traditionally believed to be large, older specimens of S. inermis; and (2) the ‘red white’, which is found in the Seto Inland Sea and Kagoshima and includes putative hybrids of S. cheni and S. inermis. We assessed the biological identity of both morphotypes by comparing their morphological and genetic variations with those of the three species. The meristic traits of both morphotypes overlapped with those of the three species. The ‘big red’ morphotype showed significant differences in body proportions and otolith structure, whereas the ‘red white’ morphotype differed mainly in otolith features. Phylogenetic analyses of mitochondrial and nuclear genes did not separate these morphotypes into distinct lineages. However, the ‘big red’ morphotype exhibited unique mutations at the rhodopsin gene. Analyses of microsatellite loci indicated that the divergence of both morphotypes is as large as the divergence observed between sister species within the genus. Phylogenetic analyses of genomic data placed the ‘big red’ morphotype in a basal position in relation to the three species and supported the separation of the ‘red white’ morphotype from S. cheni and S. inermis. Genomic scan analyses comparing the ‘big red’ and ‘red white’ morphotypes with S. inermis and S. cheni, respectively, indicate that genes involved in fertilization, egg hatching, immunity, and thermal resilience are under divergent selection. Overall, the results suggest that both morphotypes could represent previously undescribed cryptic species, warranting further investigation to confirm their status as independent taxa. Full article

One of the most common problems in drilling operations is lost circulation, which can significantly increase well costs and lead to issues such as pipe sticking, blowouts, and even well closures. Identifying thief zones using analytical models is especially difficult, and there are no robust equations available in the literature due to a wide range of influential parameters, both controllable and uncontrollable. These parameters include operational factors, as well as the physical properties of the rock and drilling fluid. This study presents an artificial intelligence-based model designed to predict lost circulation zones. It investigates the underexplored potential of WV-curves for feature selection. Traditionally used to represent the spectral characteristics of training data, their role in feature selection has not been widely examined in the literature. The presentation of WV-curves is modified, and their effectiveness in identifying the optimal number of input and hidden neurons is evaluated. In this research study, a total of 15,000 data points were used and collected from oil wells in the Middle East. The artificial neural network (ANN) model exhibited a remarkable ability to accurately predict the locations of lost circulation zones based on the collected data, achieving an impressive accuracy of 94.5%. This is a significant achievement when compared to existing ANN models in the literature. The results highlight the strength of the ANN model in predicting lost circulation locations across a wide range of data collected from various wells in the Middle East. In addition, this model takes into account a diverse set of drilling operational parameters, as well as rock characteristics and fluid properties, offering a broader approach compared to other available ANN models. This advancement will also greatly facilitate future studies, enabling the prediction of lost circulation zones, and enabling advanced planning of appropriate prevention and remediation methods during the well planning phase to reduce the risk of lost circulation. Nevertheless, it should be noted that one limitation of the proposed methodology relates to data availability, as comprehensive formation parameters were not fully accessible; the inclusion of additional formation data may offer opportunities for further improvement in future studies. Full article

The Tianbao melon (Cucumis melo subsp. agrestis) is a highly valued regional horticultural crop, yet its sustainable development is severely constrained by a narrow genetic base and widespread varietal admixture in the market. In this study, a panel of 32 Tianbao melon accessions was systematically evaluated by integrating field-based phenotypic assessment with genome-wide single-nucleotide polymorphism (SNP) analysis via whole-genome resequencing. Phenotypic analysis based on ten quantitative traits revealed low overall morphological variability, indicating limited discriminatory power of morphological traits alone. In contrast, 173,497 high-quality SNPs uncovered substantial hidden genetic differentiation, partitioning the accessions into four distinct genotypic groups. Notably, accessions TB-17 and TB-27, though nearly indistinguishable morphologically, exhibited clear genetic divergence in both phylogenetic and principal component analyses. Furthermore, a panel of 20 core SNPs with conserved flanking sequences was selected, generating unique molecular fingerprint profiles for all 32 accessions and achieving high discriminatory resolution (pairwise differences ranging from 10 to 13 SNPs). These findings demonstrate that the integration of phenotypic and genome-wide SNP data provides a robust framework for genetic diversity assessment and DNA fingerprinting in Tianbao melon, offering a scientific basis for cultivar identification, intellectual property protection, and precision breeding to support sustainable development of the regional melon industry. Full article

This article studies the nonlinear wave dynamics of the recently introduced integrable combined Kairat-II-X (K-II-X) equation, which combines dynamical features of the Kairat-II and Kairat-X models. The considered model possesses relevance in nonlinear wave propagation, geometric curve dynamics, and localized optical pulse evolution, thereby providing a mathematical framework for describing curvature-driven nonlinear phenomena in higher-dimensional systems. To obtain exact analytical solutions, a symbolic neural analytical framework based on the improved Riccati neural networks (IRNNs) method is employed. The proposed framework integrates trial functions within multilayer neural network structures, where each neuron in the first hidden layer is constructed through solutions of the improved Riccati equation. The symbolic outputs obtained from the neural network computations are subsequently employed as trial functions for the integrable combined K-II-X equation. Using this framework, several classes of exact wave solutions are derived in the form of hyperbolic, trigonometric, rational, including localized solitary waves and interaction-type structures. In particular, the symbolic neural representation produces both single- and multisoliton wave profiles exhibiting nonlinear localization and interaction behavior. Furthermore, representative wave structures are illustrated through two-dimensional, three-dimensional, contour, and density visualizations to examine the qualitative influence of governing parameters on wave amplitude, localization, propagation behavior, and interaction patterns. The reported results demonstrate the capability of the IRNNs framework to generate diverse nonlinear wave structures in integrable higher-dimensional systems and provide a useful analytical reference for future investigations in nonlinear science and applied mathematical physics. Full article

This article investigates food poverty in South Tyrol, a generally affluent region, to understand how socio-economic changes—particularly the COVID-19 pandemic—have reshaped patterns of vulnerability within local food systems and challenged social sustainability. Using a qualitative approach, the study draws on interviews with institutional and third-sector actors, adults involved in local food networks, and focus groups to capture diverse perspectives on access to food. The findings reveal a coexistence of overall economic prosperity with hidden forms of food insecurity and unequal access to healthy and sustainable food. Although official statistics report relatively low levels of childhood overweight and obesity, certain groups—including elderly individuals, migrant families, and low-income households—face increasing challenges due to rising living costs and constrained access to nutritious food. The pandemic functioned as a temporary stressor that exposed pre-existing fragilities while also encouraging adaptive responses within local welfare systems. In particular, alternative food networks such as solidarity purchasing groups and emerging food cooperatives play a complementary role by promoting food autonomy, social support, and dignity-based assistance. These initiatives highlight forms of need not always captured by traditional welfare mechanisms. The study concludes that addressing food poverty in high-income contexts requires integrated, place-based strategies that combine social inclusion, nutritional education, intersectoral governance, and community-driven food practices. Full article

As critical underground engineering structures, wellbores may suffer complex structural deterioration and hidden safety hazards may be encountered during drilling. Multi-source sensor monitoring data provides an effective data basis for structural health perception and early warnings for wellbore structures at risk. The inherent diversity of formation conditions and the dynamic disturbances during drilling jointly lead to the differentiated presentation of drilling loss types, among which fractured, permeable, and vuggy losses are the most typical. This paper focuses on fractured wellbore leakage, regards wellbore leakage as an important structural failure form of underground drilling engineering structures. In-depth analysis and research on the structural deterioration mechanism of wellbore leakage were conducted, and we propose a wellbore leakage prediction method based on the improved sparrow search algorithm (ISSA) optimized gradient boosting decision tree (XGBoost). First, the Sobol sequence is adopted to replace the random initialization strategy, combined with the opposition-based learning mechanism; then, an adaptive Levy flight search mechanism is introduced to dynamically adjust the population ratio of discoverers and vigilantes; finally, intelligent optimization technologies are integrated to reconstruct the position update strategies of discoverers, followers, and vigilantes, enhancing the optimization adaptability of the algorithm. Relying on multi-field sensor monitoring datasets collected from actual drilling engineering, this paper compares the proposed model with wellbore leakage prediction models built by classical machine learning algorithms, and verifies its generalization ability on different datasets. Experimental data indicate that the improved algorithm exhibits significant advantages in optimization accuracy, enabling the proposed model to achieve an AUC improvement of 4.46%, along with accuracy (95.1%), precision (94.9%), recall (94.7%), and F1-score (94.2%). On this basis, the ISSA was applied to the hyperparameter optimization of XGBoost, constructing the ISSA-XGBoost prediction model. The method has high accuracy and good generalization ability in fractured wellbore leakage prediction, and it can realize intelligent health monitoring of underground wellbore structures, including early warnings. This study provides a reliable sensing data analysis scheme and technical support for structural health monitoring and hazard prevention in drilling engineering. Full article

Accurate species delimitation is fundamental yet challenging, particularly in recently diverged, phenotypically conservative taxa such as bats. The “Rhinolophus macrotis group” represents one of the most taxonomically contentious groups among horseshoe bats. During field surveys in Southwest China, we discovered an unidentified Rhinolophus sp. occurring sympatrically with R. osgoodi and R. episcopus, sharing broad morphological affinities with recognized species of the “R. macrotis group.” To explore its taxonomic status, we employed an integrative approach combining morphological, acoustic, and multi-locus genetic (mitogenomic and nuclear) data. Phenotypically, Rhinolophus sp. closely resembles R. osgoodi but can be distinguished by its divergent echolocation resting frequency. Genetically, while mitochondrial data deeply nested Rhinolophus sp. within R. osgoodi with a shallow divergence time, phylogenies based on two nuclear introns positioned it closer to two other species, R. episcopus and R. siamensis. Species delimitation based on these genetic markers revealed a pattern of mitochondrial subdivision contrasted by overly conservative nuclear signals. Such mito-nuclear discordance suggests a complex evolutionary history that complicates taxonomic assignments. Given that only three specimens of Rhinolophus sp. were available, which precludes a robust assessment of intraspecific variation, we provisionally designate it as a candidate lineage within the “R. macrotis group,” warranting future validation with additional comprehensive evidence. This study highlights the indispensable utility of integrative taxonomy in uncovering hidden diversity and provides insights into chiropteran evolutionary history. Full article

Recent studies of understudied habitats, particularly caves, have revealed previously unrecognised cyanobacterial diversity. In this study, we used a metabarcoding approach to assess cyanobacterial communities in lampenflora developing in the most visited sections of Stopić Cave, Serbia. Two visually distinct lampenflora types were analysed: aerophytic lampenflora on exposed surfaces and submerged lampenflora within retained water, along with key environmental parameters. A wide range of Cyanobacteria was detected, including cave-adapted, rock-dwelling, atmophytic taxa from various habitats (deserts, thermal and saline environments), as well as species typically associated with freshwater and saline environments. Notably, many of the documented taxa have only recently been described. Dominant Cyanobacteria (>10%) included those assigned to Cyanothece aeruginosa, Loriellopsis cavernicola, Marileptolyngbya sina, Neochroococcus gongqingensis, Pseudocyanosarcina phycocyania, Scytonema hofmanii and Thainema salinarum, while representatives of the genera Chalicogloea, Crocosphaera, Dulcicalothrix, Gloeothece, Kovacikia, Timaviella, and Trichocoleus each contributed ≥1% of the community. In addition, Vampirovibrio chlorellavorus, a representative of Candidatus Melainabacteria, the non-photosynthetic relative of Cyanobacteria and an obligate parasite of Chlorella species, was detected in all aerophytic lampenflora. Full article

This paper examines the trustworthiness of generative AI as a tourism recommender system by analyzing how ChatGPT5.2 responds to an open-ended, zero-shot prompt: “Recommend me a list of 10 German Christmas Markets.” Using German Christmas markets as a case study, outputs, texts in reasoning panels, and cited sources of fifteen replicates (carried out over five consecutive days) were systematically documented and analyzed. The results show a consistent and patterned selection which is dominated by a small canon of markets (Nürnberg, Dresden, Köln, München, and Stuttgart). The generative AI model does not neutrally sample from the entire pool of approximately 2000 German markets but instead reproduces a narrow canon of “iconic” destinations. Analysis of reasoning traces and follow-up conversations demonstrates that ChatGPT5.2 applies hidden selection criteria, including canonical status, landmark setting, branding strength, and perceived trip-planning usefulness, while also introducing undisclosed filters such as geographic spread across Germany and stylistic diversity. Although the model claims to use source triangulation and quality checks, the evidence shows substantial reliance on tourism marketing pages, travel media, blogs, and social media, especially for descriptive commentary. The study concludes that generative AI tourism recommendations are useful but non-neutral and should be interpreted as “curated,” bias-bearing constructs rather than transparent information retrieval. The implications of this on tourism management and the marketing of Christmas markets are discussed. Full article

Pangenomics has become an important framework for representing genetic diversity beyond a single linear reference genome. In agricultural species, it improves access to structural variants (SVs), copy number variations (CNVs), presence/absence variations (PAVs), and non-reference regulatory or coding sequences that may contribute to domestication, adaptation, and breeding traits. This review summarizes recent progress in long-read sequencing, telomere-to-telomere (T2T) assembly, and graph-based genome analysis, with emphasis on both livestock and crop systems. We first define the conceptual boundary between pangenome representations and reference-based variant catalogs. We then compare three major technical routes: variant integration, reference-guided iterative graph construction, and reference-free graph construction. Their performance is evaluated in terms of accuracy, scalability, coordinate consistency, reference bias, computational demand, annotation transfer, and suitability for downstream breeding questions. We further discuss how pangenome resources support hidden variant discovery, QTL and GWAS interpretation, environmental adaptation analysis, and multi-omics-based candidate prioritization. Importantly, we highlight unresolved limitations, including graph complexity, pipeline-dependent SV calls, incomplete functional annotation, weak cross-study comparability, and the difficulty of distinguishing causal variants from linked or neutral variation. This review therefore treats pangenome studies as connected but non-equivalent evidence: resource-building studies establish representational breadth, method papers define technical feasibility, and trait-focused studies provide varying levels of biological support. Apparent inconsistencies among studies are interpreted as signals of differences in sampling, genome complexity, validation depth, and graph construction strategy rather than as simple disagreements. Full article

Cave-dwelling harvestmen (Opiliones: Laniatores: Gonyleptoidea) include trogloxenes, troglophiles and troglobites, frequently represented as monotypic genera, likely reflecting taxonomic practice rather than true diversity. Giupponia Pérez-González & Kury, 2002 is one such case: it was erected for the blind troglobite G. chagasi from limestone caves in Serra do Ramalho, southwestern Bahia, Brazil, and has remained monotypic for more than two decades. Here, we describe two additional troglobitic species from caves in the same karst area, thereby expanding the genus and providing an updated diagnosis and an identification key for males. The new species share core troglomorphic traits with G. chagasi (complete eye loss and depigmentation) and a distinctive suite of external and genital characters that support their placement in Giupponia, including a theta-type dorsal scutum with wide ridged grooves, a prominent preocular mound with paired spiniform armature, an enlarged ocularial apophysis, and a characteristic penial configuration with a pyriform ventral plate, a parabolic distal cleft and a robust stylus bearing a dorsal projection, with the glans lacking dorsal/ventral processes. We further discuss the morphological evidence bearing on the suprageneric placement of Giupponia within Gonyleptoidea Sundevall, 1833, highlighting affinities with lineages traditionally treated in Pachylinae and possible relationships with Ampycidae Kury, 2003 or Tricommatinae Roewer, 1912. These findings underscore the hidden diversity of the Serra do Ramalho subterranean fauna and the need for integrative phylogenetic analyses to resolve the evolutionary origin of extreme troglomorphisms in Brazilian gonyleptids. Full article

Large language models (LLMs) are increasingly adopted for Verilog code generation, yet existing benchmarks assume English-only prompts, overlooking the linguistic diversity of the global FPGA engineering community. We introduce Multi-VerilogEval, the first multilingual Verilog benchmark, built from 156 unique underlying tasks instantiated in four languages (English, Japanese, Hindi, and Mongolian), yielding 624 language-specific test cases. Our evaluation of four representative LLMs reveals a silent failure pattern: syntactic correctness remains high (∼90%) across languages, but functional correctness degrades by up to 23.9% for non-English prompts in open-source and domain-specific models, while commercial models remain near-parity. Hidden-state analysis suggests that multilingual bias is associated with persistent cross-lingual representation divergence throughout the network, which becomes most pronounced in the final layers that directly drive token generation. As fine-tuning and common prompt-based mitigations remain impractical or unreliable for multilingual RTL, we propose HE-ICL (Hard-Example In-Context Learning), a train-free method that constructs few-shot hard-example demonstrations from cross-lingually difficult cases. HE-ICL closes 80–100% of the multilingual gap without any parameter updates, achieving near-parity with or exceeding the English reference level across all evaluated HE-ICL settings. Full article

Accurate estimation of the state of health (SOH) and prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) are critical for ensuring system reliability and safety across diverse energy storage applications. This paper proposes a hybrid deep learning framework that integrates an axial-embedding Transformer (AxEmbTrans) encoder and a bidirectional LSTM (BiLSTM) module for the joint estimation of SOH and RUL. The AxEmbTrans encoder employs axial attention with abstract embeddings to capture global dependencies among multidimensional health features at reduced computational complexity compared to standard self-attention, while the BiLSTM models local temporal dynamics and short-term degradation fluctuations across consecutive cycles, with its bidirectional structure enhancing robustness against transient noise. Informative health features are extracted from charge–discharge curves, grouped into temporal, energy, and thermal categories, and fused using local linear embedding (LLE) for nonlinear dimensionality reduction. An improved Newton–Raphson-based optimizer (INRBO) is introduced to automatically tune the framework’s key hyperparameters, including the hidden dimension, number of attention heads, number of BiLSTM units, and learning rate, incorporating directional similarity modulation and multi-elite guidance to overcome the convergence instability of the standard NRBO. Extensive experiments on NASA and Maryland datasets demonstrate that the proposed method consistently outperforms baselines in both SOH and RUL prediction, achieving higher accuracy, improved robustness, and better cross-condition generalization. Full article

Current fuzzing techniques for industrial control protocols (ICPs) encounter notable challenges, including model training instability, limited sample diversity, and the inability to manage complex state dependencies in protocol interactions. To address these issues, this paper presents SD-Fuzz, a state-aware fuzzing framework that integrates a discrete denoising diffusion probabilistic model (DDPM) with an online Hidden Markov Model (HMM). The discrete DDPM is designed to generate syntactically valid and diverse protocol messages using cosine noise scheduling and Denoising Diffusion Implicit Model (DDIM) sampling, while the HMM performs unsupervised learning of state transitions from real traffic to guide the creation of logically consistent multi-step interaction sequences. The framework is evaluated on three representative Modbus/TCP slave implementations. Evaluations based on 5 h benchmark campaigns across multiple independent runs indicate that SD-Fuzz achieves a mean test case recognition rate (TCRR) of 91.3% and an HMM-inferred state transition coverage of 50.1%, exhibiting statistically significant improvements over the evaluated baselines. Furthermore, an extended 8 h vulnerability mining campaign demonstrates its capability to trigger deep-seated exceptions, including buffer overflows and protocol state violations, which are typically challenging to access using traditional stateless approaches. This work illustrates the feasibility of combining diffusion-based generation with lightweight state inference for automated vulnerability discovery in industrial control systems. Directions for future work include validation on physical programmable logic controller (PLC) hardware to acquire internal code coverage feedback. Full article

Network technology has advanced rapidly in recent years, and distributed denial-of-service (DDoS) attacks have grown more diverse, stealthy, and large-scale. Traditional detection approaches struggle to process long network traffic sequences and locate sparse attack signals hidden in massive normal traffic, which makes accurate and efficient DDoS detection an urgent requirement. This paper presents an end-to-end DDoS detection model built on the Mamba architecture. We use one-dimensional convolutions to extract local features and smooth noise, which strengthens the model’s ability to capture bursty attack behaviors. Then, taking advantage of Mamba’s linear complexity and selective scanning mechanism, the model models long traffic sequences, filters out redundant information, and concentrates on potential attack patterns. With global feature aggregation and a classification layer, the model realizes accurate attack recognition. Experiments conducted on the CIC-DDoS2019 dataset show that our model obtains better performance in weighted F1 score, precision, and recall, while also improving inference efficiency. The model is suitable for high-precision, low-latency DDoS detection in real network environments. Full article