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Background and Objectives: Breast cancer surgery is increasingly performed in older patients with multimorbidity, in whom cardiovascular disease and frailty may substantially modify perioperative risk, including vulnerability to heart failure decompensation and other major medical complications. However, most available studies report global perioperative complication rates and composite medical endpoints, with heart failure events only rarely captured as dedicated outcomes, and operative technique, cardiovascular comorbidity, and frailty are often treated as separate domains rather than components of an integrated risk framework. Materials and Methods: We conducted a systematized narrative review with a structured literature search in PubMed/MEDLINE, Scopus, and Web of Science from inception to 31 January 2026, including original studies of adult patients undergoing breast-conserving surgery, mastectomy, and/or reconstruction that reported early postoperative outcomes in relation to comorbidities, cardiovascular risk, or frailty. Eligibility assessment, data extraction, and qualitative synthesis followed key PRISMA 2020 principles, and findings were organized into three prespecified domains: surgical complexity, cardiovascular vulnerability (including patients with heart failure where reported), and frailty. Results: Nineteen studies (retrospective cohorts, registry-based analyses, and large database studies, primarily ACS NSQIP) met inclusion criteria, encompassing diverse breast surgery populations, including elderly, metastatic, and reconstructive cohorts. Across datasets, escalation from breast-conserving surgery to mastectomy and then to increasingly complex reconstruction was associated with a stepwise increase in perioperative complications, reoperations, bleeding, and, in selected series, catastrophic events. Preexisting cardiovascular disease and systemic vascular pathology significantly amplified postoperative morbidity even in procedures considered low or intermediate cardiac risk, with signals that patients with underlying heart failure carry particularly heightened vulnerability, although HF-specific events were infrequently reported as separate endpoints. Frailty, mainly assessed using modified frailty indices, consistently emerged as a strong, age-independent predictor of 30-day complications, mortality, and readmissions across surgical types, including both breast-conserving and reconstructive procedures. Conclusions: Early postoperative outcomes after breast cancer surgery are associated with the interaction between surgical complexity, cardiovascular comorbidity (with limited HF-specific reporting), and frailty rather than by operative technique alone. In this context, our synthesis primarily reflects overall cardiovascular vulnerability in comorbid and frail patients, with heart failure risk inferred indirectly from the available data. These findings support a patient-centered, risk-adapted surgical strategy in which the extent and timing of surgery and reconstruction are tailored to each patient’s cardiovascular profile and frailty status, with preferential use of breast-conserving or less complex procedures in vulnerable individuals. Integrating standardized frailty assessment and cardio-oncologic evaluation into preoperative workflows, and prospectively validating this tri-axial framework in dedicated cohorts, may improve perioperative risk stratification and reduce the burden of postoperative medical complications in an aging breast cancer population. Full article

Event cameras capture sparse, high-temporal-resolution visual information, making them attractive for challenging scenarios with fast motion and severe illumination changes. However, event-based depth models trained on one real-world benchmark often degrade substantially when transferred to another, revealing a practical cross-dataset domain shift between real sensor datasets. In this work, we study parameter-efficient adaptation from MVSEC to DSEC using a frozen VFM-based recurrent depth backbone. We systematically compare several parameter-efficient fine-tuning (PEFT) strategies, including Bias-only, Adapter, Decoder Weight Tuning, ConvLSTM-only, and FiLM-based modulation, under labeled few-shot adaptation. Across three random seeds, Bias-only achieves the best few-shot accuracy, reaching 0.189 AbsRel with 150 calibration samples. Decoder-side FiLM provides the best accuracy–efficiency trade-off, maintaining stable performance while updating only 2048 parameters, and reaches 0.176 AbsRel when trained with the full DSEC training set under our protocol. Our study shows that tuning native pretrained parameters is a strong baseline in this specific MVSEC → DSEC event-depth adaptation setting, whereas higher-capacity auxiliary modules are less effective under limited target-domain supervision. These results establish a controlled MVSEC → DSEC benchmark and provide practical guidance for adapting event-based monocular depth models under cross-dataset transfer. Full article

Autonomous tracked amphibious robotic systems operating across water and land environments are essential for coastal inspection, disaster response, environmental monitoring, and complex terrain exploration. However, discontinuous water–land dynamics, unstable medium switching, and safety-critical control under environmental uncertainty pose significant challenges to existing amphibious navigation and path planning methods, where global reachability and adaptive decision-making are difficult to unify. Motivated by these challenges, this paper proposes CD-HSSRL, a Cross-Domain Hierarchical Safe-Switching Reinforcement Learning framework for autonomous tracked amphibious navigation. Specifically, a Cross-Domain Global Reachability Planner is developed to construct unified cost representations across heterogeneous water–land environments, a Hierarchical Safe Switching Policy enables stable medium-transition decision-making through option-based policy decomposition with switching regularization, and a Safety-Constrained Continuous Controller integrates action safety projection and risk-sensitive reward shaping to ensure collision-free control during complex shoreline interactions. These components are jointly optimized to achieve robust cross-domain navigation. The experimental results in the Gazebo + UUV simulation environment show that the proposed method demonstrates competitive performance compared with baseline approaches, achieving higher success rates and lower collision rates across water, land, and transition environments. In particular, in cross-domain scenarios, the proposed method improves success rates by approximately 20% compared to conventional RL methods while maintaining stable performance under environmental disturbances. Robustness and ablation studies further verify the effectiveness of hierarchical switching and safety-constrained control mechanisms. Overall, this work establishes an integrated framework for safe and robust cross-domain navigation of tracked amphibious robotic systems, providing new insights into hierarchical safe-switching architectures for multi-medium autonomous robots. Full article

Deploying highly generalizable deepfake detection systems on resource-constrained edge devices poses a significant technical challenge for conventional end-to-end large models that rely heavily on computational resources. Extracting multi-source physical prior features is a viable approach under limited computational power; however, in few-shot scenarios, the dimensional mismatch of heterogeneous features is prone to causing downstream classifiers to overfit. To mitigate this bottleneck, this paper proposes a “static feature extraction–central normalization alignment–independent downstream decision” decoupled detection system for few-shot cross-domain tasks on edge devices. The front end of the system constructs an 856-dimensional comprehensive feature reservoir, and a lightweight residual normalization adapter $g_{\varphi }$ is introduced as the central support module. This module explicitly compresses the intra-class variance of heterogeneous features, providing a smoothly aligned manifold base for downstream classifiers. Experimental results indicate that this decoupled architecture demonstrates consistent stability in few-shot ($K=10$) cross-domain evaluations. When encountering intra-family cross-domain shifts and cross-mechanism distribution shifts from diffusion models, the accuracy reaches 84.9% and 76.1%, respectively. Compared to representative end-to-end meta-learning baselines (e.g., MAML), the relative error rate is reduced by over 30%. Furthermore, after completing the asynchronous offline pre-processing (approximately 897 ms) at the front end, a single-image online classification query requires only 7.7 ms under a simulated single-core CPU constraint, satisfying the low-latency requirements for lightweight deployment on edge devices. Finally, combined with empirical observations, this paper discusses the performance boundaries of the architecture in cross-mechanism metric mismatch scenarios, providing a low-barrier, robust engineering defense scheme for resource-constrained environments. Full article

The increasing vulnerability of global food systems—exacerbated by the pandemic, climate change, and disruptions to international supply chains—has highlighted the importance of local food production for sustainability, food security, and rural resilience. At the same time, the LOHAS (Lifestyles of Health and Sustainability) value system is gaining prominence, shaping consumer demand for locally produced, environmentally responsible, and health-oriented products. While the existing literature predominantly addresses LOHAS consumers and local food systems as separate research domains, limited empirical attention has been paid to the value-based alignment between LOHAS principles and local food producers, particularly from a territorial and place-based perspective. This study seeks to address this gap by examining how LOHAS value dimensions are reflected in the self-identification and operational practices of local food producers, and by analyzing how such value alignment may be interpreted as contributing to the sustainability and resilience of territorially embedded rural production systems. From a systems perspective, LOHAS-related value alignment may be interpreted as a potential coordination mechanism that may contribute to strengthening feedback loops between producers and consumers and may enhance the adaptive capacity of short food supply chains as socio-ecological systems. The empirical analysis draws on an online survey conducted in the second quarter of 2024 among 73 local producers operating in Zala and Vas counties in Western Hungary. Factor analysis and cluster analysis were applied to identify underlying value structures and producer typologies. The results reveal two distinct producer clusters, one of which exhibits a strong alignment with LOHAS values. Producers within this cluster place particular emphasis on sustainability, environmental responsibility, health consciousness, and authenticity, alongside a pronounced commitment to local embeddedness and community-oriented practices. Overall, the findings demonstrate that LOHAS-related values are not confined to the consumer side but are increasingly embedded in territorially grounded local production models. This value alignment may contribute to strengthening short food supply chains rooted in specific geographical contexts, thereby contributing to the long-term socio-economic and environmental sustainability of rural regions. Full article

Chicken (Gallus gallus domesticus) domestication, likely associated with dry-rice farming in central Thailand, has led to substantial loss of ancestral immune-related genetic diversity in commercial chicken lineages. This study addresses allelic loss by providing the first comprehensive analysis of the highly polymorphic BG1 gene, an MHC-linked marker across the wild–domestic interface in Thailand and Vietnam, using high-depth Illumina amplicon sequencing. Genomic DNA from 47 Thai and Vietnamese chicken populations was extracted using a salting-out protocol following ethical sampling. Allelic variation was examined by targeting the BG1 intron 15–exon 16 region using triplicate PCR and Salus Pro NGS sequencing. Evolutionary dynamics and selection pressures were analyzed using AmpliSAS, MrBayes, and Datamonkey, while AlphaFold 3 was used to predict and validate 3D protein structures. We identified 98 novel alleles and 172 polymorphic sites within the BG1 intron 15–exon 16 region encoding an Ig-like domain. Extensive allele sharing between indigenous chickens and red junglefowl indicated strong balancing selection and trans-species polymorphism. Selection analyses showed that purifying selection conserved structural integrity at codons 9, 13, and 18, while variation at other sites enhanced immune recognition. AlphaFold 3 modeling confirmed conservation of the β-sandwich fold across variants, maintaining stability of the Immunoreceptor Tyrosine-based Inhibition Motif (ITIM). Thus, despite the regional gene flow, geographic isolation has shaped distinct signatures, as evidenced by the presence of 38 unique Thai and 9 unique Vietnamese alleles in addition to breed-specific private markers in the Betong (BG1*TH88), Decoy (BG1*TH91), and Tre (BG1*VN54) populations. A notable adaptive outlier under positive selection (ω = 1.357) was detected in the Dong Tao population, suggesting a recent selective sweep. These findings support the mission of the Siam Chicken Bioresource Project (SCBP) to utilize indigenous breeds as genetic reservoirs and provide a molecular basis for restoring resilience traits in domestic poultry to enhance global food security. Full article

Background: Erectile dysfunction (ED) is a common complication of type 2 diabetes and obesity and significantly affects patients’ quality of life. Nursing care for patients with metabolic multimorbidity requires a holistic, structured approach. The International Classification for Nursing Practice (ICNP^®) enables standardized formulation of nursing diagnoses, interventions, and outcomes and supports structured and individualized ICNP^®-based care planning. Aim: This study aimed to develop and present an ICNP^®-based nursing care plan for a patient with erectile dysfunction associated with type 2 diabetes and obesity and to demonstrate the applicability of ICNP^® in holistic nursing management of chronic disease. Methods: A descriptive single-case study was conducted in 2025 in a cardiology ward in Poland. Data were collected using a nursing interview, observation, medical documentation analysis, and standardized tools (IIEF-5, SF-36v2). Based on a comprehensive assessment of physical, psychological, and social status, nursing diagnoses, interventions, and expected outcomes were formulated according to ICNP^® terminology. Results: The patient presented with poorly controlled diabetes, class I obesity, moderate erectile dysfunction, reduced testosterone levels, and decreased quality of life, particularly in psychosocial domains. Key ICNP^® nursing diagnoses included erectile dysfunction, deficient knowledge, obesity, disturbed psychological status, impaired endocrine function, impaired cardiovascular function, and impaired adaptation. Individualized ICNP^®-based interventions focused on metabolic control, lifestyle modification, sexual health support, education, and psychosocial support. Implementation of the care plan was associated with improvements in health behaviors, disease knowledge, and psychological well-being. Conclusions: ICNP^® provides a useful framework for structured and comprehensive nursing care in patients with diabetes-related erectile dysfunction and multimorbidity. Case-based ICNP^® care planning supports holistic management, interdisciplinary collaboration, and quality improvement in chronic disease nursing. Full article

Cells contain an unexpectedly large diversity of lipid molecules. Modern lipidomics studies have revealed that even a single cell type can harbor hundreds to thousands of distinct lipid species that differ in headgroup structure, acyl chain length, and degree of unsaturation. While this remarkable diversity is now well established, its biological significance remains incompletely understood. Why do cells maintain such complex lipidomes? In this review, we examine several conceptual frameworks that may help explain the origin and functional significance of lipid diversity. First, the physical properties of biological membranes impose constraints on lipid composition, as variations in lipid structure influence membrane fluidity, curvature, thickness, and phase behavior. Second, lipids can regulate membrane protein function through specific interactions and through the physical environment of the lipid bilayer. Third, lipid metabolism generates signaling molecules that participate in diverse regulatory pathways. Fourth, lipid metabolic networks continuously remodel membrane composition, producing dynamic lipidomes that can adapt to physiological conditions. Finally, evolutionary processes have shaped membrane lipid composition across different domains of life, suggesting that lipid diversity may reflect long-term adaptation to functional and environmental constraints. Taken together, these perspectives suggest that lipid diversity is unlikely to be a simple byproduct of metabolism. Instead, the cellular lipidome may emerge from the interplay of membrane biophysics, metabolic network architecture, protein regulation, and evolutionary pressures. Understanding why cells contain thousands of lipid species therefore represents an important challenge for modern cell biology and may reveal fundamental principles governing the organization of biological membranes. Full article

Cross-domain semantic classification of 3D point clouds remains challenging due to strong domain shifts between heterogeneous sensing modalities. Most existing classification frameworks are domain-specific, limiting their use in integrated land–water mapping applications. This study evaluates the transferability of classical geometric machine learning classifiers between terrestrial and underwater point cloud domains without target-domain retraining. Experiments were conducted using terrestrial data acquired with a Leica BLK360 terrestrial laser scanner (TLS) and underwater point clouds collected with a Blueview BV5000 mechanical scanning sonar (MSS). Two dimensionality-based frameworks, CANUPO–Support Vector Machine (SVM) and 3DMASC–Random Forest (RF), were implemented in CloudCompare and assessed under intra-domain and cross-domain configurations. Strong intra-domain performance was achieved, with terrestrial–terrestrial accuracies of 0.99 for CANUPO–SVM and 0.97 for 3DMASC. In underwater evaluation, CANUPO maintained high accuracy (0.97), whereas 3DMASC decreased to 0.86 due to increased variability in the submerged data. Under cross-domain transfer, CANUPO achieved 0.93 accuracy for terrestrial-to-underwater and 0.89 for underwater-to-terrestrial classification, while 3DMASC demonstrated stable generalisation with 0.95 accuracy in both directions. Overall, dimensionality-based geometric descriptors capture stable structural cues across sensing environments, providing an interpretable and efficient pathway for applications such as hydrographic surveying, coastal monitoring, and underwater search-and-rescue detection. Future work will extend validation to larger datasets and explore domain adaptation strategies to further reduce cross-modality domain shift. Full article

Cities exhibit sound patterns that vary across locations and time, while transmitting raw audio introduces communication and privacy concerns. We present a federated TinyML architecture for real-time urban sound classification on microcontroller-class edge devices. A compact audio embedding network is deployed as a frozen feature extractor, while a lightweight classifier head is trained on-device and shared via MQTT, enabling communication-efficient collaborative learning. The system is evaluated on ESP32 (Espressif Systems, Shanghai, China) hardware under cross-dataset transfer from UrbanSound8K to SONYC. Domain shift reduces baseline accuracy from 90.39% to 78.27%, while local adaptation and federated aggregation improve accuracy to approximately 85%, recovering most of the performance loss. Repeated aggregation further improves macro-F1 and class balance across heterogeneous data. Embedded measurements confirm real-time inference (~250 ms per window) with negligible overhead, while each update exchanges only a compact classifier head (~1.2 kB). These results demonstrate that adaptive classification can be achieved on resource-constrained nodes in distributed smart-city networks. Full article

Objective: This study investigates the optimization of surface EMG (sEMG) classification for seven transtibial movements using short analysis windows (64 ms) suitable for real-time control of below-knee prostheses. Methods: We systematically evaluated feature engineering strategies, dimensionality reduction techniques, and classification approaches using linear Support Vector Machines on four-channel sEMG data from the transtibial region. We compared amplitude-based versus derivative-based time-domain features, integrated frequency-domain features, and implemented multi-window majority voting with 50% overlap. Results: Evaluated across nine subjects (four male, five female), the optimized system achieves a population-level accuracy of $70.16\%\pm 7.09\%$ with multi-window majority voting (per-subject range: 60.71–78.57%), with voting consistently improving accuracy over single-window classification by $+7.06$% on average. We demonstrate that PCA provides zero benefit for linear classifiers when all features are retained. Documented failed approaches include adaptive windowing and spectral entropy features. Conclusion: Careful feature engineering combining time-domain (MAV2, RMS, VAR, MAX, LOG, IEMG) and frequency-domain features (MPF, MF, band powers) with multi-window voting substantially recovers accuracy losses from aggressive window reduction while maintaining sub-100 ms latency suitable for prosthetic control. This work provides a validated methodology across multiple subjects for optimizing EMG classification latency–accuracy trade-offs, demonstrates that PCA is unnecessary for linear classifiers with well-engineered features, and documents negative results to guide future prosthetic control research. Full article

This paper investigates federated multi-centre liver tumour classification from contrast-enhanced CT under realistic data heterogeneity and domain shift. To address the practical constraint that medical data are often siloed across institutions, we develop a FedProx-based federated learning pipeline that enables collaborative training without exchanging raw patient data. Using the LiTS dataset as the training domain, we construct a slice-level binary classification task based on voxel-level annotations, while rigorously assessing out-of-distribution generalisation on an external held-out dataset, 3D-IRCADb. We conduct comprehensive experiments across multiple backbone architectures, including ResNet-50, EfficientNet-B3, ViT-B/16, and MobileNetV3-Small, comparing FedProx and FedAvg under three heterogeneity intensities (IID, mild non-IID, and severe non-IID). Furthermore, we evaluate transfer learning strategies, ranging from frozen backbones to partial fine-tuning of the last stage, and perform ablations on the proximal coefficient $\mu$ and local epochs E to characterise optimisation behaviour. Our results show that FedProx is generally comparable to FedAvg, with slightly more stable behaviour in some heterogeneous settings. We also observe a clear validation-to-external gap, indicating that external-domain robustness remains challenging and requires cautious interpretation for deployment. ImageNet pretraining yields consistent gains, particularly for data-sparse clients, while partial fine-tuning enhances adaptation to CT-specific features. Finally, MobileNetV3-Small offers a favourable performance–efficiency trade-off by reducing communication payload and computation cost, supporting practical deployment on resource-constrained clinical edge devices. Full article

This study reviews and summarizes existing research on how small and medium-sized construction enterprises adopt Building Information Modeling (BIM), while also highlighting potential areas for future investigation. The analyses aimed to address two research questions: RQ1: What research areas are explored in scientific publications on the use of BIM in small and medium-sized enterprises? RQ2: What future research directions should be pursued regarding the implementation and development of BIM in SMEs? A bibliometric analysis and science-mapping analysis was conducted on 162 Scopus-indexed publications (2007–2025) using Excel, VOSviewer and Biblioshiny, complemented by a state-of-the-art review of 69 recent studies (2022–2025). Keyword analyses revealed five thematic clusters: implementation and adaptation, collaboration and integration, construction industry digitalization, project management, and information systems. Within the identified areas, a state-of-the-art review was conducted to indicate the main research domains and directions for future research. Emerging topics include Industry 4.0-enabled digitalization, common data environments, interoperability, decision-making, human resource management, and safety and risk assessment. Future studies should examine managerial competencies, behavioral drivers of adoption and value creation in resource-constrained contexts. Policymakers and professional bodies should combine capacity building, incentives and lightweight interoperable tools to lower entry barriers for SMEs. Integrating bibliometric mapping with qualitative synthesis, this paper offers an evidence-based research agenda and guidance to support BIM diffusion in SMEs. Full article

Due to the scarcity of high-quality, specialized datasets for coal mining equipment operation and maintenance (O&M) and the poor adaptability of large language models to domain-specific scenarios, the reliability of actual mining O&M cannot be guaranteed. To address this, this paper investigates the construction of vertical-domain large language models for coal mining equipment O&M scenarios under limited fine-tuning data. First, to tackle the lack of O&M scenario data, a safety-guided evolutionary self-instruction method (SafeEvol-Instruct), is developed by integrating Self-Instruction, Evol-Instruct, and Rule-Based Filtering. This approach achieves the unified fusion of scalable generation, deep evolution, and safety filtering on limited O&M data, resulting in the construction of scenario-specific datasets for system status assessment, equipment fault diagnosis, maintenance plan formulation, and preventive maintenance. Second, to account for the distinct characteristics of different O&M tasks, a hybrid fine-tuning strategy (SynergyLoRA) is proposed based on the Qwen2.5-7B-Instruct foundation model. This strategy incorporates middle-layer LoRA, top-layer LoRA, middle-layer IA³, Prompt Tuning, and Prefix Tuning to enable specialized training of vertical-domain models for each O&M scenario. Finally, the constructed Coal Mining Equipment O&M Large Language Model (CMEOM-LLM) is evaluated through ablation studies across various scenarios, validating the effectiveness of the proposed methods. Experimental results demonstrate that, in the system status assessment scenario, CMEOM-LLM achieves improvements of 4.9%, 1.5%, and 1.4% over the Qwen model in accuracy, recall, and F1-score, respectively. In the equipment fault diagnosis scenario, CMEOM-LLM outperforms Qwen by 7.4% in accuracy, with BLEU-4 and ROUGE-L scores increasing by 6.6% and 6.5%, respectively. In the maintenance plan formulation scenario, CMEOM-LLM surpasses ChatGLM with improvements of 6.6%, 6.5%, and 8.5% in ROUGE-L, BLEU-4, and human evaluation, respectively. In the preventive maintenance scenario, CMEOM-LLM achieves improvements of 7.1% and 8.9% over Qwen in ROUGE-L and BLEU-4, along with a 0.69-point increase in human evaluation scores. This paper provides an effective approach for knowledge management in coal mining equipment O&M. Full article

The Real Maestranza de Caballería in Seville features one of the most prominent Spanish bullrings, characterized by a notable architectural design. Its distinctive ovoid geometry resulted from a protracted construction history (1761–1881), during which the floor plan adapted to pre-existing urban structures. Beyond its architectural significance, the sounds perceived within such venues constitute traces of collective memory and form part of an intangible cultural heritage relevant for understanding the sociocultural context of such spaces. This work provides an acoustic characterisation of the bullring through field measurements. Reverberation time and other monaural and binaural descriptors were determined using 3D impulse responses obtained from strategically placed sources and receivers. This analysis is complemented by examining the sound energy distribution of early reflections in the time–frequency domain to define the acoustic signature of the venue, namely the characteristic pattern of early reflections that unequivocally determines its sound response, and identify the provenance of reflections. In the Maestranza, music and silence are hallmarks of its identity, contributing to a complex auditory environment. The results highlight how its geometry and tiered seating create a differentiated sound field, potentially contributing to the preservation of the site as a cultural landmark. Full article