Search Results (1,572)

Population decline is reshaping rural development and requiring village planning to balance land-use efficiency with changing community needs. This study examines planning-led land-use restructuring in Gushanqian Village, a shrinking peri-urban village in Shandong Province, China. Under the Village Spatial Plan (2021–2035), commercial service land was reduced from 9.35 to 1.66 ha and industrial land increased from 1.42 to 10.30 ha, while the total area of construction land remained unchanged. By integrating planning-document analysis with a questionnaire survey of 132 villagers, the study connects documented statutory land-use adjustments with residents’ multidimensional perceptions, residential retention intention, and overall planning satisfaction. Firth logistic regression was used to analyse retention intention, while multiple linear regression was applied to planning satisfaction, with age and residential status included as covariates. More favourable perceptions of planning information, agricultural space, ecological space, and residential space were positively associated with retention intention, whereas industrial expansion perception was negatively associated with retention intention but showed the strongest positive association with overall planning satisfaction. This divergence highlights that support for an industrially oriented planning direction does not necessarily translate into a stronger intention to remain. The case demonstrates the value of examining the social implications of stock-oriented land reallocation alongside its spatial outcomes and provides a non-Western basis for comparison in research on planning responses to rural shrinkage. Full article

We introduce triproximity, a conceptual framework that organizes tree–soil greenhouse gas (GHG) interactions across three spatial dimensions: (i) horizontal distance from tree stems, (ii) vertical soil profile depth, and (iii) structural position relative to tree components including the stem itself as a gas conduit. This addresses a critical and previously unquantified methodological gap in the literature. Despite the inherent spatial heterogeneity of tree-based agricultural systems, where molecular oxygen gradients structured by root macropore networks, rhizosphere demand, and canopy-mediated moisture redistribution govern CO₂, N₂O, and CH₄ fluxes across distances of just a few meters from the stem, most studies report GHG emissions from single locations without documenting distance from trees, effectively assuming spatial homogeneity where none exists. Following PRISMA guidelines, we systematically reviewed 107 field-based studies identified through a Scopus search (December 2025) of tree-based systems published between 2010 and 2025. Only 37.4% of studies explicitly reported measurement distance from trees, a proportion that has not improved despite a nearly four-fold increase in publication volume since 2020. Through narrative synthesis, we show that CH₄ uptake follows the most consistent spatial response, with higher oxidation rates in the near-tree zone across diverse system types; N₂O responses are context-dependent and governed by competing substrate availability and moisture controls; and CO₂ fluxes show no universal spatial pattern yet respond predictably to specific proximity dimensions once the dominant source term is identified. Stem-level gas transport remains virtually unmeasured across the dataset, likely biasing ecosystem GHG budgets systematically. We propose a minimum triproximity-based sampling protocol for five major tree-based system types and call for journals to adopt spatial reporting as a minimum submission standard. This review was not pre-registered and received no external funding. Full article

Airborne dust plumes are difficult to localize in underground mine-face video because the scene is dark, illumination moves with machinery, and dust is confused with lamp bloom, reflective steel, and water-spray aerosol. We present DustVeil, a label-free two-stage system for image-space plume localization. Its software teacher combines background-referenced veiling (C1), local texture decay (C2), and absolute dark-channel response (C3) with a probabilistic soft-OR, then applies glare and chroma gates. The teacher returns a dimensionless response map in [0, 1], a binary plume mask and the corresponding image-area ratio; it does not estimate dust concentration, particle-size distribution, respirable exposure, or hazard categories. Teacher outputs from 342 frames in 114 clips/24 sessions supervise a 0.47 M parameter TinyU-Net. Evaluation uses a 144-image synthetic calibration set and a 72-frame real test set drawn from 72 clips in 18 sessions, with all roles separated at clip and session levels. Thresholds are selected only on synthetic masks and frozen before real scoring. After replacing per-image score normalization with fixed baseline-normal calibration and using reference implementations of the anomaly methods, DustVeil obtains IoU/F1 of 0.366/0.500 and the lowest clean-frame false-positive area (3.7% versus 13.9–59.9%). A separate water-spray set quantifies visual specificity. TinyU-Net runs at 610 FPS for network-only inference and 233 FPS aggregate in the measured six-stream decode-to-mask pipeline; optical flow is excluded from these figures. The validated scope is six fixed visible-light RGB cameras with camera-specific unlabelled calibration at one site, rather than concentration monitoring or camera-disjoint deployment. Full article

Mass-customized production introduces variants faster than certified asset models can be rebuilt. This feasibility study presents an active–reactive architecture for type-3 Asset Administration Shells (AASs) in which two submodel templates separate the reactive layer from independently deployable decision services. On the requester side, ActiveSteps represents a variant’s process sequence and references the capability each step needs, so a product’s AAS becomes a type-3 on demand. On the provider side, ActiveSkills maps offered skills to the capabilities that realize them, so one provider-core image serves every resource and only its domain services are scenario-specific. The type-2 to type-3 upgrade adds these submodels to both sides and costs one restart of the provider alone; subsequent rollouts needed none, so consumers were not interrupted. Evaluation used a university robotic screwdriving demonstrator, not a live production line. Of 104 calls for proposal, 102 were answered at a median of 4 s. Median requester activation was 8 s over 100 activations. Capability checking over 454 balanced cases with five repetitions reached an $F1$ of 89.6% and a 10.8% false-positive rate. An evaluation costs USD $7.0\times {10}^{-4}$. These results support feasibility for asynchronous coordination in the evaluated cell; scalability and cross-vendor interoperability were untested. Full article

The importance of understanding how feature engineering contributes to breast cancer metastasis analytics is growing as artificial intelligence (AI) technologies rapidly transform the field of precision oncology. This systematic review collated, thematically synthesised, and analysed the evidence related to feature engineering processes, multimodal integration techniques and explainability mechanisms used in AI-driven breast cancer metastasis analytics. Following PRISMA 2020, studies from 2020 to 2026 were retrieved from MEDLINE, Scopus, Web of Science, Embase and IEEE Xplore. A total of 50 empirical studies investigating AI, feature engineering, radiomics and multimodal approaches for breast cancer metastasis prediction were included. The research findings reveal that contemporary AI-based breast cancer metastasis prediction is predominantly based on imaging-derived features, while clinical, pathological, biomarker and molecular variables increasingly enhance multimodal model development. Frequently applied techniques were observed across feature extraction, preprocessing, reproducibility assessment, dimensionality reduction, feature integration and explainability, all of which helped to create more interpretable, robust and clinically meaningful predictive systems. The study crafts an evidence-informed conceptual 5Fs framework comprising feature extraction or generation, feature preparation and transformation, feature selection and dimensionality reduction, feature construction and integration, and feature interpretability and explainability. The framework highlights the feature-centric nature of AI-driven breast cancer metastasis analytics and emphasises the importance of interpretable, multimodal and potentially clinically translatable predictive systems. Full article

Background: Inverted-bearing reverse total shoulder arthroplasty (rTSA), using a polyethylene glenosphere with a metal humeral liner, may reduce polyethylene wear, osteolysis, and scapular notching. This study evaluated long-term survivorship, complications, radiographic findings, and clinical outcomes of a large-glenosphere inverted-bearing rTSA design. Methods: A retrospective single-surgeon cohort included 394 primary rTSAs (363 patients; mean age 73.8 ± 8.5 years) performed between 2006 and 2018 using the same implant system. Clinical follow-up was available for 123 patients at a mean of 9.4 years. Outcomes included ASES, Oxford Shoulder Score (OSS), Subjective Shoulder Value (SSV), and pain VAS. Radiographs obtained at ≥8 years were assessed for scapular notching, glenoid loosening, and humeral osteolysis/stress shielding. Kaplan–Meier survival analysis was performed. Results: Thirty of the 257 rTSAs with revision follow-up (11.7%) underwent revision, most commonly for glenoid loosening or instability. Among patients with long-term radiographs, scapular notching occurred in 8.3% of all-polyethylene glenospheres and was limited to grade 1. Humeral osteolysis/stress shielding was present in 36%, although no humeral components were revised for loosening. Median final follow-up PROMs were ASES 75.8, OSS 42, SSV 78, and pain VAS 1. Conclusions: Large-glenosphere inverted-bearing rTSA demonstrated satisfactory long-term outcomes, low notching rates, and acceptable survivorship. The favourable notching profile should be interpreted in the context of the larger eccentric glenosphere design, which precludes attribution of this finding to bearing material alone. Full article

Varroa destructor is the leading cause of honey bee colony losses globally and is increasingly implicated in winter colony mortality in New Zealand. This study used data from the New Zealand Colony Loss Survey to evaluate the relationship between specific varroa management practices and over-winter colony losses among 5203 hobbyist beekeepers surveyed between 2023 and 2025. We find that beekeepers who did not treat for varroa experienced loss rates of 49.4% compared with 30.7% among beekeepers who treated. Conditional on treatment, colony losses declined as the diversity of products used increased and as treatment applications were spread throughout the year. For example, beekeepers using four or more products experienced losses of 21.2%, while those treating in three or more seasons of the year experienced losses of 24.8%. Multiple autumn treatments were also associated with lower loss rates. No statistically significant differences were observed among major treatment types when hobbyist beekeepers used a single treatment type in a single season of the year. While apiary size is negatively associated with loss rates, beekeeper experience is not. The results demonstrate that treating for varroa, product rotation, treatment throughout the year, and intensified autumn control are associated with substantially improved over-winter colony survival among hobbyist beekeepers. Full article

Background: Germline pathogenic variants in BRCA1/2 increase breast and ovarian cancer risk, but their prevalence in Aboriginal and Torres Strait Islander (herein referred to as Aboriginal) families has never been studied. Consequently, their contribution to breast cancer in this population is unknown. Methods: This retrospective cohort study included 259 Aboriginal women and 789 age- and remoteness-matched non-Aboriginal women diagnosed with breast cancer (2001–2016). We assessed family history, genetic service referral and testing rates, and pathogenic variant rates in the context of testing access. Results: Family history data were available for more Aboriginal than non-Aboriginal cases (46% vs. 34%, p < 0.001), with no difference in reported first-degree relatives with breast (22.5% vs. 22.4%, p = 0.98) or ovarian cancer (4.2% vs. 2.2%, p = 0.29). Similar proportions were referred to genetic services for testing eligibility (5.4% vs. 7.2%, p = 0.392). Of those referred, similar proportions were offered testing (86% vs. 81%, p = 0.66). Across the full cohort, BRCA1/2 pathogenic variants were detected in 0.77% of Aboriginal versus 2.4% of non-Aboriginal women (p = 0.127); any pathogenic gene variant was detected in 0.77% versus 3.0% (p = 0.039). Conclusions: Pathogenic genetic variants were detected less frequently in Aboriginal women, although, in keeping with international clinical practice, most women diagnosed with breast cancer were not tested. Given that only a small proportion of the cohort underwent germline testing, this study cannot distinguish whether the lower detection rate reflects a genuinely lower population level prevalence of pathogenic variants or differences in historical referral, selection, and testing practices. These findings are hypothesis-generating and require validation in larger Australian Aboriginal studies including unselected populations with whole-population testing data. Full article

The derivatives of the inverse of a one-to-one function are needed in a range of applied contexts, from random variate generation and molecular simulation to nuclear physics and bias reduction for maximum likelihood estimates, yet existing treatments derive them by ad hoc, application-specific means without a unifying framework. Here, we give the general derivative of the inverse of a one-to-one function, firstly by a recurrence formula, secondly by repeated differentiation, and thirdly—and most explicitly—in closed form using the partial exponential Bell polynomials associated with Faà di Bruno’s chain rule, providing a single representation that subsumes and extends earlier case-specific results and that can be taken to arbitrary order. We illustrate the practical value of these results in mathematical statistics, applying them to bias reduction for maximum likelihood estimates in one-parameter exponential families, including the gamma shape parameter and canonical regression models. Python programs implementing the recurrence and the Bell polynomial representations are included. Full article

Real-time multilingual communication (RTMC) is increasingly important in academic, governmental, defense, and clinical settings, where cloud-based speech processing may raise privacy, data sovereignty, connectivity, and cost concerns. This study presents Yapzek Stream, a fully offline multilingual broadcasting and speech-translation platform that performs speech recognition, translation, and text-to-speech synthesis within institution-owned infrastructure. The system integrates Web Real-Time Communication (WebRTC)-based broadcasting with locally hosted artificial intelligence (AI) services and provides multilingual audio delivery, browser-based access, recording, transcript and subtitle export, and institutional authentication. The platform was evaluated through an implementation-oriented analysis and a university pilot involving two live sessions with 10 and 50 participants and 12 semi-structured interviews. Estimated end-to-end processing latency was 1.8–4.1 s, while pilot observations ranged from approximately 2 s for short utterances to 4.5–5 s for longer speech. The results indicate the feasibility of offline multilingual broadcasting for privacy-preserving and institution-controlled communication. Deployment-specific accuracy and translation evaluation, controlled scalability and GPU-utilization measurements, robustness testing, and larger-scale user-acceptance studies remain for future work. Full article

Background: People in prison have a higher prevalence of adverse childhood experiences (ACEs) compared to the general population. This has been found across different cultural contexts. While treatment programs for people in prison have moderate completion rates, the impact of ACEs on peoples’ treatment and outcomes has been neglected in research, especially for men. Methods/Results: In a series of studies with men attending violent offending treatment programs, it was shown that the ability to complete treatment was substantially impacted by adverse childhood experiences. The association between diver-sity of childhood adversities and treatment outcomes was not better explained by posttraumatic symptoms or antisocial, borderline or psychopathic personality traits. Conclusions: Replication of these associations in various cultural contexts, implications for forensic assessment and treatment practice should be considered. Future research should investigate the pathways for these associations, including physiological and hormonal changes due to the adverse experiences, and acute stress reactivity in interpersonal interactions. Full article

AI-generated synthetic media are evolving from tools of digital manipulation into a practical antidote for persistent challenges in digital health implementation. However, the computational capabilities that characterise these technologies, their applications and downstream health-related benefits remain fragmented and insufficiently synthesised. This systematic review identified the computational capabilities that characterise AI-generated synthetic media in health, examined their applications and benefits, and developed an integrative framework linking these domains. Twenty-four studies published between 2021 and 31 May 2026 were included. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT) and findings were synthesised through thematic analysis. The synthesis revealed an integrated set of capabilities spanning photorealistic medical-image generation, modality-specific synthesis of clinical images and physiological signals, synthetic non-image health-data creation, preservation of statistical distributions, temporal patterns and clinical relationships, generation of diverse, novel and non-memorised samples and controlled transformation of medical and audiovisual content. Privacy-oriented synthesis and deepfake detection emerged as distinct components supporting privacy-conscious data use, clinical verification and healthcare safety. These demonstrated capabilities were linked to empirically evaluated and indicated applications, including data augmentation, AI model training, diagnostic model development, privacy-oriented health-data sharing, medical education, patient-facing communication, therapeutic support, clinical safety, health-system analytics and planning. The resulting Computational Capability–Application–Benefit (CAB) Framework conceptualises synthetic media as an evidence-graded pathway distinguishing demonstrated computational capabilities, evaluated health-related applications and reported, indicated or potential downstream benefits requiring further validation. AI-generated synthetic media, therefore, represent an emerging computational infrastructure with potential to support safer, privacy-conscious, adaptive and data-intensive healthcare. Full article

Background/Objectives: Health challenges associated with metabolic syndrome (MetS) have stimulated investigations into the effectiveness of nutraceutical interventions, including beetroot juice (BRJ), in mitigating MetS risk factors. Methods: This repeated-measures crossover trial (n = 16 healthy participants) investigates the effects of daily ingestion of two different BRJs [United Kingdom beetroot juice (UKBR), 1280 mg nitrate, 147 mg betalains; New Zealand beetroot juice (NZBR), 1120 mg nitrate, 506 mg betalains] and placebo (PL) on plasma nitrate and nitrite levels, MetS biomarkers and systemic vascular resistance (SVR). Participants consumed the juices for 6 days, with measures taken on day 7 fasted (pre-dose) and 2 h and 5 h post ingestion (acute). Results: On day 7, pre-dose plasma nitrate levels were significantly elevated for UKBR and NZBR compared to PL (297.2, 177.1 and 64.2 µmol/L, respectively). On day 7, UKBR achieved significantly higher plasma nitrate levels 2 h and 5 h post consumption than NZBR (2 h: 750.3 vs. 577.7 µmol/L; 5 h: 703.4 vs. 510.1 µmol/L) and PL at all timepoints. Plasma nitrite was elevated pre-dose for UKBR only and for both juices relative to PL at 2 h and 5 h. On day 7, pre-dose systolic blood pressure (SBP) was significantly lower for NZBR than PL (−4.96 mmHg) and 2 h post-ingestion for both UKBR (−5.63 mmHg) and NZBR (−4.98 mmHg) compared with PL. Systemic vascular resistance was 11.6% lower after 6 days of consumption of NZBR relative to PL (ratio 0.884, 95% CI 0.803 to 0.974). Conclusions: Supplementation with BRJ reduced SBP relative to placebo, and the betalain-rich NZBR also reduced SVR, whereas the higher-nitrate UKBR did not. This suggests that betalains and nitrates may play an important role in these improvements. The Australia New Zealand Clinical Trials Registry ID for this study is: ACTRN12622000714785. Full article

Planning debates increasingly recognise that climate adaptation, community wellbeing, and tourism development cannot be treated as separate domains; yet existing place-based frameworks rarely integrate Indigenous governance, multidimensional wellbeing, and visitor dynamics within a single cohesive model. This article develops the Relational Resilient Place Framework as a conceptual model for understanding how culturally grounded landscapes can balance Indigenous authority, local community interests, and destination pressures. Drawing on mātauranga Māori (Indigenous knowledge) scholarship, therapeutic landscapes research, and the destination governance literature, the framework positions place as relational infrastructure. In this model, ecological vitality (mauri), collective wellbeing, cultural integrity, and visitor engagement are treated as interdependent rather than competing objectives. The framework is structured through six lenses: partnership integrity, cultural safety, community wellbeing, climate transition capacity, visitor–resident alignment, and long-term stewardship. The article establishes a conceptual basis for evaluating the theoretical conditions under which places function as resilient systems across rural, regional, and destination contexts. It argues that Indigenous governance principles provide a structural foundation for aligning wellbeing and tourism without reproducing extractive or growth-centric logics. In doing so, the framework demonstrates how Indigenous ontologies can move beyond symbolic inclusion to underpin resilient, non-extractive approaches to place-based planning. Full article

Artificial intelligence (AI) has emerged as a transformative analytical paradigm for modelling cancer progression using medical imaging and complementary clinical data. This systematic review collates and analyses AI-powered systems and techniques for cancer progression risk analytics and prediction. It provides an empirical examination of algorithmic approaches, imaging-derived feature domains, and clinical metrics that underpin progression prediction. Following the PRISMA 2020 reporting guidance, empirical English-language studies published between 2020 and the final search cutoff date of 15 May 2026 were identified from PubMed, EBSCOhost, Google Scholar and Web of Science. Forty (40) eligible studies spanning diverse cancer types were included, appraised using the MMAT and synthesised narratively. The evidence shows that both classical machine learning models and deep learning architectures are widely used to extract predictive information from radiological data. Radiomic descriptors of intratumoural heterogeneity, tumour morphology, functional imaging biomarkers and multiscale transform-based features consistently demonstrate strong associations with disease progression. Imaging-derived features were linked to clinically meaningful progression endpoints, including progression-free survival, disease-free survival, recurrence and metastasis, while clinical and molecular covariates supported risk stratification. This study further develops the AI-driven cancer progression risk analytics framework (AI CanPRAF), which integrates AI taxonomies, imaging phenotypes, clinical context, progression analytics and decision support into one clinically oriented model. The results demonstrate the growing role of multimodal AI systems in the development of clinically grounded cancer progression analytics and prediction solutions. Full article