Search Results (185)

Mortality in smallholder cattle and buffalo systems is climate-driven, but the signal is not uniform: heat and cold stress, flooding, and climate-sensitive disease act through different pathways, yet livestock loss models usually compress them into one elevated-mortality state. The paper builds a climate-state-dependent mortality model for the Thai national herd, separating an endemic baseline from a temperature-extreme and a moisture- and disease-driven regime. A 100,000-iteration Monte Carlo model, calibrated to the 2024 herd and a 2017 farmer survey at 2026 prices, generates the annual loss distribution and decomposes it by driver. The study is a calibrated scenario analysis, not an empirical estimation, so every result is conditional on the calibration and bounded by sensitivity analysis. Endemic mortality governs the average year, about 81% of expected loss but none of the extreme tail; the tail belongs entirely to the two climate regimes, with the moisture- and disease-driven regime carrying roughly 69% of losses beyond the 95th percentile and the temperature regime about 31%. This split holds across low-, medium-, and high-severity scenarios and a baseline range from 0.07 to 0.12, so it is structural: the driver of the typical year is not the driver of the catastrophe. Under a reduced-form behavioral layer with an assumed destocking response, generous payouts would raise stocking pressure 10% to 16% above a sustainable carrying capacity benchmark, so an adaptation instrument could degrade the rangeland it protects. The findings argue for regime-specific risk financing, for pairing insurance with heat and animal health adaptation, and for treating the carrying capacity externality as a design parameter. Full article

Water scarcity, high non-productive soil evaporation, and poor vegetation establishment are major constraints on the sustainable ecological restoration of reconstructed open-pit mine dumps in arid and semi-arid regions. Conventional surface-applied water replenishment can result in rapid evaporative loss, thereby reducing the ecological benefits obtained from limited water resources. However, whether redistributing water into deeper reconstructed soil layers can simultaneously reduce non-productive evaporation, stabilize the root-zone hydrothermal environment, and improve vegetation growth remains insufficiently verified. In this study, a deep-profile soil water replenishment (DPSWR) device was tested in reconstructed mine-dump soil columns planted with locally adapted Stipa. Surface-applied water replenishment (CK) and DPSWR were compared using a single-run simulated rainfall comparison, soil water-retention and water-loss measurements, continuous temperature and moisture monitoring at 10 and 40 cm depths, and plant growth indicators. In the rainfall-simulation comparison, DPSWR showed lower cumulative water loss across the tested rainfall intensities and improved water-retention stability; the evaporation rate under CK was approximately 1.3 times that under DPSWR, whereas final soil water-holding capacity under DPSWR was approximately 2.4 times that under CK. Root fresh weight, plant fresh weight, and seedling number were significantly higher under DPSWR than under CK (p < 0.01), and maximum plant height and root length also increased significantly (p < 0.05). Under equal water-input conditions, DPSWR reduced non-productive water loss, prolonged soil water retention, and supported vegetation establishment. These findings suggest that DPSWR may provide a more water-efficient approach to the sustainable restoration of reconstructed mine dumps in water-limited regions. Full article

Earthworms play crucial roles in soil fertility and are vital to sustainable agriculture and environmental stability. Synchronously, they are highly sensitive to their environment, which limits their abundance and activities. Existing research primarily focuses on their protein-coding gene responses at different chemicals and/or temperature stresses. This study utilized the tropical earthworm Eudrilus eugeniae as a model to investigate how the expression of protein-coding genes and microRNAs are influenced in the anterior tissues at different moisture conditions (30%, 80%, and 100% water-holding capacity). We revealed that molecular chaperones sHsp20, Hsp70, a CHORD-containing protein, and HspBP1-like genes were differentially regulated, and Hsp70 family genes served as hub genes in this process. In addition, a novel lineage-specific microRNA, with an opposite expression trend, was predicted to target three chaperone genes (CHORD-containing protein, Hsp90-like, and Hsp40). Pull-down and dual-luciferase assays further verified their potential interactions. This study suggests holistic cooperation between transcriptional and post-transcriptional mechanisms in the anterior tissues of earthworms facing soil moisture variation and provides new insights into the effect of moisture on the adaptation of biological molecules in earthworms. Full article

Digital transformation is portrayed both as a lever of territorial convergence and as a driver of polarisation between urban cores and peripheries, yet its effect on regional economic resilience has rarely been measured systematically. This study transposes the Economic Resilience Index framework from the national to the regional scale, building a Regional Resilience Index (R-ERI) for 236 NUTS2 regions of the EU-27 from Eurostat indicators, anchored in the capacities of absorption, recovery, and adaptation and measuring resilience as a capacity rather than as a realised shock trajectory. Two complementary models are estimated: a spatial Durbin panel with two-way fixed effects (2018–2023), spanning the COVID-19 pandemic and 2022 energy shocks, and an exploratory cross-sectional difference model exploiting regional artificial intelligence (AI) adoption data disaggregated by NACE branch (2023–2025). The results show that resilience is strongly spatially autocorrelated (Moran’s I between 0.66 and 0.74; p = 0.001); that digital connectivity generates a positive indirect effect on neighbouring regions despite a negative own-region effect; and that the synergy hypothesis—that digitalisation yields more resilience when combined with traditional sectors—does not hold robustly, the interaction being null in the panel and only marginally positive in the AI layer (p = 0.10). We conclude that digital connectivity is not, on its own, an automatic convergence mechanism, and that cohesion policy should account for each region’s sectoral structure and peripheral position. Full article

The expansion of Internet-of-Things (IoT) deployments in 5G New Radio (NR) networks has made periodic measurement reporting a growing burden for low-mobility devices, which benefit little from frequent updates yet must report as often as highly mobile ones. At present, User Equipment (UE) transmits MeasurementReport messages at a fixed ReportInterval—typically 240 ms—regardless of mobility. This continuous transmission needlessly depletes UE battery energy and consumes critical uplink signaling capacity. This paper proposes a five-level adaptive ReportInterval selection scheme driven by the statistical properties of Reference Signal Received Power (RSRP) and Signal-to-Interference-plus-Noise Ratio (SINR). A low-mobility criterion combines four statistical conditions—the variance and gradient of both RSRP and SINR—through a logical AND, while a two-stage hysteresis mechanism (a 3 dB margin and a 2 s holding timer) suppresses unnecessary level transitions. The scheme is slice-agnostic: By relying on observed signal statistics rather than network-slice labels, it serves low-mobility mMTC and stationary eMBB devices while leaving URLLC and high-mobility UEs at their standard configuration. In Monte Carlo simulations over the 3GPP TR 38.901 Urban Micro (UMi) channel model (200 UEs, 300 s, 100 iterations), the algorithm attains a classification accuracy of 91.32% and a sensitivity of 98.77%. Based on the DRX energy model, it yields an estimated 10.87% reduction in average UE power (from 28.15 to 25.09 mW) together with a 51.09% reduction in the network-wide MeasurementReport count. The hysteresis mechanism cuts level transitions by a factor of 31.33 (from 6852.7 to 218.7 per iteration), substantially lowering RRC reconfiguration signaling. Operating at O(n) complexity on the gNodeB and using only conventional MeasConfig signaling, the scheme requires no protocol additions or UE-side modifications, making it directly deployable on existing 3GPP Release 17 infrastructure as a gNB-side software update. Full article

Predicting the mechanical properties of bolted connection nodes in prefabricated Concrete-Filled Steel Tube (CFST) column–steel beam assemblies remains challenging due to complex nonlinear relationships, high degrees of parameter coupling, and limited generalization capabilities of traditional empirical formulas. This study proposes a data-driven prediction model integrating an Improved Particle Swarm Optimization (IPSO) algorithm with a Deep Neural Network (DNN). Drawing upon 196 sets of experimental data on CFST column–steel beam nodes with Extended Hollo-Bolt (EHB) connections from the published literature, the model employs bolt diameter, steel tube wall thickness, concrete compressive strength, beam–column cross-sectional parameters, and connection configuration parameters as input variables, while designating ultimate moment capacity, initial stiffness, and joint ductility coefficient as prediction targets. A multi-layer DNN is constructed to capture the highly nonlinear mapping between structural parameters and mechanical responses. The IPSO algorithm, enhanced with adaptive inertia weight and Lévy flight perturbation, performs global optimization of the network weights and hyperparameters to improve convergence speed and prediction stability. Five-fold cross-validation is embedded within the IPSO fitness evaluation loop to guide hyperparameter selection, while dropout regularization and early stopping are applied during final training to mitigate overfitting; prediction performance is ultimately verified on an independent hold-out test set. Experimental results demonstrate that the proposed IPSO-DNN model outperforms a tuned shallow neural network (SNN), Support Vector Regression (SVR), and Random Forest (RF) models across the coefficient of determination ($R^2$), root mean square error (RMSE), and mean absolute error (MAE), effectively capturing the nonlinear mechanical characteristics of CFST nodes under complex loading conditions. Full article

Given the urgent need to regulate oxidative stress microenvironments in chronic wound healing, hydrogel dressings that simultaneously integrate antioxidant, antibacterial, mechanically adaptive, and biocompatible properties are highly desirable. In this study, a natural polymer-based composite hydrogel dressing loaded with zinc-quercetin nanozyme (Zn-Q) was designed. The gel skeleton was constructed via a dual network of photocrosslinked methacrylated silk fibroin (SilMA) and mercaptolated hyaluronic acid (HA-SH) via thiol-ene click chemistry, with the catalase (CAT)-like Zn-Q nanozyme encapsulated in situ within the network, thereby achieving synergy between chemical crosslinking and dynamic metal-polyphenol coordination. Systematic characterization revealed that Zn-Q nanozyme adopted a stable octahedral coordination configuration, and its continuous porous structure exposed abundant catalytically active sites. The composite hydrogels exhibited a highly interconnected, three-dimensional (3D) porous morphology, with swelling ratios that increased significantly with Zn-Q nanozyme content (up to around 1082%). Rheological and mechanical tests demonstrated that although incorporating the nanozyme reduced the storage modulus, the reversible physical crosslinks formed via hydrogen bonding and coordination interactions endowed the material with excellent tensile toughness and energy-dissipation capacity, exhibiting typical Mullins softening behavior. Functional evaluation showed that Zn-Q nanozyme conferred superior free radical scavenging capability to the hydrogels and exerted dose-dependent inhibition against both Staphylococcus aureus and Escherichia coli. Furthermore, the hydrogels exhibited favorable adhesion to various wet organs and heterogeneous material surfaces, with hemolysis rates below 5% and cell viability exceeding 100% after 3 days of culturing with fibroblasts, confirming their excellent hemocompatibility and cytocompatibility. This study provides an experimental basis for developing a new type of wound repair materials that integrate antioxidant, anti-infective, and mechanically adaptive properties, holding significant application potential in oxidative stress-related tissue repair fields. Full article

Peripheral neurons have an intrinsic capacity for repair, albeit still challenging. How the nervous system responds to the cellular stress imposed by nerve injury and adjunct therapies impacts axon regeneration and functional outcomes. Here, we summarize some of the key primarily axonal and neuronal adaptive stress responses and mechanisms that underlie the ability of peripheral neurons to regenerate an axon. This includes activation of the unfolded protein response and endoplasmic reticulum membrane-associated molecules, namely Luman/CREB3, a transmembrane basic leucine zipper transcription factor that regulates the encoding of beneficial adaptive stress responses that drive the ability of an injured sensory neuron to regenerate their axon. We also highlight an emerging novel non-invasive strategy, therapeutic acute intermittent hypoxia, that imposes a level of beneficial adaptive stress that can alter gene programs induced by the injury to enhance regeneration in a manner akin to the more invasive yet highly effective electrical nerve stimulation. The ability to manipulate and significantly elevate the intrinsic adaptive stress/repair responses of injured peripheral neurons holds therapeutic promise, with accumulating evidence supporting its clinical use. Full article

Production system may affect meat quality and muscle metabolic characteristics in Tibetan sheep. In this study, the biceps femoris muscles of twelve 3-year-old Tibetan sheep with similar body weights were used as experimental materials during a 6-month experimental period. The housed group (n = 6) was defined as the control group (C group), whereas the grazing group (n = 6) was defined as the L group. Meat quality measurement, nutritional composition analysis, untargeted lipidomics, and amino acid metabolomics (AAM) were integrated to investigate the effects of contrasting grazing and house-feeding production systems on meat quality and metabolic characteristics in Tibetan sheep. The results showed that cooking loss and drip loss were significantly decreased, whereas water-holding capacity (WHC) was significantly increased in the L group. However, shear force was also increased, indicating that grazing and house-feeding systems were associated with differences in muscle WHC and shear force. The L group exhibited significant alterations in lipid composition and increased concentrations of several n-3 polyunsaturated fatty acids and increased levels of omega-3 polyunsaturated fatty acids (n-3 PUFAs), including α-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), suggesting that grazing and house-feeding systems were associated with differences in the lipid nutritional profile of muscle. Lipidomic analysis showed that the differential lipids were mainly enriched in triacylglycerols (TGs), phosphatidylethanolamines (PEs), and phosphatidylcholines (PCs), and several PUFA-containing TGs and membrane lipid molecules were closely associated with meat quality traits. AAM analysis showed that branched-chain amino acids (BCAAs), including L-leucine and L-valine, as well as N,N-dimethylglycine, were upregulated in the L group, whereas kynurenine and 1-methyl-L-histidine were downregulated. These findings suggest that BCAA metabolism and tryptophan–kynurenine metabolism were associated with metabolic differences observed between production systems in muscle metabolic adaptation. However, amino acid metabolomics analysis revealed that no amino acid metabolites remained significant after FDR correction, and thus the observed pathway-level changes (e.g., BCAA metabolism and tryptophan–kynurenine pathway) should be interpreted as nominal and exploratory findings. Overall, the results indicate that feeding systems were associated with alterations in the lipid and amino acid metabolic profiles of the biceps femoris muscle in Tibetan sheep, which were further associated with differences in muscle WHC, shear force, lipid nutritional composition, and the profile of flavor precursors. This study provides a theoretical basis for optimizing plateau meat sheep production systems and developing high-quality Tibetan sheep meat products. Full article

Aero-engine safety and prognostics and health management (PHM) rely on robust vibration-based fault diagnosis. However, many deep learning studies on rotating machinery are evaluated under random train–test splits that mix hardware instances and may obscure the domain shift faced in deployment. This paper presents a protocol-driven end-to-end baseline for multi-sensor aero-engine-relevant vibration diagnosis on the HIT inter-shaft bearing benchmark. Six synchronous vibration channels are segmented into fixed-length windows, standardized using source-domain statistics, and classified by a compact 1D CNN backbone with and without squeeze-and-excitation (SE) channel attention. A deeper ResNet1D baseline is further introduced to examine whether increasing backbone capacity improves cross-bearing generalization under the same source-only training protocol. We compare random segment-level splits with bearing-level cross-splits that hold out entire bearings as unseen target domains, and we report deployment-oriented indicators including balanced accuracy, false-alarm rate (FAR), and miss rate over five random seeds. Under random splits, the compact CNN baseline reaches near-ceiling test accuracy, confirming that the benchmark is readily separable under in-domain interpolation. In contrast, cross-bearing evaluation reveals severe degradation: in the representative split, the baseline CNN accuracy collapses to approximately 15% with near-zero normal-class recall, while ResNet1D improves fault sensitivity but still retains a high FAR above 88%. Additional cross-bearing permutations further show that this degradation is not attributable to a single unfavorable source–target split. These findings indicate that, under the tested source-only backbones and protocols, distribution mismatch is a dominant bottleneck for deployment-ready cross-bearing diagnosis. The results establish a reproducible baseline for protocol-driven evaluation in aero-engine PHM and motivate future work on domain adaptation, domain generalization, calibration, and sequential decision logic. Full article

Real-time, free-breathing, ungated cardiac magnetic resonance imaging (CMR) is a clinically valuable alternative to conventional breath-held, ECG-gated cine imaging for patients who cannot sustain breath holds or produce reliable cardiac rhythms, including pediatric, arrhythmic, and respiratory-compromised populations. Achieving diagnostic image quality in this setting requires aggressive k-space undersampling and sophisticated reconstruction. Because no fully sampled reference exists for such acquisitions, supervised deep learning is not directly applicable, motivating unsupervised, subject-specific methods. Existing approaches typically rely on low-dimensional continuous latent spaces, which can limit their capacity to represent concurrent cardiac and respiratory motions as distinct states and may suffer from posterior collapse. We introduce VQ-SToRM (Vector-Quantized Smoothness Regularization on Manifolds), an unsupervised framework that adapts the Vector-Quantized Variational Autoencoder to real-time CMR by replacing the continuous latent manifold of prior existing methods with a learned discrete codebook. The encoder, decoder, and codebook are trained jointly on the undersampled non-Cartesian k-t space data of a single subject. On free-breathing, ungated spiral acquisitions from healthy volunteers, VQ-SToRM accurately resolved cardiac and respiratory motion across all phases of the cardiac cycle. A systematic ablation study identified a compact configuration—a codebook of only five embeddings of dimension ten—as optimal, indicating that a small discrete codebook is sufficient to represent the dominant cardiac and respiratory motion content. Compared with V-SToRM and Time-DIP, VQ-SToRM achieved smoother frame-to-frame transitions and comparable or superior signal-to-noise and contrast-to-noise ratios with lower variance across frames and datasets, offering a promising path toward clinically practical real-time CMR. Full article

Climate change is intensifying flood risks globally, yet preparedness behaviors vary dramatically across governance contexts. While past disaster research suggests that institutional trust enables individual preparedness, this relationship remains unexplored in high-capacity governance systems where citizens hold exceptionally strong confidence in government response. We examined this dynamic in the United Arab Emirates, where several surveys have found extremely high levels of public confidence in the local government institutions. In our survey of 900 respondents in the emirates of Dubai and Sharjah we also found that 97% of the respondents had confidence in local government institutions. However, interestingly we also found that while 77% of residents reported past experience with floods, household flood preparedness was markedly low. Using covariance-based structural equation modeling, we tested whether government trust mediates relationships between flood experience, risk perception, and household preparedness. The results revealed that government trust exhibited a strong negative association with flood preparedness, suggesting that institutional confidence may suppress rather than enable household protective action. Notably, flood experience was associated with reduced government trust, likely reflecting the impact of disappointment with service restoration times that exceeded individual expectations. This erosion of trust created positive mediation, indicating that flood experience was associated with increased preparedness. Conversely, higher risk perception was associated with increased trust, which was associated with reduced preparedness through negative mediation. Direct relationships between flood experience and preparedness were statistically non-significant, indicating complete mediation through the trust pathway. Socioeconomic status was positively associated with flood preparedness, with wealthier residents displaying higher protective behaviors. While these findings seem to challenge conventional disaster preparedness theory, the results align with the moral hazard and dependency arguments. Our results show that state-led disaster management in high-capacity governance systems may inadvertently create dependency that increases systemic vulnerability crowding out endogenous adaptive behavior. Building resilience in such contexts requires reframing institutional trust to emphasize shared responsibility rather than externalized protection. Full article

This study evaluated the effects of replacing 25% of wheat straw with dried carob (Ceratonia siliqua) leaves and twigs, either untreated or treated with 5% sodium hydroxide (NaOH), on growth performance, nutrient digestibility, carcass traits, meat quality, blood metabolites, and rumen microbial populations in Assaf lambs. Twenty-four male lambs (2.5 months old; 29 ± 0.5 kg) were randomly assigned to three dietary treatments (n = 8): a control diet containing wheat straw as the sole roughage source, supplemented with a concentrate feed, a diet with 25% untreated carob leaves and twigs (UCL), and a diet with 25% NaOH-treated carob leaves and twigs (TCL). Following a 14-day adaptation period, lambs were fed the corresponding experimental diet for 14 weeks. Carob inclusion improved growth performance, with UCL lambs showing the highest average daily gain (214 g/d) compared with TCL (201 g/d) and control (160 g/d), resulting in improved feed conversion ratio (9.02 vs. 5.68 and 5.63, respectively) (p < 0.001). Blood urea nitrogen was reduced (p < 0.001) in UCL lambs (26.8 vs. 38.5 mg/dL in control), suggesting improved nitrogen retention. Digestibility responses differed between treatments (p < 0.001), as TCL increased dry matter digestibility to 72.6% compared with 65.4% (UCL) and 63.6% (control), indicating enhanced nutrient utilization following NaOH treatment. Both UCL and TCL increased (p < 0.001) carcass weights (up to 24.7 vs. 21.0 kg in control), while TCL achieved the highest dressing percentage (46.6% vs. 43.4%). Meat quality traits were generally unaffected in terms of color (lightness, redness, and yellowness) and water-holding capacity; however, shear force decreased from 33.6 N (control) to 30.0 N (TCL), indicating improved tenderness. Carob inclusion modified meat composition by increasing (p < 0.001) lipid content (12.0–12.2 vs. 9.6%) and improving fatty acid profile, with reduced saturated fatty acids (53.4–56.5 vs. 61.4%) and increased α-linolenic acid (2.04 vs. 1.58%), leading to a lower n-6/n-3 ratio (5.54–5.61 vs. 6.45). Rumen fermentation was also affected (p < 0.001), as carob diets increased total bacterial populations and reduced protozoal counts, suggesting shifts toward more efficient microbial activity. In conclusion, replacing 25% of wheat straw with carob leaves improved growth performance and feed efficiency, with untreated carob primarily enhancing nitrogen utilization and treated carob improving fiber digestibility and carcass yield. These findings support the use of carob by-products as a viable alternative feed resource, although responses depend on processing method and targeted production outcomes. Full article

Background/Objectives: Large language models (LLMs) and generative AI are rapidly being integrated into healthy ageing initiatives for tasks ranging from companionship and cognitive support to personalised health advice and reduction in social isolation among older adults. Current ethical discussions predominantly address bias, privacy, and accuracy, leaving unresolved three critical governance questions: How do LLM sentiments towards transformative technologies diverge from human values in ageing contexts? What epistemic status do LLM outputs hold when applied to geriatric care? When is trust in those outputs justified for older adults? And who bears responsibility when AI-informed decisions affect functional ability or well-being? Methods: The framework was developed through normative conceptual analysis, synthesizing philosophical principles of medical knowledge and trust, ethical theories of responsibility, empirical evidence on LLM sentiment divergence, digital ageism, and applications of AI in geriatric care (structured searches in PubMed, PhilPapers, and relevant databases, January 2020–March 2026). Results: The integrated framework produces (i) adaptation of SAIA for multidimensional evaluation of human–machine value convergence specific to healthy ageing values (functional ability, autonomy, dignity, equity); (ii) a four-tier classification of LLM outputs tailored to geriatric scenarios; (iii) conditions for warranted trust calibrated to age-related vulnerabilities such as cognitive decline and digital divide; and (iv) responsibility allocation via RACI models with testable hypotheses linking governance design to trust calibration and patient safety outcomes. Conclusions: Without explicit societal alignment and epistemic governance, generative AI risks reinforcing benevolent ageism, automation bias, and responsibility gaps in healthy ageing. The 2025–2027 period offers a decisive window to shape institutional norms that place functional capacity, human dignity, and value convergence at the centre of AI deployment in geriatric care. Full article

Turpan Black (TBL) and Altay (ALT) sheep are indigenous breeds adapted to extreme heat and severe cold in their respective native environments. However, the mechanisms underlying their divergent meat quality remain unclear. Using longissimus dorsi muscle from 15 TBL and 15 ALT sheep, we integrated phenotypic evaluation with non-targeted metabolomics and proteomics to elucidate the impact of environmental adaptation on ovine meat quality. Compared to the cold-adapted ALT sheep, the heat-tolerant TBL sheep exhibited lower post-mortem pH, reduced cooking loss, smaller muscle fiber cross-sectional area, and elevated selenium and magnesium levels. Multi-omics identified 99 differentially expressed proteins and 364 differentially expressed metabolites. Core divergence was enriched in lipid and amino acid metabolism and stress response networks, particularly the Apelin signaling, glycerophospholipid metabolism, and ferroptosis pathways. Lipid remodeling driven by glycerophospholipid metabolism emerged as a critical bridge linking adaptation to meat quality. Notably, glycero-3-phosphocholine, regulated by GPCPD1 and related enzymes, maintained cell membrane homeostasis and osmotic pressure, thereby enhancing water-holding capacity and tenderness. These findings reveal the multi-omics basis of climate-driven divergence in ovine meat quality, offering theoretical support for breeding stress-resilient, high-quality indigenous sheep breeds in extreme environments. Full article