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Ustica is a Sicilian island for which most of the information available on the informal group of sea slugs comes from old and non-specific studies. Consequently, the aim of this study is to provide an updated list of the sea slugs of the Ustica Island Marine Protected Area (MPA). This study, carried out using the “photographic capture technique” in two surveys (early autumn and late spring), led to the finding of 32 species and 14 families of sea slugs. Overall, considering both the literature and current data, a total of 77 species and 33 families of sea slugs have been documented on this island. While these numbers might indicate high species richness compared to other previously investigated Sicilian islands, they might also reflect the fact that Ustica Island is the only one for which numerous malacological studies have been performed. Full article

The northern part of the Great Xing’an Range in China hosts a prominent Au mineralization belt, where Mesozoic clastic rock-hosted Au deposits represent the mineralization type. A study of the Baoxinggou Au deposit in this region might provide new perspectives on the mineralization mechanisms of these Mesozoic clastic-rock-hosted Au deposits. This study investigated the age of mineralization, origins and evolution of the ore-forming fluids, and sources of the ore-forming materials in this deposit. Rubidium–Sr dating of sulfides yielded a mineralization age of 119 ± 2 Ma. Fluid inclusion analyses revealed that the ore precipitated from fluids with temperatures of 105–415 °C and salinities of 4.3–8.8 wt.% NaCl equivalent. Hydrogen and O isotopic data show that the ore-forming fluids were of magmatic origin and, during mineralization, the proportion of meteoric waters increased gradually and eventually dominated the late mineralization stage. Fluid mixing was the primary ore-forming mechanism. Sulfur isotopic data for pyrite and chalcopyrite (δ³⁴S_V–CDT = −4.35‰ to −0.91‰) and Pb isotopic ratios (²⁰⁶Pb/²⁰⁴Pb = 18.429–18.477; ²⁰⁷Pb/²⁰⁴Pb = 15.581–15.591) indicate the ore-forming materials were magmatic in origin, with a similar source as an Early Cretaceous diorite and mixed crust–mantle materials. The results indicate the Baoxinggou Au deposit is a magmatic–hydrothermal deposit. Full article

The performance of remote sensing-based mineral alteration extraction is significantly restricted in the vegetation area. Spectral unmixing is one of the effective methods to address the vegetation problem during mineral alteration extraction. However, the spectral curves of different tree species vary a lot; if multiple tree species are regarded as a whole during the spectral unmixing stage, the proportions of vegetation would be estimated with more errors. The purpose of this study was to verify the effects of dominant tree species classification on spectral unmixing and reconstruction, and to apply the proposed method to the mineral alteration extraction practice. To accomplish this, the Shabaosi gold deposit region in Mohe City, China, with an area of 650 km², was selected as the study area. Firstly, reference spectral curves, GaoFen-1/6 (GF-1/6) satellite imageries, ZiYuan-1F (ZY-1F) satellite imageries, Sentinel-1B satellite synthetic aperture radar (SAR) data, the ALOS digital elevation model (DEM), and sub-compartment dominant tree species data were collected; subsequently, simulated mixed-pixel reflectance images of ZY-1F, reflectance images of GF-1/6, ZY-1F, backscattering data of Sentinel-1B, slope, aspect, and 5484 tree species samples were derived from the collected data. Secondly, to verify the effect of dominant tree species classification on mineral alteration extraction, the reference spectra of pine, oak, goethite, and kaolinite were used to construct a simulated ZY-1F mixed-pixel image, and spectral unmixing and reconstruction experiments were conducted. Thirdly, fourteen independent variables were selected from the derived data, five dominant tree species classification models were trained and tested using tree species samples via the ResNet50 algorithm, and the pine- and birch-dominated parts were segmented from the ZY-1F images. Fourthly, minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer auto-clustering, and spectral angle mapper (SAM) methods were separately applied to the pine- and birch-dominated parts of ZY-1F images to extract and identify endmembers; subsequently, the fully constrained least squares (FCLS) and linear spectral unmixing (LSU) methods were separately applied to the pine- and birch-dominated parts to estimate endmember proportions and generate spectrally reconstructed ZY-1F images. Fifthly, the pine- and birch-dominated parts of spectrally reconstructed ZY-1F images were mosaiced, and the SAM was utilized to extract mineral alteration in the study area. The result showed that in the spectral unmixing and reconstruction experiment, the spectral reconstruction error declined from 0.0594 (simulated ZY-1F image without segmentation) to 0.0292 and 0.0388 (simulated ZY-1F image that was segmented by pine- and oak-dominated parts), suggesting that dominant tree species classification could improve the accuracy of spectral unmixing and reconstruction and help obtain a more reliable mineral alteration extraction result. In the study area, the tested overall accuracies (OA) and Kappa coefficients of the five dominant tree species classification models were 0.75 ± 0.03 and 0.50 ± 0.05, respectively, suggesting that conducting dominant tree species classification was feasible in dense vegetation areas and could facilitate mineral alteration extraction. After segmenting the ZY-1F image by pine- and birch-dominated parts and spectral reconstruction, eight main types of alteration, including kaolinite, vesuvianite, montmorillonite, rutile, limonite, mica, sphalerite, and quartz, were identified, and nine mineral alteration areas (MA) were delineated accordingly. Full article

Introduction: Cardiovascular disease (CVD) is a leading cause of non-cancer death among cancer survivors, attributable to cardiotoxic therapies and cardiovascular risk factors. General population risk prediction tools, including ASCVD (Atherosclerotic cardiovascular disease), Framingham’s Score, and PREVENT (Predicting Risk of Cardiovascular Disease EVENTS), lack cancer-specific variables. We evaluated whether these models, even after statistical optimization, could predict cardiovascular mortality in cancer survivors. Methods: Using the National Health and Nutrition Examination Survey (NHANES) 2001–2018, linked with National Death Index (NDI) mortality data, we conducted a retrospective analysis of 634 and 429 cancer survivors, respectively, across model-specific cohorts free of baseline cardiovascular disease. Discrimination was assessed for ASCVD, Framingham Score, and PREVENT using standardized thresholds of 7.5% and 20%, as well as Youden-optimized cutoffs. Area under the curve (AUC) comparisons were performed using the DeLong non-parametric method. Results: Standard thresholds showed suboptimal discrimination across all models (AUCs: ASCVD 0.56, Framingham 0.53, PREVENT 0.64). In contrast, Youden-optimized AUCs (ASCVD: 0.68; PREVENT: 0.71; all p < 0.001, DeLong test). Optimization increased the “low-risk” group’s mortality rate from 2.8% to 4.1% (RR = 1.47), suggesting improved statistical fit came at the cost of overestimating the risk. Optimized thresholds outperformed conventional cutoffs, underscoring the necessity for recalibrated, cohort-specific risk stratification in cancer survivors. Conclusions: Standard risk scores have inadequate discrimination for cardiovascular mortality prediction in cancer survivors. Threshold recalibration improves statistical metrics but does not resolve the structural failure of these models to account for cardiotoxic exposure. Development of cardio-oncology-specific risk models incorporating oncologic exposures is therefore warranted. Full article

Dielectric and magnetic spherical hollow shells are employed in many applications as standard building units. These structures are commonly subjected to size reduction to obtain a high surface area/volume ratio, a property that is in favor of specific applications. However, the size reduction enhances the importance of physical mechanisms that originate from surfaces, such as the depolarization effect. Here we tackle the problem of dielectric and magnetic spherical hollow shells, consisting of a linear, homogeneous and isotropic parent material, subjected to an external potential, $U_{ext}\left(\mathit{r}\right)$, of any spatial form (either dc (static) or ac of low-frequency (quasistatic limit)). By applying the method-of-linear-recursive-solution (MLRS) to the Laplace equation, we calculate analytically the internal, $U_{int}\left(\mathit{r}\right)$, and total, $U_{tot}\left(\mathit{r}\right)$, potentials in respect to the external one, $U_{ext}\left(\mathit{r}\right)$. From $U_{int}\left(\mathit{r}\right)$ and $U_{tot}\left(\mathit{r}\right)$ we calculate all relevant scalar and vector physical entities of interest. The MLRS unveils straightforwardly the existence of two distinct depolarization factors, $N_l=l/(2l+1)$ and $N_{l+1}=(l+1)/(2l+1)$, both depending on the degree, $l$, however not on the order, $\mathrm{m}$, of the mode of the external potential, $U_{ext}^{(l,m)}\left(\mathit{r}\right)$. These depolarization factors, $N_l$ and $N_{l+1}$, originate from the outer, $r=\mathrm{b}$, and inner, $r=\mathrm{a}$, surfaces and are accompanied by two extrinsic susceptibilities, ${\chi }_{e,l}^{ext}={\chi }_e^{}/(1+N_l{\chi }_e^{})$ and ${\chi }_{e,l+1}^{ext}={\chi }_e^{}/(1+N_{l+1}{\chi }_e^{})$, respectively. Importantly, $N_l+N_{l+1}=1$, irrespective of the degree, $l$, as it should. The properties of spherical hollow shells are investigated through analytical modeling and detailed simulations, with emphasis on application-relevant scenarios including resonance phenomena in scattering, quantitative materials characterization, and shielding/distortion. The generic MLRS strategy provides a flexible and reliable route for analyzing depolarization processes in other dielectric and magnetic building-unit geometries encountered in practice. Full article

This study investigates how a network’s structural and relational capabilities condition the influence of social CRM capabilities on innovation novelty, highlighting a deeper network paradox. Drawing on survey evidence from social enterprises, the analyses indicate that social CRM capabilities meaningfully contribute to the generation of novel innovations. Yet the two network capabilities move in opposite directions: structural capability amplifies the innovative gains derived from social CRM, whereas relational capability tends to dilute them. These divergent effects reflect the simultaneous pull of structural-hole and network-closure mechanisms within the same organizational setting. The results suggest that organizations aiming to translate social CRM investments into innovation may benefit more from structurally expansive network positions than from tightly embedded relational ties. Future work could employ longitudinal and cross-institutional designs to strengthen causal insight and broaden the study’s applicability. Full article

The increasing adoption of servitization has led many manufacturing companies to operate simultaneously in manufacturing and service activities, creating dual-sector business models characterized by heightened organizational complexity. Although prior research acknowledges that both internal capabilities and contextual conditions shape organizational outcomes, fewer studies have examined these variables within the same empirical model in companies operating under both manufacturing and service logics. Drawing on the resource-based view and contingency theory, this study examines the effects of organizational culture, organizational commitment, knowledge management, environmental uncertainty, and employee retention on organizational performance in dual-sector companies, while also assessing whether these relationships vary by company size. Survey data were collected from 433 employees working in dual-sector companies and were analyzed using confirmatory factor analysis, covariance-based structural equation modeling, and supplementary hierarchical regression analysis. The findings indicate that environmental uncertainty and employee retention did not receive empirical support as independent direct predictors in the structural model. Organizational commitment, knowledge management, and two dimensions of organizational culture—consistency and adaptability—are significant positive predictors of perceived organizational performance. The moderation analysis does not provide strong evidence that company size changes these relationships, although the interaction suggests that environmental uncertainty may be more consequential in large firms. This study contributes to research on servitization by showing that, in dual-sector companies, performance is most strongly associated with internal capabilities that support coordination, shared meaning, and knowledge integration across manufacturing and service activities. For managers, the results highlight the importance of strengthening commitment, adaptive coordination, and cross-domain knowledge processes rather than relying on retention efforts alone. Full article

Methanol is the simplest C1 oxygenated compound possessing the highest hydrogen-to-carbon ratio and can therefore be used as an effective hydrogen carrier. Furthermore, it can be easily transported by land and sea because it is liquid at room temperature and atmospheric pressure. Methanol can be converted into hydrogen via methanol steam reforming (MSR), aqueous-phase reforming of methanol (APRM), or aqueous methanol dehydrogenation (AMDH). In this review, various catalysts for MSR, APRM, and AMDH are summarized. Highly active and stable catalysts that can operate under low steam-to-methanol ratios are needed to increase the economics of the MSR process. Compared with the MSR process, the APRM process is rather simple because the water–gas shift reaction can occur simultaneously; however, more constraints exist in the selection of active metals and supports to ensure high activity and stability under APRM conditions. The inherently low reaction rate compared to MSR and the structural vulnerability of the catalyst under severe hydrothermal conditions are obstacles that the APRM catalysts must overcome. The low intrinsic catalytic activity and the high cost of homogeneous catalysts represent fundamental limitations inherent to AMDH catalysts. Based on a literature survey of MSR, APRM, and AMDH catalysts, some future research directions are also discussed. Full article

Central nervous system (CNS) disorders represent a growing healthcare burden, and various drugs are developed for their treatment. However, the blood–brain barrier (BBB) prevents over 98% of therapeutics from reaching brain tissue. Intranasal delivery provides a promising alternative by exploiting olfactory and trigeminal nerve pathways to circumvent the BBB. This review surveys recent advances in nose-to-brain delivery technologies, from carrier design to evaluation methods. Polymeric and lipid-based nanocarriers show enhanced mucosal penetration and prolonged residence time, and microneedle platforms further enable controlled drug release with minimal discomfort. To evaluate these delivery strategies, sensor-integrated organ-on-chip models provide more physiologically relevant testing than static cultures. Although persistent challenges such as rapid mucociliary clearance and formulation stability remain, combining nanotechnology with microfluidic devices and computational modeling shows potential for developing patient-specific therapeutics. Full article

The circular economy (CE) can help businesses use resources more efficiently, but empirical evidence on CE adoption among non-European SMEs remains limited. This study examines CE practices, benefits, challenges, and future intentions in 220 Saudi Arabian SMIs. A structured survey collected data on four CE practice domains (resource efficiency, waste management, eco-design, and reverse logistics), four benefit dimensions (economic, environmental, operational, and reputational), four challenge dimensions (financial, organizational, technical, and regulatory), and six future intention items. CE adoption was moderate (M = 3.29 on a five-point scale) and balanced across all four practice domains, with resource efficiency scoring highest (M = 3.32). Benefit scores averaged 3.46, far outpacing challenges (M = 2.78). This benefit surplus of 0.68 points (on a five-point scale) indicates that Saudi SMIs perceive CE as worthwhile and view its barriers as manageable rather than prohibitive. Together, perceived benefits and perceived challenges explained 54.3% of the variance in CE adoption (R² = 0.543) in multiple regression analysis. Reducing perceived challenges may be a more effective lever for promoting CE adoption than amplifying perceived benefits, as challenges exerted a larger absolute standardised effect (β = −0.50) than perceived benefits (β = 0.39). Once perceptions were controlled, perceived benefits and challenges significantly predicted future CE intentions, but current CE practices did not. According to the Theory of Planned Behavior’s attitudinal pathway, firms without CE experience can develop strong forward-looking intentions if the business case is convincing and barriers are perceived as manageable. Technical and organizational barriers outweighed financial ones, indicating the need for capacity-building interventions over supplementary financing, unlike European findings. About 79% of respondents were neutral or positive about government-supported CE expansion. CE adoption did not differ significantly by firm size, geographic location, or ownership structure, suggesting that Vision 2030’s sustainability messaging has established a broad baseline of CE awareness across Saudi SMIs. Full article

The recycling of pesticide packaging waste is crucial for the sustainable development of agriculture and the advancement of ecological civilization. However, the current recycling management still faces challenges. This study adopts a dynamic analytical framework of “ex-ante behavioral cognition and post-event outcome perception” to investigate the impact of digitalization on farmers’ recycling behavior of pesticide packaging waste. The analysis draws on data from the 2020 China Rural Revitalization Survey and examines two dimensions of digitalization: digital technology access and digital technology usage. The findings indicate that integrating digital technologies into farming practices significantly increases the likelihood of farmers participating in pesticide packaging waste recycling programs. These results remain robust after conducting robustness checks and addressing potential endogeneity issues. A heterogeneity analysis reveals that the promotional effect of digitalization varies significantly across different categories of rural elite status, cooperative membership, education level, pesticide spraying methods, and income structure. Mechanism testing further indicates that hazard cognition regarding pesticide packaging serves as a mediating factor in the impact of both digital technology access and usage on farmers’ recycling behavior. In contrast, farmers’ satisfaction with their living environment mediates only the effect of digital technology usage on recycling behavior. Overall, these findings provide both theoretical and empirical support for the hypothesis that digitalization can facilitate the recycling of pesticide packaging waste and enhance the ecological effectiveness of agricultural policy governance. Full article

Promoting farmers’ adoption of low-carbon technologies constitutes a vital pathway for achieving agricultural sustainability and a key measure for implementing the national dual-carbon strategy. Drawing upon field survey data from 602 wheat farmers in Heze City, Shandong Province, this study employs an ordered probit model to empirically examine the influence mechanism of data capital on farmers’ low-carbon technology adoption behaviour. The results show that (1) data capital significantly promotes farmers’ adoption of low-carbon technologies; (2) ecological cognition plays a significant mediating role, while economic incentives and human capital serve as positive moderators; (3) heterogeneity analysis indicates that the promoting effect of data capital is more pronounced among large-scale farmers. Based on these findings, recommendations are proposed to consolidate rural digital infrastructure, establish agricultural data service platforms, and enhance farmers’ ecological cognition, thereby fully unleashing the potential of data capital in driving agricultural sustainability. Full article

Digitalizing essential services opens up a new risk of exposing critical infrastructure to botnet infections. In a grid topology network, the neighbor-to-neighbor paths can be used by the malicious botnet to spread the infection. Previous white-hat worm launchers used heuristics and supervised learning to exterminate botnets, which demand specific conditions or a suitable dataset to be effective. Although reinforcement learning addressed these issues, it requires a longer time to train. This article proposes a framework to shorten training and improve the effectiveness of reinforcement learning. The framework applies four key principles: (1) surveying the network status with multi-tensor input, (2) removing irrelevant actions via a novel Chebyshev-based masking strategy, (3) reinforcing key actions with rewards, and (4) optimizing rewards for winning. Four reinforcement learning algorithms are implemented to evaluate the framework, which are vanilla policy gradient, deep Q-network, proximal policy optimization, and MuZero in a stylized grid topology network simulation. An ablation study indicates that the masking used in identify accounts for the majority of the improvement, whereas multi-channel in Survey alone can reduce performance without complementary masking, rewards, and optimization. With the mean winning rate improved by 49.129% and mean win efficiency improved by 118.8031% against our previous work, the framework effectiveness is confirmed in stylized simulations. Full article

Kolmogorov–Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications. Full article