Search Results (928)

To boost the local microalgae sector, Switzerland needs to better understand the current state of the industry, which is not fully represented in the existing literature. Only by identifying the strengths and weaknesses of the Swiss microalgae industry, will the country be able to develop strategies toward a strong and sustainable sector in the future. This work provides the first structured assessment of Switzerland’s fragmented and poorly documented microalgae sector through desktop research and an online survey of the country’s microalgae stakeholders. First, research articles with Swiss authors and patents with Swiss applicants were mapped. Then, a survey consisting of 8 questions was designed to gather information about the location, purpose, employees, production capacity, activities, and installations of 42 organizations with a research and/or commercial focus. The growing number of organizations working with microalgae in Switzerland is dominated by small companies (<50 employees) that provide services rather than biomass or bioproducts. Microalgae biomass production is about 2 tons DW per year and is also dominated by small-scale producers (<100 kg DW per year). One third of Swiss companies that sell microalgae-based products produce their own biomass abroad or purchase from abroad. Our findings highlight the growth potential of the Swiss microalgae sector. This systematic summary of research interests, technological innovations, and current market parameters is the first step toward future improvements in the sector. Full article

Laurinterol, a halogenated sesquiterpene produced by red algae of the genus Laurencia, is one of the most characteristic compounds within the laurane and cyclolaurane families. This review compiles and examines current knowledge on laurinterol, integrating evidence on its occurrence, biosynthesis, biological activities, and structural features. Within a functional and ecological framework, laurinterol is proposed as an archetypal Smart Secondary Metabolite (SSM), a concept that reflects the convergence of structural singularity, high abundance within its biosynthetic context, broad biological activity, multi-target interactions, and ecological or chemotaxonomic relevance. This perspective highlights its role in adaptive processes within producing organisms and associated trophic networks. Laurinterol exhibits a broad bioactivity profile, including antimicrobial, antimycobacterial, cytotoxic, antiparasitic, enzyme inhibitory, antifouling, and insecticidal or repellent effects. Structure–activity relationship (SAR) studies remain limited and are mainly developed in specific models, particularly against Naegleria fowleri. The current intellectual property landscape related to laurinterol, including patent applications, granted patents, and technological development trends, is also examined. Overall, this review positions laurinterol as a structurally distinctive and functionally relevant marine metabolite within chemical ecology and marine natural products research. Full article

Firms can gain a competitive advantage through a strategic patent portfolio, wherein patents elucidate technological advancements and establish legal barriers that keep competitors out. However, patents do not provide a perpetual monopoly within the prevailing open innovation paradigm, which means that firms should keep up with innovation input and patent applications to preserve their market dominance. Fostering technological breakthroughs in the patent network thus becomes a critical issue. Anchored in the theoretical views of open innovation, this study conducts an empirical analysis of patent data to examine how patent network structural features influence the technologies’ breakthrough tendency in the field of autonomous driving (AD). The findings indicate that centrality metrics such as degree centrality, harmonic centrality, and betweenness centrality within AD patent networks exert significant influence on technological breakthrough tendency, and the patent family size plays a moderating role in these relationships. Moreover, this research advances theoretical insights for patent strategy formulation in emerging firms of AD, with broader implications for other technology-intensive sectors. Full article

Pipeline valves play a crucial role in oil and gas exploration, production, transportation, and storage, and a systematic understanding of patent technologies in this field can help identify innovation trends and formulate research and development (R&D) strategies. This study collected more than 5000 pipeline-valve-related patents worldwide from 2006 to 2025, including 2292 invention patents, and adopted a progressive patent analytics approach integrating statistical analysis, network analysis, text mining, and high-value invention patent analysis. The results show that innovation activity in this field has remained active over the past two decades, especially since 2016, when the number of patent publications exceeded 300 in almost every year. China, Russia, the United States, South Korea, and Canada are the major sources of patent activity, with Chinese enterprises and universities making important contributions in terms of patent quantity. However, the analysis of high-value invention patents indicates that representative patents from the United States, Canada, and Russia also have a strong influence. Core innovation directions cover multiple pipeline valve applications in oil and gas extraction, transportation, and storage, with valve control systems and mechanical structures constituting the dominant technologies. The ten identified technological themes and their evolution show that technological innovation in this field has gradually expanded from mechanical improvements in traditional valve bodies, sealing components, and pressure relief devices to diversified directions such as wellhead control, intelligentization, and low-carbon development. The analysis of high-value invention patents further confirms this trend, indicating that pipeline valve technology is being reshaped from a relatively mature mechanical technology field into an integrated technological system that combines mechanical reliability, intelligent control, and other dimensions. Full article

Background: Meta-analyses suggest an association between patent foramen ovale (PFO) and white-matter hyperintensities (WMH), but pooled effect sizes do not clarify applicability to routine transthoracic echocardiography (TTE) or provide patient-level risk estimates. Objective: The objective of this study was to evaluate the association between TTE-detected PFO and MRI-defined WMH in routine practice and to develop a practice-anchored framework (PAMAP) that translates literature-derived evidence into individualized risk. Methods: We performed a retrospective, single-center, propensity-matched analysis of 149 adults undergoing TTE and brain MRI (37 PFO-positive, 112 controls). The primary endpoint was WMH (Fazekas ≥ 1). PAMAP synthesized 12 studies; 4 eligible studies were pooled using random-effects meta-analysis to derive a locked shunt coefficient (OR 3.65). The locked model used age and shunt (H); embolic context (E), and atrial stress (A) were neutral until refit. Transportability was assessed at the cohort level (expected vs. observed prevalence) and patient level (calibration, discrimination), followed by a minimal prespecified refit. Results: WMH was more frequent in PFO-positive versus control participants (54% vs. 32%). Literature-based expected prevalence approximated observed prevalence, supporting transportability. The locked model showed acceptable performance (calibration intercept 0.106; slope 0.912; Brier 0.188; AUC 0.756). A parsimonious refit improved performance (Brier 0.176; AUC 0.783), with the shunt term remaining significant (OR 2.45, 95% CI 1.23–4.88). Conclusions: PAMAP translates meta-analytic associations into a transportable patient-level risk framework. In routine TTE-defined PFO populations, the WMH association is preserved, suggesting that incidental PFO may mark early subclinical cerebral injury and enabling calibrated, individualized risk assessment. Full article

Digital transformation is a key driver of green innovation in agribusiness. While the positive impact of digital transformation on firm innovation has been well documented, its multidimensional nature and heterogeneous associations on agribusiness green innovation remain underexplored. This study deconstructs digital transformation into five business dimensions and two structural features, using a sample of 155 Chinese A-share listed agricultural companies from 2011 to 2021. By combining an explainable machine learning framework integrating Bayesian-optimized XGBoost and SHAP, we identify individual and interaction predictive effects of each feature on green innovation measured by green patent applications. The results reveal correlational evidence that governance digitalization is the dominant predictive driver of agricultural green innovation, followed by institutional digitalization. Merely expanding the scope of digital transformation delivers no substantial improvements in green-patent-based innovation outputs. Different digital dimensions present notable heterogeneous nonlinear correlations with distinct threshold characteristics. We further find significant synergistic interaction linkages across digital dimensions, where coordinated multi-dimensional digital development is critical to fully unlocking the green innovation potential of digital transformation. These findings provide insights for agribusiness managers and policymakers to prioritize digital investment and facilitate low-carbon transition. Full article

Background and Objectives: Lung ultrasound (LUS) has fundamentally transformed neonatal respiratory diagnostics, offering a radiation-free, bedside-applicable modality capable of guiding surfactant therapy, characterizing pulmonary pathology, and monitoring treatment response in real time. While surfactant replacement therapy is firmly established for neonatal respiratory distress syndrome (RDS), its role in acute complications—specifically pulmonary hemorrhage (PH) and pneumothorax (PTX)—remains uncertain and heterogeneous in clinical practice. This review examines how LUS-based phenotyping can improve the diagnostic precision and therapeutic sequencing of surfactant administration in these high-risk scenarios, and how comorbidities such as hemodynamically significant patent ductus arteriosus, persistent pulmonary hypertension, sepsis, and coagulopathy modulate clinical outcomes. Materials and Methods: We conducted a structured narrative review of studies published from 2020 onward, sourced from PubMed, Web of Science, Semantic Scholar, and Mendeley, using PRISMA-inspired selection principles. The search combined terms including “lung ultrasound,” “neonatal POCUS,” “surfactant therapy,” “pulmonary hemorrhage,” “neonatal pneumothorax,” and “LUS score.” Studies focusing on neonatal populations, clinical LUS applications, and surfactant use in PH and PTX were prioritized. Results: Quantitative LUS scoring systems (range 0–18) predict surfactant need and re-dosing with AUC values of 0.85–0.87, outperforming clinical estimates alone. In PH, LUS reveals dense consolidation with alveolar flooding patterns, guiding the timing of rescue surfactant after hemodynamic stabilization; response monitoring via serial LUS is feasible and informative. In PTX, hallmark signs—absent lung sliding, loss of B-lines, and the pathognomonic lung point—allow diagnosis within seconds, guiding immediate thoracentesis and subsequent surfactant administration if underlying RDS is confirmed. Nationally implemented LUS protocols in neonatal intensive care units have demonstrated significant reductions in radiation exposure without compromising diagnostic accuracy. Conclusions: LUS-guided decision algorithms—integrating ultrasonographic phenotyping, quantitative scoring, and hemodynamic assessment—represent the current best framework for individualizing surfactant therapy in neonatal PH and PTX. Standardization of POCUS training and protocol implementation in neonatal units is essential. Prospective multicenter trials are urgently needed to define optimal indications, timing, and dosing in these vulnerable populations. Full article

This study examines whether digital–intelligent technology innovation supports sustainable urban transition by improving urban pollution–carbon synergy in China. Using panel data for 278 prefecture-level cities from 2012 to 2023, we measure digital–intelligent technology innovation by the per capita intensity of patent applications in key digital–intelligent technology fields and construct an urban pollution–carbon synergy index based on a global non-radial directional distance function combined with data envelopment analysis. The results show that digital–intelligent technology innovation is positively associated with urban pollution–carbon synergy, and this finding remains robust to alternative variable definitions, sample adjustments, alternative frontier settings, and supplementary identification strategies. Further analyses suggest that the relationship is stage-dependent rather than purely linear, with stronger sustainability gains emerging after critical development thresholds are crossed. Channel analyses indicate that green technological innovation, digital inclusive finance, and AI firm agglomeration are important routes through which digital–intelligent innovation is translated into environmental governance capacity. Additional analyses show that the effect is stronger on the carbon mitigation dimension than on the pollution reduction dimension, is more pronounced in cities with higher human capital and more developed financial technology, and exhibits both temporal persistence and spatial spillover effects. In addition, digital–intelligent technology innovation is associated with higher energy efficiency, lower total energy consumption, and lower PM2.5, SO₂, total CO₂ emissions, and CO₂ intensity. Overall, these findings contribute to the sustainability literature by showing that digital–intelligent innovation can facilitate sustainable urban transition when it is effectively transformed through green innovation, financial support, and local application scenarios. Full article

Enhancing engineering education remains a critical priority in universities, particularly in foundational science courses, where the development of cognitive competencies, flexibility, and creative thinking is essential. This study evaluates the outcomes associated with the implementation of the Design Thinking (DT) methodology on the creativity of 154 students from a private university in Peru, serving as a foundational stage for its future integration with interactive technologies. A quantitative approach was adopted using a pre-experimental design with pre-test and post-test. The CREA instrument (Creative Intelligence) was employed to measure the evolution of its dimensions across 10 practical sessions involving development boards. The findings indicated that the intervention was associated with statistically significant improvements across all creative dimensions. Notably, students generated 49 unique technological projects, resulting in 22 scientific articles accepted in international conferences and 27 patent applications submitted in Peru (INDECOPI). It is concluded that fostering creativity through Design Thinking serves as a relevant pedagogical framework, as it is associated with students’ cognitive openness and supports the development of sustainable solutions within their professional training as future engineers. Full article

This study investigates the predictive contribution of digital infrastructure to GDP per capita growth across 27 European Union Member States over the period 1995–2024, using a balanced panel of 810 country–year observations and an explainable machine learning framework. An XGBoost model trained on six World Bank indicators—fixed broadband subscriptions, internet users, mobile subscriptions, patent applications, R&D expenditure, and secure internet servers—achieves a training R² of 0.804 and a test R² of 0.430 under temporal out-of-sample validation spanning the COVID-19 structural break. TreeSHAP decomposition identifies fixed broadband as the strongest predictor of model-estimated GDP per capita growth (mean |SHAP| = 0.948; bootstrap rank 1 in 78% of 50 resamples; Friedman Chi-square (5) = 168.16, p < 0.001), providing predictive support for Hypothesis H1. Innovation indicators, represented by patent applications and R&D expenditure, exceed the pre-specified materiality threshold, providing predictive support for H2, while SHAP dependence plots reveal pronounced non-linear threshold patterns consistent with S-curve diffusion theory, supporting H3. Temporal SHAP decomposition identifies three structural phases: broadband dominance (1995–2007), crisis-induced reconfiguration (2008–2013), and quality convergence (2014–2024). The framework reconciles contradictory findings from prior literature by visualizing the complete functional form of the broadband–growth relationship without imposing a parametric specification. Full article

The V-Model is widely used in safety-critical engineering; however, its application to rapid prototyping remains challenging due to limited integration between lifecycle governance and executable validation workflows. This paper addresses end-to-end mechatronic ECU development through the general objective of designing an integrated, prototyping-oriented software platform that operationalizes a V-Model-based rapid prototyping lifecycle with explicit, process-level traceability. Four specific objectives structure the contribution. First, bibliometric analyses of the keywords “V-Model” and “Rapid Prototyping Platforms,” conducted using the Web of Science database and VOSviewer, highlight the limited integration of structured lifecycle approaches with practical prototyping workflows. Second, the V-Model is adapted for mechatronic ECU development by introducing domain-specific decomposition and a dependency-driven testing sequence. Third, the BIOComProP (Basic Input Output Communication Protocol Platform)—comprising reusable ECU firmware, PC based test software, and a dedicated request–response communication protocol—is developed, and its testing capabilities are mapped to V-Model phases. Finally, a logic-based workflow is defined to translate patent derived requirements into executable development and validation steps. The results demonstrate staged verification aligned with technical dependencies, structured traceability across development activities, and firmware reuse across multiple prototypes, offering a coherent and reproducible approach for rapid prototyping of mechatronic ECU systems without relying on heavyweight MBSE or ALM toolchains. Full article

Chirality has emerged as a critical determinant in the design, efficacy, and environmental behavior of modern insecticides. While a significant proportion of agrochemicals are inherently chiral, most are still commercialized as racemic mixtures, despite well-documented differences in biological activity, toxicity, and degradation pathways between enantiomers. In this review, we provide a comprehensive and critical analysis of advances in the stereoselective synthesis and resolution of chiral insecticides, with particular emphasis on neonicotinoids, pyrethroids, and oxadiazines, including indoxacarb. A systematic survey of the literature (1985–2025), including peer-reviewed articles and patents, reveals that multiple strategies have been developed to access enantiomerically enriched compounds, including asymmetric organocatalysis, transition-metal catalysis, chiral-pool approaches, biocatalytic transformations, and chromatographic resolution techniques. Among these, recent developments in photoredox catalysis, recyclable metal complexes, and enzyme-mediated processes have significantly improved enantioselectivity and scalability, bridging the gap between academic methodologies and industrial applications. Despite these advances, challenges remain in achieving cost-effective, sustainable, and universally applicable asymmetric processes. Importantly, the relationship between stereochemistry and biological performance underscores the need for integrating synthetic chemistry with toxicological and environmental studies. Future directions point toward the incorporation of green chemistry principles, continuous-flow processes, and computational tools, including machine learning and molecular modeling, to accelerate the rational design of enantiopure agrochemicals. This review highlights both the progress achieved and the critical gaps that must be addressed to realize the potential of stereoselective insecticide development fully. Full article

The growth of pistachio (Pistacia spp.) production, particularly in America, Iran, and Turkey, has resulted in the production of substantial quantities of by-products, including shells, fruit husks, leaves, resins, and tree pruning waste. Although these fractions contain a range of beneficial secondary compounds, they are rarely used. Recent advances in nanotechnology and materials engineering suggest that they can be used as inexpensive, renewable raw materials in the fields of agriculture, the food industry, cosmetics, pharmaceuticals, and environmental engineering. Pistachio by-products have been shown to exhibit antioxidant, antibacterial, antifungal, and anticorrosive properties, making them good candidates for use in active packaging systems and nanoencapsulation. They may also be valuable as fertilisers, animal feed, and composite materials. They have been found to have potential roles in industrial pollutant removal and energy storage, and as ‘green’ precursors for nanomaterials. This review summarises the current knowledge on the multifunctional applications of Pistacia spp. by-products, emphasising their importance in implementing circular economic strategies and sustainable industrial development. This study also addresses the practical application of pistachio by-products, as well as the widely appreciated nuts, within the domain of patents, thereby illustrating the prospective pathway of currently emerging solutions in the context of sustainable development. Full article

Screed mortars are extensively used in construction, yet their durability and environmental footprint remain key challenges. This study evaluates the effects of partially replacing natural sand with polymeric waste aggregates (25–55% by volume) on the mechanical, hygric, and deformation-related properties of cementitious screed mortars. The proposed material solution, protected under patent ES2973008, results in a significant reduction in density of up to 26.87% while decreasing natural aggregate consumption by as much as 55%, improving workability and ease of application. Experimental results indicate reductions in flexural and compressive strength with increasing polymer content; however, the obtained strength levels remain suitable for self-leveling mortars and applications subjected to pedestrian traffic or light loads. In contrast, the incorporation of polymeric aggregates leads to marked improvements in durability-related parameters, including reductions in drying shrinkage of up to 25.2%, Young’s modulus of up to 73%, capillary water absorption of up to 72.34%, and water vapour permeability of up to 6.53%. These combined effects reflect a pronounced increase in elastic deformation capacity, dimensional stability, and resistance to moisture ingress, thereby reducing susceptibility to shrinkage-induced cracking and freeze–thaw damage. Overall, the results demonstrate that polymeric waste incorporation enables the development of lighter, more crack-resistant, and more durable screed mortars, achieving a favourable balance between mechanical performance and long-term durability while contributing to sustainability and circular economy objectives. Full article

The NEET category refers to the proportion of young people who are neither employed nor in education or training. The success of Sustainable Development Goal 8 largely depends on reducing the number of NEETs, one of its sub-goals. This study examines the long-term impact of gross domestic product, human development, social globalization, and patent applications on NEET in eight EU countries during 1991–2021, within the framework of SDG 8. For long-run estimation, the study employs panel data techniques that account for cross-sectional dependence and heterogeneity, specifically the Augmented Mean Group (AMG) and Regularized Common Correlated Effects (RCCE) estimators. According to country-specific findings, PA has a statistically significant effect in reducing NEET rates in France and Spain, while human development has a similar effect in Portugal. In contrast, economic growth and social globalization do not exhibit statistically significant effects on NEET rates at the country level. The results underscore that, in high-income EU countries, policies designed to simultaneously enhance human development and innovation capacity are central to tackling the NEET issue, consistent with the objectives of Sustainable Development Goal 8. The study contributes to the literature by providing a comparative empirical assessment of NEET determinants within a framework that accounts for cross-country heterogeneity and multiple structural factors. Full article