Search Results (3,347)

Layered sodium trititanate (Na₂Ti₃O₇) is a promising anode material for sodium-ion batteries due to its suitable charge/discharge plateaus, cost-effectiveness, and eco-friendliness. However, its slow Na⁺ diffusion kinetics, poor electron conductivity, and instability during cycling pose significant challenges for practical applications. To address these issues, we developed a template-free method to synthesize Na₂Ti₃O₇-C hollow microspheres. The synthesis began with polymerization-induced colloid aggregation to form a TiO₂–urea–formaldehyde (TiO₂-UF) precursor, which was then subjected to heat treatment to induce inward crystallization, creating hollow cavities within the microspheres. The hollow structure, combined with a conductive carbon matrix, significantly enhanced the cycling performance and rate capability of the material. When used as an anode, the Na₂Ti₃O₇-C hollow microspheres exhibited a high reversible capacity of 188 mAh g⁻¹ at 0.2C and retained 169 mAh g⁻¹ after 500 cycles. Additionally, the material demonstrated excellent rate performance with capacities of 157, 133, 105, 77, 62, and 45 mAh g⁻¹ at current densities of 0.5, 1, 2, 5, 10, and 20C, respectively. This innovative approach provides a new strategy for developing high-performance sodium-ion battery anodes and has the potential to significantly advance the field of energy storage. Full article

Additive manufacturing must be highlighted as an innovative technology with the capacity to produce objects with complex and customized geometries using a diverse range of raw materials. Despite its significant potential, research compiling and evaluating the specific contributions of additive manufacturing in the field of chemical engineering was scarce in both quantitative and qualitative terms. Similarly, the application of chemical engineering tools to additive manufacturing has not been specifically reviewed. Therefore, this work conducted a comprehensive review of the scientific literature covering these issues using bibliometric analysis. The search encompassed the entirety of the scientific literature up to the year 2023, yielding 3761 documents in the Scopus database. The principal findings of this bibliometric analysis indicated an exponential growth in the number of publications, which suggests a rising scientific interest in this field. The analysis revealed that English was the dominant language in the documents, and articles constituted the most common document type, indicating the quality and maturity of the research. The thematic distribution proved to be multidisciplinary, with a primary focus on engineering and materials science, as well as basic sciences. The United States was the foremost contributor to scientific production, followed by China and Germany. Keyword analysis and scrutiny of the most cited documents enabled the identification of the main topics, which were found to include biofabrication and biomedical applications. Moreover, bibliometric network analysis using the software SciMAT (v 1.1.06) yielded the corresponding strategic diagrams, evolution maps, and thematic networks, which provided a comprehensive overview of trends and research gaps. The considerable interest in the application of additive manufacturing to biofabrication and other biomedical purposes has overshadowed the specific applications within the chemical engineering field, while the potential contributions that chemical engineering could make to the field of additive manufacturing have been eclipsed too. On the one hand, applications focused on process intensification in chemical engineering could benefit from additive manufacturing to design advanced microreactors and other miniaturized devices or to produce more efficient heat exchangers, catalysts, and adsorbents with complex geometries and separation membranes with innovative materials and structures. On the other hand, life cycle assessment and optimization are established chemical engineering tools that should be more extensively employed in the context of additive manufacturing to ensure a more sustainable outcome. Full article

The high-quality development of specialized, refined, distinctive, and innovative enterprises (SRDIEs) is essential for advancing an innovation-driven strategy. This paper investigates the impact of financial technology (Fintech) on sustainable innovation within SRDIEs that face financing challenges, analyzing it from supply-side, demand-side, and environmental perspectives. We utilize fuzzy-set Qualitative Comparative Analysis (fSQCA) and Necessary Condition Analysis (NCA) to explore the configurational paths and complex causal effects of Fintech in facilitating the innovation of SRDIEs amid financing challenges. By employing a combination of NCA and fsQCA, this study identifies several effective pathways through which Fintech enhances the innovation efficiency of SRDIEs. We develop an integrative model to enhance innovation inputs, outputs, and sustainability. The key findings include the following: (1) Fintech significantly enhances innovation output, supported by business efficiency and digital intelligence; (2) two distinct pathways for achieving high-innovation inputs are identified, driven by Fintech intensity and effective credit allocation, with specialization and financial mismatches serving as auxiliary factors; (3) the core conditions of Fintech intensity and the financing environment, along with competitive banking, promote innovation motivation and sustainability in highly specialized enterprises. The conclusions of this study provide both theoretical and practical insights for SRDIEs to tackle innovation challenges characterized by an “inability to innovate”, a “lack of willingness to innovate”, and “ineffectiveness in innovation”, enabling their transition from merely being “able to innovate” and “daring to innovate” to becoming “proficient in sustainable innovation”. These findings offer differentiated sustainable innovation solutions for enterprises through three avenues: capacity building on the demand side, channel optimization on the supply side, and ecological cultivation on the environmental side. Full article

The COVID-19 pandemic devastated businesses globally, leading to significant economic and social challenges. Micro, Small and Medium Enterprises (MSMEs) are particularly vulnerable to environmental turbulence, while their survival and resilience are critical to the national economic recovery of countries globally. This study adopted a qualitative approach to examine the strategic responses adopted by MSMEs and the impact of the strategies on survival and resilience during the COVID-19 pandemic period. The sample size comprises ten (10) MSMEs and data collection using a semi-structured interview schedule. The key findings indicate that the MSMEs adopted cost reduction and diversification strategies to mitigate the impact of the abrupt COVID-19 lockdowns and the subsequent financial and cashflow problems, threats to business continuity and survival. The study conclusions indicate that adopting a combination of cost reduction and diversification strategies improved the business continuity and survival of MSMEs during the COVID-19 pandemic. The study recommendations include the need for government and stakeholders to enhance the capacity and competence of MSMEs in strategic and operational planning, investing in technology, innovation, creativity, training and development of strategic agility and fostering adaptive organisational cultures that enhance flexibility, resilience and survival when faced with economic and business environment disruptions in future. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

Background: China’s pharmaceutical regulatory framework is undergoing a pivotal shift from a traditional “command-control” model to a “lifecycle regulation” approach, aiming to balance drug safety, innovation, and accessibility. This study systematically examines the evolution, achievements, and challenges of China’s regulatory reforms, offering insights for global pharmaceutical governance. Methods: Using a mixed-methods approach integrating historical analysis, policy text mining, and case studies, we reviewed the pharmaceutical laws and regulations enacted since 1949, supplemented by case studies (e.g., COVID-19 vaccine emergency approvals) and a comparative analysis with international models (e.g., U.S. FDA and EU EMA frameworks). The data were sourced from authoritative platforms such as the PKULAW database, criminal law amendments, and international regulatory texts. Results: China’s regulatory evolution is categorized into four phases: Emergence (1949–1984), Foundational (1985–2000), Deepening Reform (2001–2018), and Lifecycle Regulation (2019–present). The revised Drug Administration Law (2019) institutionalized risk management, dynamic GMP inspections, and post-market surveillance, marking a transition to holistic lifecycle oversight. Key milestones include the introduction of the Vaccine Management Law (2019) and stricter penalties under the Criminal Law Amendment (XI) (2020). Conclusions: China’s lifecycle regulation model demonstrates potential to harmonize safety and innovation, evidenced by improved API export compliance (e.g., 15% increase in international certifications by 2023) and accelerated approvals for breakthrough therapies (e.g., domestically developed PD-1 inhibitors). However, challenges persist, including uneven enforcement capacities, tensions between conditional approvals and risk mitigation, and reliance on global supply chains. These findings provide critical lessons for developing countries navigating similar regulatory dilemmas. Full article

This study experimentally examines the flexural performance, crack formation patterns, and failure mechanisms of glass fiber-reinforced polymer (GFRP) bar-reinforced concrete beams with varying concrete compressive strengths (low, moderate, and high), addressing a gap in the current literature. Furthermore, it employs an innovative machine learning approach to enhance analysis. Nine RC beams reinforced with GFRP bars, having concrete compressive strengths of low (CC20), moderate (CC30), and high (CC40), each measuring 150 × 200 × 1100 mm, were fabricated and tested under three-point bending conditions. Through the integration of three-point bending tests and machine learning-based prediction models, this study connects experimental findings with advanced analytical approaches. One of the key innovations in this study is the use of eighteen ML regression models implemented with Python’s PyCaret library, achieving an impressive average prediction accuracy of 91.5% for RC beam deflection values. In particular, the Ada Boost Regressor and Gradient Boosting Regressor models performed exceptionally well on GFRP bar-reinforced concrete beams, providing the highest number of consistent and highly accurate predictions, making them very useful tools for GFRP bar-reinforced beam ultimate load-carrying capacity/deflection predictions. The outcomes identified clear failure mechanisms: RC beams with CC20, CC30, and CC40 concrete compressive strengths typically developed a single, large flexural crack at the midpoint. Although the ultimate load-carrying capacity of GFRP bar RC beams improved with higher concrete compressive strength, CC20 and CC30 beams displayed more ductile failure behavior than CC40 beams. The ultimate load-carrying capacity of CC40 RC beams was determined to be approximately 74% higher than that of CC20 RC beams. Regardless of the concrete compressive strength class, the absence of shear cracks and the prevention of sudden failure under bending in GFRP bar-reinforced concrete beams are considered major advantages of using GFRP bar reinforcement. Full article

Breast cancer (BC) tops the list of causes for female fatalities globally, with the elusive triple-negative breast cancer (TNBC) constituting 10–20% of all cases. Current clinical strategies for combating TNBC encompass a multifaceted approach, including surgical intervention, radiation therapy, chemotherapy, and advanced targeted drugs and immunotherapies. While these modalities have catalyzed significant advancements in TNBC management, lingering limitations continue to pose formidable challenges. There is an acute need for novel therapeutics in the realm of TNBC treatment. Natural products (NPs) have emerged as a rich reservoir for pharmaceutical innovation, owing to their extraordinary range of structures and physicochemical properties. Scholars have reported diverse evidence of NPs’ efficacy against TNBC. This review aims to comprehensively explore the bioactive constituents, specifics and commonalities of chemical structure, and pharmacological mechanisms of NPs, specifically examining their multifaceted roles in impeding TNBC. NPs, which have recently garnered significant interest, are intriguing in terms of their capacity to combat TNBC through multifaceted mechanisms, including the suppression of tumor cell proliferation, the induction of apoptosis, and the inhibition of tumor metastasis. These natural agents primarily encompass a range of compounds, including terpenoids, glycosides, phenolic compounds, and alkaloids. An in-depth exploration has unveiled their involvement in key signaling pathways, including the transforming growth factor-beta (TGF-β), vascular endothelial growth factor A (VEGFA), phosphoinositide 3-kinase/protein kinase B (PI3K/AKT), Wingless/Int-1 (Wnt) /β-catenin, and mitogen-activated protein kinase (MAPK) pathways. Meanwhile, this review also looks at the challenges and opportunities that arise from harnessing natural compounds to influence TNBC, while outlining the prospective trajectory for future research in the field of NPs. Full article

This paper presents findings from the LEOPARD project, part of the LEAP-RE program, a joint European Union (EU) and African Union initiative to advance renewable energy solutions. The study employs a simulation-based approach to optimize solar-integrated microgrid configurations for rural electrification. The project deployed a solar-integrated pilot microgrid at the Songhai agroecological center in Benin to address key challenges, including load profile estimation, energy balancing, and diesel dependency reduction. A hybrid methodology integrating predictive modeling, real-time solar and weather data analysis, and performance simulations was employed, leading to a 65% reduction in diesel reliance and an LCOE of EUR 0.47/kWh. Quality control measures, including compliance with IEC 61215 and IEC 62485-2 standards, ensured system reliability under extreme conditions. Over 150 days, the system consistently supplied energy, preventing 10.16 tons of ${CO}_2$ emissions. Beyond the Benin pilot, the project conducted feasibility assessments in Senegal to evaluate microgrid replicability across different socio-economic and environmental conditions. These analyses highlight the scalability potential and the economic viability of expanding solar microgrids in rural areas. Additionally, this research explores innovative business models and real-time diagnostics to enhance microgrid sustainability. By providing a replicable framework, it promotes long-term energy access and regional adaptability. With a focus on community involvement and capacity building, this study supports efforts to reduce energy poverty, strengthen European–African collaboration, and advance the global clean energy agenda. Full article

To accelerate the dissipation of excess pore water pressure, enhance the bearing capacity of piles, and mitigate long-term settlement in soft ground, a novel green and lowcarbon pile foundation technology, termed the precast drainage pile (PDP) technology, is proposed. This innovative approach integrated precast pipe piles with prefabricated vertical drains (PVDs) attached to their sides. The piles were installed using static pile pressing and were subsequently subjected to vacuum-induced negative pressure to facilitate soil consolidation, which enhances the resource utilization rate of pile foundations and promotes the sustainable utilization of soft soil foundations. To investigate the bearing characteristics of the PDP, this study combined the shear displacement method for piles with the consolidation theory of soft soil foundations. A calculation model for the load-settlement behavior of precast piles, accounting for the influence of vacuum-induced soil consolidation, was derived, establishing a method for analyzing the load transfer mechanism of PDPs. The reliability of the theoretical model was validated through comparisons with engineering test results. Building on this foundation, the influence of factors such as consolidation period and pile length on the bearing characteristics of PDPs was analyzed. The results demonstrated that, compared to a 10 m precast pile without drainage, the ultimate bearing capacity of single piles with drainage durations of 3, 7, 14, and 28 days increased by 7.3%, 12.7%, 20.3%, and 29.6%, respectively. Furthermore, under a 7-day drainage condition, the bearing capacity of piles with lengths of 10 m, 20 m, and 30 m increased by 12.7%, 12.8%, and 13.1%, respectively. Overall, the findings of this study provide a theoretical basis for the research, development, and design calculations of this new sustainable pile technology. Full article

Machine learning (ML) and deep learning (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation and innovation in various industries by integrating AI-driven solutions. Understanding ML and DL is essential to logically analyse the applicability of ML and DL and identify their effectiveness in different areas like healthcare, finance, agriculture, manufacturing, and transportation. ML consists of supervised, unsupervised, semi-supervised, and reinforcement learning techniques. On the other hand, DL, a subfield of ML, comprising neural networks (NNs), can deal with complicated datasets in health, autonomous systems, and finance industries. This study presents a holistic view of ML and DL technologies, analysing algorithms and their application’s capacity to address real-world problems. The study investigates the real-world application areas in which ML and DL techniques are implemented. Moreover, the study highlights the latest trends and possible future avenues for research and development (R&D), which consist of developing hybrid models, generative AI, and incorporating ML and DL with the latest technologies. The study aims to provide a comprehensive view on ML and DL technologies, which can serve as a reference guide for researchers, industry professionals, practitioners, and policy makers. Full article

In this experimental work, sloshing tests were performed with containers filled with water at 50% of their volume capacity. Two boundary conditions were considered: uncoated containers and containers with hydrophobic coated walls. In addition, several aspect ratios $\lambda$ (container width over length) were tested. We characterized two regimes, the first when the container is periodically forced at a frequency lower than its resonant frequency and the second after the forcing is suddenly stopped. In each case, the amplitude of the waves was measured. Several surprising results were found. First, in the forced regime, the sloshing amplitude was lower in the hydrophobic containers than in the containers with the non-hydrophobic walls, despite the free-slip condition in the former case. Second, the damping after sudden stoppage was much higher in the containers with hydrophobic walls than in the uncoated containers. This behavior is explained by the collision of waves with oil-coated walls, which generates a lower load pressure. Finally, we found that the damping depends on the dimension of the container through $\lambda$, and is greater when $\lambda =1.00$. These experimental findings open the way for further innovative research. Full article

Thalassemia and sickle cell disease remain the most common life-threatening non-communicable diseases in children worldwide and an increasing burden on affected families and health services. Significant progress has been made in terms of technologies to improve access to a cure by both allogeneic and autologous gene-modified hematopoietic stem cell transplantation (HSCT). However, the high cost of cutting-edge treatments often places them beyond the reach of individual families or even national healthcare systems. Advances in frugal innovation and simplified HSCT procedures for low-risk transplants have significantly reduced the costs and complexities associated with HSCT without compromising on quality and outcomes. Because of the geographical distribution of hemoglobinopathies, i.e., largely in low- and middle-income countries (LMICs), HSCT cost optimization has the potential to impact a huge number of patients, increasing hope for a cure and health-related quality of life normalization, which in turn may affect supportive care compliance. Furthermore, because of the high burden of disease, LMIC transplant centers are rapidly increasing in number and developing unique expertise for the cure of thalassemia and sickle cell disease, particularly in India, where the Sankalp India Foundation with the support of DKMS and Cure2Children has implemented several cost-conscious transplant services. In fact, the very high success rate, increasing cost-effectiveness of transplantation, as well as the chronic nature of these conditions make them ideal initial candidates for start-up transplant centers, so it is likely that the global capacity for a cure for severe hemoglobinopathies will substantially increase in the years to come. Full article

With global climate challenges intensifying, the cement industry, as a major CO₂ emitter, has attracted significant attention regarding its emission reduction potential and strategies. Advanced economies like the European Union use carbon pricing to spur innovation, while emerging countries focus on incremental solutions, such as fuel substitution. Combining LMDI decomposition and the LEAP model, this study examines Jiangsu Province as a test bed for China’s decarbonization strategy, a highly efficient region with carbon intensity 8% lower than the national average. Historical analysis identifies carbon intensity, energy mix, energy intensity, output scale, and economic effects as key drivers of emission changes. Specifically, the reduction in cement production, real estate contraction, lower housing construction, and reduced production capacity are the main factors curbing emissions. Under an integrated technology strategy—including energy efficiency, fuel and clinker substitution, and CCS—CO₂ emissions from Jiangsu’s cement sector are projected to decrease to 17.28 million tons and 10.9 million tons by 2060 under high- and low-demand scenarios, respectively. Clinker substitution is the most significant CO₂ reduction technology, contributing about 60%, while energy efficiency gains contribute only 3.4%. Despite the full deployment of existing reduction methods, Jiangsu’s cement industry is expected to face an emissions gap of approximately 10 million tons to achieve carbon neutrality by 2060, highlighting the need for innovative emission reduction technologies or carbon trading to meet carbon neutrality goals. Full article

Since the first documented case of Cephenemyia stimulator in Spain in 2001, this myiasis has experienced rapid expansion, with the first case also detected in 2021 in Portugal. Between January 2020 and October 2024, a study was carried out in northern Spain (Cantabria, Galicia, País Vasco and Principado de Asturias) and Portugal (Bragança, Viana do Castelo and Vila Real) to evaluate the presence and distribution of this myiasis in these areas. The prevalence of infestation was 76.6 ± 4.72% (95% CI) and 38.78 ± 13.78% (95% CI), with a mean intensity of 62.27 ± 104.40 and 37.74 ± 36.84 larvae per animal in Spain and Portugal, respectively. Regarding age, larval intensity was significantly higher in young animals (109.76 ± 148.59) than in adults (32.22 ± 39.01) and old animals (33.64 ± 34.21) (p < 0.05). These results reflect a large increase in the prevalence and importance of younger animals in the spread of the disease. Therefore, it is very important to control and manage populations, focusing on the youngest animals. Full article