Search Results (104,635)

This paper explores the interaction between immigrant small and medium-sized enterprises (SMEs) and the entrepreneurial ecosystems of their home and host countries. These SMEs, often established out of necessity due to economic hardship or unemployment, contrast with opportunity-driven ventures that seek to exploit new market prospects. Employing a dynamic capabilities framework, the study investigates how immigrant SMEs identify and mobilize critical resources in unstable environments, and how these resources are dynamically adapted and transformed to meet organizational objectives while responding to ecosystem changes, ultimately cultivating competitive advantages. Interviews were conducted with 25 immigrant entrepreneurs from eight countries, and the interviews took place within their business environments, with native speakers of the interviewees′ home countries present to ensure accuracy and authenticity. The findings highlight that immigrant entrepreneurs effectively leverage resources from both home and host country ecosystems, with human capital being the most essential asset. Additionally, the role of immigrants′ social networks is emphasized as a crucial mechanism for resource acquisition. This study further illustrates the positive contributions of immigrant enterprises to entrepreneurial ecosystems, revealing a co-evolutionary relationship whereby the businesses and ecosystems both mutually evolve. This co-evolution strengthens the resilience and sustainability of immigrant ventures, significantly enhancing their long-term success. Full article

This study presents an optimized container-stowage plan using reinforcement learning to tackle the complex logistical challenges in maritime shipping. Traditional stowage-planning methods often rely on manual processes that account for factors like container weight, unloading order, and balance, which results in significant time and resource consumption. To address these inefficiencies, we developed a two-phase stowage plan: Phase 1 involves bay selection using a Proximal Policy Optimization (PPO) algorithm, while Phase 2 focuses on row and tier placement. The proposed model was evaluated against traditional methods, demonstrating that the PPO algorithm provides more efficient loading plans with faster convergence compared to Deep Q-Learning (DQN). Additionally, the model successfully minimized rehandling and maintained an even distribution of weight across the vessel, ensuring operational safety and stability. This approach shows great potential for enhancing stowage efficiency and can be applied to real-world shipping scenarios, improving productivity. Future work will aim to incorporate additional factors, such as container size, type, and cargo fragility, to further improve the robustness and adaptability of the stowage-planning system. By integrating these additional considerations, the system will become even more capable of handling the complexities of modern maritime logistics. Full article

With the development of the steel industry, China’s demand for niobium is increasing. However, domestic niobium resources are not yet stably supplied and are heavily dependent on imports from abroad (nearly 100%). It is urgent to develop domestic niobium resources. The Bayan Obo deposit is the largest rare earth element deposit in the world and contains a huge amount of niobium resources. However, the niobium resource has not been exploited due to the fine-grained size and heterogeneous and scattered occurrences of Nb minerals. To promote the utilization of niobium resources in the Bayan Obo deposit, we focused on the mineralogical and geochemical characterization of six types of ores and mineral processing samples from the Bayan Obo deposit, using optical microscopes, EPMA, TIMA, and LA–ICP–MS. Our results show that: (1) the niobium mineral compositions are complex, with the main Nb minerals including aeschynite group minerals, columbite–(Fe), fluorcalciopyrochlore, Nb–bearing rutile, baotite, fergusonite–(Y), fersmite, and a small amount of samarskite–(Y). Aeschynite group minerals, columbite–(Fe), and fluorcalciopyrochlore are the main niobium-carrying minerals and should be the primary focus of industrial recycling and utilization. Based on mineralogical and geochemical investigation, the size of the aeschynite group minerals is large enough for mineral processing. Aeschynite group minerals are thus a significant potential recovery target for niobium, as well as for medium–heavy REE resources. The Nb–rich aegirine-type ores with aeschynite group mineral megacrysts are suggested to be the most significant niobium resource for mineral processing and prospecting. Combined with geological features, mining, and mineral processing, niobium beneficiation efforts of aeschynite group minerals are crucial for making breakthroughs in the utilization of niobium resources at the Bayan Obo. Full article

This paper presents a comprehensive review of scientific papers and market reports analyzing the economic competitiveness of fuel cell electric buses (FCEBs) with respect to their conventional alternatives via the total cost of ownership (TCO) methodology. We discussed the variables and data taken into account and compared the resulting outcomes by year and geographical areas. It emerged that FCBs are not currently cost competitive. The decreasing trend in acquisition and fuel costs, however, indicates potential for future competitiveness. We find that the current TCO literature on FCEBs presents several areas of uncertainty and weakness. Potential improvements can be achieved by: (i) extending the geographic coverage to Asian and African developing countries; (ii) making use of real-world data instead of simulated data, in particular, concerning acquisition costs, hydrogen costs under different pathways, fuel efficiency, and maintenance costs; (iii) clarifying the role of infrastructural costs; (iv) exploring the existence of economies of scale at fleet level; (v) distinguishing among different bus sizes. Full article

Lotus-type porous copper was fabricated using a continuous casting method in pressurized hydrogen and nitrogen gas atmospheres. This study evaluates the effects of process parameters, such as the hydrogen ratio, total pressure, and transference velocity, on the resulting pore structure. A continuous casting process was developed to facilitate the mass production of lotus-type porous copper. To achieve the desired porosity and pore diameter for large-scale manufacturing, a systematic evaluation of the influence of each process parameter was conducted. Lotus-type porous copper was produced within a hydrogen ratio range of 25–50%, a transference velocity range of 30–90 mm∙min⁻¹, and a total pressure range of 0.2–0.4 MPa. As a result, the porosity ranged from 36% to 55% and the pore size varied from 300 to 1500 µm, demonstrating a wide range of porosities and pore sizes. Through process optimization, it is possible to control the porosity and pore size. The hydrogen ratio and total pressure were found to primarily affect porosity, whereas the hydrogen ratio, transference velocity, and total pressure significantly influenced pore diameter. When considering these parameters together, porosity was most influenced by the hydrogen ratio, whereas the total pressure and transference velocity had a greater influence on pore diameter. Reducing the hydrogen ratio and increasing the transference velocity and total pressure reduced the pore diameter and porosity. This optimization of the continuous casting process enables the control of porosity and pore diameter, facilitating the production of lotus-type porous copper with the desired pore structures. Full article

Cultural assets are all movable and immovable assets that have been the subject of social life in historical periods, have unique scientific and cultural value, and are located above ground, underground or underwater. Today, the fact that most of the analyses conducted to understand the technologies of these assets require sampling and that non-destructive methods that allow analysis without taking samples are costly is a problem for cultural heritage workers. In this study, which was prepared to find solutions to national and international problems, it is aimed to develop a non-destructive, cost-minimizing and easy-to-use analysis method. Since this article aimed to develop methodology, the materials were prepared for preliminary research purposes. Therefore, it was limited to four primary colors. These four primary colors were red and yellow ochre, green earth, Egyptian blue and ultramarine blue. These pigments were used with different binders. The produced paints were photographed in natural and artificial light at different light intensities and brought to a 256 × 256 pixel size, and then trained on support vector machine, convolutional neural network, densely connected convolutional network, residual network 50 and visual geometry group 19 models. It was asked whether the trained VGG19 model could classify the paints used in archaeological and artistic works analyzed with instrumental methods in the literature with their real identities. As a result of the test, the model was able to classify paints in artworks from photographs non-destructively with a 99% success rate, similar to the result of the McNemar test. Full article

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management. Full article

Since its discovery, the phenomenon of Surface Enhanced Raman Scattering (SERS) has gradually become an important tool for analyzing the composition and structure of substances. As a trace technique that can efficiently and nondestructively detect single molecules, the application of SERS has expanded from environmental and materials science to biomedical fields. In the past decade or so, the explosive development of nanotechnology and nanomaterials has further boosted the research of SERS technology, as nanomaterial-based SERS substrates have shown good signal enhancement properties. So far, it is widely recognized that the morphology, size, composition, and stacking mode of nanomaterials have a very great influence on the strength of the substrate SERS effect. Herein, an overview of methods for the preparation of surface-enhanced Raman scattering (SERS) substrates is provided. Specifically, this review describes a variety of common SERS substrate preparation methods and explores the potential and promise of these methods for applications in chemical analysis and biomedical fields. By detailing the influence of different nanomaterials (e.g., metallic nanoparticles, nanowires, and nanostars) and their structural features on the SERS effect, this article aims to provide a comprehensive understanding of SERS substrate preparation techniques. Full article

Nanocellulose is considered a promising and sustainable biomaterial, with excellent properties of biorenewability with improved mechanical properties. As a unique natural biopolymer, it has been applied to many different industries, where efficient and environmentally friendly productions are in demand. For the first time, ferns from the class Polypodiopsida were used for the isolation of cellulose fibers, which was performed using a chemo-mechanical method. As chemical treatment plays a crucial role in the isolation of nanocellulose, it affects the efficiency of the extraction process, as well as the properties of the resulting nanocellulose. Therefore, mechanical fibrillation was performed via grinding, while the chemical process consisted of three different treatments: alkali treatment, bleaching, and acid hydrolysis. In three different experiments, each treatment was separately prolonged to investigate the differing properties of isolated nanocellulose. Structural analysis and morphological analysis were investigated by SEM, EDS, FT-IR, and DLS. The thermal stability of cellulose fibers was investigated by TGA/DSC. The morphology of obtained nanocellulose was confirmed via SEM analysis for all samples, with particles ranging from 20 nm up to 600 nm, while the most consistent sizes were observed for NC3, ranging from 20 to 60 nm. FT-IR spectra showed prominent absorption peaks corresponding to cellulose, as well as the absence of absorption peaks, corresponding to lignin and hemicellulose. The EDS confirmed the elemental purity of nanocellulose, while TGA/DSC indicated higher thermal stability of nanocellulose, compared to untreated fern, which started to degrade earlier than nanocellulose. Such characteristics with unique properties make nanocellulose a versatile biomaterial for the industrial production of cellulosic materials. Full article

This paper presents an efficient and secure method for updating firmware in IoT devices using LoRaWAN network resources and communication protocols. The proposed method involves dividing the firmware into fragments, storing them in the application server’s database, and transmitting them to remote IoT devices via downlink messages, without necessitating any changes to the device’s class. This approach can be replicated across any IoT LoRaWAN device, offering a robust and scalable solution for large-scale firmware updates while ensuring data security and integrity. The proposed method significantly reduces the downtime of IoT devices and enhances the energy efficiency of the update process. The method was validated by updating a block in the program memory, associated to a specific functionality of the IoT end device. The associated Intel Hex file was segmented into 17 LoRaWAN downlink frames with an average size of 46 bytes. Upon receiving the complete firmware update, the microcontroller employs self-programming techniques that restrict the update process to specific rows of the program memory, avoiding interruptions or reboots. The update process was successfully completed in 51.33 ms, resulting in a downtime of 16.88 ms. This method demonstrates improved energy efficiency compared to existing solutions while preserving the communication network’s capacity, making it an adequate solution for remote devices in LoRaWAN networks. Full article

The samples from lunar farside have great significance for the study of the Moon, and even the solar system. Chang’e-6 landed successfully on the southern mare of the Apollo basin and returned ~2 kg of samples from lunar farside. To provide a better understanding for the background of the returned samples, we conducted detailed crater size-frequency distribution (CSFD) measurements in the Chang’e-6 landing region, the southern mare of the Apollo basin. The southern mare is divided into the western mare (W region) and the eastern mare (E region), and then subdivided into five subunits (W1, W2, W3, W4, W5) and three units (E1, E2, E3), respectively, according to the elevation, TiO₂, and FeO abundances. Within the W2 and W5 region, more detailed subunits were separated out. The results show that the southern mare surface was active during two epochs, the Imbrian period and the Eratosthenian period. The basalt eruption lasted for ~1.7 Ga, from 3.28 Ga of the eastern mare to 1.54 Ga of the western mare. The W region is younger than the E region, while the three units of the E region have an age of ~3.2 Ga. The ages of the western mare basalts range from 2.98 Ga to 1.54 Ga, lasting for 1.4 Ga. It is worth noting that the age of the basalt at the Chang’e-6 sampling site is ~1.68 Ga, indicating the samples returned may include components with this very young age. Full article

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields. Full article

Climate change is a significant and urgent threat, gaining traction in the scientific community around the globe and requiring immediate action across many sectors. In this context, the digital economy could provide a mutually beneficial solution by utilizing innovation and technical breakthroughs to establish a sustainable future that addresses environmental deterioration, promotes economic growth, and encourages energy conservation. Against this background, this study examined the diffusion of innovation modeling-based factors affecting small and medium-sized firms’ (SMFs) adoption of the Internet of Things (IoT) technology and its impact on SMFs’ sustainability performance related to environmental, economic, innovation, and energy conservation perspectives. The key findings revealed that (i) the relative advantage, trialability, and observability drive IoT adoption. However, compatibility and complexity hinder IoT adoption. (ii) When prioritizing the adoption factors, the relative benefit is the strongest driver, and compatibility is the most significant barrier to IoT adoption. (iii) IoT technology adopter SMFs spent less on natural resources and more on renewable energy and environmental monitoring systems than non-adopter firms, boosting their environmental sustainability. (iv) IoT technology adopter firms had greater revenue, profits, and credit access than non-adopters and lower input costs, improving their economic sustainability. (v) IoT adopter firms spent more on innovative products than non-adopter enterprises, demonstrating innovation performance. (vi) Compared to non-adopter firms, IoT technology adopter SMFs had lower utility expenses and spent more on energy-efficient technologies. (vii) To realize the full potential of the IoT for a more sustainable and inventive future, authorities may pursue a variety of policy actions involving the strengthening and implementation of IoT technology standards and regulations, securing the incentivization of financial resources to SMFs, diverting the allocation of resources to research and development avenues, prioritizing the capacity development and environmental awareness, and focusing on IoT infrastructure development. Full article

Introduction: Breast cancer has become one of the most serious and widespread public health concerns globally, affecting an increasing number of women—and, in rare cases, men—across the world. It is the most common cancer among women across all countries. In this study, we aimed to evaluate the influence of demographic factors, medical and reproductive history, diagnostic techniques, and hormone receptor status on the development and progression of breast cancer. Materials and Methods: A total of 687 female patients from Romania underwent standard breast examination techniques, including clinical breast examination, mammography, ultrasonography, and, ultimately, breast biopsy. Statistical analysis was performed using the R programming language and RStudio software. The study included a comparative analysis and a prediction analysis for malignancy and tumor size (cumulative histological dimension) through logistic and linear regression models. Results: The comparative analysis identified several variables associated with malignancy: older age (p < 0.001), non-vulnerability (p = 0.04), no daily physical activity (p = 0.002), no re-biopsy (p < 0.001), immunohistochemistry use (p < 0.001), use of larger gauge needles (p < 0.001), ultrasound-guided biopsy (p < 0.001), and vacuum biopsy (p < 0.001). The hormone receptor statuses—estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR)—showed statistically significant differences in distribution across breast cancer B classifications. Logistic regression analysis identified ER, PR, and age as significant predictors of malignancy. Linear regression analysis revealed histopathological results, living environment, geographical region, vulnerability, prior breast examination, and the number of histological fragments as significant predictors of cumulative histological dimension. Conclusions: Our predictive models demonstrate the impact of demographic factors, medical history, diagnostic techniques, and hormone receptor status on breast cancer development and progression, accounting for a significant portion of the variance in malignancy and cumulative histological dimension. Full article

Background: The restoration of the degraded sandy grasslands in Hulun Buir is crucial for maintaining the local ecological balance and sustainable development. Caragana microphylla Lam., a shrub species widely employed in the restoration of sandy vegetation. It is essential to understand its impact on the understory vegetation and soil properties during this process. Methods: This study employed ANOVA, Pearson correlation, and redundancy analysis to systematically analyze the impact of C. microphylla on the three critical stages of desertified grassland vegetation recovery: semi-fixed dunes, fixed dunes, and sandy grasslands. It provided strategies for the restoration of desertified grassland vegetation and offered additional theoretical evidence for the role of vegetation in promoting the recovery of sandy lands. Results: (1) As the degree of vegetation recovery in desertified grasslands increases, the species richness of understory vegetation, Shannon–Wiener index, community height, and biomass also increase. Both the community height and biomass within shrublands are higher than outside, with species richness within the shrublands being higher than outside during the semi-fixed and fixed-sand land stages. (2) In both the 0~10 cm and 10~20 cm soil layers, soil water content showed an increasing trend, peaking in the sandy grassland stage (1.2%), and was higher within the shrublands than outside. The soil water content at 10~20 cm was higher than in the 0~10 cm layer. In both layers, clay and silt content gradually increased with the degree of vegetation recovery in the sandy land, and higher within the shrublands than outside, while the opposite was true for sand content. (3) In both soil layers, soil organic carbon gradually increased with the degree of vegetation recovery, peaking in the sandy grassland stage (4.12 g·kg⁻¹), and was higher within the shrublands than outside. Total nitrogen increased from the semi-fixed-sand land stage to the fixed-sand land stage, with higher levels within the shrublands than outside at all stages. Soil pH within the shrublands decreased as the degree of vegetation recovery increased. There was no significant change in the total phosphorus content. (4) In both soil layers, soil physicochemical characteristics accounted for 59.6% and 46.9% of the vegetation changes within and outside the shrublands, respectively, with the main influencing factors being the soil particle size, total nitrogen, soil water content, and soil organic carbon. Conclusions: In the process of sandy grassland restoration, C. microphylla facilitates the growth and development of vegetation by enhancing the underlying soil physicochemical properties, specifically regarding the soil particle size distribution, soil water content, soil organic carbon, and total nitrogen. Full article