While there has been much discussion about the large infrastructure needs in Asia and the Pacific, less attention has been paid to public expenditure efficiency in infrastructure services delivery. New constructions are not the only solution, especially when governments have limited capital to invest. Globally, new infrastructure projects face delays and cost overruns, leading to an inefficient use of public resources. The root causes include the lack of transparency in project selection, the lack of project preparation, the silo approach by public entities in assessing feasibility studies, and the lack of public sector capacity to fully develop a bankable pipeline of projects. To tackle these issues, governments need a smarter investment approach and to do so, enhancing public service efficiency is very crucial. The paper suggests a “whole life cycle” (WLC) approach as the main strategic solution for the discussed issues and challenges. We expand the definition of WLC to include the entire life cycle of the infrastructure asset from need identification to its disposal. The stages comprise planning, preparation, procurement, design, construction, operation and maintenance, and disposal. This is because we believe any efficient or inefficient decision throughout such a wide life cycle influences the quality of public services. Hence, in this holistic approach, infrastructure life cycle consists of four phases: planning, preparation, procurement, and implementation. Governments could enhance public efficiency and thus improve access to finance throughout the WLC by several solutions. These are (i) preparing infrastructure master plan and pipelines and long-term budgeting during the planning phase; (ii) establishing framework and guidelines and improving governance during preparation phase; (iii) promoting standardization, transparency, open government, and contractual consistency during the procurement phase; and finally (iv) continued role of government and total asset management during the implementation phase. In addition to these phase-specific means, key WLC solutions include proper use of technology, capacity building, and private participation in general and public-private partnership (PPP) in particular.

The physical-mechanical characteristics of leather are crucial in the tanning industry since they determine whether the leather satisfies quality standards for various product manufacture. This study's goal was to assess the physical-mechanical characteristics of leather that could be washed and used for garments after the Zetestan-GF polymer was added during the tanning process. The data gathered from the physical-mechanical analysis of two treatments—one a control with white leather (T1) and the other with leather treated with Zetestan-GF polymer (T2)—were compared for the development of this work. Each treatment was performed in triplicate, undergoing three washes, yielding a total of 24 samples for analysis. Following the acquisition of the leather, a control was applied and the various treatments were compared. SAS software version 9.0 was utilized for the data's statistical analysis. The physical-mechanical properties of the control leather and the leather treated with Zetestan-GF polymer were compared using a one-way ANOVA, and any differences in the means (p < 0.05) were assessed using the Tukey test. The findings showed that while the polymer's application during the tanning process affects the parameters of softness, tensile strength, elongation percentage, and dry and wet flexometry, it has no effect on the lastometry parameter. In conclusion, the physical-mechanical characteristics of the product made by tanning cow hides can be greatly impacted by the inclusion of a polymer.

In the current competitive global marketplace, innovation is key for high-tech firms to thrive. Open innovation offers a promising approach, but its effectiveness remains unclear. Therefore, this research explored the connection between open innovation, knowledge management capability, and innovation performance within high-tech firms. We used a mediation approach to highlight the central role of knowledge management capability in the relationship between open innovation and innovation performance. We used a survey questionnaire approach to collect data from the 462 employees of high-tech firms on open innovation, knowledge management capability, and innovation performance using a convenient sampling technique. We used partial least square structural equations modeling through PLS-SEM statistics. Results indicated that open innovation has a direct, positive and significant connection with innovation performance. Similarly, the current research serves as a pioneering exploration into mediation analysis, highlighting the mediating role of knowledge management capability that influences the relationship between open innovation and innovation performance. Empirical studies offer valuable insights for leaders of high-tech firms, guiding them to identify effective knowledge management practices and determine the ideal extent of open innovation to boost innovation performance. The current study reveals novel insights into the benefits of knowledge management capability in enhancing open innovation efforts within firms. This research provides valuable implications and future research directions.

The livelihood of ethnic minority households in Vietnam is mainly in the fields of agriculture and forestry. The percentage of ethnic minorities who have jobs in industry, construction, and services is still limited. Moreover, due to harsh climate conditions, limited resources, poor market access, low education level, lack of investment capital for production, and inadequate policies, job opportunities in the off-farm and non-farm activities are very limited among ethnic minority areas. This paper assessed the contribution of livelihood diversification activities to poverty reduction of ethnic minority households in Son La Province of Vietnam. The analysis was based on the data using three stages sampling procedure of 240 ethnic minority households in Son La Province. The finding showed that the livelihood diversification activities had positively significant contribution to poverty reduction of ethnic minority households in Son La Province. In addition, the factors positively affecting the livelihood choices of ethnic minority households in Son La Province of Vietnam are education level, labor size, access to credit, membership of associations, support policies, vocational training, and district. Thus, improving ethnic minority householder’s knowledge through formal educational and training, expanding availability of accessible infrastructure, and enhancing participation of social/political associations were recommended as possible policy interventions to diversify livelihood activities so as to mitigate the level of poverty in the study area.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Researchers from all over the world have been working tirelessly to combat the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) COVID-19 pandemic since the World Health Organization (WHO) proclaimed it to be a pandemic in 2019. Expanding testing capacities, creating efficient medications, and creating safe and efficient COVID-19 (SARS CoV-2) vaccinations that provide the human body with long-lasting protection are a few tactics that need to be investigated. In clinical studies, drug delivery techniques, including nanoparticles, have been used since the early 1990s. Since then, as technology has advanced and the need for improved medication delivery has increased, the field of nanomedicine has recently seen significant development. PNPs, or polymeric nanoparticles, are solid particles or particulate dispersions that range in size from 10 to 1000 nm, and their ability to efficiently deliver therapeutics to specific targets makes them ideal drug carriers. This review article discusses the many polymeric nanoparticle (PNP) platforms developed to counteract the recent COVID-19 pandemic-related severe acute respiratory syndrome coronavirus (SARS-CoV-2). The primary subjects of this article are the size, shape, cytotoxicity, and release mechanism of each nanoparticle. The two kinds of preparation methods in the synthesis of polymeric nanoparticles have been discussed: the first group uses premade polymers, while the other group depends on the direct polymerization of monomers. A few of the PNPs that have been utilized to combat previous viral outbreaks against SARS-CoV-2 are also covered.