An Overview of Sustainability Content in Higher Education: Applications for University Landscape Architecture Programs

Higher education institutions worldwide have recognized the importance of integrating sustainability into their programs, with over 600 universities offering courses focused on sustainable development. This trend has led to the emergence of Education for Sustainable Development (ESD). This multidimensional approach aims to empower individuals to create a sustainable future by integrating environmental, social, and economic systems. In particular, ESD has been implemented in various aspects of higher education, such as course content, teaching methodologies, curriculum design, and faculty roles. Design and planning education are critical components of shaping future decision-makers who will positively and negatively impact society and the environment. However, despite its potential to tackle complex design challenges, sustainability education in landscape architecture (LA) has received less attention from academia than other design and planning disciplines. As such, there is a need to prioritize integrating ESD into LA education to prepare future professionals for addressing social and environmental challenges. The objective of this dissertation is to investigate the integration of ESD in LA education and to identify the approaches utilized and the benefits and challenges of integrating ESD into LA programs. The research method combines quantitative and qualitative research approaches, including surveys, syllabi, and document analysis. Therefore, the findings of this paper will inform LA educators and practitioners on best practices for integrating ESD into LA programs, preparing future professionals to address complex social and environmental challenges

A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement

Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents the typical performance metrics and explores public datasets for constructing these measures. The results show that accurate (semantic) segmentation is inevitable not only for fine-grained greenness measures but also for the qualitative evaluation of landscape analyses for planning amidst the incomplete explainability of the DL model. Also, the unsupervised domain adaptation (UDA) in aerial images is addressed to overcome the scale changes and lack of labeled data for fine-grained greenness measures. This review contributes to helping researchers understand the recent breakthroughs in DL-based segmentation technology for challenging topics in UGS research

Extrusion-Based 3D Printing of Molecular Sieve Zeolite for Gas Adsorption Applications

Extrusion based 3D printing is one of the emerging additive manufacturing technologies used for printing range of materials from metal to ceramics. In this study, we developed a customized 3D printer based on extrusion freeform fabrication technique, such as slurry deposition, for 3D printing of different molecular sieve zeolite monoliths like 3A, 4A, 5A and 13X to evaluate their performance in gas adsorption. The physical and structural properties of 3D printed zeolite monoliths will be characterized along with the gas adsorption performance. The Brunauer–Emmett–Teller (BET) test of 3D printed samples will be performed for calculation of the surface area, which will give us the capacity of gas absorption into 3D printed zeolite. The BET surface area test showed good results for Zeolite 13X compared to available literature. The surface area calculated for 3D – printed Zeolite 13X was 767m2/g and available literature showed 498 m2/g for 3D – printed Zeolite 13X. The microhardness values of 3D – printed Zeolite samples were measured using a Vicker hardness tester. The hardness value of the 3D - printed Zeolite samples increased from 8.3 ± 2 to 12.5 ± 3 HV 0.05 for Zeolite 13X, 3.3 ± 1 to 7.3 ± 1 HV 0.05 for Zeolite 3A, 4.3 ± 2 to 7.5 ± 2 HV 0.05 for Zeolite 4A, 7.4 ± 1 to 14.0 ± 0.5 HV 0.05 for Zeolite 5A, before and after sintering process, respectively. The SEM analysis was performed for 3D printed samples before and after sintering to evaluate their structural properties. The SEM analysis reveals that all 3D – printed Zeolite samples retained their microstructure after slurry preparation and also after the sintering process. The porous nature of 3D – printed Zeolite walls was retained after the sintering process

Thermal properties of La2Zr2O7 double-layer thermal barrier coatings

La2Zr2O7 is a promising thermal barrier coating (TBC) material. In this work, La2Zr2O7 and 8YSZ-layered TBC systems were fabricated. Thermal properties such as thermal conductivity and coefficient of thermal expansion were investigated. Furnace heat treatment and jet engine thermal shock (JETS) tests were also conducted. The thermal conductivities of porous La2Zr2O7 single-layer coatings are 0.50–0.66 W m−1 °C−1 at the temperature range from 100 to 900°C, which are 30–40% lower than the 8YSZ coatings. The coefficients of thermal expansion of La2Zr2O7 coatings are about 9–10 × 10−6 °C−1 at the temperature range from 200 to 1200°C, which are close to those of 8YSZ at low temperature range and about 10% lower than 8YSZ at high temperature range. Double-layer porous 8YSZ plus La2Zr2O7 coatings show a better performance in thermal cycling experiments. It is likely because porous 8YSZ serves as a buffer layer to release stress

How Are Politicians Informed? Witnesses and Information Provision in Congress

How are politicians informed and who do politicians seek information from? The role of information has been at the center for research on legislative organizations but there is a lack of systematic empirical work on the information that Congress seeks to acquire and consider. To examine the information flow between Congress and external groups, we construct the most comprehensive dataset to date on 74,082 congressional committee hearings and 755,540 witnesses spanning 1960-2018. We show descriptive patterns of how witness composition varies across time and committee, and how different types of witnesses provide varying levels of analytical information. We develop theoretical expectations for why committees may invite different types of witnesses based on committee intent, inter-branch relations, and congressional capacity. Our empirical evidence shows how committees' partisan considerations can affect how much committees turn to outsiders for information and from whom they seek information

The ubiquitin receptor S5a/Rpn10 links centrosomal proteasomes with dendrite development in the mammalian brain

SummaryProteasomes drive the selective degradation of protein substrates with covalently linked ubiquitin chains in eukaryotes. Although proteasomes are distributed throughout the cell, specific biological functions of the proteasome in distinct subcellular locales remain largely unknown. We report that proteasomes localized at the centrosome regulate the degradation of local ubiquitin conjugates in mammalian neurons. We find that the proteasomal subunit S5a/Rpn10, a ubiquitin receptor that selects substrates for degradation, is essential for proteasomal activity at centrosomes in neurons and thereby promotes the elaboration of dendrite arbors in the rodent brain in vivo. We also find that the helix-loop-helix protein Id1 disrupts the interaction of S5a/Rpn10 with the proteasomal lid and thereby inhibits centrosomal proteasome activity and dendrite elaboration in neurons. Together, our findings define a function for a specific pool of proteasomes at the neuronal centrosome and identify a biological function for S5a/Rpn10 in the mammalian brain

Analysis of carotenoid accumulation and expression of carotenoid biosynthesis genes in different organs of Chinese cabbage (Brassica rapa subsp. pekinensis)

The relationship between carotenoid accumulation and expression of carotenoid biosynthesis genes was investigated in the flowers, stems, young leaves, old leaves, and roots of Chinese cabbage (Brassica rapa subsp. pekinensis). Quantitative real-time PCR analysis showed that the mRNA levels of BrPSY, BrPDS, BrZDS, BrLCYB, BrLCYE, BrCHXB, and BrZEP leading to the production of carotenoids were highest in the flowers or the leaves and lowest in the roots of Chinese cabbage. In contrast, the mRNA expression of BrNCED, a gene involved in abscisic acid (ABA) biosynthesis, was highest in the roots. High-performance liquid chromatography revealed that carotenoids, namely, lutein and β-carotene, were distributed predominantly in the flowers and leaves, with very little in the underground organ, the roots. Specifically, old leaves contained 120.3 μg/g lutein and 103.93 μg/g β-carotene, which is the most potent dietary precursor of vitamin A. Moreover, we found a relatively large amount of cis isomers of β-carotene, namely, 9-cis β-carotene and 13-cis β-carotene, in Chinese cabbage. These results provide insight into carotenoid biosynthetic mechanisms in Chinese cabbage and may be helpful in the metabolic engineering of carotenoid biosynthesis in plants

Unleashing the full potential of Hsp90 inhibitors as cancer therapeutics through simultaneous inactivation of Hsp90, Grp94, and TRAP1

Cancer therapeutics: Extending a drug's reach A new drug that blocks heat shock proteins (HSPs), helper proteins that are co-opted by cancer cells to promote tumor growth, shows promise for cancer treatment. Several drugs have targeted HSPs, since cancer cells are known to hijack these helper proteins to shield themselves from destruction by the body. However, the drugs have had limited success. Hye-Kyung Park and Byoung Heon Kang at Ulsan National Institutes of Science and Technology in South Korea and coworkers noticed that the drugs were not absorbed into mitochondria, a key cellular compartment, and HSPs in this compartment were therefore not being blocked. They identified a new HSP inhibitor that can reach every cellular compartment and inhibit all HSPs. Testing in mice showed that this inhibitor effectively triggered death of tumor cells, and therefore shows promise for anti-cancer therapy. The Hsp90 family proteins Hsp90, Grp94, and TRAP1 are present in the cell cytoplasm, endoplasmic reticulum, and mitochondria, respectively; all play important roles in tumorigenesis by regulating protein homeostasis in response to stress. Thus, simultaneous inhibition of all Hsp90 paralogs is a reasonable strategy for cancer therapy. However, since the existing pan-Hsp90 inhibitor does not accumulate in mitochondria, the potential anticancer activity of pan-Hsp90 inhibition has not yet been fully examined in vivo. Analysis of The Cancer Genome Atlas database revealed that all Hsp90 paralogs were upregulated in prostate cancer. Inactivation of all Hsp90 paralogs induced mitochondrial dysfunction, increased cytosolic calcium, and activated calcineurin. Active calcineurin blocked prosurvival heat shock responses upon Hsp90 inhibition by preventing nuclear translocation of HSF1. The purine scaffold derivative DN401 inhibited all Hsp90 paralogs simultaneously and showed stronger anticancer activity than other Hsp90 inhibitors. Pan-Hsp90 inhibition increased cytotoxicity and suppressed mechanisms that protect cancer cells, suggesting that it is a feasible strategy for the development of potent anticancer drugs. The mitochondria-permeable drug DN401 is a newly identified in vivo pan-Hsp90 inhibitor with potent anticancer activity

Machine Learning in Additive Manufacturing: A Review

In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested