Agricultural land abandonment affects millions of hectares of cultivated lands globally. While ending cultivation can lead to spontaneous reforestation and ecological benefits, the resulting landscapes often have lower social and agricultural benefits than the native forests and agricultural systems they replace, especially when non-native species dominate successional pathways. This is the case in many Pacific Islands including Hawaiʻi, where approximately 45 % of agricultural lands are unmanaged and non-native forests make up nearly 40 % of total forest cover. Agroforestry systems that integrate native and non-native culturally important plants present a potential pathway to increase social and ecological benefits of unmanaged agricultural lands; however, understanding what the restoration potential is of different agroforestry systems remains a question. We collaborated with a Native Hawaiian-led, community-based organization to explore this potential. We asked, 1) does the composition of agroforestry species planted (i.e., treatment) affect restoration success, and if so, do other factors mediate the effect of treatment, and 2) how do ecological conditions two years after starting restoration compare to conditions pre-restoration? We set up ten 12 × 15 m restoration plots and one reference plot on former pasture land regenerating as non-native forest. Then, we used a functional trait-based approach to select two agroforestry species mixes. Both mixes had high cultural value and each had traits to address a different primary ecological goal: erosion control and early successional facilitation. We monitored the plant communities before restoration and at six months, one year, and 1.5 years post-planting. We used multivariate analysis and structural equation modeling (SEM) to analyze the differences between treatments over time. We found that measures of restoration success did not vary significantly between treatments but did change from baseline. Results of the SEM indicated that understory weed cover was a significant driver of understory cover of agroforestry species, but that variability in agroforestry cover was primarily a result of management factors not tested in the model. This study provides a first step in documenting what non-native forest to agroforest transitions can look like. Our findings suggest that non-native fallows have a high potential for restoration through agroforestry in Pacific Island ecosystems.
Green recycling of poly(lactic acid) (PLA) has become the future trend for its large-scale application to cope with the “white pollution” associated. In the present study, solvent-free catalytic hydrogenolysis of PLA powder was developed with a carbon utilization of 99 % and 100 % conversion. The primary products were oxygenated liquid fuels with HHV = 29.9 MJ/kg and CO gas (∼99 %, disregard of hydrogen reactant) without the formation of any solid residues. Such hydrogenolysis process was further verified by discarded PLA straws, which possessed 95 % carbon recovery, and the derived oil held an HHV = 30.3 MJ/kg. Benefiting from the solvent-free process and near-zero-waste products, this technique provides an extraordinarily sustainable pathway for recycling PLA and its spent plastics.
This paper reviews recent developments in deep learning-based crack segmentation methods and investigates their performance under the impact from different image types. Publicly available datasets and commonly adopted performance evaluation metrics are also summarized. Moreover, an image dataset, namely the Fused Image dataset for convolutional neural Network based crack Detection (FIND), was released to the public for deep learning analysis. The FIND dataset consists of four different image types including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused image by combining the raw intensity and raw range image. To validate and demonstrate the performance boost through data fusion, a benchmark study is performed to compare the performance of nine (9) established convolutional neural network architectures trained and tested on the FIND dataset; furthermore, through the cross comparison, the optimal architectures and image types can be determined, offering insights to future studies and applications.
At a global scale, there are two major cultural groups engaged in seabird conservation—Indigenous Peoples and Local Communities (IPLC) and neocolonial conservationists. With the latter being the dominant perspective in this book, this chapter aspires to bring balance by focusing mainly on the IPLC perspective. Indigenous worldviews predate the emergence of and are therefore not born from a neoclassical worldview that perceives a separation between humanity and nature—with the latter being referred to as ecosystems. The generalized Indigenous worldview perceives humanity and nature existing as interconnected parts of a single system (a.k.a., social-ecological systems), and therefore systems of Indigenous resource management emerged that are founded in a conservation ethic. Aspects of this worldview and approaches to resource management have been adopted by many Local Communities around the world. By understanding this, we can set the stage to build a bridge between both groups in an effort to achieve more effective approaches to seabird conservation.
Breadfruit (Artocarpus altilis (Parkinson) Fosberg var. Ma'afala is an important traditional staple food crop of Samoa and Tonga that was recently introduced into commercial cultivation worldwide. To determine the impact of environmental conditions on fruit and nutritional characteristics, we evaluated the performance of Ma'afala trees planted at 23 sites across Hawai'i. Proximate analysis revealed that edaphic properties impacted energy, protein and moisture content and climate factors impacted fat and fiber content. Application of fertilizer was correlated with differences in starch content and composition. Fruit size was found to increase with water balance and soil organic content. Soil micronutrient content was not correlated with fruit micronutrient content, however, soil nitrogen levels were correlated with higher nitrogen content of fruits. We used climate modeling to predict performance of Ma'afala in different ecosystems. Our data demonstrate that ecological niche models for breadfruit cultivation may be useful in determining productivity, but not necessarily for fruit quality or nutrition. These results show that modelled future climate scenarios did not negatively impact the nutritional quality of breadfruit and the importance of breadfruit as a resilient food crop for the future.
The Mojave Desert contains the hottest, driest regions in North America and is also one of the most ecologically intact regions in the contiguous United States. However, a confluence of factors including urbanization, climate change, and energy development are rapidly transforming this ecoregion. As a result of these growing threats, even common, widespread Mojave Desert endemics are at risk of being driven to extinction by the end of the 21st century. Ironically, renewable energy development that could delay or even reverse the effects of climate change in the region is also a potentially significant source of habitat loss for these same organisms. Protecting the Mojave therefore presents difficult choices about how to select among different conservation priorities. We argue that these choices will necessarily involve compromises in which protections for some habitats will have to be prioritized while allowing development in other areas. We review the state of conservation in the Mojave and use the Mojave Desert's iconic Joshua trees (Yucca brevifolia and Y. jaegeriana) as a case study to describe a framework for identifying habitats that should be given the highest levels of protection to ensure climate change resilience. Finally, using existing spatial data, we evaluate land use and conservation status in the Mojave. The result identifies considerable scope for compromise between conservation and renewable energy development. Although our examples are specific to the Mojave, we argue that these recommendations apply broadly to many biological communities threatened by climate change.
- Hanyi Yang
- Lili Du
- Guohui Zhang
- Tianwei Ma
The information of network-wide future traffic speed distribution and its propagation is beneficial to develop proactive traffic congestion management strategies. However, predicting network-wide traffic speed propagation is non-trivial. This study develops a traffic flow dependency and dynamics based deep learning aided approach (TD²-DL), which predict network-wide high resolution traffic speed propagation by explicitly integrating temporal-spatial flow dependency, traffic flow dynamics with deep learning method techniques. Specifically, we first develop a graph theory-based method to identify the local temporal-spatial traffic dependency of each road among neighboring roads adaptive to the prediction horizon and traffic delay. Then, traffic speed propagation on every road is mathematically described by v-CTM based on traffic initial and boundary conditions. Next, the long short-term memory (LSTM) model is employed to predict boundary conditions factoring the traffic temporal-spatial dependency and historical data predicted by v-CTM. In this way, we well couple the physical models (traffic dependency and v-CTM) with the deep learning approach, and further make them coevolution under this framework. Last, an EKF is used to assimilate predicted traffic speed predicted by v-CTM coupled with the LSTMs and the field traffic data; an FNN is introduced to impute missing and corrupted data for improving the traffic speed prediction accuracy. The numerical experiments indicated that the TD²-DL predicted the network-wide traffic speed propagation in 30 minutes with accuracy varying from 85%-98%. It outperformed the tested models recently developed in literature. The ablation experimental results confirmed the significance of factoring traffic dependency and integrating data imputation and assimilation techniques for improving the prediction accuracy.
The ability to measure the activation of the unfolded protein response (UPR) in plants is important when they are exposed to stressful environments. To this end, we developed a unique and versatile biosensor-reporter system to indicate the activation of UPR in living plant cells. The small cytoplasmically spliced intron from the bZIP60 locus was incorporated into the 5’ end of the GFP gene, creating the 35S::bZIP60 intron:GFP construct. When this construct is transiently expressed in Arabidopsis protoplasts, the presence of the bZIP60 intron prevents GFP mRNA from being translated under non-UPR conditions. However, when UPR is activated, the IRE1 kinase/ribonuclease splices this intron from the GFP mRNA and its translation proceeds, generating GFP fluorescence. We demonstrated the utility of the system in Arabidopsis leaf protoplasts treated with DTT, which is a chemical inducer of UPR, followed by visualization and quantification using confocal microscopy. The 35S::bZIP60 intron:GFP construct was also expressed in protoplasts from an overexpressor line containing the coding sequence for the UPR-induced, protein folding chaperone, protein disulfide isomerase-9 (PDI9). PDI9 also influences the strength of the UPR signaling pathway. Protoplasts from WT and PDI9 overexpressor plants treated with DTT exhibited significantly higher GFP fluorescence relative to untreated protoplasts, indicating that the bZIP60 intron was spliced from the GFP mRNA in response to activation of UPR. RT-PCR further confirmed the higher induction of PDI9 and bZIP60 (total and spliced) mRNA levels in DTT-treated protoplasts relative to controls. This system can be adapted for monitoring crop stress and for basic studies dissecting the UPR signaling pathway.
The settler colonial state of Hawaii has fostered tourism as its primary economic activity, despite its being only one‐fifth to a quarter share of the economy. As a result, the push to reopen tourism in the face of COVID‐19 pandemic conditions, which ground the industry to a near halt in 2020, has been acute. Based on our long‐term involvement with UNITE HERE! Local 5 and our participation‐observation of union members’ activities since May 2020, we examine worker‐led safety protocols and practices to promote public health in the face of state and industry actors’ conscious exclusion of their expert knowledge in order to revive tourism. This exclusion put barriers in the way of hotel workers returning safely to their jobs and ultimately cost lives. We call this self‐destructive urge “autoimmune capitalism,” an autophagic assemblage that consumes the mostly immigrant and Indigenous workers integral to the operation of tourism in the state. As tourism returns, hotel workers continue to organize for life‐affirming practices even as their radical care to ensure community well‐being gets absorbed as an invisible and uncompensated component of the pandemic service economy.
In recent years, there has been a tremendous increase in the application of information and communication technologies (ICTs) in critical infrastructures, such as energy grids and communication infrastructures. Smart grids, smart cities, including novel power and energy systems, etc. are current ICT developments that are becoming more complex by the day. It is foreseeable that the future of power and energy systems will be different from today's situation due to much more decentralization, enhanced communication, monitoring capabilities, big data, and cyber‐security threat. Although various machine learning and deep learning models have been applied to the related tasks, there is still a lack of powerful techniques to equip power and energy systems for a more complex future. Recent advances in artificial intelligence techniques have led Google to develop a new technique, federated deep learning. The use of this new technique would have many advantages, such as the ability to present a global server, cover difficulties in data collection, privacy preservation, high‐performance computing, and practical data adaptation. This technique has been used in various fields recently and has attracted much attention and interest. However, it has rarely been applied in power and energy systems. In this chapter, we propose to introduce the federated algorithm in detail, present its most interesting features, and summarize recent applications of this technique in power and energy systems.
Background Management actions that address local-scale stressors on coral reefs can rapidly improve water quality and reef ecosystem condition. In response to reef managers who need actionable thresholds for coastal runoff and dredging, we conducted a systematic review and meta-analysis of experimental studies that explore the effects of sediment on corals. We identified exposure levels that ‘adversely’ affect corals while accounting for sediment bearing (deposited vs. suspended), coral life-history stage, and species, thus providing empirically based estimates of stressor thresholds on vulnerable coral reefs. Methods We searched online databases and grey literature to obtain a list of potential studies, assess their eligibility, and critically appraise them for validity and risk of bias. Data were extracted from eligible studies and grouped by sediment bearing and coral response to identify thresholds in terms of the lowest exposure levels that induced an adverse physiological and/or lethal effect. Meta-regression estimated the dose–response relationship between exposure level and the magnitude of a coral’s response, with random-effects structures to estimate the proportion of variance explained by factors such as study and coral species. Review findings After critical appraisal of over 15,000 records, our systematic review of corals’ responses to sediment identified 86 studies to be included in meta-analyses (45 studies for deposited sediment and 42 studies for suspended sediment). The lowest sediment exposure levels that caused adverse effects in corals were well below the levels previously described as ‘normal’ on reefs: for deposited sediment, adverse effects occurred as low as 1 mg/cm ² /day for larvae (limited settlement rates) and 4.9 mg/cm ² /day for adults (tissue mortality); for suspended sediment, adverse effects occurred as low as 10 mg/L for juveniles (reduced growth rates) and 3.2 mg/L for adults (bleaching and tissue mortality). Corals take at least 10 times longer to experience tissue mortality from exposure to suspended sediment than to comparable concentrations of deposited sediment, though physiological changes manifest 10 times faster in response to suspended sediment than to deposited sediment. Threshold estimates derived from continuous response variables (magnitude of adverse effect) largely matched the lowest-observed adverse-effect levels from a summary of studies, or otherwise helped us to identify research gaps that should be addressed to better quantify the dose–response relationship between sediment exposure and coral health. Conclusions We compiled a global dataset that spans three oceans, over 140 coral species, decades of research, and a range of field- and lab-based approaches. Our review and meta-analysis inform the no-observed and lowest-observed adverse-effect levels (NOAEL, LOAEL) that are used in management consultations by U.S. federal agencies. In the absence of more location- or species-specific data to inform decisions, our results provide the best available information to protect vulnerable reef-building corals from sediment stress. Based on gaps and limitations identified by our review, we make recommendations to improve future studies and recommend future synthesis to disentangle the potentially synergistic effects of multiple coral-reef stressors.
Background Native Hawaiians are disproportionately affected by type 2 diabetes mellitus (DM), a chronic metabolic, non-communicable disease characterized by hyperglycemia and systemic inflammation. Unrelenting systemic inflammation frequently leads to a cascade of multiple comorbidities associated with DM, including cardiovascular disease, microvascular complications, and renal dysfunction. Yet few studies have examined the link between chronic inflammation at a cellular level and its relationship to standard DM therapies such as diabetes-specific lifestyle and social support education, well recognized as the cornerstone of clinical standards of diabetes care. This pilot study was initiated to explore the association of monocyte inflammation using epigenetic, immunologic, and clinical measures following a 3-month diabetes-specific social support program among high-risk Native Hawaiian adults with DM. Results From a sample of 16 Native Hawaiian adults with DM, monocytes enriched from peripheral blood mononuclear cells (PBMCs) of 8 individuals were randomly selected for epigenomic analysis. Using the Illumina HumanMethylation450 BeadChip microarray, 1,061 differentially methylated loci (DML) were identified in monocytes of participants at baseline and 3 months following a DM-specific social support program (DM-SSP). Gene ontology analysis showed that these DML were enriched within genes involved in immune, metabolic, and cardiometabolic pathways, a subset of which were also significantly differentially expressed. Ex vivo analysis of immune function showed improvement post-DM-SSP compared with baseline, characterized by attenuated interleukin 1β and IL-6 secretion from monocytes. Altered cytokine secretion in response to the DM-SSP was significantly associated with changes in the methylation and gene expression states of immune-related genes in monocytes between intervention time points. Conclusions Our pilot study provides preliminary evidence of changes to inflammatory monocyte activity, potentially driven by epigenetic modifications, 3 months following a DM-specific SSP intervention. These novel alterations in the trajectory of monocyte inflammatory states were identified at loci that regulate transcription of immune and metabolic genes in high-risk Native Hawaiians with DM, suggesting a relationship between improvements in psychosocial behaviors and shifts in the immunoepigenetic patterns following a diabetes-specific SSP. Further research is warranted to investigate how social support influences systemic inflammation via immunoepigenetic modifications in chronic inflammatory diseases such as DM.
The Angkor empire (9-15th centuries CE) was one of mainland Southeast Asia's major civilizations, with a 3000 km² agro-urban capital located in northwest Cambodia. Since 2010, the Greater Angkor Project has been investigating occupation areas within Angkor's urban core. This work has identified temple enclosures as important residential areas that made up part of Angkor's civic-ceremonial center. In this paper, we review excavations from residential areas within Angkor Wat's temple enclosure. We concentrate on evidence for residential patterning by focusing on our 2015 excavations, one of the largest horizontal excavations of a single occupation mound within Angkor's civic-ceremonial center. These data offer further evidence for archaeological patterns of residential occupation within the Angkor Wat temple enclosure and a comparative dataset for future research of habitation areas within Angkor as well as domestic spaces in other urban settings.
Many science phenomena are described as interacting particle systems (IPS). The mean field limit (MFL) of large all-to-all coupled deterministic IPS is given by the solution of a PDE, the Vlasov Equation (VE). Yet, many applications demand IPS coupled on networks/graphs. In this paper, we are interested in IPS on a sequence of directed graphs, or digraphs for short. It is interesting to know, how the limit of a sequence of digraphs associated with the IPS influences the macroscopic MFL. This paper studies VEs on a generalized digraph, regarded as limit of a sequence of digraphs, which we refer to as a digraph measure (DGM) to emphasize that we work with its limit via measures. We provide (i) unique existence of solutions of the VE on continuous DGMs, and (ii) discretization of the solution of the VE by empirical distributions supported on solutions of an IPS via ODEs coupled on a sequence of digraphs converging to the given DGM. Our result extends existing results on one-dimensional Kuramoto-type networks coupled on dense graphs. Here we allow the underlying digraphs to be not necessarily dense which include many interesting graphical structures such that stars, trees and rings, which have been frequently used in many sparse network models in finance, telecommunications, physics, genetics, neuroscience, and social sciences. A key contribution of this paper is a nontrivial generalization of Neunzert's in-cell-particle approach for all-to-all coupled indistinguishable IPS with global Lipschitz continuity in Euclidean spaces to distinguishable IPS on heterogeneous digraphs with local Lipschitz continuity, via a measure-theoretic viewpoint. The approach together with the metrics is different from the known techniques in Lp-functions using graphons and their generalization by means of harmonic analysis of locally compact Abelian groups. Finally, to demonstrate the wide applicability, we apply our results to various models in higher-dimensional Euclidean spaces in epidemiology, ecology, and social sciences.
Experience in research facilitates development of science identity and encourages undergraduate student persistence along the pathway to careers in science, technology, engineering , and math (STEM). Participation in authentic research can foster identity development by influencing a sense of belonging, recognition, interest, and performance and competence in science. We examine science identity in a group of five community college women in marine science during a 2-year study in which students participated in a research experience. We used interviews, surveys, identity artifacts, and significant circles before and after the research experience in a thematic analysis to explore identities and examine their intentions, interests, perspectives, and aspirations for a future career. Participation in research provided opportunities for students to gain conceptual understanding of themselves and their abilities in science as well as explore and clarify their professional interests. This work builds upon our current understanding by providing evidence that conceptualization of career trajectories and self as a science professional is an important component of identity. Exploring career options and developing professional conceptualization are critical components in science research experiences and warrants additional study to understand the role of professional conceptualization in shaping student trajectories in STEM.
Plastics are one of the most used materials in the world. Their indiscriminate use and inappropriate disposal have led to inevitable impacts, for instance ingestion, on the environment arousing the attention of the global community. In addition, plastic ingestion studies are often written in scientific jargon or hidden behind paywalls, which makes these studies inaccessible. GLOVE is an online and open-access dashboard database available at gloveinitiative.shinyapps.io/Glove/ to support scientists, decision-makers, and society with information collected from plastic ingestion studies. The platform was created in the R environment, with a web interface developed through Shiny. It already comprises 530 studies, including all biological groups, with 245,366 individual records of 1458 species found in marine, freshwater, and terrestrial environments. The main goal of the GLOVE dashboard database is to improve data accessibility by being a scientifically useful grounded tool for designing effective and innovative actions in the current scenario of upcoming global and local agreements and actions on plastic pollution.
International transfer pricing and tax compliance are currently receiving a great deal of attention among tax policy makers and multinational enterprises (MNEs). In particular, the OECD Action Plan on Base Erosion and Profit Shifting (OECD, 2013) and subsequent updates to the OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations (OECD, 2017; OECD, 2022) have driven a shift among MNEs toward more compliance-based transfer pricing strategies. One of the significant challenges for MNEs relates to the international transfer pricing for intra-group services based on the OECD Guidelines, Chapter VII. This intra-group transaction is subject to massive attention from tax authorities as service cost allocations between MNE group companies are often significant due to highly centralized intra-group service arrangements. Current cost accounting textbooks largely ignore the implications of tax regulation for intra-group service cost allocations. This case study develops your critical thinking skills, particularly regarding how to determine tax-compliant transfer prices for intra-group services in accordance with OECD Transfer Pricing Guidelines, Chapter VII, and the arm’s length principle. Furthermore, it enhances your ability to reflect on the implications of tax regulation for cost and management accounting.
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