An analysis of long-term effects of climate change and socioeconomic activities on grassland productivity of inner Mongolia

In recent years, researchers have recognized the complexity of the interactions between the ecological system and the economic development of human society. However, the complicated relationships overwhelm traditional statistical procedures and require an innovative approach to investigate their dynamics. We proposed this study to provide a unique perspective in analyzing the long-term causal relationships between the grassland productivity, climate change, and socioeconomic development of Inner Mongolia Autonomous Region (IMAR) of China. Our attempt began with acquiring remotely sensed satellite imagery, climatic variations, and aggregated annual reports of the socio-economy of the IMAR in vegetation growing seasons for 15 years. The spatial and temporal dissimilarities of the raw observations prevented us from exploiting the potential of this valuable dataset; thus, we interpolated and extrapolated the data to generate a panel dataset with consistent spatial and temporal resolutions. Then, we took another step to preprocess the panel data by applying a signal filter to isolate the long-term trend of change from the inter- and intra-annual cyclic patterns and used the trends as the input for a panel data model. The results from our statistical analysis indicated that the independent variables explained the variations in the dependent variable extremely well, while the polynomial terms of climatic variables were significant with limited marginal effect and most of the climatic variables showed negative linear impact on the grassland productivity. In the meantime, we found not all socioeconomic variables we attempted to include into the model significantly affected grassland productivity, especially the variables describing the financial status of the IMAR residents

One Way to Subjectivity: Bell hooks on Postmodernism Blackness

Postmodernism is a new style that emerged in American culture after World War II, characterized by formalization, decentralization and commercialization. Bell hooks’ Postmodernism Blackness argues that there is an antagonistic relationship between whites and minorities. In turn, postmodern art is an important way for minorities to challenge white hegemony and build subjectivity. The author takes Captivity by Sherman Alexie, a postmodernist writing on the Other as an example to explore the validity of this theory. On the acknowledgement of the Anglo world’s long-standing oppression of Indian ethnicity, Captivity deconstructs the white imagination of Indian ethnicity and reconstructs a narrative about white, Native American as well their relationship from an Indian perspective. At the same time, the protagonist of Indian ethnicity in Captivity has an ambiguous relationship with the white character and presents an image that conforms to white stereotypes, reflecting the author Alexie’s oscillating emotional position between whiteness and Indianness, which is rooted in his specific individual experience. Through an analysis of the context as well the text, it can be seen that bell hooks successfully portray the merging of postmodernist claims of decentrality and minority liberation demands, reflecting the significance of postmodernism for minority construction of subjectivity, but simplifies the issue of identity, ignores the complexity of identity in the real situation, and thus sees national liberation as the only way out for the ethnic minorities

Field deployment process transformation in IBM PC services

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (p. 57).Field service, as an important focus area of service operations, has increasingly become a critical component of the overall service offering by high-tech enterprises. Enhancing productivity by optimizing field services could bring significant benefits to the organization. This thesis investigates the field deployment process in IBM PC services and attempts to identify potential areas of improvements by applying principles in capacity management, customer-oriented services, as well as IT technologies, such as database and the Internet. In addition, demand statistics are analyzed to provide important insights into the limitations of the existing largely manual planning and scheduling process. A transformation plan is developed, with due consideration to both the capacity and efficiency of the Customer Solution Center and the overall experience of the end users.by Siyu Fan.M.Eng

DiP: Learning Discriminative Implicit Parts for Person Re-Identification

In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods extract part features in an explicit manner, by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, a set of attributes and auxiliary losses are introduced to further improve the learning of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks

Quantum Sensing of Free Radical Generation in Mitochondria of Human Keratinocytes during UVB Exposure

Ultraviolet (UV) radiation is known to cause skin issues, such as dryness, aging, and even cancer. Among UV rays, UVB stands out for its ability to trigger problems within cells, including mitochondrial dysfunction, oxidative stress, and DNA damage. Free radicals are implicated in these cellular responses, but they are challenging to measure due to their short lifetime and limited diffusion range. In our study, we used a quantum sensing technique (T1 relaxometry) involving fluorescent nanodiamonds (FNDs) that change their optical properties in response to magnetic noise. This allowed us to monitor the free radical presence in real time. To measure radicals near mitochondria, we coated FNDs with antibodies, targeting mitochondrial protein voltage-dependent anion channel 2 (anti-VDAC2). Our findings revealed a dynamic rise in radical levels on the mitochondrial membrane as cells were exposed to UVB (3 J/cm2), with a significant increase observed after 17 min.</p

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

Macrophages—bone marrow mesenchymal stem cells crosstalk in bone healing

Bone healing is associated with many orthopedic conditions, including fractures and osteonecrosis, arthritis, metabolic bone disease, tumors and periprosthetic particle-associated osteolysis. How to effectively promote bone healing has become a keen topic for researchers. The role of macrophages and bone marrow mesenchymal stem cells (BMSCs) in bone healing has gradually come to light with the development of the concept of osteoimmunity. Their interaction regulates the balance between inflammation and regeneration, and when the inflammatory response is over-excited, attenuated, or disturbed, it results in the failure of bone healing. Therefore, an in-depth understanding of the function of macrophages and bone marrow mesenchymal stem cells in bone regeneration and the relationship between the two could provide new directions to promote bone healing. This paper reviews the role of macrophages and bone marrow mesenchymal stem cells in bone healing and the mechanism and significance of their interaction. Several new therapeutic ideas for regulating the inflammatory response in bone healing by targeting macrophages and bone marrow mesenchymal stem cells crosstalk are also discussed