Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer-Learning-Based Temperature Uncertainty Reduction Algorithm for Large Scale Oil Tank Ground Settlement Monitoring

Sensors operating in open-air environments can be affected by various environmental factors. Specifically, ground settlement (GS) monitoring sensors installed in oil tanks are susceptible to non-uniform temperature fields caused by uneven sunshine exposure. This disparity in environmental conditions can lead to errors in sensor readings. To address this issue, this study aimed to analyze the impact of temperature on GS monitoring sensors and establish a mapping relationship between temperature uncertainty (fluctuations of measurement caused by temperature variation) and temperature variation. By collecting the temperature information and inferring the temperature uncertainty being introduced, this interference can be removed. However, it is crucial to note that in real-world complex scenarios, the relationship between temperature uncertainty and temperature variation is not always a constant positive correlation, which limits the data available for certain periods. Moreover, the limited availability of data presents a challenge when analyzing the complex mapping relationship. To overcome these challenges, a transfer-learning-based algorithm was introduced to develop a more accurate model for predicting temperature uncertainty based on temperature variation, even with limited data. Subsequently, a practical test was conducted to validate the proposed algorithm’s performance. The results demonstrated that the algorithm outperformed a simple linear fitting model using the least squares method (LSM), achieving an improvement of up to 21.9%. This outcome highlights the algorithm’s potential for enhancing the performance of GS sensors in daytime monitoring and contributing to the safe operation of oil tank facilities and infrastructure health monitoring.
Asynchronous Distributed Coordinated Hybrid Precoding in Multi-Cell mmWave Wireless Networks

Asynchronous distributed hybrid beamformers (ADBF) are conceived for minimizing the total transmit power subject to signal-to-interference-plus-noise ratio (SINR) constraints at the users. Our design requires only limited information exchange between the base stations (BSs) of the mmWave multi-cell coordinated (MCC) networks considered. To begin with, a semidefinite relaxation (SDR)-based fully-digital (FD) beamformer is designed for a centralized MCC system. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the FD beamformer into its analog and baseband components and construct a hybrid transmit precoder (TPC). However, the centralized TPC design requires global channel state information (CSI), hence it results in a high signaling overhead. An alternating direction based method of multipliers (ADMM) technique is developed for a synchronous distributed beamformer (SDBF) design, which relies only on limited information exchange among the BSs, thus reducing the signaling overheads required by the centralized TPC design procedure. However, the SDBF design is challenging, since it requires the updates from the BSs to be strictly synchronized. As a remedy, an ADBF framework is developed that mitigates the inter-cell interference (ICI) and also control the asynchrony in the system. Furthermore, the above ADBF framework is also extended to the robust ADBF (R-ADBF) algorithm that incorporates the CSI uncertainty into the design procedure for minimizing the the worst-case transmit power. Our simulation results illustrate both the enhanced performance and the improved convergence properties of the ADMM-based ADBF and R-ADBF schemes.
Domain-Enhanced Attention Enabled Deep Network for Behavioral Health Identification from 911 Narratives

Appropriately addressing behavioral health challenges is a problem in the United States. Behavioral health challenges can manifest in the form of crimes committed by individuals with mental illness, social and domestic violence, etc., which may result in emergency calls to first responders. For example, in domestic violence situations, involved parties may need various supports and resources if no criminal charges are filed. In these instances, when a 911 call is made, it mobilizes the entire first responder system (e.g., police, fire, EMT), each of whom write their respective incident reports. A majority of the calls that first responders are addressing are social and behavioral in nature, which they are often ill-equipped to handle. The recurring nature of these calls and limited available resources, first responders encounter challenges in providing appropriate responses to community residents. Therefore, early identification of these cases through incident reports could guide service providers and first responders to take appropriate actions on behalf of the community resident. However, the presence of noise and contextual variability within these unstructured public narrative reports makes it challenging for identifying social and behavioral health based cases. To address this challenge, in this study, we propose a novel model to analyze the first responders’ public narratives: the model learns contextual features automatically from the public narratives by employing self-attention techniques and incorporates automatically extracted domain knowledge by comparing expert-given keywords to represent document features to identify behavioral health cases. Unlike conventional approaches, integrating domain expert knowledge with the contextual representation of the public narrative report enriches the model’s understanding of social and behavioral health cues and enhances its overall performance. Extensive evaluation showcases the efficacy and effectiveness of our proposed novel model. Our evaluation shows that our model identifies behavioral health with an accuracy of 82% and F1-score of 86%, outperforms the baseline models.
Exploring Texture Ensembles by EÆcient Markov Chain Monte Carlo { Towards a "Trichromacy" Theory of Texture

This article presents a mathematical de nition of texture { the Julesz ensemble (h), which is the set of all images (de ned on Z) that share identical statistics h. Then texture modeling is posed as an inverse problem: given a set of images sampled from an unknown Julesz ensemble (h ), we search for the statistics h which de ne the ensemble. A Julesz ensemble (h) has an associated probability distribution q(I;h), which is uniform over the images in the ensemble and has zero probability outside. In a companion paper[32], q(I;h) is shown to be the limit distribution of the FRAME (Filter, Random Field, And Minimax Entropy) model[35] as the image lattice ! Z. This conclusion establishes the intrinsic link between the scienti c de nition of texture on Z and the mathematical models of texture on nite lattices. It brings two advantages to computer vision. 1). The engineering practice of synthesizing texture images by matching statistics has been put on a mathematical foundation. 2). We are released from the burden of learning the expensive FRAME model in feature pursuit, model selection and texture synthesis. In this paper, an eÆcient Markov chain Monte Carlo algorithm is proposed for sampling Julesz ensembles. The algorithm generates random texture images by moving along the directions of lter coeÆcients and thus extends the traditional single site Gibbs sampler. This paper also compares four popular statistical measures in the literature, namely, moments, recti ed functions, marginal histograms and joint histograms of linear lter responses in terms of their descriptive abilities. Our experiments suggest that a small number of bins in marginal histograms are suÆcient for capturing a variety of texture patterns. We illustrate our theory and algorithm by successfully synthesizing a number of natural textures.
Income inequality in today’s China

Significance We document a rapid increase in income inequality in China’s recent past, capitalizing on newly available survey data collected by several Chinese university survey organizations. By now, China’s income inequality not only surpasses that of the United States by a large margin but also ranks among the highest in the world, especially in comparison with countries with comparable or higher standards of living. We argue that China’s current high income inequality is significantly driven by structural factors attributable to the Chinese political system, the main structural determinants being the rural-urban divide and the regional variation in economic well-being. Using multiple data sources, we establish that China's income inequality since 2005 has reached very high levels, with the Gini coefficient in the range of 0.53–0.55. Analyzing comparable survey data collected in 2010 in China and the United States, we examine social determinants that help explain China’s high income inequality. Our results indicate that a substantial part of China’s high income inequality is due to regional disparities and the rural-urban gap. The contributions of these two structural forces are particularly strong in China, but they play a negligible role in generating the overall income inequality in the United States, where individual-level and family-level income determinants, such as family structure and race/ethnicity, play a much larger role.
Platelet Transfusion – The New Immunology of an Old Therapy

Platelet transfusion has been a vital therapeutic approach in patients with hematologic malignancies for close to half a century. Randomized trials show that prophylactic platelet transfusions mitigate bleeding in patients with acute myeloid leukemia. However, even with prophylactic transfusions, as many as 75% of patients, experience hemorrhage. While platelet transfusion efficacy is modest, questions and concerns have arisen about the risks of platelet transfusion therapy. The acknowledged serious risks of platelet transfusion include viral transmission, bacterial sepsis, and acute lung injury. Less serious adverse effects include allergic and non-hemolytic febrile reactions. Rare hemolytic reactions have occurred due to a common policy of transfusing without regard to ABO type. In the last decade or so, new concerns have arisen; platelet-derived lipids are implicated in transfusion-related acute lung injury after transfusion. With the recognition that platelets are immune cells came the discoveries that supernatant IL-6, IL-27 sCD40L, and OX40L are closely linked to febrile reactions and sCD40L with acute lung injury. Platelet transfusions are pro-inflammatory, and may be pro-thrombotic. Anti-A and anti-B can bind to incompatible recipient or donor platelets and soluble antigens, impair hemostasis and thus increase bleeding. Finally, stored platelet supernatants contain biological mediators such as VEGF and TGF-β1 that may compromise the host versus tumor response. This is particularly of concern in patients receiving many platelet transfusions, as for acute leukemia. New evidence suggests that removing stored supernatant will improve clinical outcomes. This new view of platelets as pro-inflammatory and immunomodulatory agents suggests that innovative approaches to improving platelet storage and pre-transfusion manipulations to reduce toxicity could substantially improve the efficacy and safety of this long-employed therapy.
Novel Actions of Growth Hormone in Podocytes: Implications for Diabetic Nephropathy

The kidney regulates water, electrolyte, and acid-base balance and thus maintains body homeostasis. The kidney’s potential to ensure ultrafiltered and almost protein-free urine is compromised in various metabolic and hormonal disorders such as diabetes mellitus (DM). Diabetic nephropathy (DN) accounts for ~20–40% of mortality in DM. Proteinuria, a hallmark of renal glomerular diseases, indicates injury to the glomerular filtration barrier (GFB). The GFB is composed of glomerular endothelium, basement membrane, and podocytes. Podocytes are terminally differentiated epithelial cells with limited ability to replicate. Podocyte shape and number are both critical for the integrity and function of the GFB. Podocytes are vulnerable to various noxious stimuli prevalent in a diabetic milieu that could provoke podocytes to undergo changes to their unique architecture and function. Effacement of podocyte foot process is a typical morphological alteration associated with proteinuria. The dedifferentiation of podocytes from epithelial-to-mesenchymal phenotype and consequential loss results in proteinuria. Poorly controlled type 1 DM is associated with elevated levels of circulating growth hormone (GH), which is implicated in the pathophysiology of various diabetic complications including DN. Recent studies demonstrate that functional GH receptors are expressed in podocytes and that GH may exert detrimental effects on the podocyte. In this review, we summarize recent advances that shed light on actions of GH on the podocyte that could play a role in the pathogenesis of DN.
Integrating the Stress Response: Lessons for Neurodegenerative Diseases from C. Elegans

All cells possess surveillance and homeostatic mechanisms to adjust protein biogenesis to the demands of growth, differentiation, ageing and environmental stress. However, under certain circumstances, these mechanisms fail to adequately respond to proteotoxic imbalances and result in the accumulation of mis-folded proteins. In humans, this can lead to neurode-generation and other protein conformational diseases. To protect itself, the cell employs highly conserved stress responses and chaperone networks to maintain protein-folding homeostasis (proteostasis). Although the regulation of stress responses, such as the heat-shock response, and of proteostasis have been widely considered to be cell autonomous, recent studies using Caenorhabditis elegans have shown that these processes are regulated by neuronal signaling and endocrine pathways and integrated into other functions of the organism. The hierarchical control of the cellular proteostasis machinery affords insight into the organization of stress regulatory networks in multi-cellular organisms and offers novel targets for the treatment of human protein conformational diseases. Protein misfolding and the stress response Cells respond to rapid changes in their protein biogenesis requirements or exposure to proteotoxic environmental conditions, such as heat, oxidative stress or transition metals, by inducing a highly conserved program of gene regulation (the stress response) [1–4]. Studies on unicel-lular organisms such as bacteria and yeast and on isolated Drosophila and mammalian cells have shown that the stress response is triggered cell autonomously by changes in the intracellular flux of misfolded proteins that accompany these physiological or environmental challenges [1– 4]. However, the genetic program implemented by isolated cultured cells in response to stress can differ from that of organs or whole organisms exposed to similar stress regi-mens [5,6]. The exposure of an organism to stress conditions can result in a selective and asynchronous activation of the stress response in different tissues [6,7]. Moreover, in human diseases of protein misfolding, such as Parkinson's disease and Huntington's disease, only certain cells and tissues are at risk. Despite the accumulation of damaged proteins, the stress response is not appreciably induced [3,8–10] in the affected cells, indicating that additional levels of control can exist at the orga-nismal level. Recent advances in the understanding of stress responses and proteostasis in C. elegans show that the cell-autonomous stress responses of individual cells are regulated by neuronal and endocrine signaling to yield an integrated systemic response [11–13]. C. elegans has proven valuable as a model system to study this issue [4,14– 22] (Box 1). Here, we summarize studies …
Error Bounds for Real Function Classes Based on Discretized Vapnik-Chervonenkis Dimensions

The Vapnik-Chervonenkis (VC) dimension plays an important role in statistical learning theory. In this paper, we propose the discretized VC dimension obtained by discretizing the range of a real function class. Then, we point out that Sauer\'s Lemma is valid for the discretized VC dimension. We group the real function classes having the in¯nite VC dimension into four categories by using the discretized VC dimension. As a byproduct, we present the equidistantly discretized VC dimension by introducing an equidistant partition to segmenting the range of a real function class. Finally, we obtain the error bounds for real function classes based on the discretized VC dimensions in the PAC-learning framework.
Matrix metalloproteinases and their inhibitors in connective tissue remodeling

Matrix metalloproteinases are an important group of zinc enzymes responsible for degradation of the extracellular matrix components such as collagen and proteoglycans in normal embryogenesis and remodeling and in many disease processes such as arthritis, cancer, periodontitis, and osteoporosis. A matrixin family is defined, comprising at least seven members that range in size from Mr 28000 to 92000 and are related in gene sequence to collagenase. All family members are secreted as zymogens that lose peptides of about 10,000 daltons upon activition. Latency is due to a conserved cysteine that binds to zinc at the active center. Latency is overcome by physical (chaotropic agents), chemical (HOCl, mercurials), and enzymatic (trypsin, plasmin) treatments that separate the cysteine residue from the zinc Expression of the metalloproteinases is switched on by a variety of agents acting through regulatory elements of the gene, particularly the AP‐1 binding site. A family of protein inhibitors of Mr 28,500 or less binds strongly and stoichiometrically in noncovalent fashion to inhibit members of the family. The serum protein α2‐macroglobulin and relatives are also strongly inhibitory. —Woessner, J. F. , Jr. Matrix metalloproteinases and their inhibitors in connective tissue remodeling. FASEB J. 5: 2145–2154; 1991.
Cross-domain query answering: Using Web scrapper and data integration

Information drives today's businesses and the internet is a powerhouse of information. So data integration gives the user with a unified view of all heterogeneous data sources. The basic service provided by data integration is query processing. But if we are considering a query that involves multiple domains, then we find that general purpose search engines fail to answer such queries and domain specific search services cover only one domain. Hence currently the only solution to this problem is to pose the query separately to dedicated services and feed the result of one as input to another. Our consideration could be drawn from the work in data integration, where two basic approaches has been proposed to specify the mapping between global ontology and a set of services, that are GAV and LAV. This paper presents a model, providing fully automated support to multi domain queries. This model (a) integrates different kind of services into a global ontology (b) covers query formulation aspects over global ontology and query rewriting in terms of local services (c) Several web related services that helps to conquer the problem and the package that we have offered here is XAMPP Control panel.
Performance and Determinants of India’s GDP Since 1950: A Quantitative Analysis

The planned economic development during the 40 years period (1950-91) has showed a mixed scenario. For example per annum growth in GDP during first fifteen years 1950-65 was found at 4 percent while during the period 1967-80 it declined marginally and stood at merely 3.45 percent per annum. However the decade of eighties which witnessed improvement in Agricultural sector, Mining & Manufacturing sector, Service sector and Export sector has resulted in 5.46 per cent per annum growth in the GDP. In this connection it is significant to observe that the overall growth in GDP during the period 1950 to 1991 was not very impressive due to constant pulls and pressures. In the early 1991, Indian economy faced several economic crises, like fiscal imbalances, mounting inflationary pressures and severe balance of payment crisis etc. The congress government, which assumed office at the end of June 1991, responded quickly to these problems. As a rescue measure, a series of new policy measures were announced in July 1991 by the government of India. These are also known as Structural Adjustment Programmes (SAPs) of 1991.The major thrust of present paper is to evaluate the performance (for the period 1950 to 2006) as well as determinants (for the period 1975 to 2006) of Gross Domestic Product (GDP) of Indian economy.
On three levels in analysis of dynamic capabilities of enterprises

In accordance with the connotation of dynamic capabilities, this Article herein divides the dynamic capabilities of enterprises into three levels as follows: the first level in analysis of dynamic capabilities of enterprises is “operational environment-core capability-competitive advantage”, including two aspects as the response of core capability to the environment and the correspondence of competitive advantage to core capability; the second level is centered on the analysis of response efficiency of core capability to operational environment as “environmental pre-warning-decision-making-capability transformation”, which is the embodiment of the “flow” in the theory of dynamic capabilities and concretion and materialization of the connotation of the “flow” in the framework of D.T. Teece; while the third level in analysis of dynamic capabilities is to go deep into three flows as environmental pre-warning, decision-making and capability transformation explore their respective operational efficiency, i.e. pre-warning capability, decision-making capability and transforming capability.