Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Thermal and Physical properties of Biofield Treated Acrylamide and 2-Chloroacetamide

Acrylamide (AM) and 2-chloroacetamide (CA) are widely used in diverse applications such as biomedical, drug delivery, waste water treatment, and heavy metal ion removal. The objective of this study was to evaluate the influence of biofield treatment on physical and thermal properties of amide group containing compounds (AM and CA). The study was performed in two groups (control and treated). The control group remained as untreated, and biofield treatment was given to treated group. The control and treated compounds were characterized by X-ray diffraction (XRD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and surface area analysis. XRD of treated AM showed decrease in intensity of peaks as compared to control sample. However, the treated AM showed increase in volume of unit cell (0.16%) and molecular weight (0.16%) as compared to control. The crystallite size was decreased by 33.34% in treated AM as compared to control Whereas, the XRD diffractogram of treated CA showed increase in intensity of crystalline peaks as compared to control. The percentage volume of unit cell (-1.92%) and molecular weight (-1.92%) of treated CA were decreased as compared to control. However, significant increase in crystallite size (129.79%) was observed in treated CA as compared to control. DSC of treated AM showed increase in melting temperature as compared to control sample. Similarly, the treated CA also showed increase in melting temperature with respect to control. Latent heat of fusion (ΔH) was significantly changed in treated AM and CA as compared to control samples. TGA showed increase in thermal stability of treated AM and CA which was evidenced by increase in thermal decomposition temperature (Tmax) as compared to control. Surface area analysis of treated AM showed increase (31.6%) in surface area as compared to control. However, a decrease (30.9%) in surface area was noticed in treated CA as compared to control. Study results suggest that biofield treatment has significant impact on the physical and thermal properties of AM and CA.
Development of accelerated aging test for ESD/EMI protective materials and electrical discontinuity at seams and interconnections

Many studies of new protective ESD/EMI materials, such as antistats, and conductive members of a seam or interconnection, such as metals that make up corrodible seams and interconnections, should be regarded as incomplete. Little or no attention has been paid to changes in essential material properties exposed to various environments because the test takes too much time. A procedure for development of accelerated tests is described. There are a number of variables that can be used to reduce the time usually required for accelerated aging tests. These variables include, but are not limited to, temperature, percent relative humidity, mechanical and electrical properties. Examples from the literature are cited to show how temperature can be used to develop acceleration factors to reduce the time for aging in outgassing and resistivity aging. Corrosion reactions can degrade the ESD/EMI protected product by promoting electromagnetic emissions and also producing electrical discontinuity at seams (EDS) and interconnections (EDI). EDS can result in secondary ESD events. EDI can result in circuit malfunctioning. Under these conditions, temperature can be used to produce large acceleration factors, thus reducing the time to simulate the lifetime aging process of the electronic product.
A Sensitivity Analysis to Separate Bias Due to Confounding from Bias Due to Predicting Misclassification by a Variable That Does Both

Variables that predict misclassification of exposure, outcome, or a confounder cannot be controlled by techniques that adjust for predictors of risk. They must be controlled by external adjustments. We confronted an analysis in which a variable predicted misclassification of the exposure and of a confounder. The same variable confounded the exposure-outcome relation. The analysis focused on the relation between less-than-definitive therapy and breast cancer mortality in the 5 years after diagnosis. Receipt of less-than-definitive prognostic evaluation predicted misclassification of definitive therapy (the exposure) and stage (a confounder). Prognostic evaluation also confounded the therapy-breast cancer mortality relation. We used a sensitivity analysis to separate the misclassification biases from the confounding bias. The relative hazard associated with less-than-definitive therapy in the original multivariable model equaled 1.75 (95% confidence interval = 1.02–3.00). The median estimate in 2,500 repetitions of the sensitivity analysis was a relative hazard of 1.64, and 90% of the estimates fell between 1.47 and 1.83. The sensitivity analysis suggests that less-than-definitive therapy confers an excess relative hazard of breast cancer mortality in the 5 years after diagnosis. The original analysis, which adjusted for confounding by prognostic evaluation but not its misclassification biases, overestimated the relative hazard.
Application of Imperialist Competitive Algorithm for Optimum Iterative Learning Control Model

This study introduce the robust iterative learning control (ILC) model for uncertain single input-single output (SISO) linear time invariant (LTI) control systems. In large cases of industrial robot employments it is a fact that the robot is learned to do the same task repeatedly several times. By looking at the control fault in the independent and several iterations of the same task it becomes obvious that it is actually highly repetitive. The ILC permits to iteratively compensate for and, therefore, cancel this repetitive fault. In this article different issues of iterative learning control are investigated. Although stability margin is the main significant in practice the model aspect is also bolded. some design schemes for iterative learning control techniques are introduced, consisting 1st order as well as 2nd order iterative learning control and parameter balancing. The basic characters of the model are that: The control signal is continuous and the coefficient of controller is optimized. Therefore it is chattering-free compared with the robust ILC using conventional 1st-order sliding mode method. In the suggested method, free variables of controller have valuable figure in the performance of system. Hence we propose Imperialist Competitive Algorithm (ICA) for selecting the excellent value of these parameters. The suggested method is examined on robotic manipulator case and computer simulations show that the proposed method has high performance.
When trees collide: an approximation algorithm for the generalized Steiner problem on networks

We give the first approximation algorithm for the generalized network Steiner problem, a problem in network design. An instance consists of a network with link-costs and, for each pair $\{i,j\}$ of nodes, an edge-connectivity requirement $r_{ij}$. The goal is to find a minimum-cost network using the available links and satisfying the requirements. Our algorithm outputs a solution whose cost is within $2\lceil \log_2(r+1)\rceil$ of optimal, where $r$ is the highest requirement value. In the course of proving the performance guarantee, we prove a combinatorial min-max approximate equality relating minimum-cost networks to maximum packings of certain kinds of cuts. As a consequence of the proof of this theorem, we obtain an approximation algorithm for optimally packing these cuts; we show that this algorithm has application to estimating the reliability of a probabilistic network.
Challenges in Community Resilience Planning and Opportunities with Simulation Modeling

The importance of community resilience has become increasingly recognized in emergency management and post-disaster community well-being. To this end, three seismic resilience planning initiatives have been conducted in the U.S. in the last decade to envision the current state of community resilience. Experts who participated in these initiatives confronted challenges that must be addressed for future planning initiatives. We interviewed eighteen participants to learn about the community resilience planning process, its characteristics, and challenges. Conducting qualitative content analysis, we identify six main challenges to community resilience planning: complex network systems; interdependencies among built environment systems; inter-organizational collaboration; connections between the built environment and social systems; communications between built environment and social institutions' experts; and communication among decision-makers, social stakeholders, and community members. To overcome the identified challenges, we discuss the capability of human-centered simulation modeling as a combination of simulation modeling and human-centered design to facilitate community resilience planning.
Assessing Pairing and Data Exchange Mechanism Security in the Wearable Internet of Things

The consumer wearable economy is a rapidly growing sector with an ever increasingly number of use cases mostly focused on the quantified self. Whether used for fitness tracking, mobile health monitoring, or as remote controllers for connected smartphone apps, wearables typically come equipped with a wide variety of different sensors such as accelerometers, pulsometors, and thermometers to capture data such as, respectively, the user's movements, heart-rate, and temperature. Once data is captured it is then typically wirelessly transmitted, using Bluetooth LE (low energy) to an awaiting smartphone. Since the data may be sensitive and/or personally identifiable, it is critical that this exchange and the pairing mechanisms used to set up the connection remain secure and resilient to eavesdropping attacks. This paper empirically evaluates the data exchange mechanisms of a variety of major commercial wearable products to determine if, and how well, the products live up to this security constraint. As part of this effort, the work also investigates the three different types of Bluetooth LE pairing strategies at a packet and protocol level. The results show presumably secure pairing strategies have glaring security vulnerabilities that affect all of the devices examined. In addition to this publication, efforts are underway to report these vulnerabilities to US-CERT.
Chapter 1 Managing Design Change with Functional Blueprints

Long-lived complex electromechanical systems, such as vehicles or industrial machinery, often need to be adapted for new uses or new environments. Adapting the design for such a system is frequently complicated by the fact that they are often tightly integrated, such that any change will have consequences throughout the design, and must take many different aspects of the system into consideration. Functional blueprints simplify adaptation by incorporating the reasons for design decisions and their consequences directly into the specification of a system. This allows a human designer to be supported by automated reasoning that can identify potential conflicts, suggest design fixes, and propagate changes implicit in the choices of the designer. This chapter presents the functional blueprints approach in detail, including both review of prior work and new results. 1.
Structural modeling using overlapped group penalties for discovering predictive biomarkers for subgroup analysis

The identification of predictive biomarkers from a large scale of covariates for subgroup analysis has attracted fundamental attention in medical research. In this article, we propose a generalized penalized regression method with a novel penalty function, for enforcing the hierarchy structure between the prognostic and predictive effects, such that a nonzero predictive effect must induce its ancestor prognostic effects being nonzero in the model. Our method is able to select useful predictive biomarkers by yielding a sparse, interpretable, and predictable model for subgroup analysis, and can deal with different types of response variable such as continuous, categorical, and time-to-event data. We show that our method is asymptotically consistent under some regularized conditions. To minimize the generalized penalized regression model, we propose a novel integrative optimization algorithm by integrating the majorization-minimization and the alternating direction method of multipliers, which is named after \texttt{smog}. The enriched simulation study and real case study demonstrate that our method is very powerful for discovering the true predictive biomarkers and identifying subgroups of patients.
How forelimb and hindlimb function changes with incline and perch diameter in the green anole, Anolis carolinensis

SUMMARY The range of inclines and perch diameters in arboreal habitats poses a number of functional challenges for locomotion. To effectively overcome these challenges, arboreal lizards execute complex locomotor behaviors involving both the forelimbs and the hindlimbs. However, few studies have examined the role of forelimbs in lizard locomotion. To characterize how the forelimbs and hindlimbs differentially respond to changes in substrate diameter and incline, we obtained three-dimensional high-speed video of green anoles (Anolis carolinensis) running on flat (9 cm wide) and narrow (1.3 cm) perches inclined at 0, 45 and 90 deg. Changes in perch diameter had a greater effect on kinematics than changes in incline, and proximal limb variables were primarily responsible for these kinematic changes. In addition, a number of joint angles exhibited greater excursions on the 45 deg incline compared with the other inclines. Anolis carolinensis adopted strategies to maintain stability similar to those of other arboreal vertebrates, increasing limb flexion, stride frequency and duty factor. However, the humerus and femur exhibited several opposite kinematic trends with changes in perch diameter. Further, the humerus exhibited a greater range of motion than the femur. A combination of anatomy and behavior resulted in differential kinematics between the forelimb and the hindlimb, and also a potential shift in the propulsive mechanism with changes in external demand. This suggests that a better understanding of single limb function comes from an assessment of both forelimbs and hindlimbs. Characterizing forelimb and hindlimb movements may reveal interesting functional differences between Anolis ecomorphs. Investigations into the physiological mechanisms underlying the functional differences between the forelimb and the hindlimb are needed to fully understand how arboreal animals move in complex habitats.
Context-aware operational decision support

The paper presents research on context-aware operational decision support for emergency management. Ontology-based context model is proposed for organization of context-aware decision support Two types of context are used: abstract context that is an ontology-based model integrating information and knowledge relevant to a decision situation, and operational context that is an instantiated abstract context within the personalization of decision support systems multi-role decision support is offered proposed ideas are illustrated through a case study of disaster relief and evacuation.
EventNet Version 1.1 Technical Report

EventNet is a large-scale video corpus and event ontology consisting of 500 events associated with event-specific concepts. In order to improve the quality of the current EventNet, we conduct the following steps and introduce EventNet version 1.1: (1) manually verify the correctness of event labels for all videos; (2) remove the YouTube user bias by limiting the maximum number of videos in each event from the same YouTube user as 3; (3) remove the videos which are currently not accessible online; (4) remove the video belonging to multiple event categories. After the above procedure, some events may contain only a small number of videos, and therefore we crawl more videos for those events to ensure every event will contain more than 50 videos. Finally, EventNet version 1.1 contains 67,641 videos, 500 events, and 5,028 event-specific concepts. In addition, we train a Convolutional Neural Network (CNN) model for event classification via fine-tuning AlexNet using EventNet version 1.1. Then we use the trained CNN model to extract FC7 layer feature and train binary classifiers using linear SVM for each event-specific concept. We believe this new version of EventNet will significantly facilitate research in computer vision and multimedia, and will put it online for public downloading in the future.
Dendritic Cell-Induced Th1 and Th17 Cell Differentiation for Cancer Therapy

The success of cellular immunotherapies against cancer requires the generation of activated CD4+ and CD8+ T-cells. The type of T-cell response generated (e.g., Th1 or Th2) will determine the efficacy of the therapy, and it is generally assumed that a type-1 response is needed for optimal cancer treatment. IL-17 producing T-cells (Th17/Tc17) play an important role in autoimmune diseases, but their function in cancer is more controversial. While some studies have shown a pro-cancerous role for IL-17, other studies have shown an anti-tumor function. The induction of polarized T-cell responses can be regulated by dendritic cells (DCs). DCs are key regulators of the immune system with the ability to affect both innate and adaptive immune responses. These properties have led many researchers to study the use of ex vivo manipulated DCs for the treatment of various diseases, such as cancer and autoimmune diseases. While Th1/Tc1 cells are traditionally used for their potent anti-tumor responses, mounting evidence suggests Th17/Tc17 cells should be utilized by themselves or for the induction of optimal Th1 responses. It is therefore important to understand the factors involved in the induction of both type-1 and type-17 T-cell responses by DCs.
Epitope Mapping of Avian Influenza M2e Protein: Different Species Recognise Various Epitopes

A common approach for developing diagnostic tests for influenza virus detection is the use of mouse or rabbit monoclonal and/or polyclonal antibodies against a target antigen of the virus. However, comparative mapping of the target antigen using antibodies from different animal sources has not been evaluated before. This is important because identification of antigenic determinants of the target antigen in different species plays a central role to ensure the efficiency of a diagnostic test, such as competitive ELISA or immunohistochemistry-based tests. Interest in the matrix 2 ectodomain (M2e) protein of avian influenza virus (AIV) as a candidate for a universal vaccine and also as a marker for detection of virus infection in vaccinated animals (DIVA) is the rationale for the selection of this protein for comparative mapping evaluation. This study aimed to map the epitopes of the M2e protein of avian influenza virus H5N1 using chicken, mouse and rabbit monoclonal or monospecific antibodies. Our findings revealed that rabbit antibodies (rAbs) recognized epitope 6EVETPTRN13 of the M2e, located at the N-terminal of the protein, while mouse (mAb) and chicken antibodies (cAbs) recognized epitope 10PTRNEWECK18, located at the centre region of the protein. The findings highlighted the difference between the M2e antigenic determinants recognized by different species that emphasized the importance of comparative mapping of antibody reactivity from different animals to the same antigen, especially in the case of multi-host infectious agents such as influenza. The findings are of importance for antigenic mapping, as well as diagnostic test and vaccine development.
How does satisfaction with the health-care system relate to patient experience?

OBJECTIVE To explore what determines people's satisfaction with the health-care system above and beyond their experience as patients. METHODS Data on health system responsiveness, which refers to the manner and environment in which people are treated when they seek health care, provides a unique opportunity to better understand the determinants of people's satisfaction with the health-care system and how strongly this is influenced by an individual's experience as a patient. The data were obtained from 21 European Union countries in the World Health Survey for 2003. Additive ordinary least-squares regression models were used to assess the extent to which variables commonly associated with satisfaction with the health-care system, as recorded in the literature, explain the variation around the concept of satisfaction. A residual analysis was used to identify other predictors of satisfaction with the health-care system. FINDINGS Patient experience was significantly associated with satisfaction with the health-care system and explained 10.4% of the variation around the concept of satisfaction. Other factors such as patient expectations, health status, type of care, and immunization coverage were also significant predictors of health system satisfaction; although together they explained only 17.5% of the observed variation, while broader societal factors may largely account for the unexplained portion of satisfaction with the health-care system. CONCLUSION Contrary to published reports, people's satisfaction with the health-care system depends more on factors external to the health system than on the experience of care as a patient. Thus, measuring the latter may be of limited use as a basis for quality improvement and health system reform.
Cumulative sum analysis of learning curve for video-assisted mini-laparotomy partial nephrectomy in renal cell carcinoma

Abstract Video-assisted mini-laparotomy surgery (VAMS), a hybrid of open and laparoscopic surgical techniques, is an important surgical approach in the field of partial nephrectomy. The learning curve for VAMS partial nephrectomy has not been studied to date; we therefore, evaluated this learning curve. We prospectively evaluated 20 consecutive patients who underwent VAMS partial nephrectomy performed by a single surgeon (YEY) between March 2015 and December 2016. The learning curve was evaluated using the cumulative sum method. The measure of surgical performance was composed of 3 parameters (total operation time [Op time], warm ischemic time [WIT], and estimated blood loss [EBL]). Among the 20 patients who underwent VAMS partial nephrectomy, the mean age was 54.6 years. The mean Op time and WIT were 172.5 and 28.8 minutes, respectively. The learning curve for the Op time, WIT, and EBL consisted of 3 unique phases: phase 1 (the first 7 cases), phase 2 (the next 5 to 7 cases), and phase 3 (all subsequent cases). Phase 1 represents the initial learning curve, and the phase 2 plateau represents the period of expert competency. Phase 3 represents when one is competent in VAMS partial nephrectomy. The learning curve for VAMS partial nephrectomy is relatively short and after a learning curve of approximately 7 cases, the surgeon became familiar with VAMS partial nephrectomy; after 12 to 14 cases, the surgeon became competent in this procedure.
…Fell Upas Sits, the Hydra-Tree of Death , or the Phytotoxicity of Trees

The use of natural products that can serve as natural herbicides and insecticides is a promising direction because of their greater safety for humans and environment. Secondary metabolites of plants that are toxic to plants and insects—allelochemicals—can be used as such products. Woody plants can produce allelochemicals, but they are studied much less than herbaceous species. Meanwhile, there is a problem of interaction of woody species with neighboring plants in the process of introduction or invasion, co-cultivation with agricultural crops (agroforestry) or in plantation forestry (multiclonal or multispecies plantations). This review describes woody plants with the greatest allelopathic potential, allelochemicals derived from them, and the prospects for their use as biopesticides. In addition, the achievement of and the prospects for the use of biotechnology methods in relation to the allelopathy of woody plants are presented and discussed.