Miracle-dz/UniFAR-FTUs huggingface dataset:
AXIN2 and SNAIL expression predict the risk of recurrence in cutaneous squamous cell carcinoma after Mohs micrographic surgery

Recurrence is a common complication observed during cutaneous squamous cell carcinoma (cSCC) treatment; however, biomarkers for predicting recurrence in cSCC remain unknown. The present study aimed to investigate the predictive value of axis inhibition protein 2 (AXIN2) and SNAIL expression in cSCC recurrence. AXIN2 and SNAIL expression was evaluated using immunohistochemistry in 111 cSCC tissue samples obtained from 18 patients who presented recurrence (recurrence interval, 1–91 months) and 93 patients who did not experience recurrence following Mohs micrographic surgery (MMS) during the follow-up period (156 months). Nomogram construction was performed using patients' clinicopathological characteristics and AXIN2 and SNAIL protein expression. The results demonstrated that high AXIN2 (histoscore >100) and SNAIL (histoscore >100) expression was detected in 35 and 44 cSCC tissues, respectively. Furthermore, the expression levels of AXIN2 and SNAIL were significantly associated in patients with cSCC (P=0.001). AXIN2 and SNAIL expression levels were significantly associated with tumor size (P=0.021 and P=0.044, respectively) and recurrence of cSCC (P=0.017 and P=0.042, respectively). In addition, the results of the Kaplan-Meier curve analysis revealed that recurrence-free survival was significantly associated with tumor size (P=0.025), differentiation status (P<0.001), AXIN2 expression (P=0.001) and SNAIL expression (P=0.001). Furthermore, the results of the multivariate analysis demonstrated that age (P=0.043), AXIN2 expression (P=0.001) and SNAIL expression (P=0.045) were independent risk factors for cSCC recurrence in the present cohort. A nomogram for predicting the 1-, 2-, 3-, and 5-year recurrence-free survival was developed for patients with cSCC by including independent risk factors with a concordance index of 0.75. The results suggested that high AXIN2 and SNAIL expression may be considered as potential risk factors for cSCC recurrence. This nomogram may therefore be useful to assess the probability of recurrence in patients with cSCC following MMS.
From Values to Behavior: Proposition of an Integrating Model

Human values are at the heart of our lives. We all hold a set of values that influence our actions. The protection of the natural environment is no exception to this rule. That is why the study of human values is key to reaching the imperative of sustainability. In this conceptual paper, we aim to (1) integrate the key theories and models explaining the influence of human values on behavior and to (2) identify factors that might have previously been overlooked. Drawing from a range of disciplines, this article proposes an integrated model mapping the influence of human values on behavior. It also puts forward the concept of consequences as an emerging factor that could play an important role in this relationship. Recommendations are to extend the research to an empirical investigation of the model and to develop the definition of the concept of consequences and the role they play in the influence of values on consumer behavior.
The Association Between High Mortgage Debt and Financial Well-Being in Old Age: Implications for the Financial Education Field

Over the last few decades the share of older homeowners (age 62 and older) with mortgage debt has doubled, while the typical amount of outstanding debt relative to home values among this group has tripled. Older homeowners still paying off mortgage debt face high rates of housing cost burdens (paying more than 30 percent of income for housing), leaving less income for other necessities. In addition, homeowners with higher mortgage debt have less housing equity to tap for critical needs and face the ongoing risk of foreclosure. For these reasons, higher levels of mortgage debt may be expected to create lower levels of financial well-being among these individuals. Using both descriptive and multivariate approaches, this paper explores two issues related to these trends. First, it examines the relationship of financial well-being with both the incidence of mortgage debt and housing cost burdens based on the CFPB’s Financial WellBeing Scale as applied to data from the 2016 National Financial Well-Being Survey. Second, it assesses the association between the use of mortgage debt among older adults and measures of financial skill, knowledge, planning, and saving habits. The results indicate that both higher monthly housing costs and the presence of high levels of mortgage debt are associated with lower financial well-being scores and increased likelihood of having a score below 50, which is associated with greater likelihood of material hardship. The results also indicate that older adults with higher financial skill scores and a greater propensity to save are less likely to have higher levels of mortgage debt. The findings suggest that reductions in mortgage debt among older homeowners can improve financial well-being and that efforts to improve financial skill and encourage savings could potentially help bring about such reductions.
An Examination of the Role Discrepancy, Depressive symptoms, and Turnover Intention among the Jordanian Nursing Workforce

This dissertation has also been disseminated through both the ProQuest Dissertations and Theses database and OhioLINK repository. ProQuest dissertation/thesis number: 3367779; ProQuest document ID: 304860717. Link to OhioLINK: http://rave.ohiolink. edu/etdc/view? acc_num=case1243957418. The author still retains copyright.
Performance analysis of present cup anemometers

The cup anemometer, wind speed sensor developed by T.R. Robinson in the 19 th century, remains today as the best option in relation to important scientific and economic sectors such as the meteorology sector or the wind energy sector. Despite the great advances reached by new technologies as sonic anemometry, LIDAR or SODAR, the cup anemometer is the most demanded wind speed sensor thanks to its balance between the accuracy, reliability, endurance and the cost. In the present paper, the work carried out in relation to this instrument at the IDR/UPM Institute is briefly summarized, and then the results from the last research testing campaigns are included. The output signal of the first class cup anemometers such as Thies CLIMA First Class, Thies CLIMA 4.3350, and Vector Instruments is analyzed to obtain insights on the instrument accuracy. It is found that three accelerations of the rotor are converted into a pulsed output signals, leading to some error if that is not taken into account. Besides, the way the output signal is registered in order to correlate the output frequency with the wind speed has proven to be also a source of error. Two ways of extracting the output frequency, namely by Counting Pulses (CP), and by using FFT are compared. Results indicate that the wind speed errors are six times larger in the case of using FFT.
Understanding Retention of IT Professionals in Vendor Organizations: A Job Embeddedness Model

A major challenge facing the IT outsourcing industry is the high rate of turnover among IT professionals. Skilled and talented IT professionals are assets to organizations. When they leave, loses in the forms of human, knowledge, and social capital can be so substantial that it affects the operations of both vendors and their clients. Therefore, an important agenda for vendors is to understand the factors that will attract employees to stay with their current organizations so that effective retention programs can be formulated to keep the best IT employees productively engaged for a long-term. This paper adapts the Job Embeddedness Model to present a framework that identifies factors binding IT professionals to their current organizations. The framework captures the working environment of IT professionals in the IT outsourcing industry – where not only are they part of vendor organizations, but they are also highly enmeshed with client organizations.
Genomic Inference of Inbreeding in Alexander Archipelago Wolves (Canis lupus ligoni) on Prince of Wales Island, Southeast Alaska

Habitat loss and climate change are increasingly resulting in reduction and fragmentation of wildlife populations. Populations that have experienced fragmentation and decreases in abundance are at heightened risk of inbreeding due to reduced opportunities to mate with unrelated conspecifics. Prolonged or extensive inbreeding can result in inbreeding depression via the exposure of deleterious alleles in long runs of homozygosity. Alexander Archipelago wolves (Canis lupus ligoni) on Prince of Wales Island (POW) in Southeast Alaska are a small, isolated population of conservation concern that have experienced habitat loss and high harvest rates, and present an ideal system in which to explore questions about inbreeding. We present a custom hybridization capture that generates 37,082 genome-wide single nucleotide polymorphism (SNP) genotypes and over 500,000 SNP genotype likelihoods, allowing us to characterize and understand the extent of genomic inbreeding. We found that FROH revealed different patterns of inbreeding across three populations of Alexander Archipelago wolves when compared to FH. Estimates of FH revealed no differences between the three populations, while FROH revealed that wolves in the southeast portion of the study area had the greatest total proportion of the genome in runs of homozygosity, including short runs from inbreeding in distant ancestors. Wolves on POW had more long runs of homozygosity, indicating more frequent mating between individuals with recent common ancestors, likely due to smaller recent historical population size on POW. We demonstrate the advantage of using genomics to assess inbreeding via FROH (versus FH), which allows for inference about the timing and severity of inbreeding. Wolves on POW exhibit similar inbreeding patterns as wolves in Isle Royale National Park, a population that was founded by two to three individuals and has demonstrated severe inbreeding depression. This result is important for management (e.g. informing habitat use, population targets, and harvest quotas) and demonstrates the benefit of using high-resolution genomic data to infer individual inbreeding so proactive management can be applied to ensure the long-term sustainability of the populations.
Android Malware Detection Using Fine-Grained Features

Nowadays, Android applications declare as many permissions as possible to provide more function for the users, which also poses severe security threat to them. Although many Android malware detection methods based on permissions have been developed, they are ineffective when malicious applications declare few dangerous permissions or when the dangerous permissions declared by malicious applications are similar with those declared by benign applications. This limitation is attributed to the use of too few information for classification. We propose a new method named fine-grained dangerous permission (FDP) method for detecting Android malicious applications, which gathers features that better represent the difference between malicious applications and benign applications. Among these features, the fine-grained feature of dangerous permissions applied in components is proposed for the first time. We evaluate 1700 benign applications and 1600 malicious applications and demonstrate that FDP achieves a TP rate of 94.5%. Furthermore, compared with other related detection approaches, FDP can detect more malware families and only requires 15.205 s to analyze one application on average, which demonstrates its applicability for practical implementation.
Design and Testing of a Vertical Take-Off and Landing UAV Optimized for Carrying a Hydrogen Fuel Cell with a Pressure Tank

Flight endurance is still a bottleneck for many types of unmanned air vehicle (UAV) applications. While battery technology improves over the years, for flights that last an entire day, batteries are still insufficient. Hydrogen-powered fuel cells offer an interesting alternative but pose stringent requirements on the platform. The required cruise power must be sufficiently low and flying with a pressurized tank poses new safety and shape constraints. This paper proposes a hybrid transitioning UAV that is optimized towards carrying a hydrogen tank and fuel cell. Hover is achieved using 12 redundant propellers connected to a dual Controller Area Network (CAN) bus and dual power supply. Forward flight is achieved using a tandem wing configuration. The tandem wing not only minimizes the required wingspan to minimize perturbations from gusts during hover, but it also handles the very large pitch inertia of the inline pressure tank and fuel cell very well. During forward flight, 8 of the 12 propellers are folded while the tip propellers counteract the tip vortexes. The propulsion is tested on a force balance and the selected fuel cell is tested in the lab. Finally, a prototype is built and tested in-flight using battery power. Stable hover, good transitioning properties, and stable forward flight are demonstrated.
Knowledge sharing and business process: The mediating role of organizational factors

Knowledge is considered as a vital asset, an important fundamental driver and significant resource in banking industry. In current era, banks face positive consequences of sharing knowledge in workplace. Knowledge management is significant and reliable source for achieving excellence in organizational performance. But there is thirst in literature to have research on knowledge sharing and its significance in improving the business process of banking sector. This research has been designed to empirically investigate the significance of knowledge sharing on a business process with the mediating role of organizational factors. The researchers collect data through a self-administrative questionnaire. Drawing on insights from 200 valid respondents from various banks of Pakistan, SEM and Path modeling approach is used to examine the relationships between knowledge sharing and business process, using AMOS software. The finding of the study demonstrates that the organizational culture factors and knowledge sharing practices directly improve the banking performance and process in Pakistan. The statistical test accepted the hypothesis that intrinsic organizational factors mediated the business process through knowledge sharing at a 1% level of significance. The research is valuable as it provides useful information related to bank employees to engage in knowledge sharing practices.
Hypoglycemia During Therapy of Diabetes

Hypoglycemia, caused by treatment with a sulfonylurea, a glinide, or insulin coupled with compromised defenses against the resulting falling plasma glucose concentrations, is the limiting factor in the glycemic management of diabetes. It causes recurrent morbidity in most people with type 1 diabetes mellitus (T1DM) and many with advanced type 2 diabetes mellitus (T2DM) and is sometimes fatal; it limits maintenance of euglycemia over a lifetime of diabetes; and it impairs physiological and behavioral defenses against subsequent hypoglycemia. In addition to drug selection and application of diabetes treatment technologies, minimizing hypoglycemia in diabetes includes acknowledging the problem, considering each of the risk factors and applying the principles of intensive glycemic therapy. For most people with diabetes who are at risk for, or suffering from, iatrogenic hypoglycemia these principles include selecting appropriate individualized glycemic goals and providing structured patient education that will often reduce the incidence of hypoglycemia. That is typically coupled with short-term scrupulous avoidance of hypoglycemia that will often reverse impaired awareness of hypoglycemia. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT. ORG.
Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.
Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.
Trends in herbal usage – a survey study

Folk medicine is quite common for primary health care in Bulgaria, and the tradition of plant usage is of importance for the development of pharmaceutical formulations. The objective of this study was to provide up-to-date information regarding trends in herbal usage among citizens in the city of Plovdiv, Bulgaria. The study involved 114 respondents for a period of 60 calendar days in 2018. Chamomile (Matricaria recutita L.) and thyme (Thymus vulgaris L.) were the most popular plants among the respondents. The common cold is the most frequent example for seeking herbals and other botanicals. Herbal teas are most preferred when it comes to daily consumption. Answers also revealed plants used in households; reasons for herbal consumption, and opinions on common statements concerning herbal stereotypes.
Chapter 1 Introduction : Issues in studying early alphabets

An open-access on-line version of this book is available at: http://books.casematepublishing. com/ Understanding_relations_between_Scripts_II_Early_alphabets. The online work is licensed under the Creative Commons Attribution 3.0 Unported Licence. To view a copy of this license, visit http:// creativecommons.org/licenses/ by/3.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA. This licence allows for copying any part of the online work for personal and commercial use, providing author attribution is clearly stated.
Detection of nearby UAVs using CNN and Spectrograms

In this work, we address the problem of drone detection flying nearby another UAV. Usually, computer vision could be used to face this problem by placing cameras on board the patrolling UAV. However, visual processing is prone to false positives, sensible to light conditions and potentially slow if the image resolution is high. Thus, we propose to carry out the detection by using an array of microphones mounted with a special array on board the patrolling UAV. To achieve our goal, we convert audio signals into spectrograms and used them in combination with a CNN architecture that has been trained to learn when a UAV is flying nearby and when it is not. Clearly, the first challenge is the presence of ego-noise derived from the patrolling drone itself through its propellers and motor’s noise. Our proposed CNN is based on the Google’s Inception v.3 network. The Inception model is trained with a dataset created by us, which includes examples of when an intruder drone flies nearby and when it does not. We tested our approach with three different drone platforms, achieving a successful detection of 97.93% for when an intruder drone flies by and 82.28% for when it does not. The dataset used for this work will be available as well as the code.
FAKE CAN BE REAL IN GANS

In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples can be reconsidered as real ones during the training process. This strategy can reduce the gradient value that generator receives in the region where gradient exploding happens. We show that the theoretical equilibrium between the generators and discriminations actually can be seldom realized in practice. And this results in an unbalanced generated distribution that deviates from the target one, when fake datepoints overfit to real ones, which explains the non-stability of GANs. We also prove that, by penalizing the difference between discriminator outputs and considering certain fake datapoints as real for adjacent real and fake sample pairs, gradient exploding can be alleviated. Accordingly, a modified GAN training method is proposed with a more stable training process and a better generalization. Experiments on different datasets verify our theoretical analysis.