Miracle-dz/UniFAR-FTUs huggingface dataset:
The human toxicity of marijuana

The pathophysiological effects of marijuana smoke and its constituent cannabi‐ noids were reported first from in‐vitro and in‐vivo experimental studies. Marijuana smoke is mutagenic in the Ames test and in tissue culture and cannabinoids inhibit biosynthesis of macromolecules. In animals, marijuana or Δ‐trahydrocannabinol (THC), the intoxicating material it contains, produces symptoms of neurobehavioural toxicity, disrupts all phases of gonadal or reproductive function, and is fetotoxic. Smoking marijuana can lead to symptoms of airway obstruction as well as squamous metaplasia. Clinical manifestations of pathophysiology due to marijuana smoking are now being reported. These include: long‐term impairment of memory in adolescents; prolonged impairment of psychomotor performance; a sixfold increase in the incidence of schizophrenia; cancer of mouth, jaw, tongue and lung in 19‐30 year olds; fetotoxicity; and non‐ lymphoblastic leukaemia in children of marijuana‐smoking mothers.
Prevalence of Vulvovaginal Candidiasis and Frequency of Candida Species in Women

Background and purpose: Vulvovaginal candidiasis (VVC) often referred to as a yeast infection which is a common gynecologic problem, affecting %75 of women during their lifetimes. More than 40% of affected women will have two or more VVC episodes. Candida albicans is the most common agent of VVC; however non-albicans Candida species have increasingly been identified as the cause of vulvovaginitis. The objectives of this study were to determine the prevalence of vulvovaginal candidiasis and frequency of Candida species in women of different age groups referred to gynecology obstetrics clinic at Kashan-Iran from 2008 to 2010. Materials and methods: Vaginal samples were collected from 232 patients suspected of contracting vulvovaginitis based on clinical manifestations examined by gynaecologist. All collected samples were cultured on Chromagar and Sabouraud's dextrose agar which incubated at 35°C in dark for 72 h. Identification and determination of yeast isolates were performed based on morphologic, physiologic and biochemical criteria. Results: A total of 105 patients were diagnosed with vulvovaginal candidiasis. Thirteen (12.3%) patients out of 105 were identified as recurrent vulvovaginal candidiasis. The results revealed that Candida albicans was the most commonly identified species of pathogenic yeasts. The majority of symptoms among the positive patients were vulvovaginal itching (82.9%). Moreover, the results showed that significant differences between the signs of itching and discharge among the positive and negative cultures. Conclusion: Although the incidence of non-Candida albicans in different type of candidiasis is dramatically increased, in the present study, C. albicans was the most predominant species related to vulvovaginal candidiasis Therefore, identification and consideration of potential etiological agents involved in RVVC is recommended.
Ultralow area and power self-localization in sensor motes

The self-localization problem is becoming every day much more important especially with autonomous vars/robots. Since these devices has to work without any supervision, they need to know constantly which is their position inside the space where they are moving. This problem can be solved in many diff erent ways and using many technologies. However there is not a better method: the quality of the solution is related mainly to the application where it is adopted. This thesis proposes a new approach to this problem and design a circuit in order to accomplish the task. On the contrary of the standard solution adopted in literature, the design of the circuit has been done using an analog electronics rather than digital one. This allows to keep the structure as small, giving the opportunity to produce a fi nal device that can be used in many diff erent applications (from robotics to medicine). The analog approach guarantees also a low power consumption, particular important where the device has not a big battery. After the theory and the circuit, it is proposed the design of the main components (OTA and OpAmp) needed to implement the circuit. In particular the OTA has to exploit all the input available and the output curve has to be as linear as possible in order to t inside the design. Then both the OTA and the OpAmp have been implemented and simulated. At the end, it is shown an example of the results expected by the theory and the one derived from the simulation.
Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation

Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on the them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimalpower flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to batteries lifetime, it becomes unavoidable to develop battery lifetime models, which do not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetimeoriented battery models have to be integrated in power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source is developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance to current operating conditions. Here, the change in charging/ discharging rate (C-rate), overcharging/undercharging of the battery (depth-of-discharge), and the temperature are taken in consideration to define machine model states. The End-of-Lifetime (EoL) is defined as deviation between nominal and current ampere-hour (Ah)-throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serve as a prerequisite forits integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.
On the effectiveness of blockchain against cryptocurrency attacks

—Cryptocurrencies are being widely adopted to perform various online-based transactions; therefore, they are required to maintain a consensus to ensure a secured transaction. Blockchain comprises a distributed ledger, which holds digital records of individual crypto-transactions. Besides recording a particular activity, blockchain also ensures that the contents of the ledger are decided based on agreements of distinct participants. Various consensus mechanisms are followed by blockchain to ensure blocks are being summed up representing legitimate data on the network. However, the major consensus protocols comprise various limitations; and these are prone to different types of cyber attacks, such as Distributed denial-of-service, 51% attack, Double-spending, Long-range attack. In this paper, we analyze several attack vectors that can cause serious security threats due to the loopholes in the consensus mechanism. Our study involves examining 3 signiﬁcant consensus mechanisms, which are followed by major cryptocurrencies. We also discuss the limitations of individual consensus mechanisms and demonstrate their robustness towards various attack vectors. We conclude that, although blockchain comprises proper consensus mechanisms to enhance secured crypto-transaction, unfortunately, it is not strong enough to defend against some cryptocurrency attacks which could discourage some users to adopt this technology.
A comparison of the Subjective Workload Assessment Technique (SWAT) and the NASA-Task Load Index (TLX) under immediate and delayed rating conditions

A COMPARISON OF THE SUBJECTIVE WORKLOAD ASSESSMENT TECHNIQUE (SWAT) AND THE NASA-TASK LOAD INDEX (TLX) UNDER IMMEDIATE AND DELAYED RATING CONDITIONS Gandy, Robyn Anne University of Dayton, 1991 Advisor: Dr. F. Thomas Eggemeier When practical constraints in operational and laboratory environments require a delay between the performance of a task and its workload evaluation, the accuracy of the subjective ratings could be questioned. Despite its potential importance, little information exists which addresses the relationship between delay intervals and the sensitivity of the Subjective Workload Assessment Technique (SWAT) and the NASA-Task Load Index (TLX) ratings to variations in task demands. Twenty four undergraduate psychology students performed a Sternberg memory search task at three levels of difficulty, followed by immediate or delayed workload ratings. One half of the subjects reported workload using SWAT while the other half used TLX. The delay period was 15 minutes, during which an intervening task was performed. For the Sternberg task, both accuracy and reaction iii times were found to differ significantly as a function of memory set size, thus confirming the presence of three distinct levels of workload in the task. The SWAT technique was sensitive tc differences in workload at all three levels, while TLX was not able to discriminate between the two lowest levels of difficulty. Neither the SWAT nor TLX ratings differed significantly as a function delay. SWAT was rated as somewhat more difficult than TLX to use at the scale development phase. However, ease-of-use ratings associated with the two techniques did not differ at the scale use phase. The sensitivity and ease-of-use results suggest possible trade-offs in the selection of a subjective rating measurement technique. These trade-offs may be associated with the type of task to be rated and its demand levels, as well as with the environment in which the task is to be performed.
Changes in the EMG temporal pattern of pre and post-landing of ankle and foot muscles in volleyball players with functional instability

INTRODUCTION: The ankle sprain is one of the most common injuries in athletes, including volleyball. 90% of ankle injuries in volleyball occur during landing after a blocking maneuver. The most common complication following ankle sprains is functional instability (FI), a condition that affect about 52% of the patients that suffered an ankle sprains . Functional ankle instability (FI) has been defined as a tendency for the foot to give way after an ankle sprain with no evidence of ligament injury. Hence, FI is an impairing condition for volleyball performance since it interferes in its basic skills. AIMS: The purpose of this study was to compare the EMG activation patterns of tibialis anterior (TA), peroneus longus (PL) and gastrocnemius lateralis (GL) in volleyball players with and without FI during landing after the blocking movement. METHODS: EMG activity was acquired for 21 subjects (mean age 20 ± 4 yrs) with FI (IG) and 19 control ones (CG). Linear envelopes were calculated for both groups for the time period between 200 ms before and 200 ms after the instant of impact, and time and magnitude of peak occurrence were extracted from the envelopes. Groups were compared using T test (α < 0.05). RESULTS: IG subjects showed a later peak occurrence for TA (CG = -107.4 ± 29.6 ms; IG = -134.0 ± 26.0 ms) and PL (CG = -11.0 ± 55.9 ms; IG = -41.7 ± 49.8 ms) and a lower peak magnitude for TA (CG = 68.5 ± 17.2%; FIG = 81.2 ± 28.8%) and PL (CG = 72.9 ± 27.3%; FIG = 59.1 ± 16.0%). CONCLUSIONS: These results suggest that individuals with FI present a later and lower activation pattern of muscular activity and different activation magnitudes that predispose them to ankle sprains, even in the absence of an anatomical damage.
Modeling Relationship Maps for The Factors of Purchasing Management System According to Selected Key Drivers Using Fuzzy Cognitive Maps

Strategic management is the philosophy of the creation which provides a goal, a plan and a model for the competitive advantage. Companies define their own goals and generate the plan for goal according to this management philosophy. However, company can utilize successively from this philosophy so long as it is able to be adapted to all management systems such as product, inventory, manufacturing and etc. Purchasing management system is the one of them. It is a basic management unit where decisions, goals and plans are structured according to production and customer requirements which give the competitive advantage to each company. All of them can be managed strategically by being focused on the appropriate purchasing conditions including low-cost, high quality, and on-time delivery. In this study, we will determine variables and the relationships between them which affect in achieving the strategies of the company in terms of purchasing management system. Fuzzy cognitive mapping method will be selected in order to find them out because this method creates a map by using some strategies for system. The results will be presented at the end of the study.
Enrollment Projection Using Markov Chains: Detecting Leaky Pipes and the Bulge in the Boa

While the use of Markov chains is widely used in business and industry, their use within higher education has been sporadic. Furthermore, when used to predict enrollment progression, the majority of these models use student level as the classification variable. This study uses grouped earned credit hours to track the movement of students from one academic term to the other to better identify where students enter or leave the institution. Results from this study indicate a high level of predictability from one year to the next and the use of the credit hour flow matrix can aid administrators in identifying trends and anomalies within the institution’s enrollment management process.
Irreducible Isolated Subtalar Dislocation: A Case Report

Subtalar dislocations are uncommon and account for approximately 1% of all dislocations. Optimal management is by immediate closed reduction under general anesthesia. We report 3 cases of irreducible, isolated subtalar dislocation that required an open procedure. Closed reduction failed in 2 patients with lateral dislocation due to interposition of the posterior tibialis tendon caused by a large tear of the flexor retinaculum. The flexor retinaculum was accurately reconstructed after the reduction. In the third case, a medial dislocation, a displaced extensor retinaculum prevented relocation of the talar head and required resection. We also discuss the mechanisms for irreducible subtalar dislocations.
Piezoelectricity: a literature review for power generation support

It is undeniable the potential of piezoelectric materials as an energy source. However, do current methods have the potential to be applied widely. To answer this, we require to know their current value according to socio-urban and environmental conditions. This paper presents the first literature review to study the ways of most successful piezoelectric forms of generation, implemented today, and a comparison between them according to their energy potential, as well as the socioeconomic implications presented. Results give a clearer picture of the challenges and advantages of piezoelectric materials as a means of power generation support, showing the positive practical implications of the implementation of piezo systems.
Interconnection Performance Analysis of Single Phase Neural Network Based NPC and CHB Multilevel Inverters for Grid-connected PV Systems

This paper presents the interconnection performance analysis of single-phase cascaded H-bridge and neutral point clamped multilevel inverters for grid-connected photovoltaic (PV) applications. Interconnection performance analysis of both inverters was investigated using fixed and variable bands hysteresis current control strategies to ensure a lower current’s total harmonic distortion (THD). An intelligent control method based on neural networks was introduced to extract maximum power from the PV modules and to achieve optimal operation of the whole system when connected to the utility grid. Control algorithm was implemented in a microcontroller with interrupt routines priority. Both simulation and experimental results are presented to verify the performance of the proposed control methods. In addition, islanding detection capability of the two topologies was investigated .
Skin Lesion Classification Based on Convolutional Neural Networks

Melanoma causes the majority of skin cancer deaths. The population level of melanoma has increased over the past 30 years. It kills around 9.320 people in the US every year. Melanoma can often be found early, when it is most likely to be cured. Medical diagnoses using digital imaging with machine learning methods have become popular because of their ability to recognize patterns in digital images. Image diagnosis accuracy allows disease cured at an early stage. This paper proposes a simulation that can be used for early detection of skin cancer that can help dermatologists to distinguish melanomas from other pigmented lesions on the skin. Some researchers have developed a system using machine learning algorithms used to classify skin lesions from dermoscopy images of human skin. In this study, we proposed Convolutional Neural Network (CNN) to our model. CNN is very efficient for image processing because feature extractors can be optimized, applied to each feature image position. The results of skin lesion classification of benign nevi and melanoma based on CNN models produces high accuracy (area under the receiver operator characteristics (ROC) curve (AUC) is 92.59 %, sensitivity is 89.47%, specificity is 100.0%, precision is 100 % and F1 score is 94.44 %).
An Improved Model for System-Level Energy Minimization on Real-Time Systems

Energy-efficient scheduling has become an essential branch in the evolution of real-time systems due to requirements for reliability and portability. Such scheduling algorithms exploit the varying voltage/frequency capability of a processor to reduce its energy consumption. However, most of these algorithms assume an inaccurate linear correlation between memory latency and core frequency. To make the matter worse, the inclusion of caches has tarnished the predictability of such systems while also increasing the total energy consumption. Therefore, improved system and task models are crucial for further progression in this domain. In this paper, we propose improvements to existing models in order to incorporate the above-mentioned complexities. We then propose techniques to minimize core-, cache-and system-level energy consumptions. Extensive simulations demonstrate the effectiveness of our approach and the impact each proposed technique has on total energy consumption of the processor.
The Application and Improvement of Deep Neural Networks in Environmental Sound Recognition

Neural networks have achieved great results in sound recognition, and many different kinds of acoustic features have been tried as the training input for the network. However, there is still doubt about whether a neural network can efficiently extract features from the raw audio signal input. This study improved the raw-signal-input network from other researches using deeper network architectures. The raw signals could be better analyzed in the proposed network. We also presented a discussion of several kinds of network settings, and with the spectrogram-like conversion, our network could reach an accuracy of 73.55% in the open-audio-dataset “Dataset for Environmental Sound Classification 50” (ESC50). This study also proposed a network architecture that could combine different kinds of network feeds with different features. With the help of global pooling, a flexible fusion way was integrated into the network. Our experiment successfully combined two different networks with different audio feature inputs (a raw audio signal and the log-mel spectrum). Using the above settings, the proposed ParallelNet finally reached the accuracy of 81.55% in ESC50, which also reached the recognition level of human beings.
Advertisement of Multiple Paths in BGP: Implementation Report

This document reports the results of an ADD-PATH implementation survey. The survey had 22 questions about implementations' support for advertising multiple paths in BGP. After a brief summary of the results, each response is listed. This document contains responses from six implementers who completed the survey. The editor did not use external means to verify the accuracy of the information submitted by the respondents. The respondents are considered experts on the products they reported on.
A Model of Magnetic Field and Braking Torque in Liquid-Cooled Permanent-Magnet Retarder Accounting for the Skin Effect on Permeability

Braking torque is the most important performance parameter for a permanent magnet retarder used in heavy vehicles. This paper proposes a model of the permanent magnet retarder for braking torque at different speeds considering the skin effect, the magnetic leakage, and the actual path of the eddy currents. Based on the magnetic equivalent circuit and the piecewise function method, the static and the transient air-gap flux density models are established, and their accuracy is verified by comparison to the finite element method (FEM). The permeability distribution curve of the retarder stator is obtained by a simple permeability-measuring instrument, considering the skin effect on the permeability of the stator. Thus, the effective permeability function of the stator is obtained at different rotational speeds, and the transient air-gap flux density model is optimized using the effective permeability function. The simulation results showed that the optimized transient air-gap flux density of the calculation model was in good agreement with the FEM. Finally, the calculation model of the eddy current braking torque was verified by the bench test.
Incidents in high-volume elongated crowd facilities: A simulation-based study

This paper presents the main findings of a simulation-based study to evaluate incidents in pedestrian/crowd tunnels and similar elongated confined facilities, with high-volume heterogeneous traffic. These incidents, when occur, imposes hazardous conditions that always result in significant number of fatalities. The aim of this study is to understand how these facilities perform under different irregular scenarios and possibly identify potential causes of accidents. The problem of studying incidents in large-scale high-volume pedestrian facilities is that these incidents are difficult to expect or replicate. Thus, studying these facilities through real-life scenarios is almost impossible. Accordingly, a micro-simulation assignment model for multidirectional pedestrian movement is used for this purpose. The model adopts a Cellular Automata (CA) discrete system, which allows detailed representation of the pedestrians’ walkways in the tunnel. The modeling approach captures crowd dynamics through representation of behavioral decisions of heterogeneous pedestrians at the individual level. Several experiments are conducted to study the pedestrian flow in the proposed tunnel considering different operational scenarios including demand levels, heterogeneous traffic, evacuation scenario, and tunnel blockage. Results show that flow of large pedestrian volumes through a long confined linear structure, such as a tunnel, are subject to the same flow dynamics as we observe with vehicular traffic. In particular, they are subject to the formation of “clumps” and shock waves that can rapidly propagate and lead to inefficient operation, including flow breakdown with stop-and-go waves.