Miracle-dz/UniFAR-FTUs huggingface dataset:
Food, Masculinity and Gender-based Violence in Sally Andrew’s Recipes for Love and Murder (2015)

Summary This article offers a reading of Sally Andrew’s debut murder mystery novel, Recipes for Love and Murder: A Tannie Maria Mystery (2015), from the angle of critical food studies. The article explores how the novel’s depiction of food relates to notions of masculinity and power against the backdrop of widespread gender-based violence in South Africa today. I argue that the protagonist and narrator’s reverent, restorative relationship with food represents a gentle yet powerful feminine counternarrative to the violent masculinities of subjugation embodied in Fanie’s dogmatic religious ideology, Dirk’s oppressive military indoctrination, and Cornelius’s cruel hunting practices. Beyond providing mere escapism, this supposedly “popular” novel can therefore be seen as delivering sharp, timely social commentary.
Preparation of carbon nanotube arrays nanocomposites filled with Prussian blue and electrochemical sensing of hydrogen peroxide

Abstract In this work, A nanocomposite film composed of multi-walled carbon nanotube arrays (MWCNTA) filled with Prussian Blue (PB) was prepared, and it was successfully applied in the electrochemical Sensing of H2O2. The nanostructure of MWCNTA/PB was characterized by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The electrochemical behavior was investigated by cyclic voltammetry (CV). The excellent catalytic reduction performance of H2O2 by MWCNTA/PB was observed and applied as a H2O2 electrochemical sensor. It is found that the linear detection range of the sensor is 10 μM to 110 μM and the sensitivity is 0.407 μA μM–1, as well as the detection limit is 0.21 μM. The interference experiment results showed that uric acid, dopamine, glucose and ascorbic acid could not affect the electrochemical detection of H2O2, indicating that the sensor has good stability and anti-interference.
Dopaminergic Inhibition of Na+ Currents in Vestibular Inner Ear Afferents

Inner ear hair cells form synapses with afferent terminals and afferent neurons carry signals as action potentials to the central nervous system. Efferent neurons have their origins in the brainstem and some make synaptic contact with afferent dendrites beneath hair cells. Several neurotransmitters have been identified that may be released from efferent terminals to modulate afferent activity. Dopamine is a candidate efferent neurotransmitter in both the vestibular and auditory systems. Within the cochlea, activation of dopamine receptors may reduce excitotoxicity at the inner hair cell synapse via a direct effect of dopamine on afferent terminals. Here we investigated the effect of dopamine on sodium currents in acutely dissociated vestibular afferent calyces to determine if dopaminergic signaling could also modulate vestibular responses. Calyx terminals were isolated along with their accompanying type I hair cells from the cristae of gerbils (P15-33) and whole cell patch clamp recordings performed. Large transient sodium currents were present in all isolated calyces; compared to data from crista slices, resurgent Na+ currents were rare. Perfusion of dopamine (100 μM) in the extracellular solution significantly reduced peak transient Na+ currents by approximately 20% of control. A decrease in Na+ current amplitude was also seen with extracellular application of the D2 dopamine receptor agonist quinpirole, whereas the D2 receptor antagonist eticlopride largely abolished the response to dopamine. Inclusion of the phosphatase inhibitor okadaic acid in the patch electrode solution occluded the response to dopamine. The reduction in calyx sodium current in response to dopamine suggests efferent signaling through D2 dopaminergic receptors may occur via common mechanisms to decrease excitability in inner ear afferents.
Archaeosomes and Gas Vesicles as Tools for Vaccine Development

Archaea are prokaryotic organisms that were classified as a new domain in 1990. Archaeal cellular components and metabolites have found various applications in the pharmaceutical industry. Some archaeal lipids can be used to produce archaeosomes, a new family of liposomes that exhibit high stability to temperatures, pH and oxidative conditions. Additionally, archaeosomes can be efficient antigen carriers and adjuvants promoting humoral and cellular immune responses. Some archaea produce gas vesicles, which are nanoparticles released by the archaea that increase the buoyancy of the cells and facilitate an upward flotation in water columns. Purified gas vesicles display a great potential for bioengineering, due to their high stability, immunostimulatory properties and uptake across cell membranes. Both archaeosomes and archaeal gas vesicles are attractive tools for the development of novel drug and vaccine carriers to control various diseases. In this review we discuss the current knowledge on production, preparation methods and potential applications of archaeosomes and gas vesicles as carriers for vaccines. We give an overview of the traditional structures of these carriers and their modifications. A comparative analysis of both vaccine delivery systems, including their advantages and limitations of their use, is provided. Gas vesicle- and archaeosome-based vaccines may be powerful next-generation tools for the prevention and treatment of a wide variety of infectious and non-infectious diseases.
Evaluación del efecto del proceso de extrusión en harina de quinua (chenopodium quinoa willd) normal y germinada

Quinoa (Chenopodium quinua Willd) germinated seeds shows nutritional value and body assimilation differences respect to normal seeds, therefore, sought to identify the extrusion process effect over flour from normal and germinated quinoa. Proximal analysis, pasting curves, Water Absorption Index (WAI) and Water Solubility Index (WSI). After extrusion process, proximal analysis, WAI and WSI performed to flours. Changes in germinated flour was found respect to normal flour like protein increase (15,06 g/100 g sample to 15,74 g/100 g sample), gelation properties, WAI and WSI variations. Significant differences was found on germinated flour due to extrusion process applied, over WSI and WAI (2,5274 g gel/g D.M. in normal quinoa flour increased to 5,8761 g gel/g D.M. in extruded quinoa flour. Thus, 2,5829 g gel/g D.M. for no extruded germinated quinoa flour, increased to 5,4197 g gel/g D.M. for extruded germinated quinoa flour).
5G D2D Transmission Mode Selection Performance & Cluster Limits Evaluation of Distributed AI and ML Techniques

5G D2D Communication promises improvements in energy and spectral efficiency, overall system capacity, and higher data rates. However, to achieve optimum results, it is essential to select wisely the Transmission mode of the D2D Device to form clusters in the most advantageous positions in terms of Sum Rate and Power Consumption. Towards this end, this paper investigates the use of Distributed Artificial Intelligence (DAI) and innovative D2D, Machine Learning (ML) approaches (i.e., DAIS, FuzzyART, DBSCAN and MEC) to achieve satisfactory results in terms of Spectral Efficiency (SE), Power Consumption (PC) and execution time, with the creation of clusters and back-hauling links in D2D network under existing Base Station. Additionally, this paper focuses on a small number of Devices (i.e., <=200), targeting the identification of the limits of each approach in terms of the low number of devices. More specifically, we investigate when an operator must consider implementing a D2D network (that requires extra complexity), therefore when the cluster members are sufficient enough to achieve better results than the classic mobile network. So, this research identifies where it is beneficial to form a cluster, investigate the critical point that gains increases rapidly and in the end, examine the applicability of 5G requirements. Additionally, prior work presented a Distributed Artificial Intelligence (DAI) Solution/Framework in D2D, and a DAIS Transmission Mode Selection (TMS) plan was proposed. In this paper, DAIS is further examined, improved in terms of thresholds evaluation (i.e., Weighted Data Rate (WDR), Battery Power Level (BPL)), evaluated, and compared with other approaches (AI/ML). The results obtained demonstrate the exceptional performance of DAIS and FuzzyART, compared to all other related approaches in terms of SE, PC, execution time and cluster formation. Also, results show that the investigated AI/ML approaches are also beneficial for Transmission Mode Selection (TMS) in 5G D2D communication, even with fewer devices (i.e., >=5 for clustering, >=50 for backhauling) as lower limits.
Visual-Pressure Fusion for Underwater Robot Localization With Online Initialization

The motion of underwater robot is usually slow, which leads to large scale error easily introduced in the positioning method based on monocular Visual Inertial Odometry (VIO). To solve the above problem, we present a positioning method based on visual-pressure fusion in this letter. Firstly, it is proved by observability analysis that when the robot's motion is not on the same horizontal plane, the system scale can be uniquely determined by monocular image and depth information; secondly, a novel visual-pressure initialization method is proposed, which can calculate the system scale and initial attitude with respect to world frame online; finally, we integrate our method into current advanced monocular Simultaneous Localization And Mapping (SLAM) system, and the effectiveness and superiority of the method are verified by experiments on simulation datasets and real datasets.
Closing the Loop on Cognitive Radar for Spectrum Sharing

We investigate three emerging topics essential for the development of cognitive radar (CR) for spectrum sharing: the response time (RT) of the CR, the autonomous regulation of the perception-action cycle (PAC), and the regulation of cognition. The RT measures the latency of all algorithms/hardware and is examined with respect to enabling capabilities of software-defined systems for rapid flexibility and responsiveness. The autonomous regulation of the PAC determines “how fast the CR can interact with the environment” as well as “how fast the CR should interact with the environment. ” The regulation of the PAC is explored with respect to pulse-to-pulse waveform agility to coexist successfully with dynamic radio frequency emitters in the ambient electromagnetic environment (EME) and the consequence of modifying the waveform within the coherent processing interval. Finally, the regulation of cognition determines how to select a particular CR technique appropriately for a given dynamic environment. This selection requires a high level, or metadecision process to identify the appropriate CR technique as the EME changes over time and we therefore concentrate discussion on the newly emerging topic for radar called metacognitive radar. The exploration of these three topics include a review of past and current research with discussion of possible future research.
Identifying the Effect of Data Breach Publicity on Information Security Awareness Using Hierarchical Regression

The technological evolution has formed new challenges for organizations to safeguard their information as digital assets. Information Security Awareness (ISA) is the cognitive state where individuals comprehend information security, threats, and the capability to develop preventive strategies. Prior studies discovered that human mistakes or misbehavior is the most vulnerable link in information security due to insufficient security awareness. There were massive data breaches reported throughout the years globally. Literature shows that individuals will develop their evaluations of risks and sense of security awareness when receiving security risk information such as data breach incidents. These indications motivated us to examine the effect of an unexplored factor, that is, data breach publicity (DBR) on ISA. The purpose of this research is to discover if DBR significantly improves a model’s ability to predict ISA and its magnitude in influencing ISA. A 3-stage hierarchical linear regression approach was used to build up the model with prior known influential factors to predict ISA. To the extent of our knowledge, there is no study reported to date regarding the implication of DBR on ISA. Our main findings reveal that DBR significantly explains 6.7% of ISA and achieves the highest coefficient comparing with prior known factors. Our research contributes to a novel discovery of a new factor that significantly influences ISA and its magnitude in increasing ISA. This discovery implies the need to incorporate the knowledge of data breach incidents into ISA-related educative programs or strategies to increase ISA.
Massive Machine-Type Communication and Satellite Integration for Remote Areas

Despite immense progress along different tracks, wireless connectivity for machine applications in remote areas is still very challenging. To address this vital need, in this article, we discuss and advocate the convergence between low-pow-er wide area network (LPWAN) grade massive machine-type communication (mMTC) wireless technologies and satellite (especially low Earth orbit, LEO) systems. In the article, we discuss the alternative implementation approaches allowing such convergence and highlight some of their pros, cons, and challenges. Furthermore, we obtain more in-depth insight into the matter by simulating and analyzing the LoRaWAN LPWAN sensors' performance served by a LEO satellite-based gateway. Our results demonstrate the feasibility of such a system and illustrate some of the relevant trade-offs between the network configurations and communication performance. These results motivate us to take a more profound look at such systems and the challenges they introduce. We highlight some of them and the potential directions for further studies in the final sections.
An Adaptively Parameterized Algorithm Estimating Respiratory Rate from a Passive Wearable RFID Smart Garment

Currently, wired respiratory rate sensors tether patients to a location and can potentially obscure their body from medical staff. In addition, current wired respiratory rate sensors are either inaccurate or invasive. Spurred by these deficiencies, we have developed the Bellyband, a less invasive smart garment sensor, which uses wireless, passive Radio Frequency Identification (RFID) to detect bio-signals. Though the Bellyband solves many physical problems, it creates a signal processing challenge, due to its noisy, quantized signal. Here, we present an algorithm by which to estimate respiratory rate from the Bellyband. The algorithm uses an adaptively parameterized Savitzky-Golay (SG) filter to smooth the signal. The adaptive parameterization enables the algorithm to be effective on a wide range of respiratory frequencies, even when the frequencies change sharply. Further, the algorithm is three times faster and three times more accurate than the current Bellyband respiratory rate detection algorithm and is able to run in real time. Using an off-the-shelf respiratory monitor and metronome-synchronized breathing, we gathered 25 sets of data and tested the algorithm against these trials. The algorithm’s respiratory rate estimates diverged from ground truth by an average Root Mean Square Error (RMSE) of 4.1 breaths per minute (BPM) over all 25 trials. Further, preliminary results suggest that the algorithm could be made as or more accurate than widely used algorithms that detect the respiratory rate of non-ventilated patients using data from an Electrocardiogram (ECG) or Impedance Plethysmography (IP).
Determinação dos parâmetros elétricos de motores de indução por meio de dados construtivos

Este artigo propõe o desenvolvimento de uma metodologia para determinar os parâmetros elétricos nominais de motores de indução utilizando os dados construtivos obtidos em placas e catálogos dos motores. Nesta proposta, utilizou-se o método de Newton para determinar os parâmetros nominais da máquina, bem como implementou-se uma análise no domínio do tempo durante a partida do motor estabelecendo um comparativo entre a impedância de entrada obtida na partida e a impedância de entrada calculada com base nos parâmetros nominais. Por meio dessa comparação foi possível comprovar a eficácia da metodologia proposta. Os parâmetros nominais estimados podem ser utilizados em análises comparativas com os parâmetros reais, a fim de identificar eventuais necessidades de manutenção devido a problemas relacionados a eficiência energética e perdas. Além disso, os parâmetros elétricos são amplamente utilizados no controle vetorial de motores de indução.
Improving Human-Centric Software Defect Evaluation, Reporting, and Fixing

Defect reporting customarily exists in most applications and web sites to support issue reporting by end users and for developers to receive actionable feedback. However, the impact of "human-centric" issues - such as age, gender, language, culture, physical and mental challenges, and socio-economic status - is often overlooked in the development process and during product inception and defect reporting. Most defect reporting tools lack necessary human-centric features to enable a challenged user to adequately navigate and report defects i.e. do not take into account the human differences between end users that cause defects for them in their software and make reporting of such defects difficult for them. Most defect reporting tools also lack sufficient defect report structuring, reporting guidance, and do not emphasize the possible perceived severity of the defect to developers from end users who are very different to them. If users are unable to report defects due to usability issues, this makes those same defect reports difficult to understand by the developer. In this paper, we aim to improve human-centric defect reporting by employing cognitive walkthrough with diverse end user personas, develop a prototype of an improved defect reporting tool, and evaluate the prototype’s defect reporting process from end user and developer perspectives. Our findings offer a foundation for improved human-centric defect evaluation, reporting and fixing.
Aplicações de Processamento de Linguagem Natural para o Setor de Energia

Técnicas de Processamento de Linguagem Natural (PLN) podem ser aplicadas em domínios específicos para extração de informação. Este trabalho apresenta aplicações de PLN em documentos do Diário Oficial da União (DOU) relativos ao setor elétrico para a automação de back-office e similaridade entre palavras com uma representação vetorial refinada para o domínio específico. Foi obtida maior similaridade entre as palavras energia, elétrica, geração e ANEEL com o uso desta representação refinada.
Combining Mobile Crowdsensing and Wearable Devices for Managing Alarming Situations

Dangerous events such as accidental falls, allergic reactions or even severe panic attacks can occur spontaneously and within seconds. People experiencing alarming situations like these often require assistance. Modern wearable devices such as smartphones or smartwatches can be used to detect these situations by utilising the plethora of sensors built into them. Mobile Crowdsensing Systems (MCS) might be used to manage the detected alarming situations. To handle these events, an MCS requires integration with mobile sensory devices, as well as the voluntary participation of people willing to help. The contributions of this paper are twofold. First, we enhance the capabilities of an MCS by enabling the integration of various Bluetooth wearable devices. Second, we perform a simulation that models different scenarios that represent dangerous events. Through the MCS simulation, we identify essential parameters that need to be considered when building such a system. The simulation can also be used to find the optimal configuration of the MCS.
Weighted Reward for Reinforcement Learning based Test Case Prioritization in Continuous Integration Testing

Test Case Prioritization (TCP) based on the continuous decision of Reinforcement Learning (RL) has achieved a successful application for test cases optimization in Continuous Integration (CI). The reward functions of RL describe how a test case "ought" to be executed in next integration, of which the design is usually based on the historical executions of the test case. The Average Percentage of Historical Failure (APHF) had been considered as one of the best reward function which has a strong correlation with the recent failure executions of a test case. However, for a test case with many historical failures but passes in recent cycles, the APHF value may be low. In this paper, two novel reward functions are proposed focusing on the impact of failure position in test case history execution sequence, which are the Average Position Exponential Weight (APEW) reward function and the Average Position Quadratic Weight (APQW) reward function, respectively. Both APEW and APQW carry out weight design of failure position but with different weights. We theoretically prove the issue of the only strong correlation with recent failure executions, and also prove that both proposed reward functions can reflect the quantity of historical failures and the distribution of these failures. Experimental verification on 10 industrial-level data sets show that the proposed reward functions can effectively improve the fault detection capability of test cases.
A Review of Security Standards and Frameworks for IoT-Based Smart Environments

Assessing the security of IoT-based smart environments such as smart homes and smart cities is becoming fundamentally essential to implementing the correct control measures and effectively reducing security threats and risks brought about by deploying IoT-based smart technologies. The problem, however, is in finding security standards and assessment frameworks that best meets the security requirements as well as comprehensively assesses and exposes the security posture of IoT-based smart environments. To explore this gap, this paper presents a review of existing security standards and assessment frameworks which also includes several NIST special publications on security techniques highlighting their primary areas of focus to uncover those that can potentially address some of the security needs of IoT-based smart environments. Cumulatively a total of 80 ISO/IEC security standards, 32 ETSI standards and 37 different conventional security assessment frameworks which included 7 NIST special publications on security techniques were reviewed. To present an all-inclusive and up-to-date state-of-the-art research, the review process considered both published security standards and assessment frameworks as well as those under development. The findings show that most of the conventional security standards and assessment frameworks do not directly address the security needs of IoT-based smart environments but have the potential to be adapted into IoT-based smart environments. With this insight into the state-of-the-art research on security standards and assessment frameworks, this study helps advance the IoT field by opening new research directions as well as opportunities for developing new security standards and assessment frameworks that will address future IoT-based smart environments security concerns. This paper also discusses open problems and challenges related to IoT-based smart environments security issues. As a new contribution, a taxonomy of challenges for IoT-based smart environment security concerns drawn from the extensive literature examined during this study is proposed in this paper which also maps the identified challenges to potential proposed solutions.