Miracle-dz/UniFAR-FTUs huggingface dataset:
The musician effect: does it persist in spectrally degraded cochlear implant simulations?

Cochlear implants (CIs) are auditory prostheses that restore hearing via electrical stimulation of the auditory nerve. Compared to normal acoustic hearing, sounds transmitted through the CI are spectro-temporally degraded, causing difficulties in challenging listening tasks such as speech intelligibility in noise and perception of music. In normal hearing (NH), musicians have been shown to better perform than non-musicians in auditory processing and perception, especially for challenging listening tasks. This ‘musician effect’ was attributed to better processing of pitch cues, as well as better overall auditory cognitive functioning in musicians. Does the musician effect persist when pitch cues are degraded, as it would be in signals transmitted through a CI? To answer this question, NH musicians and non-musicians were tested while listening to unprocessed signals or to signals processed by an acoustic CI simulation. The task increasingly depended on pitch perception: 1) speech intelligibility (words and sentences) in quiet or in noise, 2) vocal emotion identification, and 3) melodic contour identification. For speech perception, there was no musician effect with the unprocessed stimuli, and a small musician effect only for word identification in one noise condition, in the CI simulation. For emotion identification, there was a small musician effect for both. For melodic contour identification, there was a large musician effect for both. Overall, the effect was stronger as the importance of pitch in the listening task increased. This suggests that the musician effect may be more rooted in pitch perception, rather than in a global advantage in cognitive processing (in which musicians would have performed better in all tasks). The results further suggest that musical training before (and possibly after) implantation might offer some advantage in pitch processing that could partially benefit speech perception, and more strongly emotion and music perception.
Article 9.indd

Sulfated polysaccharides derived from seaweed have shown great potential for use in the development of new drugs. In this study, we observed that a low-molecular-weight sulfated polysaccharide from Caulerpa racemosa, termed CrSP, could interact with secretory phospholipase A2 (sPLA2) isolated from Crotalus durissus terrifi cus venom. When native sPLA2 (14 kDa) was incubated with CrSP, they formed a molecular complex (sPLA2:CrSP) with a molecular mass of 32 kDa, approximately. Size exclusion chromatography experiments suggested that CrSP formed a stable complex with sPLA2. We belived that sPLA2 and SPCr are involved an ionic interaction between negatively charged CrSP and the positively charged basic amino acid residues of sPLA2, because this interaction induced signifi cant changes in sPLA2 enzymatic and pharmacological activities. CrSP caused a signifi cant increase in sPLA2 enzymatic and bactericidal activity and increased its edematogenic effect. A pharmacological assay showed that the myotoxic activity of sPLA2:CrSP is unrelated to its enzymatic activity and that sPLA2:CrSP may have a practical application as a natural antibacterial agent for use in humans and commercially raised animals.
Intergenerational Effects of Trade Liberalization: Longitudinal Estimation of a Dynamic Structural Model of Labor Mobility

Most studies of the distributional effect of trade focus on static models, comparing effects across industries or skill groups. By contrast, this paper provides US estimates of a dynamic longitudinal model of sectoral choice with mobility costs in an open economy subject to trade shocks. Parameter estimates are used for simulations of the dynamic model (in a general equilibrium framework), that show: 1. Differences in sectoral mobility of younger versus older workers, 2. The speed with which workers can find new jobs in other sectors if they lose their jobs because of free trade, 3. Gradual adjustment of wages and labor in response to a trade shock, and 4. The welfare effects of a hypothetical trade liberalization in the metal manufacturing sector (which has been especially vulnerable to trade shocks in the past, the steel industry in particular). The results suggest that moving costs are large (more than one year's wage) and increase with age. Both a worker's welfare loss in the metal sector and welfare gain in the other sectors increase with sectoral experience. Older workers' distribution of gains and losses from trade liberalization has a very large variance while young workers' distribution shows little deviance. The results also suggest that older workers take longer to find new jobs than younger workers. and Panayiotis Pourpourides. All errors are mine.
αα-Amylase and of Cucurbita maxima

The aim of the present study was to investigate, α activities of the aqueous extract of activity with their phytoelemental profile quantitatively assessed by Laser Spectroscopy (LIBS). Diabetes can be managed by controlling postprandial hyperglycemia which can be achieved by inhibiting carbohydrate hy αα inhibitory activity of 46.03±1.37% with IC αα used as a reference. LIBS analysis showed the presence of certain phytoelements viz. Mg, Ca, K and Na which are well known glycemic elements and hence could be responsible for inhibitory activity of car inhibitory action of glycemic elemental profile further validates their role in controlling diabetes and hence maxi hyperglycemia causing type 2 diabetes mellitus. Keywords
The Role of Domestic Integrated Battery Energy Storage Systems for Electricity Network Performance Enhancement

Low carbon technologies are necessary to address global warming issues through electricity decabonisation, but their large-scale integration challenges the stability and security of electricity supply. Energy storage can support this transition by bringing flexibility to the grid but since it represents high capital investments, the right choices must be made in terms of the technology and the location point in the network. Most of the potential for storage is achieved when connected further from the load, and Battery Energy Storage Systems (BESS) are a strong candidate for behind-the-meter integration. This work reviews and evaluates the state-of-the-art development of BESS, analysing the benefits and barriers to a wider range of applications in the domestic sector. Existing modelling tools that are key for a better assessment of the impacts of BESS to the grid are also reviewed. It is shown that the technology exists and has potential for including Electric Vehicle battery reuse, however it is still mostly applied to optimise domestic photovoltaic electricity utilisation. The barriers to a wider integration are financial, economic, technical, as well as market and regulation. Increased field trials and robust numerical modelling should be the next step to gain investment confidence and allow BESS to reach their potential.
Modifying water nipples for newborn pigs

and Modifying water nipples for newborn pigs . Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 43 :5.1-5.4. The use of bite nipples by piglets during the first six days after birth was monitored by time-lapse video recording. Simple bite nipples, mounted at a downward angle, were not discovered by most piglets within the six days, even after the nipple had been modified to drip water continuously. However, two modifications resulted in most piglets discovering the nipple within three days. These modifications were (1) mounting the nipple close to the floor with an upward angle and (2) adding a short length of chain to the valve lever. piglets, le haut, et; 2) on a ajouté une petite chaîne au levier de la valve.
Fuzzy-PID controller for an energy efficient personal vehicle: Two-wheel electric skateboard

The two-wheeled electric skateboard (TWS) is designed for a personal vehicle. A Fuzzy-PID control strategy is designed and implemented for controlling its motion. Basically, motions control of the TWS is performed by balancing the pitch position of the TWS. Performance of the designed controller is demonstrated experimentally. The Fuzzy algorithm updates the PID gains and therefore it can handle the changing of the TWS load. Contribution of Fuzzy-PID in reducing the electric energy consumption, which is an important issue in electrical system, is also evaluated. The Fuzzy-PID successes to reduce the electric energy consumption of the TWS compared to the conventional PID.
Human activity recognition by using convolutional neural network

In recent years, many researchers have studied the HAR (Human Activity Recognition) system. HAR using smart home sensor is based on computing in smart environment, and intelligent surveillance system conducts intensive research on peripheral support life. The previous system studied in some of the activities is a fixed motion and the methodology is less accurate. In this paper, vision-based studies using thermal imaging cameras improve the accuracy of motion recognition in intelligent surveillance systems. We use one of the deep learning architectures widely used in image recognition systems called Convolutional Neural Networks (CNN). Therefore, we use CNN and thermal cameras to provide accuracy and many features through the proposed method.
Data Mining and Statistical Analysis on Smart City Services Based on 5G Network

Mobile edge computing in 5G network is emerging as a very promising computation architecture by pushing computation and storage closer to end users with both strategically deployed and opportunistic processing and storage resources. Baidu cloud provides network services which can be deployed in 5G network recently. The network services such as weather forecast service and city road map service are typical applications for smart city. We analysis Baidu website data in this paper by our data mining method and related software. Clustering, outlier detection, prediction, and statistical methods are used to evaluate these smart city services, and the analysis result give suggestions to improve design and development of our 5G services (API website).
A model-driven approach towards designing and analysing secure systems for multi-clouds

Cloud computing is a paradigm that is utilised by cloud service providers to offer computational resources as procurable services to consumers. Whereby security is a primary concern to both consumers and cloud service providers, as confidential data and processes are offloaded to third party providers when utilising cloud services. Addressing security is therefore a non-trivial task due to the importance of the enabling technology, such as virtualisation, coupled with social factors considering consumer needs and physical properties such as geo-location influencing jurisdiction on resources and processes. Consumers have security needs to ensure their data and processes are kept secure, where cloud service providers are responsible for identifying security requirements in cloud computing environments and providing assurance that security needs are satisfied through transparency. In order to provide transparency of security requirements in cloud computing environments, concepts unique to the cloud computing paradigm such as multi-tenancy, dissemination of resources and responsibility needs to be captured. By adapting a security requirements engineering approach, security issues are identified early in the software development life-cycle. This ensures the security needs within a system is identified and well-understood before committing to implementation and deployment, where it is exponentially more difficult to address underlying security issues. In this thesis we present the Secure Cloud Environment Framework(SCEF), a decision support framework enabling developers to elicit cloud security requirements from cloud computing environments. The SCEF consists of a cloud modelling language, a secure cloud process and a set of cloud security analysis techniques. In the SCEF we make explicit, the exact definition and relationship of cloud security requirements within the scope of a cloud computing environment, encompassed and expressed at an organisational, application and infrastructure level of abstraction. The overall contribution to knowledge is the SCEF, which allows developers to model cloud computing environments in order to understand and address cloud security issues. The cloud modelling language is a contribution as it extends the Secure Tropos methodology, which enhances the existing security paradigm through domain-specific cloud computing concepts. The secure cloud process is a contribution as it provides developers with a methodological approach when applying the
Node activity based trust and reputation estimation approach for secure and QoS routing in MANET

Achieving safe and secure communication in MANETs is a key challenge due to its dynamic nature. A number of security studies disclose that reputation management systems are able to be effectual with less overhead. The reputation of a node is calculated by using automated assessment algorithms depend on predefined trust scheme. This paper proposes a Node Activity-based Trust and Reputation estimation (NA-TRE) approach for the security and QoS routing in MANET. NA-TRE aims to find trust estimation and reputation of a node. The NA-TRE approach monitors the activity changes, packet forwarding or dropping in a node to find the status of the node. The various activities of a node like Normal State (NS), Resource Limitation State (RS) and Malicious State (MS) are monitored. This status of a node is helpful in computing trust and reputation. In this paper NA-TRE approach compared with existing protocols AODV, FACE and TMS to evaluate the efficiency of MANET. The experiment results show that 20% increasing of throughput, 10% decrease of overhead and end to end delay.
Augmented SRC for face recognition under quality distortions

In the last two decades, numerous methods have been developed to offer a formulation to the face recognition problem under scene-dependent conditions. However, these methods have not considered image quality degradations resulting from capture, processing, and transmission such as blur and occlusion due to packet loss, under the same scene variations. Although deep neural networks are achieving state-of-the-art results on face recognition, the existing networks are susceptible to quality distortions. In this work, the authors propose an augmented sparse representation classifier (SRC) framework to improve the performance of the conventional SRC in the presence of Gaussian blur, camera shake blur, and block occlusions, while preserving its robustness to scene-dependent variations. In their evaluation of the SRC framework, they present a feature sparsity concentration and classification index that is capable of assessing the quality of features in terms of recognition accuracy as well as class-based sparsity concentration. For this purpose, they consider three main types of features including image raw pixels, histogram of oriented gradients and deep learning visual geometry group (VGG) Face. The obtained performance results show that the proposed method outperforms state-of-the-art sparse-based and blur-invariant methods.
User Preference Dynamics on Evolving Social Networks - Learning, Modeling and Prediction

The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks. Online social networks contain rich information about social interactions and relations, becoming essential source of knowledge for the understanding of user preferences evolution. In this thesis, we investigate the interplay between user preferences and social networks over time. We use temporal networks to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Our findings show that we can predict user preference changes by just observing how her social network structure evolves over time.
Power system voltage collapse prediction using a new line stability index (NLSI-1): A case study of the 330-kV nigerian national grid

The cumulative number of historical and recent power system outages substantiates the fact that further studies are necessary for an improved solution to the issue of voltage instability on the grid and the subsequent system collapse. Voltage collapse is a serious reliability issue which inhibits the objective of running a reliable and secure power system network. In this study, a new line stability index (NLSI_1) for predicting voltage collapse is presented. The new index considers a switching logic which is derived from the difference of voltage angle between the two load buses. The index is deployed for performance analysis using the 28-bus, 330-kV Nigeria National Grid (NNG). The simulation implemented in MATLAB shows that the index gives the same results as Line stability index (Lmn) and Fast Voltage Stability Index (FVSI) indices. The base case and the contingency scenarios were considered during the simulation. The base case analysis using the NNG values of all the three indices FVSI, Lmn, and NLSI_1 for simulation generates a value less than one for the entire lines which implies that the NNG is stable in this mode. The values of the three indices are almost the same, which confirms the accuracy of the novel index developed. The analysis for the contingency case reveals that the load bus 16 (Gombe) which has the lowest, maximum permissible reactive load of 139.5MVAR is the weakest; also power line 16-19 is identified as the critical line. The result of the simulation confirms that the accuracy was improved by using NLSI_1.
Optimization for Smooth Paths

Path planning for games and robotics applications typically consists of finding the straight-line shortest path through the environment connecting the start and goal position. However, the straight-line shortest path usually contains abrupt, nonsmooth changes in direction at path apex points, which lead to unnatural agent movement in the pathfollowing phase. Conversely, a smooth path that is free of such sharp kinks greatly improves the realism of agent steering and animation, especially at the path start and goal positions, where the path can be made to align with the agent facing direction. Generating a smooth path through an environment can be challenging because there are multiple competing constraints of total path length, path curvature, and static obstacle avoidance that must all be satisfied simultaneously. This chapter describes an approach that uses convex optimization to construct a smooth path that optimally balances all these competing constraints. The resulting algorithm is efficient, surprisingly simple, free of special cases, and easily parallelizable. In addition, the techniques used in this chapter serve as an introduction to convex optimization, which has many uses in fields as diverse as AI, computer vision, and image analysis. A source code implementation can be found on the book’s website (http://www.gameaipro.com).
Short term load forecast of Kano zone using artificial intelligent techniques

Load forecast provides useful information for effective electricity dispatch, planning for future expansion and significantly enhances operational efficiency. Conventional techniques yield unsatisfactory forecast which results in high energy losses and in turn leads to high operational cost and suppressed electricity demand. This paper presents hybrid neuro fuzzy (HNF) and Nonlinear Auto-Regressive with eXogeneous input (NARX) neural network for the short term load prediction of Kano region Nigeria. Simulation results obtained demonstrated the generalization capabilities of the models in predicting the load accurately well by achieving MAPE of 0.025% and 0.6551% for the HNF model and NARX network model respectively. The models could serve as promising tool for predicting Kano Zone load demand.
Rapid development of data for shallow transfer RBMT translation systemsfor highly inflective languages

The article describes a new way of constructing rule-based machine tran slatio systems (RBMT), in particular shallow-transfer RBMT suited for related languages. The article describes methods that automate parts of the construction process. The methods were evaluated on a case study: the construction of a fully functional machine translation s ystem of closely related language pair Slovenian Serbian. The Slovenian language and The Serbian language belong to the group of southern Slavic languages that were spoken mostly in the former Yugoslavia. The economies of the nations where these language s are spoken are closely connected and younger generations, the post-Yugoslavia breakage generations, have difficulties in mutual comm unication, so there is a big interest in construction of such translation system. The system is based on Apertium (Oller and Forcada, 2006) , an open-source shallow-transfer RBMT toolkit. Thorough evaluation of the translation system is presented and conclusions present the strong and the weak points of this approach and explore the grounds for further work.
Nonocclusive Mesenteric Ischemia Resulting from Acute MyocardialInfarction of Left Main Trunk after the Recovery from Cardiac Arrest by Percutaneous Cardiopulmonary System: A Case Report

A 77-year old male was ambulated to our institution after he had been found unconsciousness in the street. On admission, he complained about severe chest pain. His systolic blood pressure was 58 mmHg; therefore, he was diagnosed to be in cardiogenic shock. A 12-lead electrocardiography (ECG) revealed ST elevation in the lead II, III, aVf, and V1 - V5. The episode of chest pain and abnormal ECG findings led to the diagnosis of AMI. Coronary angiography revealed severe stenosis in the left main trunk (LMT) and the left anterior descending artery (LAD). LMT was considered as the culprit lesion in this case. During the examination, the monitoring ECG revealed cardiac arrest; therefore, cardiac resuscitation was immediately performed using percutaneous cardiopulmonary system. Ventricular fibrillation was evident; following cardiac defibrillation at 360J restored sinus rhythm. Subsequently, intraaortic balloon pumping was placed, and percutaneous coronary intervention was performed; coronary stents were successfully placed in LMT and LAD. Postoperative course was uneventful; however, he emerged with massive melena on day 13. Colonoscopy revealed cast-like stripped mucosa from the ascending to the cecum, which was suggesting of severe intestinal ischemia. Non contrast computed tomography suggested a lower intestinal perforation. Because of his circulatory condition and anticoagulant therapies after AMI, he didn’t become a candidate for surgical treatment. After provided with conservative therapy, he died of multiple organ failure resulting from panperitonitis.