Miracle-dz/UniFAR-FTUs huggingface dataset:
Event detection for smarter cities

Detecting what is going on around in near real time through analysis of social network data has become part of assessing the pulse of a city. The advances in event detection techniques enable cities to give a real-time overview of the events — ranging from music concerts and exhibitions to emergencies like fires and car accidents — and activities happening in a smart city. While previous work mostly focused on large-scale, e.g. global or national level, event detection recent development focuses on hyper-local events, that are occurring in a small region, e.g. a street corner or a certain venue rather than city-or country-level area. This paper offers a broad survey and classification of event detection techniques, identifying the key features of recent techniques and their usage in the context of smart cities. This is done by introducing them as well as comparing and categorizing different up to date techniques regarding their event definition, their mode of operation, and their qualitative and quantitative evaluation approaches. Research gaps are highlighted and future work in the area is identified.
Investigation of a Voltage-Mode Controller for a dc-dc Multilevel Boost Converter

The multilevel dc-dc boost converter (MBC) is well known for its simple circuit structure and high voltage gain. Several controllers have been previously employed for their output voltage regulation. However, due to the non-minimum phase nature of the MBC, these controllers use both the inductor current and output voltage as feedback variables. This increases the complexity of the controller design and cost of implementation. In order to overcome these drawbacks, a voltage-mode controller for the regulation of the MBC is proposed in this brief. By using the generalized reduced-order state-space model of the converter and frequency-domain techniques, the stability of the voltage-controlled converter system is analyzed. In addition, some simulation and experimental results illustrating the ability of the proposed controller to regulate a $3{x}$ dc-dc multilevel boost converter in the presence of load and line voltage variations are provided.
Digital eye strain: prevalence, measurement and amelioration

Digital device usage has increased substantially in recent years across all age groups, so that extensive daily use for both social and professional purposes is now normal. Digital eye strain (DES), also known as computer vision syndrome, encompasses a range of ocular and visual symptoms, and estimates suggest its prevalence may be 50% or more among computer users. Symptoms fall into two main categories: those linked to accommodative or binocular vision stress, and external symptoms linked to dry eye. Although symptoms are typically transient, they may be frequent and persistent, and have an economic impact when vocational computer users are affected. DES may be identified and measured using one of several available questionnaires, or objective evaluations of parameters such as critical flicker–fusion frequency, blink rate and completeness, accommodative function and pupil characteristics may be used to provide indices of visual fatigue. Correlations between objective and subjective measures are not always apparent. A range of management approaches exist for DES including correction of refractive error and/or presbyopia, management of dry eye, incorporating regular screen breaks and consideration of vergence and accommodative problems. Recently, several authors have explored the putative role of blue light-filtering spectacle lenses on treating DES, with mixed results. Given the high prevalence of DES and near-universal use of digital devices, it is essential that eye care practitioners are able to provide advice and management options based on quality research evidence.
Virtual machine placement based on degradation factor ant colony algorithm

Rational use of physical resources is the most important challenge in cloud computing. To accomplish this challenge, the system must take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a traveling salesman problem (TSP) and propose to apply degradation factor ant colony algorithm technique for optimal placing of virtual machines in the physical servers. The value of degradation factor has been calculated by resource waste model. Experimental results on Cloudsim simulation platform demonstrate that the proposed approach can achieve superior performance than the standard ant colony algorithm.
Clinical features and survival analysis of clear cell papillary renal cell carcinoma: A 10-year retrospective study from two institutions

Clear cell papillary renal cell carcinoma (CCPRCC) is a recently recognized subtype of renal cell carcinoma entity, however, little is known about its clinical features. In the present study, 26 cases of CCPRCC were screened out from two institutions. The patient data, tissue pathology, immunohistochemical phenotype, computed tomographic images and survival analysis were studied. The mean age was 53.3 years and the average tumor size was 2.5 cm. A total of 17 patients' body mass indexes were higher than the normal level. A total of 11 patients had hypertension and 6 patients had a smoking history. Histopathologically, all cases of CCPRCC exhibited a tubular and papillary architecture, small to medium-sized cuboidal tumor cells with clear cytoplasms, and a low Fuhrman nuclear grade. All tumors were encapsulated by variably thick fibrous capsules. Immunohistochemistry showed diffuse and moderate to strong cytoplasmic staining for CK7, CA IX and vimentin, but negative for AMACR and CD10 (sometimes focally positive) in all cases. According to the results of Ki67 labeling index, the expression of Ki67 in CCPRCC was much lower than that in clear cell renal cell carcinoma (CCRCC) (2.19 vs. 7.07%, P<0.001) and that in papillary renal cell carcinoma (PRCC) (2.19 vs. 6.65%, P<0.001). Radiographically, the tumors were shown as small masses with smooth contour and mixed enhancement pattern. The multiphasic attenuation curve for CCPRCC, like that for CCRCC, increased in the corticomedullary phase markedly and decreased in the nephrographic phase and excretory phase gradually. At a median follow-up period of 50 months, no cancer-specific death or tumor recurrence was observed. Considering the favorable prognosis of CCPRCC, preoperative biopsy in order to make clear the diagnosis is particularly important. In light of the present findings, partial nephrectomy for patients with CCPRCC is recommended. If the patients cannot tolerate surgery, closed monitoring or radiofrequency ablation may be considered.
Application of multi-agent systems to shared transport services: A Review

Several studies have extended the application of Multi-Agent Systems (MAS) to a wide range of different domains, including shared services with various purposes. With regard to transport systems, shared services enabled through technological solutions, especially in mobile space, have many benefits, such as mitigating traffic congestion and reducing greenhouse gas emissions, among others. In addition, with the population of large cities rising more than ever, citizens are becoming more sensitive to societal issues and other performance measures imposed by the new concept of smart cities. This work will study the literature with the objective of verifying the proposals and limitations of existing studies, prototypes and simulations, in order to provide a systematic evidence-based view of how the scientific community is applying MAS to leverage shared services for the implementation and feasibility of intelligent mobility solutions. In the course of the studies, we conclude that although some authors came to propose conceptual models and systems prototypes, the simulation component has been little explored and only synthetic data has been used. One of the reasons justified by the authors relates to the high costs of carrying out simulations. We did not find in the literature proposals that were effectively implemented and tested in the real world, but only prototypes and proposals of future developments.
Spatial services for decentralised smart green energy management

In neighbourhoods, the number of energy generators are growing. A main reason for this being the rise in people's energy needs and the possibility of local energy production. Future scenarios involve people's choices of energy type (mostly green) as well as sharing energy among neighbours. This article investigates the use of spatial services applied to a decentralised green energy management system. Spatial services are a new type of bio-inspired services spreading geographically across dispersed devices. They are agent-based and suitable for dynamically changing scenarios. We prototyped, implemented and deployed an energy management scenario involving a peer to peer innovative energy management system and a self-adaptive system. Preliminary results show the feasibility of the envisaged scenarios. Future work include investigating advanced peer to peer scenarios as well as enhancing current spatial services with reinforcement learning.
Assessment of fundamental movement skills in Australian children: the validation of a Fundamental Motor Skills Quotient (FMSQ)

Objectives: The development of proficient fundamental movement skills (FMS) in children is important for physical, cognitive and social development, and to establish a foundation for an active lifestyle. Many teachers, coaches and physical activity program deliverers use real time observation of individual skill performances during physical activities to assess proficiency in young children. However, a valid method to quantify these observations to provide an overall motor competence is not available. Design: A small pilot validation study using the Bland Altman method to compare scores on the McCarron Assessment of Neuromuscular Development (MAND) and a Fundamental Movement Skill Quotient (FMSQ) (M = 100, SD = 15) derived from a composite score for the observed mastery of 4 skills. Methods: Motor competence in 50 children aged between 7 and 8 years was determined using the MAND. These scores were compared to a FMSQ derived from observation scores for the run, hop, overhand throw and jump. Results: The correlation between the two scores was moderate (r = .35, p = .013) and the level of agreement using the Bland Altman method was acceptable. Conclusions: The FMSQ is a valid measure of motor competence when observing and teaching fundamental motor skills in young children.
The SEC and MDP: Implications of the Self-Serving Bias for Independent Auditing

Huge accounting firms, seeking to become one-stop shopping centers for clients, wish to add legal services to the audit, tax, information consulting, financial planning, litigation support, and other services they already provide—a phenomenon known as multi-disciplinary practice (MDP). MDP can aggravate problems already plaguing objectivity and independence in the auditing profession. Theoretically, auditors must be objective and independent. However, there is a large body of behavioral studies demonstrating that, overwhelmingly, people tend to behave inequitably if it benefits them and they think they can get away with it—a behavior known as the self-serving bias. Professor Prentice examines the self-serving bias by surveying the literature from psychology, decision theory, behavioral finance, and behavioral economics, and examines its operation in relevant analogous fields, such as law, medicine, and investment banking. Professor Prentice then applies what we know about the self-serving bias to the special problems of auditors’ mandatory objectivity and independence. It turns out that auditors, no less than the rest of us, behave in accordance with the self-serving bias: for example, they are reluctant to issue qualified opinions and reluctant to refuse their clients’ requests for improper accounting treatment. Moreover, they are even more reluctant to act against the interests of financially healthy clients and more likely to act in self-defensive ways with respect to financially troubled clients. Professor Prentice concludes by discussing appropriate SEC responses to the dangers created by the self-serving bias in auditors who are subject to the exacerbating effects of an MDP.
Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

AIM To discover methylated-differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC) and to explore relevant hub genes and potential pathways. METHODS The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differentially methylated genes and differentially expressed genes online using GEO2R. Functional and enrichment analyses of MDEGs were conducted using the DAVID database. A protein-protein interaction (PPI) network was performed by STRING and then visualized in Cytoscape. Hub genes were ranked by cytoHubba, and a module analysis of the PPI network was conducted by MCODE in Cytoscape software. RESULTS In total, we categorized 266 genes as hypermethylated, lowly expressed genes (Hyper-LGs) referring to endogenous and hormone stimulus, cell surface receptor linked signal transduction and behavior. In addition, 161 genes were labelled as hypomethylated, highly expressed genes (Hypo-HGs) referring to DNA replication and metabolic process, cell cycle and division. Pathway analysis illustrated that Hyper-LGs were enriched in cancer, Wnt, and chemokine signalling pathways, while Hypo-HGs were related to cell cycle and steroid hormone biosynthesis pathways. Based on PPI networks, PTGS2, PIK3CD, CXCL1, ESR1, and MMP2 were identified as hub genes for Hyper-LGs, and CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10 were hub genes for Hypo-HGs by combining six ranked methods of cytoHubba. CONCLUSION In the study, we disclose numerous novel genetic and epigenetic regulations and offer a vital molecular groundwork to understand the pathogenesis of HCC. Hub genes, including PTGS2, PIK3CD, CXCL1, ESR1, MMP2, CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10, can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of HCC.
Facial expressions based error detection for smart environment using deep learning

Creating innovative applications of ambient intelligence for smart environments became a real challenge. Indeed, the existing systems are considered as being obtrusive. Therefore, the ongoing trends consist in finding new methods which are less intrusive while being accurate and computer efficient. To that end, we introduce an ingenious way to assist the elderly and people with cognitive impairment in their daily life using emotions through facial expressions. Our hypothesis assumes the correlation between expressed emotions and the resulting errors in daily activities. In order to verify it, we introduce an experimentation protocol with neurotypical subjects. In this paper, we mainly focus on designing an approach for facial expression recognition using Convolutional Neural Network. The carried experiments with benchmark datasets provided promising results. In fact, the proposed approach outperforms the existing methods in terms of accuracy. Moreover, our approach has been implemented in an embedded system with limited resources. The main objective of this paper is to provide an effective approach recognizing facial expressions that will be used for our further experimentations.
Research on the influence maximization based on community detection

The research of influence maximization aims to select the k most influential nodes in social network, these nodes are a group of initial seed nodes for information dissemination and make the ultimate scope of influence maximum. Considering that Community-based greedy algorithm has two problems. One is that community division is unstable, and the other is that the time complexity of selecting seed nodes on the basis of community division is too high, this paper proposes an influence maximization algorithm (LPIMA) based on community detection. The algorithm selects optimized label propagation algorithm for community detection. Firstly, we use LeaderRank algorithm to quantify the influence of community nodes. And then assign candidate seed node set according to these quantified values. Finally, we use the submodel characteristic to improve greedy algorithm and mining community seed nodes from candidate seed sets. The results show that the proposed algorithm guarantees the scope of influence and improves the time efficiency in the large-scale network.
The influence of document characteristics on the quality of health web documents

The quality of consumer-oriented health information on the Web is usually assessed through the medical certification of websites. These tools are built upon quality indicators but, so far, no standard set of indicators has been defined. The objective of the present study is to explore the popularity of specific document features and their influence on the quality of health web documents, using HON code as ground truth. A set of top-ranked health documents retrieved from a major search engine was characterized in a univariate analysis, and then used in a bivariate analysis to seek features that affect documents' quality. The univariate analysis provides insights into the characteristics of the overall population of the health web documents. The bivariate analysis reveals strong relations between documents' quality and a set of features (namely split content, videos, images, advertisements, English language) that are potential quality indicators. We characterized health web documents and identified specific document features that can be used to assess whether the information in such documents is trustworthy. The main contribution of this work is to provide other features as candidate indicators of quality. Non-health professionals can use these indicators in automatic and manual assessments of health content.
Optimal sizing of battery storage units integrated into fast charging EV stations

The paper brings a mixed integer linear programming (MILP) solution for defining optimal size and operational strategy of battery storage systems (BSS) integrated with fast charging electric vehicle stations (FCS). The idea emerged as a solution for issues arising from strategies promoting installations of fast charging electric vehicle stations. Short, high power period peaks of fast charging increase the volatility of voltage in distribution networks and result in line congestions, requiring grid reinforcements by the distribution system operator (DSO). Additionally, transmission system operators (TSO) need to treat these power spikes, characterized by uncertainty of occurrence, by ensuring additional system flexibility. Coupling battery storage systems with FCS so that they serve as a buffer between the power system, reducing the stress of fast charging, and the electric vehicle (EV) users, providing the desired comfort in terms of charging speed, create multiple benefits for system participants. The results of the developed optimization model demonstrate that there is a feasible investment case for the proposed concept even in cases where only energy arbitrage is in place. Uncertainty aspects, such as unknown time of EV arrival or energy required by EVs, are considered for multiple locations where FCS are going to be installed.
Graph Algorithms for Topology Identification Using Power Grid Probing

To perform any meaningful optimization task, power distribution operators need to know the topology and line impedances of their electric networks. Nevertheless, distribution grids currently lack a comprehensive metering infrastructure. Although smart inverters are widely used for control purposes, they have been recently advocated as the means for an active data acquisition paradigm: reading the voltage deviations induced by intentionally perturbing inverter injections, the system operator can potentially recover the electric grid topology. Adopting inverter probing for feeder processing, a suite of graph-based topology identification algorithms is developed here. If the grid is probed at all leaf nodes but voltage data are metered at all nodes, the entire feeder topology can be successfully recovered. When voltage data are collected only at probing buses, the operator can find a reduced feeder featuring key properties and similarities to the actual feeder. To handle modeling inaccuracies and load non-stationarity, noisy probing data need to be preprocessed. If the suggested guidelines on the magnitude and duration of probing are followed, the recoverability guarantees carry over from the noiseless to the noisy setup with high probability.
Design factors for developing a university campus microgrid

Recent decentralization of electricity systems together with the decarbonization and several changing societal demands are giving rise to different application scenarios such as microgrids. A microgrid is a small-scale electrical system which consists of several loads and sources (conventional and renewables) that can either operate autonomously in a stand-alone mode or interconnected with the main grid. The design and development of such a smart microgrid in a university campus is proposed within the 3DMicroGrid project (funded through the ERANETMED European Union's initiative). This paper reviews the main components and characteristics of similar microgrids developed around the world. Furthermore, this study provides the design guidelines, the main functionalities, the key components and the control architecture for developing the microgrid proposed by the 3DMicroGrid project. A simulation model has been developed and initial results are demonstrated for the operation of this microgrid. The recommendations and insights are replicable to any solar priority country for future microgrids pilots.
Improving Blood Retinol Concentrations with Complementary Foods Fortified with Moringa oleifera Leaf Powder – A Pilot Study

Vitamin A deficiency (VAD) remains a major public health issue and is reported to be the cause of about 6 percent of child deaths under the age of 5 years in Africa. Inadequate dietary intake of vitamin A-rich foods is a major cause of VAD. Moringa oleifera leaf powder (MLP) is rich in nutrients particularly vitamin A and its use in infant feeding has been explored. This pilot study was designed to test the efficacy of MLP in improving blood retinol concentrations among infants in a rural district in Ghana. A subset of infants participating in a randomized controlled trial (ISRCTN14377902) were randomly assigned to receive one of the three study foods (MCL-35g and MS-5g both of which were fortified with MLP, and a third food, CF-35g, a cereal legume blend which served as the control food) in a feeding intervention that lasted for 6 weeks. Primary outcome of the pilot study was retinol levels measured in 5 ml of whole blood at baseline and endline using the iCheck™ Fluoro device. A total of 103 infant-mother pairs were recruited at baseline, of which 65 completed the study. All the infants in the study were vitamin A deficient at both baseline and endline when compared to the World Health Organization (WHO) threshold of 0. 70µmol/l. There was however a marginal non-significant increase in blood vitamin A concentrations for all three groups at endline, with higher numerical increases seen in the two Moringa supplemented groups. VAD is a significant public health problem and MLP could be an affordable and sustainable means of combatting the issue. The efficacy of MLP in improving vitamin A status of infants however needs to be ascertained in well-designed trials involving larger numbers of infants and which will last for longer periods. Such studies will also be beneficial in helping to establish the long-term acceptability of complementary foods that incorporate MLP in the target population.