Optimization in Knowledge-Intensive Crowdsourcing

We present SmartCrowd, a framework for optimizing collaborative knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by accounting for human factors in the process of assigning tasks to workers. Human factors designate workers' expertise in different skills, their expected minimum wage, and their availability. In SmartCrowd, we formulate task assignment as an optimization problem, and rely on pre-indexing workers and maintaining the indexes adaptively, in such a way that the task assignment process gets optimized both qualitatively, and computation time-wise. We present rigorous theoretical analyses of the optimization problem and propose optimal and approximation algorithms. We finally perform extensive performance and quality experiments using real and synthetic data to demonstrate that adaptive indexing in SmartCrowd is necessary to achieve efficient high quality task assignment.Comment: 12 page

Optimization methods for energy management in a microgrid system considering wind uncertainty data

Energy management in the microgrid system is generally formulated as an optimization problem. This paper focuses on the design of a distributed energy management system for the optimal operation of the microgrid using linear and nonlinear optimization methods. Energy management is defined as an optimal scheduling power flow problem. Furthermore, a technical-economic and environmental study is adopted to illustrate the impact of energy exchange between the microgrid and the main grid by applying two management scenarios. Nevertheless, the fluctuating effect of renewable resources especially wind, makes optimal scheduling difficult. To increase the results reliability of the energy management system, a wind forecasting model based on the artificial intelligence of neural networks is proposed. The simulation results showed the reliability of the forecasting model as well as the comparison between the accuracy of optimization methods to choose the most appropriate algorithm that ensures optimal scheduling of the microgrid generators in the two proposed energy management scenarios allowing to prove the interest of the bi-directionality between the microgrid and the main grid.info:eu-repo/semantics/publishedVersio

Combined optimization and regression machine learning for solar Irradiation and wind speed forecasting

Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.info:eu-repo/semantics/publishedVersio

Hybrid optimisation and machine learning models for wind and solar data prediction

The exponential growth in energy demand is leading to massive energy consumption from fossil resources causing a negative effects for the environment. It is essential to promote sustainable solutions based on renewable energies infrastructures such as microgrids integrated to the existing network or as stand alone solution. Moreover, the major focus of today is being able to integrate a higher percentages of renewable electricity into the energy mix. The variability of wind and solar energy requires knowing the relevant long-term patterns for developing better procedures and capabilities to facilitate integration to the network. Precise prediction is essential for an adequate use of these renewable sources. This article proposes machine learning approaches compared to an hybrid method, based on the combination of machine learning with optimisation approaches. The results show the improvement in the accuracy of the machine learning models results once the optimisation approach is used.info:eu-repo/semantics/publishedVersio

New methodology to detect the effects of emotions on different biometrics in real time

Recently, some problems have appeared among medical workers during the diagnosis of some diseases due to human errors or the lack of sufficient information for the diagnosis. In medical diagnosis, doctors always resort to separating human emotions and their impact on vital parameters. In this paper, a methodology is presented to measure vital parameters more accurately while studying the effect of different human emotions on vital signs. Two designs were implemented based on the microcontroller and National Instruments (NI) myRIO. Measurements of four different vital parameters are measured and recorded in real time. At the same time, the effects of different emotions on those vital parameters are recorded and stored for use in analysis and early diagnosis. The results proved that the proposed methodology can contribute to the prediction and diagnosis of the initial symptoms of some diseases such as the seventh nerve and Parkinson’s disease. The two proposed designs are compared with the reference device (beurer) results. The design using NI myRIO achieved more accurate results and a response time of 1.4 seconds for real-time measurements compared to its counterpart based on microcontrollers, which qualifies it to work in intensive care units

MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection

Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a wide range of device malfunctions, overloads, and network intrusions. As a result of this, the network\u27s normal operation and services will be disrupted. The paper proposes a new multi-variant time series-based encoder-decoder system for dealing with anomalies in time series data with multiple variables. As a result, to update network weights via backpropagation, a radical loss function is defined. Anomaly scores are used to evaluate performance. The anomaly score, according to the findings, is more stable and traceable, with fewer false positives and negatives. The proposed system\u27s efficiency is compared to three existing approaches: Multiscaling Convolutional Recurrent Encoder-Decoder, Autoregressive Moving Average, and Long Short Term Medium-Encoder-Decoder. The results show that the proposed technique has the highest precision of 1 for a noise level of 0.2. Thus, it demonstrates greater precision for noise factors of 0.25, 0.3, 0.35, and 0.4, and its effectiveness

Current Trends on Solid Dispersions:Past, Present, and Future

Solid dispersions have achieved significant interest as an effective means of enhancing the dissolution rate and thus the bioavailability of a range of weakly water-soluble drugs. Solid dispersions of weakly water-soluble drugs with water-soluble carriers have lowered the frequency of these problems and improved dissolution. Solid dispersion is a solubilization technology emphasizing mainly on, drug-polymer two-component systems in which drug dispersion and its stabilization is the key to formulation development. Therefore, this technology is recognized as an exceptionally useful means of improving the dissolution properties of poorly water-soluble drugs and in the latest years, a big deal of understanding has been accumulated about solid dispersion, however, their commercial application is limited. In this review article, emphasis is placed on solubility, BCS classification, and carriers. Moreover, this article presents the diverse preparation techniques for solid dispersion and gathers some of the recent technological transfers. The different types of solid dispersions based on the carrier used and molecular arrangement were underlined. Additionally, it summarizes the mechanisms, the methods of preparing solid dispersions, and the marketed drugs that are available using solid dispersion approaches

Dokaz i izdvajanje skupine A rotavirusa iz devine teladi u Sudanu.

A total of 332 fecal samples were collected from 245 diarrheic as well as 75 recovered and 12 healthy camel calves in four different areas in Sudan [north (River Nile), east (Gedarif), central to south (Sennar, Blue Nile) and west (Kordofan) States]. Using ELISA, 46 samples (13.9%) were found positive for group A rotavirus. Out of 46 ELISA positive calves, we found 35 diarrheic up to 3 months of age, 7 recovered 4-6 months of age and 4 clinically healthy 8-12 months of age. Using EM, 6 out of 22 ELISA positive samples showed the well defined wheel-like structure characteristic of rotavirus. The results indicate the significant role of rotavirus in the epidemiology of camel calf diarrhea in Sudan. Attempts to isolate the camel group A rotavirus in MA104 cell culture were carried out on 21 ELISA-positive samples with successful isolation of the virus in 18. The isolated viruses were identified by ELISA and EM. Cytopathic effects (CPE) were manifested as rounding, elongation, triangulation, vacuolation and granulation of cells while the cell sheet remains intact. The CPE appeared on days 3-5 on the first to the second passages after treatment of the inocula and the cell culture media with trypsin. To our knowledge this is the first report on the isolation of camel rotavirus in cell culture.Prikupljena su 332 uzorka fecesa od 245 devine teladi s proljevom, 75 teladi oporavljene od proljeva i 12 od zdrave devine teladi sa četiri različita područja u Sudanu: sjevernoga (područje rijeke Nila), istočnoga (područje Gedarif), središnjega i južnoga (područje Sennar, Plavi Nil) te zapadnoga područja (Kordofan). Pretragom imunoenzimnim testom 46 uzoraka (13,9%) bilo je pozitivno za skupinu A rotavirusa. Od tih 46 pozitivnih životinja imunoenzimnim testom, 35 s proljevom bilo je u dobi do tri mjeseca, sedam s preboljelim proljevom bilo je u dobi od četiri do šest mjeseci, a četiri životinje u dobi od osam do 12 mjeseci bile su klinički zdrave. Elektronskom mikroskopijom dokazane su čestice karakteristične za rotavirus u šest od 22 uzorka pozitivna pretragom imunoenzimnim testom. Rezultati ukazuju na znatnu ulogu rotavirusa u epizootiologiji proljeva u devine mladunčadi u Sudanu. Skupina A rotavirusa izdvojena je na MA104 staničnoj kulturi iz 18 od 21 uzorka pozitivnoga pretragom imunoenzimnim testom. Izdvojeni virusi bili su identificirani imunoenzimnim testom i elekronskom mikroskopijom. Citopatski učinak očitovao se zaokruživanjem stanica, njihovim izduživanjem, triangulacijom, vakuolacijom i granulacijom, dok je stanični sloj ostao netaknut. Citopatski učinak javljao se za tri do pet dana u prvoj i drugoj pasaži nakon obradbe inokula i staničnog medija tripsinom. Ovo je prvo izvješće o izdvajanju rotavirusa deva u staničnoj kulturi

Dental implants significantly increase adjacent tooth loss risk due to root fracture.

DESIGN This retrospective cohort study aimed to investigate the risk and variables of tooth loss for teeth adjacent to dental implants compared to teeth nonadjacent to implants. The study followed the STROBE guidelines and was approved by the Institutional Review Board. COHORT SELECTION The study included patients treated with dental implants at UCSF School of Dentistry between 2000 and 2020. The inclusion criteria for teeth adjacent to implants required the implant to support a fixed prosthesis and a follow-up period of at least 12 months. Nonadjacent teeth also required a follow-up period of at least 12 months. Teeth were excluded if they had a hopeless prognosis or were planned for extraction before the completion of restorative treatment. DATA ANALYSIS Data were extracted from electronic health records, including patient demographics, dental histories, and outcomes for teeth adjacent and nonadjacent to implants. Statistical analyses, including Kaplan-Meier survival plots, log-rank tests, and multivariate logistic regression, were used to compare tooth survival and identify aetiologies of tooth loss. RESULTS The study included 787 patients, with 2048 teeth adjacent and 15,637 teeth nonadjacent to implants. The 10-year cumulative survival rate was 89.2% for teeth adjacent to implants and 99.3% for nonadjacent teeth. Teeth adjacent to implants had a significantly higher risk of tooth loss (Odds Ratio [OR] 13.15). The primary etiology of tooth loss adjacent to implants was root fracture (45.2%), followed by caries (28.9%), periodontitis (24.1%), and endodontic failure (1.8%). For nonadjacent teeth, periodontitis was the leading cause of tooth loss (51.9%). CONCLUSIONS The study found that teeth adjacent to dental implants had a significantly higher risk of tooth loss, primarily due to root fractures. The findings suggest that dental implants may act as an iatrogenic factor, increasing the risk of complications for adjacent teeth. Conservative management of natural dentition should be prioritized, with emphasis on stringent periodontal surveillance and effective home care. Future research should focus on prospective studies to further explore these associations and improve clinical outcomes

Rabies Virus Infection in Livestock

Rabies is a lethal zoonotic encephalomyelitis and a major challenge to public and animal health. Livestock are affected by rabies mostly through bites of rapid dogs or wildlife carnivore\u27s species. They are considered as ‘dead-end’ hosts that do not transmit the virus. Rabies in livestock has been endemic in many developing countries for many years and diagnosed through clinical signs and dog-biting history. An introduction on rabies situation in farm animals will be given then subchapters including `rabies in bovines, rabies in small ruminants, rabies in swine and rabies in camelids. In each subchapter we shall discuss, epidemiology, modes of transmission, diagnosis and prevention and control measures