What Will You Do for the Rest of the Day?

Understanding and predicting human mobility is vital to a large number of applications, ranging from recommendations to safety and urban service planning. In some travel applications, the ability to accurately predict the user's future trajectory is vital for delivering high quality of service. The accurate prediction of detailed trajectories would empower location-based service providers with the ability to deliver more precise recommendations to users. Existing work on human mobility prediction has mainly focused on the prediction of the next location (or the set of locations) visited by the user, rather than on the prediction of the continuous trajectory (sequences of further locations and the corresponding arrival and departure times). Furthermore, existing approaches often return predicted locations as regions with coarse granularity rather than geographical coordinates, which limits the practicality of the prediction. In this paper, we introduce a novel trajectory prediction problem: given historical data and a user's initial trajectory in the morning, can we predict the user's full trajectory later in the day (e.g. the afternoon trajectory)? The predicted continuous trajectory includes the sequence of future locations, the stay times, and the departure times. We first conduct a comprehensive analysis about the relationship between morning trajectories and the corresponding afternoon trajectories, and found there is a positive correlation between them. Our proposed method combines similarity metrics over the extracted temporal sequences of locations to estimate similar informative segments across user trajectories. Our evaluation shows results on both labeled and geographical trajectories with a prediction error reduced by 10-35% in comparison to the baselines. This improvement has the potential to enable precise location services, raising usefulness to users to unprecedented levels. We also present empirical evaluations with Markov model and Long Short Term Memory (LSTM), a state-of-the-art Recurrent Neural Network model. Our proposed method is shown to be more effective when smaller number of samples are used and is exponentially more efficient than LSTM.</jats:p

Location Allocation and Econometrics of a Solar Chimney with 50 KW Output Power in Terms of Climate Conditions of Southern Iranian Provinces

The aim of the current research is location allocation and econometrics of solar tower (chimney) for power generating in climate conditions of southern Iranian provinces. Location allocation means determining an appropriate location for constructing a solar chimney with appropriate size including receiving surface (absorber or collector), tower height and turbine diameter for 50 kW output power so that the location is compatible with climate conditions of the selected province. Firstly, the amount of solar energy received on the earth surface in one of the southern provinces of Iran is calculated and then, all available governing equations on tower elements (receiving surface, tower height and turbine diameter) are written and solved in MATLAB. The obtained results are validated by comparing the output of computer solution for 50 kW daily power with the model produced in Manzanares, Spain, where have a similar climate conditions with south of Iran. In addition to validation with experimental data, the computer solution is re-validated with the results obtained from finite element analysis performed by FLUENT software. The obtained results show that: in low power, solar tower is not cos effective without considering economic conditions.Moreover, solar tower with small turbine diameter needs to large size receiver and high height that should be scientifically investigated. Further, after validation of results, the expected power is analyzed using the relationships between tower elements to achieve a comprehensive plan which has a complete analysis of relationships between the size and location of solar chimney and optimization process of final cost along with the plan econometrics in terms of the available facilities on the selected province

CLASSIFICATION OF TOMATO QUALITY BASED ON COLOR FEATURES AND SKIN CHARACTERISTICS USING IMAGE PROCESSING BASED ARTIFICIAL NEURAL NETWORK

Tomato (Solanum Lycopersicum) is a plantation commodity in Indonesia with a production rate that tends to increase every year. With a high economic value, maintenance is important so that the quality is getting better. The problems that arise at this time are related to the determination of the quality of tomatoes which is still done manually and depends on humans so classification using technology is considered important to be developed. Previously there has been researching related to the classification of tomatoes. However, accuracy and computation time still need to be improved. Therefore, in this research, a method of classification of tomatoes was carried out using Artificial Neural Network (ANN) Backpropagation algorithm by utilizing color features and skin characteristics based on image processing. This research followed several stages, from acquiring 300 tomato images with 3 class levels to the classification process using ANN Backpropagation. Several training scenarios and tests were conducted to select the feature combined with the highest accuracy and fastest computation time. The combination of 3 best features used is RGB color feature with shape and texture features as skin characteristic parameters. Based on training results with 210 training images, an accuracy of 100% was obtained with a computation time of 2.58 seconds per image. While test results with 90 test images, accuracy reaches 95.5% with a computing time of 1.39 seconds per image. So it can be concluded that the method used has gone well in classifying tomato image quality based on color features and skin characteristics

PENGARUH PEMBERIAN KESEJAHTERAAN PEGAWAI DAN DISIPLIN KERJA TERHADAP KEPUASAN KERJA DI LINGKUNGAN KANTOR PELAYANAN PERBENDAHARAAN NEGARA PADANG

Tujuan dari penelitian ini secara umum untuk menguji pengaruh pemberian kesejahteraan pegawai dan disiplin kerja terhadap kepuasan kerja di lingkungan Kantor Pelayanan Perbendaharaan Negara Padang. Metode penelitian ini menggunakan metode kuantitatif dengan jumlah populasi sebanyak 40 orang. Sampel dipilih menggunakan teknik total sampling. Sehingga sampel akhir dalam penelitian ini adalah sebanyak 40 orang pegawai. Teknik analisis yang digunakan adalah Analisis regresi berganda dengan bantuan SPSS. Hasil penelitian menunjukan bahwa pengaruh pemberian kesejahteraan pegawai, dan disiplin kerja menjadi faktor utama dalam menentukan tinggi rendahnya kepuasan kerja

Phylogenetic analysis and genotyping of Iranian infectious haematopoietic necrosis virus (IHNV) of rainbow trout (Oncorhynchus mykiss) based on the glycoprotein gene

Abstract Background Infectious haematopoietic necrosis (IHN) is known as one of the most contagious systemic viral diseases in salmonids which can lead to significant mortality rates and negative impacts on the salmonid farming industry. Infectious haematopoietic necrosis virus (IHNV) was first detected in rainbow trout (Oncorhynchus mykiss) farms in Iran in 2003. Objectives We conducted the present study to determine the detection of IHN genotypes in rainbow trout (O. mykiss) in farms in the central parts of Iran, using molecular and phylogenetic techniques. Methods Samples were collected from fries exhibiting clinical signs such as darkening of the skin, abdominal swelling, and loss of appetite. Phylogenetic analysis was performed by the neighbour‐joining method, using MEGA 5.1 software. For phylogenetic analysis and genotyping of IHNV from central parts of Iran, the sequences of the glycoprotein gene were determined for two Iranian isolates (Jahad‐UT1 and Jahad‐UT2). Results Phylogenetic analysis revealed that the detected strains (Jahad‐UT1 and Jahad‐UT2 isolates) are closely related (97.23%–100%) to European isolates within genogroup ‘E’. Conclusions This finding indicates that Jahad‐UT1 and Jahad‐UT2 isolates have been widely transferred to Iran from European countries. Moreover, the nucleotide diversity of these Iranian isolates showed a close relationship with the North American and Asian isolates, although the Iranian isolates were collected from a smaller geographical area and within a shorter time period between 2014 and 2015

MiRNA-related metastasis in oral cancer: moving and shaking

Abstract Across the world, oral cancer is a prevalent tumor. Over the years, both its mortality and incidence have grown. Oral cancer metastasis is a complex process involving cell invasion, migration, proliferation, and egress from cancer tissue either by lymphatic vessels or blood vessels. MicroRNAs (miRNAs) are essential short non-coding RNAs, which can act either as tumor suppressors or as oncogenes to control cancer development. Cancer metastasis is a multi-step process, in which miRNAs can inhibit or stimulate metastasis at all stages, including epithelial-mesenchymal transition, migration, invasion, and colonization, by targeting critical genes in these pathways. On the other hand, long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), two different types of non-coding RNAs, can regulate cancer metastasis by affecting gene expression through cross-talk with miRNAs. We reviewed the scientific literature (Google Scholar, Scopus, and PubMed) for the period 2000–2023 to find reports concerning miRNAs and lncRNA/circRNA-miRNA-mRNA networks, which control the spread of oral cancer cells by affecting invasion, migration, and metastasis. According to these reports, miRNAs are involved in the regulation of metastasis pathways either by directly or indirectly targeting genes associated with metastasis. Moreover, circRNAs and lncRNAs can induce or suppress oral cancer metastasis by acting as competing endogenous RNAs to inhibit the effect of miRNA suppression on specific mRNAs. Overall, non-coding RNAs (especially miRNAs) could help to create innovative therapeutic methods for the control of oral cancer metastases