Use of social network sites among depressed adolescents

Social network sites (SNSs) are relatively new phenomena, and the relationship between SNSs and psychopathology remains unclear. The purpose of this study was to evaluate the type of SNSs depressed adolescents use and the incidence of depressive disclosure on SNSs among them. The study was designed to be cross-sectional. The sample consisted of 53 adolescents diagnosed with depressive disorder, as confirmed by K-SADS-PL, and 55 non-depressed adolescents. The Children’s Depression Inventory, Social Anxiety Scale and Social Network Use Questionnaire were administered. The primary finding was that the amount of time spent on the Internet and on SNSs was significantly higher among depressed adolescents than non-depressed adolescents. Additionally, depressed adolescents reported significantly higher disclosure of anhedonia, worthlessness, guilt, loss of concentration, irritability and thoughts of suicide on SNSs. The intensity of the depression sharing was significantly higher in the depressed group. Depressed young people use social networks to express their symptoms. Adolescents’ disclosure on social networks may be able to guide relatives, friends and mental health professionals.     

Simulation platform for anticipative plant-level maintenance decision support system

Global competition and increasing customer expectations are forcing automobile manufacturers to improve their operations. Maintenance, being one of the most critical components in many industries, has a direct impact on the improvement of the overall production performance. In this paper, we introduce an anticipative plant-level maintenance decision support system (APMDSS) that provides guidance on corrective and preventive maintenance priorities based on the equipment bottleneck ranks with the objective of improving daily plant throughput. APMDSS anticipates the plant dynamics (i.e. bottlenecks, hourly buffer levels and likelihood of machine breakdowns) for upcoming shifts using starting state information of the production shift (e.g. equipment maintenance history, operational status of machines, buffer levels and scheduled production model mix). We also evaluate the performance of APMDSS using real data from an automotive body shop experiencing routine throughput difficulties due to frequent machine breakdowns. The results are compared with other methods from the literature and found to be superior in many settings.     

Scheduling the vehicles of bus rapid transit systems: a case study

Bus rapid transit (BRT) is a cost-efficient, traffic-free bus-based transportation system competing with subways. There are 205 municipalities around the world that implemented their own BRT systems. Istanbul, having the sixth-most congested traffic in the world, built its own BRT system (Metrobüs), which serves more than 830,000 people (6.45% of all public transportation usage) in a day with 6254 trips covered by its current fleet of 496 vehicles. In this study, we model the vehicle scheduling problem of Metrobüs as a multiple depot vehicle scheduling problem. The model aims to minimize the fleet size and total deadhead kilometers while covering all timetabled trips. We propose a new heuristic, trips merger (TM), to solve the model and show that there exists cost reduction opportunities in terms of both fleet size and deadhead kilometers. The proposed heuristic is a member of the state-space reduction heuristics family, which first reduces the problem size, then solves the reduced problem. Computational study reveals that TM performed better than the existing state-space reduction heuristics for the Metrobüs case.     

Blood glucose level prediction for diabetes based on modified fuzzy time series and particle swarm optimization

Blood glucose control is an essential goal for the patients who have Type‐1 diabetes (T1D). The prediction of the blood glucose levels for the next 30‐minute is crucial. If the predicted blood glucose level is in the critical ranges, and these predictions can be known in advance, then the patients can take the necessary cautions to prevent from it. In this article, we propose a modified fuzzy particle swarm optimization algorithm for the prediction of blood glucose levels of 30‐minute after the last measurement. We form the average and patient‐specific models to predict the blood glucose level of the patients. Both models are tested on two different datasets which contain patients with T1D. The experimental results are evaluated in terms of root mean squared error and Clarke error grid analysis metrics. The results indicate that our proposed modified algorithm is feasible to be applied to the prediction of blood glucose levels. In addition, this approach can assist patients with T1D for their blood glucose control.     

Design and Properties of New Functional Water – Soluble Polymers of Citraconic Anhydride (CA) and Related Copolymers

Summary The binary and ternary copolymers are synthesized by free-radical initiated binary copolymerization of citraconic anhydride (CA) with acrylamide (AAm) and terpolymerization of acrylamide (AAm) – citraconic anhydride (CA) – vinyl acetate (VA) monomer system. Co- and terpolymerization were carried out in the presence of benzoyl peroxide (BPO) as an initiator at 70 °C in benzene under nitrogen atmosphere. Structure and monomer unit composition of the copolymers, obtained from a wide range of monomer feed were determined by elemental analysis. Structural peculiarities of synthesized PAAm, [poly (AAm-alt-CA)] and [poly(AAm-co-CA-co-VA)] were characterized by using Fourier Transform Infrared (FTIR), Uv–vis, ¹H-NMR, X-ray diffraction (XRD), elemental analysis, analytical methods (titration methods) and viscometric measurements.     

Robust classification of face and head gestures in video

Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in human-computer interfaces. We develop a face and head gesture detector in video streams. The detector is based on face landmark paradigm in that appearance and configuration information of landmarks are used. First we detect and track accurately facial landmarks using adaptive templates, Kalman predictor and subspace regularization. Then the trajectories (time series) of facial landmark positions during the course of the head gesture or facial expression are converted in various discriminative features. Features can be landmark coordinate time series, facial geometric features or patches on expressive regions of the face. We use comparatively, two feature sequence classifiers, that is, Hidden Markov Models (HMM) and Hidden Conditional Random Fields (HCRF), and various feature subspace classifiers, that is, ICA (Independent Component Analysis) and NMF (Non-negative Matrix Factorization) on the spatiotemporal data. We achieve 87.3% correct gesture classification on a seven-gesture test database, and the performance reaches 98.2% correct detection under a fusion scheme. Promising and competitive results are also achieved on classification of naturally occurring gesture clips of LIlir TwoTalk Corpus.     

Flexibility in PPP contracts – Dealing with potential change in the pre-contract phase of a construction project

Public Private Partnerships (PPPs) cover a range of possible relationships between public and private parties. PPP contracts are typically used in contexts of great uncertainty, such as large construction and infrastructure projects that are realized over a longer period of time. Hence, a major challenge in PPPs is to keep construction progress cost-efficient and on schedule, under continuously changing circumstances. One way to achieve this is through clever contracting, by proactively anticipating potential change in the planning phase and providing flexible contract mechanisms that enable an effective response. The purpose of this article is to discuss potential changes and the subsequent requirement of flexibility in PPP contracts. By flexibility, we mean the ability of the contract to deal with changing circumstances. We set out to do so by studying the available literature on the subject and by analysing the case study of the Blankenburgverbinding in the Netherlands, a Design, Build, Finance and Maintain (DBFM) project that is currently in its planning phase based on 32 interviews. Our main findings are that the timely and accurate recognition of potential changes, combined with the availability of flexible coping mechanisms, provide the stakeholders with a better understanding of the challenges they face in realizing their aims in the pre-contract phase of projects. This understanding helps to better prepare a PPP contract for potential changes.     

Categorical and dimensional affect analysis in continuous input: Current trends and future directions

In the context of affective human behavior analysis, we use the term continuous input to refer to naturalistic settings where explicit or implicit input from the subject is continuously available, where in a human–human or human–computer interaction setting, the subject plays the role of a producer of the communicative behavior or the role of a recipient of the communicative behavior. As a result, the analysis and the response provided by the automatic system are also envisioned to be continuous over the course of time, within the boundaries of digital machine output. The term continuous affect analysis is used as analysis that is continuous in time as well as analysis that uses affect phenomenon represented in dimensional space. The former refers to acquiring and processing long unsegmented recordings for detection of an affective state or event (e.g., nod, laughter, pain), and the latter refers to prediction of an affect dimension (e.g., valence, arousal, power). In line with the Special Issue on Affect Analysis in Continuous Input, this survey paper aims to put the continuity aspect of affect under the spotlight by investigating the current trends and provide guidance towards possible future directions.