Well-being and problematic Facebook use

Social networking sites are getting more widespread online communication forms all over the world. Especially adolescents and emerging adults use these sites to make connection, and share something. Although, Facebook is one of the most popular sites, research is still in nascent phase on the excessive use of Facebook. Hence, it is fundamental to determine potential predictors of overuse of Facebook. The present study investigated the relationship between well-being and problematic Facebook use. Participants were 311 university students [179_(58%) female, 132_(42%) male, M_age = 20.86 years, SD = 1.61] who completed the questionnaire packet including the Bergen Facebook Addiction Scale (Andreassen, Torsheim, Brunborg, & Pallesen, 2012), the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985), the Subjective Vitality Scale (Ryan & Frederick, 1997), the Flourishing Scale (Diener et al., 2010), and the Subjective Happiness Scale (Lyubomirsky & Lepper, 1999). A stepwise regression analyses was used with four independent variables (life satisfaction, subjective vitality, flourishing, and subjective happiness) to explain variance in problematic Facebook use. The results showed that life satisfaction, subjective vitality, flourishing, and subjective happiness were significant negative predictors of problematic Facebook use. The significance and limitations of the results are discussed.     

Exploring depression and problematic internet use among college females: A multisite study

ObjectiveThe purpose of this study was to assess associations between depression and problematic internet use (PIU) among female college students, and determine whether Internet use time moderates this relationship.MethodThis cross-sectional survey included 265 female college students from four U.S. universities. Students completed the Patient Health Questionnaire-9 (PHQ-9), the Problematic and Risky Internet Use Screening Scale (PRIUSS) and self-reported daily Internet use. Analyses included multivariate analysis of variance and Poisson regression.ResultsParticipants reported mean age of 20.2 years (SD = 1.7) and were 84.9% Caucasian. The mean PHQ-9 score was 5.4 (SD = 4.6); the mean PRIUSS score was 16.4 (SD = 11.1). Participants’ risk for PIU increased by 27% with each additional 30 min spent online using a computer (RR = 1.27, 95% CI: 1.14–1.42, p < .0001). Risk for PIU was significantly increased among those who met criteria for severe depression (RR = 8.16 95% CI: 4.27–15.6, p < .0001). The PHQ-9 items describing trouble concentrating, psychomotor dysregulation and suicidal ideation were most strongly associated with PIU risk.ConclusionsThe positive relationship between depression and PIU among female college students supports screening for both conditions, particularly among students reporting particular depression symptoms.     

Sex role stereotyping is hard to kill: A field experiment measuring social responses to user characteristics and behavior in an online multiplayer first-person shooter game

Sex role stereotyping by players in first-person shooter games and other online gaming environments may encourage a social environment that marginalizes and alienates female players. Consistent with the social identity model of deindividuation effects (SIDE), the anonymity of online games may engender endorsement of group-consistent attitudes and amplification of social stereotyping, such as the adherence to gender norms predicted by expectations states theory. A 2 × 3 × 2 virtual field experiment (N = 520) in an online first-person shooter video game examined effects of a confederate players’ sex, communication style, and skill on players’ compliance with subsequent online friend requests. We found support for the hypothesis that, in general, women would gain more compliance with friend requests than men. We also found support for the hypothesis that women making positive utterances would gain more compliance with friend requests than women making negative utterances, whereas men making negative utterances would gain more compliance with friend requests than men making positive utterances. The hypothesis that player skill (i.e., game scores) would predict compliance with friend requests was not supported. Implications for male and female game players and computer-mediated communication in online gaming environments are discussed.     

Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans

PurposeTo compare the diagnostic performances of artificial neural networks (ANNs) and multivariable logistic regression (LR) analyses for differentiating between malignant and benign lung nodules on computed tomography (CT) scans.MethodsThis study evaluated 135 malignant nodules and 65 benign nodules. For each nodule, morphologic features (size, margins, contour, internal characteristics) on CT images and the patient’s age, sex and history of bloody sputum were recorded. Based on 200 bootstrap samples generated from the initial dataset, 200 pairs of ANN and LR models were built and tested. The area under the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow statistic and overall accuracy rate were used for the performance comparison.ResultsANNs had a higher discriminative performance than LR models (area under the ROC curve: 0.955 ± 0.015 (mean ± standard error) and 0.929 ± 0.017, respectively, p < 0.05). The overall accuracy rate for ANNs (90.0 ± 2.0%) was greater than that for LR models (86.9 ± 1.6%, p < 0.05). The Hosmer–Lemeshow statistic for the ANNs was 8.76 ± 6.59 vs. 6.62 ± 4.03 (p > 0.05) for the LR models.ConclusionsWhen used to differentiate between malignant and benign lung nodules on CT scans based on both objective and subjective features, ANNs outperformed LR models in both discrimination and clinical usefulness, but did not outperform for the calibration.     

Facebook use and academic performance among college students: A mixed-methods study with a multi-ethnic sample

This paper uses a mixed-methods approach to examine the relation between online academic disclosure and academic performance. A multi-ethnic sample of college students (N = 261; male = 66; female = 195; M age ≈ 22 years) responded to open-ended questions about their Facebook use. Thematic analysis revealed that over 14% of the Facebook wall posts/status updates (N = 714) contained academic themes; positive states were more frequent than negative and neutral states and students with lower GPAs expressed negative states more often. A path analysis suggested that academic performance may determine college students’ Facebook use, rather than the reverse. Implications for student support services are discussed.     

Analysis of high-Q,gallium nitride nanowire resonators in response to deposited thin films

Gallium nitride nanowires (GaN-NWs) are systems of interest for mechanical resonance-based sensors due to their small mass and, in the case of c-axis NWs, high mechanical quality (Q) factors of 10,000–100,000. We report on singly-clamped NW mechanical cantilevers of roughly 100 nm diameter and 15 μm length that resonate near 1 MHz and describe the behavior of GaN-NW resonant frequencies and Q factors following coating with various materials deposited by atomic layer deposition (ALD), including alumina (Al₂O₃), ruthenium (Ru), and platinum (Pt). Changes in the GaN-NW resonant frequencies with ALD deposition clearly distinguish conformal film growth versus island film growth. Conformal films lead to a stiffening of the NW and typically increase resonant frequency, whereas island films simply increase the NW mass and cause decreased resonant frequencies. We find that conformal growth of ALD alumina leads to stiffening of ～4 kHz per nm of alumina, in agreement with previously measured material properties. Conformal growth of Ru and Pt, respectively, qualitatively confirm our analytical predictions of positive and negative resonant frequency shifts. Island growth of ALD Ru has demonstrated a decrease in resonant frequency consistent with mass loading of ～0.2 fg for a 150 ALD-cycle film, also consistent with analytical predictions. Resonant Q factors are found to decrease with ALD film growth, offering the additional possibility of studying mechanical dissipation processes associated with the ALD-NW composite structures.     

Software sensors are a real alternative to true sensors

At the Ejby Mølle WWTP in Odense Denmark a software sensor predicts the ammonium and nitrite + nitrate concentration in real-time based on ammonium and redox potential measurements. The predicted ammonium concentration is used to control the length of the nitrification phase in a Biodenipho^® activated sludge unit because the software sensor has a shorter response time and a better up-time than the ammonium meter. The software sensor simplifies meter service and can reduce maintenance costs. The computed nitrite + nitrate concentration is an added benefit of the software sensor. On 4 different days, series of grab samples of the mixed liquor were collected in the aeration tanks. The average difference between the ammonium concentrations in the grab samples and the predicted ammonium concentration was 0.2 mgN L^?1 and the average difference between the predicted and the measured nitrite + nitrate concentration was 0.3 mgN L^?1. The agreement between the predicted and the measured ammonium concentration in the grab samples was better than the agreement between the ammonium meter and the grab samples. This was due to the shorter response time of the software sensor compared with the ammonium meter.     

#Gettinghealthy: The perceived influence of social media on young adult health behaviors

Young adults (18–25 years old) spend a majority of their waking hours with technology and young adulthood is an important developmental time period for establishing lasting health behaviors. Considering the relevance of technology and health during young adulthood the current study explored young adults (N = 34) perceptions of social media’s (e.g., social networking) influence on their health behaviors (i.e., diet and exercise) using a social ecological framework. Data was collected through eight focus groups and four individual interviews. Three themes were identified through phenomenological qualitative analysis. Young adults perceived that technology could be both a barrier and a motivator for exercise. Social media was also credited with expanding food choices through creating access to a variety of recipes, providing a venue for showcasing the food young adults eat or prepare, and distracting young adults from making positive food choices. Participants also reported that it is common to post statuses or pictures relating to exercise practices on social media during young adulthood. Young adults indicated that these posts could be inspirational or misused, depending on the context. Results are discussed in terms of theory and preliminary implications.     

A new discrete circuit for readout of resistive sensor arrays

In this paper, we present a method that simplifies the interconnect complexity of N × M resistive sensor arrays from N × M to N + M. In this method, we propose to use two sets of interconnection lines in row–column fashion with all the sensor elements having one of their ends connected to a row line and other end to a column line. This interconnection overloading results in crosstalk among all the elements. This crosstalk causes the spreading of information over the whole array. The proposed circuit in this method takes care of this effect by minimizing the crosstalk. The circuit makes use of the concept of virtual same potential at the inputs of an operational amplifier in negative feedback to obtain a sufficient isolation among various elements. We theoretically present the suitability of the method for small/moderate sized sensor arrays and experimentally verify the predicted behavior by lock-in-amplifier based measurements on a light dependent resistor (LDR) in a 4 × 4 resistor array. Finally, we present a successful implementation of this method on a 16 × 16 imaging array of LDR.     

Effects of visual feedback on medical students’ procrastination within web-based planning and reflection protocols

Procrastination is a very common problem among students that results from ineffective selfregulation. In two field-experimental studies (N = 18 and N = 49), we investigated whether visual feedback on students’ previous procrastination was effective in provoking a decrease in students’ future procrastination as well as improvements in self-regulated learning. The visual feedback was implemented as a dynamic line chart in a web-based planning and reflection protocol used once a week by medical students to record their class preparation and homework once a week. In the protocols, the students planned and reflected on their personal learning processes and they estimated retrospectively their inclination to procrastinate. The results of both studies consistently showed that presenting students a line chart that adaptively visualizes the course and extent of their self-reported previous procrastination led to a statistically significant and practically relevant decrease in their future procrastination. Furthermore, the visualization had positive effects on other variables central to self-regulated learning. The studies provide converging evidence that the inclination to procrastinate can successfully be counteracted both by a parsimonious and easy-to-implement method. They are suggestive of ways how Internet technology can be used support students’ self-regulated learning.     

Revealing halogen bonding interactions with anomeric systems: An ab initio quantum chemical studies

A computational study has been performed using MP2 and CCSD(T) methods on a series of O⋯X (X = Br, Cl and I) halogen bonds to evaluate the strength and characteristic of such highly directional noncovalent interactions. The study has been carried out on a series of dimeric complexes formed between interhalogen compounds (such as BrF, BrCl and BrI) and oxygen containing electron donor molecule. The existence and consequences of the anomeric effect of the electron donor molecule has also been investigated through an exploration of halogen bonding interactions in this halogen bonded complexes. The ab initio quantum chemical calculations have been employed to study both the nature and directionality of the halogen molecules toward the sp³ oxygen atom in anomeric systems. The presence of anomeric n_O → σ*_CN interaction involves a dominant role for the availability of the axial and equatorial lone pairs of donor O atom to participate with interhalogen compounds in the halogen-bonded complexes. The energy difference between the axial and equatorial conformers with interhalogen compounds reaches up to 4.60 kJ/mol, which however depends upon the interacting halogen atoms and its attaching atoms. The energy decomposition analysis further suggests that the total halogen bond interaction energies are mainly contributed by the attractive electrostatic and dispersion components. The role of substituents attached with the halogen atoms has also been evaluated in this study. With the increase of halogen atom size and the positive nature of σ-hole, the halogen atom interacted more with the electron donor atom and the electrostatic contribution to the total interaction energy enhances appreciably. Further, noncovalent interaction (NCI) studies have been carried out to locate the noncovalent halogen bonding interactions in real space.     

Performance assessment and tuning for exchange of clinical documents cross healthcare enterprises

BackgroundTo integrate electronic health records (EHRs) from diverse document sources across healthcare providers, facilities, or medical institutions, the IHE XDS.b profile can be considered as one of the solutions. In this research, we have developed an EHR/OpenXDS system which adopted the OpenXDS, an open source software that complied with the IHE XDS.b profile, and which achieved the EHR interoperability.ObjectiveWe conducted performance testing to investigate the performance and limitations of this EHR/OpenXDS system.MethodologyThe performance testing was conducted for three use cases, EHR submission, query, and retrieval, based on the IHE XDS.b profile for EHR sharing. In addition, we also monitored the depletion of hardware resources (including the CPU usage, memory usage, and network usage) during the test cases execution to detect more details of the EHR/OpenXDS system's limitations.ResultsIn this EHR/OpenXDS system, the maximum affordable workload of the EHR submissions were 400 EHR submissions per hour, the DSA CPU usage was 20%, memory usage was 1380 MB, the network usages were 0.286 KB input and 7.58 KB output per minute; the DPA CPU usage was 1%, memory usage was 1770 MB, the network usages were 7.75 KB input and 1.54 KB output per minute; the DGA CPU usage was 24%, memory usage was 2130 MB, the network usages were 1.3 KB input and 0.174 KB output per minute. The maximum affordable workload of the EHR queries were 600 EHR queries per hour, the DCA CPU usage was 66%, the memory usage was 1660 MB, the network usages were 0.230 KB input and 0.251 KB output per minute; the DGA CPU usage was 1%, the memory usage was 1890 MB, the network usages were 0.273 KB input and 0.22 KB output per minute. The maximum affordable workload of the EHR retrievals were 2000 EHR retrievals, the DCA CPU usage was 79%, the memory usage was 1730 MB, the network usages were 19.55 KB input and 1.12 KB output per minute; the DPA CPU usage was 3.75%, the memory usage was 2310 MB, and the network usages were 0.956 KB input and 19.57 KB output per minute.Discussion and conclusionFrom the research results, we suggest that future implementers who deployed the EHR/OpenXDS system should consider the following aspects. First, to ensure how many service volumes would be provided in the environment and then to adjust the hardware resources. Second, the IHE XDS.b profile is adopted by the SOAP (Simple Object Access Protocol) web service, it might then move onto the Restful (representational state transfer) web service which is more efficient than the SOAP web service. Third, the concurrency process ability should be added in the OpenXDS source code to improve the hardware usage more efficiently while processing the ITI-42, ITI-18, and ITI-43 transactions. Four, this research suggests that the work should continue on adjusting the memory usage for the modules of the OpenXDS thereby using the memory resource more efficiently, e.g., the memory configuration of the JVM (Java Virtual Machine), Apache Tomcat, and Apache Axis2. Fifth, to consider if the hardware monitoring would be required in the implementing environment. These research results provided some test figures to refer to, and it also gave some tuning suggestions and future works to continue improving the performance of the OpenXDS.     

Improvement of picture quality of high Xe content plasma display panels based on facing discharge suppression

This paper presents a waveform for driving a high-resolution plasma display panel (PDP) which uses a gas mixture of high Xe content. To prevent degradation of picture quality due to unstable discharges between two facing electrodes, the common electrode was biased at a negative voltage during the set-up period, and the data electrode was biased at a positive voltage during the sustain period. A pre-reset pulse was used before the first reset to reduce the reset voltage and to form a proper wall charge state for sustain discharges. This waveform could drive a 15% Xe 42 in. XGA (1024 × 768) PDP with the single-scan method. The measured black luminescence, peak luminescence, and contrast ratio were 0.45, 1490 cd/m², and 3310:1, respectively. The measured margin of the sustain voltage was better than ±10 V.     

An evaluation of algorithms for the remote sensing of cyanobacterial biomass

Most remote sensing algorithms for phytoplankton in inland waters aim at the retrieval of the pigment chlorophyll a (Chl a), as this pigment is a useful proxy for phytoplankton biomass. More recently, algorithms have been developed to quantify the pigment phycocyanin (PC), which is characteristic of cyanobacteria, a phytoplankton group of relative importance to inland water management due to their negative impact on water quality in response to eutrophication.We evaluated the accuracy of three published algorithms for the remote sensing of PC in inland waters, using an extensive database of field radiometric and pigment data obtained in the Netherlands and Spain in the period 2001–2005. The three algorithms (a baseline, single band ratio, and a nested band ratio approach) all target the PC absorption effect observed in reflectance spectra in the 620 nm region. We evaluated the sensitivity of the algorithms to errors in reflectance measurements and investigated their performance in cyanobacteria-dominated water bodies as well as in the presence of other phytoplankton pigments.All algorithms performed best in moderate to high PC concentrations (50–200 mg m^? 3) and showed the most linear response to increasing PC in cyanobacteria-dominated waters. The highest errors showed at PC < 50 mg m^? 3. In eutrophic waters, the presence of other pigments explained a tendency to overestimate the PC concentration. In oligotrophic waters, negative PC predictions were observed. At very high concentrations (PC > 200 mg m^? 3), PC underestimations by the baseline and single band ratio algorithms were attributed to a non-linear relationship between PC and absorption in the 620 nm region. The nested band ratio gave the overall best fit between predicted and measured PC. For the Spanish dataset, a stable ratio of PC over cyanobacterial Chl a was observed, suggesting that PC is indeed a good proxy for cyanobacterial biomass. The single reflectance ratio was the only algorithm insensitive to changes in the amplitude of reflectance spectra, which were observed as a result of different measurement methodologies.     

Empirical evaluation of the effects of mixed project data on learning defect predictors

ContextDefect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects (i.e. within project (WP)) through retrospective analyses. On the other hand, recent studies try to utilize data across projects (i.e. cross project (CP)) for building defect prediction models for new projects. There are no cases where the combination of within and cross (i.e. mixed) project data are used together.ObjectiveOur goal is to investigate the merits of using mixed project data for binary defect prediction. Specifically, we want to check whether it is feasible, in terms of defect detection performance, to use data from other projects for the cases (i) when there is an existing within project history and (ii) when there are limited within project data.MethodWe use data from 73 versions of 41 projects that are publicly available. We simulate the two above-mentioned cases, and compare the performances of naive Bayes classifiers by using within project data vs. mixed project data.ResultsFor the first case, we find that the performance of mixed project predictors significantly improves over full within project predictors (p-value < 0.001), however the effect size is small (Hedges′ g = 0.25). For the second case, we found that mixed project predictors are comparable to full within project predictors, using only 10% of available within project data (p-value = 0.002, g = 0.17).ConclusionWe conclude that the extra effort associated with collecting data from other projects is not feasible in terms of practical performance improvement when there is already an established within project defect predictor using full project history. However, when there is limited project history, e.g. early phases of development, mixed project predictions are justifiable as they perform as good as full within project models.     

Discovery of novel anti-leishmanial agents targeting LdLip3 lipase

Leishmaniasis is a neglected tropical disease, caused by several species of Leishmania. Being an opportunistic lipid-scavenging pathogen, Leishmania relies extensively on lipid metabolism especially for host–pathogen interaction, utilizing host lipids for energy and virulence. The rational approach is to target lipid metabolism of the pathogen focusing lipid-catabolizing lipases. The LdLip3 lipase is considered as drug target as it is constitutively expressed in both promastigote and amastigote forms. Since the LdLip3 structure is not known, we modeled its three-dimensional structure to implement structure-based drug discovery approach. Similarity-based virtual screening was carried out to identify potential inhibitors utilizing NCI diversity set on ZINC database including natural products. Implementing computational and experimental approaches, four anti-leishmanial agents were discovered. The screened molecules ZINC01821375, ZINC04008765, ZINC06117316 and ZINC12653571 had anti-leishmanial activity with IC₅₀ (% viable promastigotes vs. concentration) of 5.2 ± 1.8 μM, 13.1 ± 2.6 μM, 9.4 ± 2.6 μM and 17.3 ± 3.1 μM, respectively. The molecules showed negligible toxicity toward mouse macrophages. Based on the contact footprinting analysis, new molecules were designed with better predicted free energy of binding than discovered anti-leishmanial agents. Further validation for the therapeutic utility of discovered molecules can be carried out by the research community to combat leishmaniasis.     

Speech emotion recognition: Features and classification models

To solve the speaker independent emotion recognition problem, a three-level speech emotion recognition model is proposed to classify six speech emotions, including sadness, anger, surprise, fear, happiness and disgust from coarse to fine. For each level, appropriate features are selected from 288 candidates by using Fisher rate which is also regarded as input parameter for Support Vector Machine (SVM). In order to evaluate the proposed system, principal component analysis (PCA) for dimension reduction and artificial neural network (ANN) for classification are adopted to design four comparative experiments, including Fisher + SVM, PCA + SVM, Fisher + ANN, PCA + ANN. The experimental results proved that Fisher is better than PCA for dimension reduction, and SVM is more expansible than ANN for speaker independent speech emotion recognition. The average recognition rates for each level are 86.5%, 68.5% and 50.2% respectively.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

Design of high speed gate driver employing IZO TFTs

This paper presents a new bi-side gate driver integrated by indium-zinc-oxide thin film transistors (IZO TFTs). Our optimized operate method can achieve high speed performance by employing a lower duty ratio (25%) CK2 with its pulse located in the middle of the pulse of CK2L to fully use the bootstrapped high voltage of node Q. In addition, the size of devices is optimized by calculation and simulation, and the function of the proposed gate driver is predicted by the circuit simulation. Furthermore, the proposed gate driver with 20 stages is fabricated by the IZO TFTs process. It is shown that a 2.6 μs width pulse with good noise-suppressed characteristic can be successfully output at the condition of R_load = 6 kΩ and C_load = 150 pF. The power consumption of the proposed gate driver with 20 stages is measured as 1 mW. Hence, the proposed gate driver may be applied to the display of 4K resolution (4096 × 2160) at a frame rate of 120 Hz. Moreover, there is a good stability for the proposed gate driver under 48 h operation.