Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of Serious Games for Alleviating Depression: Systematic Review and Meta-analysis

Background Depression is a common mental disorder characterized by disturbances in mood, thoughts, or behaviors. Serious games, which are games that have a purpose other than entertainment, have been used as a nonpharmacological therapeutic intervention for depression. Previous systematic reviews have summarized evidence of effectiveness of serious games in reducing depression symptoms; however, they are limited by design and methodological shortcomings. Objective This study aimed to assess the effectiveness of serious games in alleviating depression by summarizing and pooling the results of previous studies. Methods A systematic review of randomized controlled trials (RCTs) was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The search sources included 6 bibliographic databases (eg, MEDLINE, PsycINFO, IEEE Xplore), the search engine “Google Scholar,” and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently carried out the study selection, data extraction, risk of bias assessment, and quality of evidence appraisal. Results of the included studies were synthesized narratively and statistically, as appropriate, according to the type of serious games (ie, exergames or computerized cognitive behavioral therapy [CBT] games). Results From an initial 966 citations retrieved, 27 studies met the eligibility criteria, and 16 studies were eventually included in meta-analyses. Very low-quality evidence from 7 RCTs showed no statistically significant effect of exergames on the severity of depressive symptoms as compared with conventional exercises (P=.12). Very low-quality evidence from 5 RCTs showed a statistically and clinically significant difference in the severity of depressive symptoms (P=.004) between exergame and control groups, favoring exergames over no intervention. Very low-quality evidence from 7 RCTs showed a statistically and clinically significant effect of computerized CBT games on the severity of depressive symptoms in comparison with no intervention (P=.003). Conclusions Serious games have the potential to alleviate depression as other active interventions do. However, we could not draw definitive conclusions regarding the effectiveness of serious games due to the high risk of bias in the individual studies examined and the low quality of meta-analyzed evidence. Therefore, we recommend that health care providers consider offering serious games as an adjunct to existing interventions until further, more robust evidence is available. Future studies should assess the effectiveness of serious games that are designed specifically to alleviate depression and deliver other therapeutic modalities, recruit participants with depression, and avoid biases by following recommended guidelines for conducting and reporting RCTs. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42021232969; https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=232969
Use of the Health Belief Model (HBM) to Examine Food Handlers' Practices in Five Stars Hotels at Petra City, Jordan

After decenniums of knowledge and technological advancement, a huge number of foodborne illnesses occur yearly around the world; these were occurring mostly as a result of food handlers' malpractices in food service establishments. This study aims to apply the Health Belief Model (HBM) in explaining food handlers' practices. A survey was applied where self-administrated questionnaires were collected from 130 food handlers working in five stars hotels at Petra city, Jordan. Collected data were analyzed using various statistics including descriptive tests, Pearson correlation and, the standard multiple regression. Findings confirmed the significant impact of different HBM constructs (beliefs of proper food handling benefits, their beliefs of expected barriers against proper food handling & their perceived risks of food handling malpractices) on food handlers' practices. The findings also confirmed the significant correlation between proper food handling knowledge as a modifying factor on the HBM constructs.
Wireless Technology and Data Analytics for Structural Health Monitoring of Civil Infrastructure

The aim of this research was to investigate how wireless technology, combined with data analytics, can be used for effective structural health monitoring (SHM) of civil infrastructure. Two main applications were investigated, for which wireless sensor networks (WSNs) were integrated into complete SHM solutions: (1) long-term quasi-static monitoring on a suspension bridge, and (2) temporary monitoring of pedestrian bridge vibration. In the first application, a commercial off-the-shelf WSN was used to acquire and transmit data from extensometers measuring the longitudinal deck displacement of the Tamar Bridge in the UK. Six months of displacement data were analysed in conjunction with the ambient and structural temperature data acquired from a separate monitoring system on the bridge. Empirical models were fitted to relate the deck displacement to various combinations of temperatures. Comparisons of each model’s prediction accuracy showed that the practice of estimating a suspension bridge deck’s thermal expansion based solely on the air temperature is overly simplistic. The deck displacement was predicted more accurately by considering instead the temperatures of the deck itself and of the underlying structure. In preparation for the second application, a number of indoor tests and short-term deployments on full-scale structures were carried out using an existing prototype WSN, to assess its suitability for vibration monitoring. Subsequently, an embedded data processing method was developed by adapting various signal processing techniques and combining them in sequence. The method was then programmed on the WSN, which was integrated into autonomous SHM systems deployed to monitor two in-service, multi-span pedestrian bridges in Singapore for two weeks. The wireless sensor nodes periodically acquired ambient vibration response data and processed them in a decentralised manner to extract and transmit useful results pertaining to the bridges’ response and modal properties. These results showed that the dynamic properties of the bridges were not affected significantly by the diurnal usage pattern or by the vibration amplitude. The maximum vibration levels recorded on both bridges were found to be within the limits recommended in design guides. Wireless technology has the potential to make SHM viable for a much broader range of civil structures than it is at the moment. While some WSNs are readily applicable for quasi-static monitoring, considerable development and system integration effort are required to use existing wireless technology in a reliable SHM system for dynamic monitoring.
The Fundamentals of Fashion Design

Introduction. Research: Are you fashionable? Know your subject Starting your research In fashion - John-Gabriel Harrison Sources of research The research book Mood-, theme-, and storyboards In fashion - Louise Gray Research Exercises In context - Alexander McQueen. Design: Design identity Ideal bodies Silhouette Proportion and line God is in the details Fabric, colour and texture Rendering your ideas In fashion - Richard Gray Portfolios In fashion - Peter Jensen Design Exercises In context - Nicolas Ghesquiere, Balenciaga. Fabrics and techniques: Fabric Natural fibres In fashion - Virginia James, Poetry and Wrap Man-made fibres Synthetic fibres Developments in fabric Fabric construction Surface treatments Fabric and yarn trade shows In fashion - Michele Manz, 7 for All Mankind Fabric exercises In context - Marni In fashion - Kristin Forss, Marni. Construction: Tools and machinery Construction technqiues Draping on the mannequin In fashion - Boudicca Construction techniques In context - Alber Elbaz, Lavin. Developing a collection: Who are you designing for? Genre In fashion - Colin McNair, John Varvatos Types of garment In fashion - Alan Humphrey Bennett, Bally Putting together a collection Showing a collection Branding In fashion - Winni Lok, Calvin Klein Developing a collection exercises In context - Maison Martin Margiela. Appendix. Bibliography Further resources Webography Glossary Index Picture credits Acknowledgements and credits Working with ethics.
Nonlinear design of geophysical surveys and processing strategies

The principal aim of all scientific experiments is to infer knowledge about a set of parameters of interest through the process of data collection and analysis. In the geosciences, large sums of money are spent on the data analysis stage but much less attention is focussed on the data collection stage. Statistical experimental design (SED), a mature field of statistics, uses mathematically rigorous methods to optimise the data collection stage so as to maximise the amount of information recorded about the parameters of interest. The uptake of SED methods in geophysics has been limited as the majority of SED research is based on linear and linearised theories whereas most geophysical methods are highly nonlinear and therefore the developed methods are not robust. Nonlinear SED methods are computationally demanding and hence to date the methods that do exist limit the designs to be either very simplistic or computationally infeasible and therefore cannot be used in an industrial setting. In this thesis, I firstly show that it is possible to design industry scale experiments for highly nonlinear problems within a computationally tractable time frame. Using an entropy based method constructed on a Bayesian framework I introduce an iteratively-constructive method that reduces the computational demand by introducing one new datum at a time for the design. The method reduces the multidimensional design space to a single-dimensional space at each iteration by fixing the experimental setup of the previous iteration. Both a synthetic experiment using a highly nonlinear parameter-data relationship, and a seismic amplitude versus offset (AVO) experiment are used to illustrate that the results produced by the iteratively-constructive method closely match the results of a global design method at a fraction of the computational cost. This new method thus extends the class of iterative design methods to nonlinear problems, and makes fully nonlinear design methods applicable to higher dimensional industrial
Permanent bracing design for MPC wood roof truss webs and chords

Permanent bracing of metal-plate-connected trusses is required to stabilize specific members of each truss throughout the life of the roof structure.The objectives of this research were to : 1) determine the required net lateral restraining force to brace multiple webs or chords in a row braced by one or more continuous lateral braces (CLBs) ; and 2) develop a methodology for permanent bracing design using a combination of lateral and diagonal braces. Three system analogs used to model multiple truss chords braced by n-CLBs and one or two diagonal braces were analyzed with a finite element analysis program. Single member analogs were analyzed that represented web members braced by one and two CLBs and chord members braced by n-CLBs. For analysis and design purposes, a ratio R was defined as the net lateral restraining force per web or chord divided by the axial compressive load in the web or chord. For both 2 by 4 and 2 by 6 webs braced with one CLB, the R-value was 2.3 percent for all web lengths studied, and for webs braced with two CLBs, the R-value was 2.8 percent for all web lengths studied. Calculated R-values for truss chords braced by n-CLBs, assumed to be spaced 2 feet on center, ranged from 2.3 to 3.1 percent. A design procedure was offered for determining the net lateral restraining force required for bracing multiple webs or chords in a row based on the results of the single member analogs studied.
Preventing Glass Alteration in Museum Objects using ALD Deposited Amorphous Alumina Coatings

The chemistry and kinetics of glass alteration have been topics of considerable study and debate over the past several decades. While work is still progressing in understanding the mechanisms by which glass degrades, what has not been as well studied are methods of slowing or preventing the alteration of glass surfaces and objects. This is of significant interest to the heritage science community, where the breakdown of historic glass or glass art objects is proceeding, with few viable options available to museum conservators to mitigate its effects. Experimental measurements using atomic layer deposited (ALD) aluminum oxide coatings on a model silicate glass show a significant reduction of the rate of glass alteration.
Effect of TIVA with Propofol Versus Inhalational Anathesia Plus Ketamine on Fertilization and Clinical Pregnancy Rate in ICSI

Background: In vitro fertilization is an important advancement in the treatment of infertilityin the late forty years and it has become available on outpatient bases in day care surgical units, however, pain is expressed during aspiration of oocytes and adequate pain relief is required. Various anesthetic modalities and analgesics have been tested in different studies. Theseanesthetic agents have been found in the follicular fluid and may have detrimental effects on oocyte fertilization and embryonic development. The aim of current study was to investigate the effect of type of anesthesia on fertilization and pregnancy rate in patients undergoing ICSI. Method: The study was held at the High Institute of Infertility and ART’s in AL-Nahrin University, Baghdad, Iraqfrom 1st of April 2018 to the 1st of February 2019. The study involved 80 patients during their ICSI course were randomized blindly at time of oocyte retrieval anesthesia into two groups:Group 1 included 40 patients subjected to TIVA(total intravenous anesthesia) with midazolam and propofol (group1) and Group 2 included 40 patiesnt subjected to inhalational anesthesia (isoflurane) with ketamine and Midazolam. Evaluation of fertilization,cleavage, grade 1 embryo and pregnancy rate were done to determine the most beneficialand less toxic anesthesia for in vitro fertilization patients. Results and Conclusion: TIVA with midazolam and propofol had better reproductive outcomes on fertilization rate, cleavage and grade 1 embryo rate with significant higher pregnancy rates than inhalational anesthesiawith midazolam and ketamine.
Theoretical Framework and Literature Review in Graduate Records Management Research

The paper discusses the importance, characteristics and benefits of a good literature review, and highlights some of the models/theories that may be adapted to develop the theoretical framework in graduate records management research or thesis/dissertation. The paper emanates from the author's experiences as a tutor of the research methodology module to graduate students at the School of Information Sciences, Moi University, Kenya, and of the very common problem of such graduate students being unable to undertake good reviews of the literature or develop adequate theoretical framework for their research. The paper explains the concepts of records management, models and theories, and explores the link between theories and models in scientific research. Some of the existing records management models/theories and their suitability in investigating issues relating to the management of paper and electronic records are subsequently described and analyzed. The discussion of the models presented in this paper should be of interest to students and researchers undertaking master's and doctoral programmes in records and archives management schools within and outside Africa. African Journal of Library, Archives and Information Science Vol. 18 (2) 2008: pp. 103-119
An Explanation of In-context Learning as Implicit Bayesian Inference

Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning.
The Relativistic Jet and Central Engine of Fermi Blazars

The origin of jets is one of the most important issues concerning active galactic nuclei, yet it has remained obscure. In this work, we made use of information from emission lines, spectral energy distributions, and Fermi–LAT γ-ray emission to construct a blazar sample that contains 667 sources. We note that jet power originations are different for BL Lacertae objects (BL Lacs) and flat-spectrum radio quasars (FSRQs). The correlation between jet power P jet and the normalized disk luminosity L Disk/L Edd shows a slope of −1.77 for BL Lacs and a slope of 1.16 for FSRQs. The results seem to suggest that BL Lac jets are powered by extracting black hole (BH) rotation energy, while FSRQ jets are mostly powered by accretion disks. Meanwhile, we find the accretion ratio Ṁ/ṀEdd increases with the normalized γ-ray luminosity. Based on this, we propose a dividing line, log(LBLR/LEdd)=0.25log(Lγ/LEdd)−2.23 , to separate FSRQs and BL Lacs in the diagram of L BLR/L Edd against L γ /L Edd using a machine-learning method; the method gives an accuracy of 84.5%. In addition, we propose an empirical formula, MBH/M☉≃Lγ0. 65/21.46 , to estimate BH mass based on a strong correlation between γ-ray luminosity and BH mass. Strong γ-ray emission is typical in blazars, and the emission is always boosted by a Doppler-beaming effect. In this work, we generate a new method to estimate a lower limit of Doppler factor δ and give δ BL Lac = 7.94 and δ FSRQ = 11.55.
Convolutional Motif Kernel Networks

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore, domain experts like healthcare providers need explainable predictions to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them integrating a model into their own routine. Therefore, interpretable models play a crucial role for the incorporation of machine learning into high stakes scenarios like healthcare. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that involves learning a feature representation within a subspace of the reproducing kernel Hilbert space of the position-aware motif kernel function. The resulting model enables to directly interpret and evaluate prediction outcomes by providing a biologically and medically meaningful explanation without the need for additional post-hoc analysis. We show that our model is able to robustly learn on small datasets and reaches state-of-the-art performance on relevant healthcare prediction tasks. Our proposed method can be utilized on DNA and protein sequences. Furthermore, we show that the proposed method learns biologically meaningful concepts directly from data using an end-to-end learning scheme.
A Novel Actuation Strategy for an Agile Bioinspired FWAV Performing a Morphing-Coupled Wingbeat Pattern

Flying vertebrates exhibit sophisticated wingbeat kinematics. Their specialized forelimbs allow for the wing morphing motion to couple with the flapping motion during their level flight. Previous flyable bionic platforms have successfully applied bioinspired wing morphing but cannot yet be propelled by the morphing-coupled wingbeat pattern. Spurred by this, we develop a bioinspired flapping-wing aerial vehicle entitled RoboFalcon, which is equipped with a novel mechanism to drive the bat-style morphing wings, performs a morphing-coupled wingbeat pattern, and overall manages an appealing flight. The novel mechanism of RoboFalcon allows coupling the morphing and flapping during level flight and decoupling these when maneuvering is required, producing a bilateral asymmetric downstroke affording high rolling agility. The bat-style morphing wing is designed with a tilted mounting angle around the radius at the wrist joint to mimic the wrist supination and pronation effect of flying vertebrates’ forelimbs. Wind tunnel tests indicate that the rolling moment of the asymmetric downstroke is correlated with the flapping frequency, and the wrist mounting angle can be used for tuning the angle of attack, lift-thrust configuration, and power consumption of the equilibrium flight state. The agility of RoboFalcon is assessed through several tethered rolling maneuvers and outdoor free flight tests, and we present a concise analysis of the vehicle's lateral dynamics model based on system identification. We believe that this article yields a well-performing bionic platform and provides a new actuation strategy for the morphing-coupled flapping flight.
One Pass ImageNet

We present the One Pass ImageNet (OPIN) problem, which aims to study the effectiveness of deep learning in a streaming setting. ImageNet is a widely known benchmark dataset that has helped drive and evaluate recent advancements in deep learning. Typically, deep learning methods are trained on static data that the models have random access to, using multiple passes over the dataset with a random shufﬂe at each epoch of training. Such data access assumption does not hold in many real-world scenarios where massive amounts of data are collected from a stream and storing and accessing all the data becomes impractical due to storage costs and privacy concerns. For OPIN, we treat the ImageNet data as arriving sequentially, and there is limited memory budget to store a small subset of the data. We observe that training a deep network in a single pass with the same training settings used for multi-epoch training results in a huge drop in prediction accuracy. We show that the performance gap can be signiﬁcantly decreased by paying a small memory cost and utilizing techniques developed for continual learning, despite the fact that OPIN differs from typical continual problem settings. We propose using OPIN to study resource-efﬁcient deep learning.