Miracle-dz/UniFAR-FTUs huggingface dataset:
One for all, and all for one? Identifying clusters of user behaviour in intranet search engine log files

In recent years a substantial amount of research has been focusing on how ordinary web users interact with search engines. However, these users are tacitly assumed to be a homogeneous group by researcher and vendor alike. We argue that search engine users should not be treated collectively and by applying automatic clustering technique based on self-organising maps to search engine log files from a corporate intranet, we show that users can be separated into distinguishable segments based on their search behaviour. Analysis of these segments teaches us more about (intranet) searching and when designing and implementing future tools for information seeking and retrieval, these tools can be targeted to specific segments rather than to the population as a whole. We found that a large group of users appear to be casual "fact seekers" who would benefit from higher precision, a smaller group of users were more holistically oriented and would likely benefit from higher recall, whereas a third clique of users seemed to constitute the information-seeking savvy employees. All these three groups may raise different design implications for search tool developers.
Libyan Vehicle License Plate Recognition with Support Vector Machine

Computer vision has become widely used in many aspects of our daily lives. There are a great number of applications that consider computer vision as a core part of them, such as those associated with law enforcement. This paper presents the design and implementation of a model for a vehicle building entrance, using a license plate recognition algorithm for Libyan vehicles. In addition, a small dataset of Libyan vehicles was created for research purposes. As with most recognition systems, there are mainly three stages to be distinguished: plate detection using vertical and horizontal histograms, character segmentation is performed through a connected-component labeling algorithm, and finally, optical character recognition (OCR) by using support vector machines (SVMs). An Arduino board was used to control the gate opening and closing processes according to the authorized vehicles stored in the database. Ultrasonic sensors were used to detect a vehicle stop at the gate. The system was programmed with MATLAB executed on a 2.20GHz Core i7 CPU, 8 GB RAM, Windows 10. Despite the limited size of the vehicle images dataset, the experiments showed promising performance in terms of average accuracy estimated at 83.3%, and the computation time was 5 seconds.
Journal of Experimental Psychology: Learning, Memory, and Cognition The Role of Semantic Transparency in the Processing of Spoken Compound

The question of whether lexical decomposition is driven by semantic transparency in the lexical processing of morphologically complex words, such as compounds, remains controversial. Prior research on compound processing has predominantly examined visual processing. Focusing instead on spoken word word recognition, the present study examined the processing of auditorily presented English compounds that were semantically transparent (e.g., farmyard ) or partially opaque with an opaque head (e.g., airline ) or opaque modi ﬁ er (e.g., pothole ). Three auditory primed lexical decision experiments were run to examine to what extent constituent priming effects are affected by the semantic transparency of a compound and whether semantic transparency affects the processing of heads and modi ﬁ ers equally. The results showed priming effects for both modi ﬁ ers and heads regardless of their semantic transparency, indicating that individual constituents are accessed in transparent as well as opaque compounds. In addition, the results showed smaller priming effects for semantically opaque heads compared with matched transparent compounds with the same head. These ﬁ ndings suggest that semantically opaque heads induce an increased processing cost, which may result from the need to suppress the meaning of the head in favor of the meaning of the opaque compound.
Gold Nanoparticle Smartphone Platform for Diagnosing Urinary Tract Infections

Current urinary tract infection (UTI) diagnostic methods are slow or provide limited information, resulting in prescribing antibiotic therapy before bacterial pathogen identification. Here, we adapted a gold nanoparticle colorimetric approach and developed a smartphone platform for UTI detection. We show the parallel identification of five major UTI pathogens at clinically relevant concentrations of 105 bacteria/mL using bacteria-specific and universal probes. We validated the diagnostic technology using 115 positive and 19 negative samples from patients with Escherichia coli, Proteus mirabilis, and Klebsiella pneumoniae infections. The assay successfully identified the infecting pathogen (specificity: >98% and sensitivity: 51–73%) in 3 h. Our platform is faster than culturing and can wirelessly store and transmit results at the cost of $0.38 per assay.
A Practice-based Methodology on Conducting a Collaborative Scoping Review with PRISMA-ScR Model for the Separated Refugee Youth Project

Considering the novelty of the area of a 2-year study on impact of family loss and separation on refugee youth in Toronto, the research team decided to conduct a scoping review of the existing literature as a foundation document that included the extent, range, geography, and nature of research on the topic of interest. A collaborative co-design approach for this review bought in wisdom from relevant stakeholders. Arksey and O’Malley’s framework was modified for this scoping review that substantively identified the extent and magnitude of past research, research gaps, and best practice models for conducting such exploratory research on novel ideas. This framework yielded desired output, such as selection and characteristics of sources of evidence, critical appraisal within sources of evidence, and synthesis of results for the next steps of the research. Prudent researchers and professionals in this area of research, service provider agencies, and a university librarian were consulted. The PRISMA-ScR model saved time and ensured the appropriate yield of the search items. The quality of the review process was evaluated by the Critical Appraisal Skills Programme (CASP) Qualitative Studies Checklist tool. This article displays a practical example of how the scoping review process was instrumental in a community-based research project with separated refugee youth to generate the foundational evidence for broader research. This quality-embedded process of collecting and charting data for a scoping review is transferable to similar research initiatives. The flexibility and reproducibility of this review method are commendable.
Switched-Capacitor Multilevel Inverters: A Comprehensive Review

Multilevel inverters (MLIs) with switched-capacitor (SC) units have been a widely rehearsed research topic in power electronics since the last decade. Inductorless/transformerless operation with voltage-boosting feature and inherent capacitor self-voltage balancing performance with a reduced electromagnetic interference make the SC-MLI an attractive converter over the other available counterparts for various applications. There have been many developed SC-MLI structures recently put forward, where different basic switching techniques are used to generate multiple (discrete) output voltage levels. In general, the priority of the topological development is motivated by the number of output voltage levels, overall voltage gain, and full dc-link voltage utilization, while reducing the component counts and stress on devices for better efficiency and power density. To facilitate the direction of future research in SC-MLIs, this article presents a comprehensive review, critical analysis, and categorization of the existing topologies. Common fundamental units are generalized and summarized with their merits and demerits. Ultimately, major challenges and research directions are outlined leading to the future technology roadmap for more practical applications.
Mobile Phone Addiction as an Emerging Behavioral Form of Addiction Among Adolescents in India

Introduction: The advancements in mobile phones from simple basic phones to featured phones and smartphones resulted in the penetration of technology to different groups of people irrespective of age, gender, or region. Thus, mobile phone addiction has evolved as a form of behavioral addiction found to be increasingly prevalent among adolescents too. The study aimed to determine the prevalence of mobile phone addiction among adolescents and its associated risk factors among adolescents. Method and results: A community-based, cross-sectional study was conducted among 264 adolescents (10-19 years) of low-income urban areas of Delhi. The prevalence of mobile phone addiction in the participants was 33.0% (95% CI: 27.2-38.6). The addiction was higher among boys (33.6%) than girls (32.3%) (p=0.835). Mobile phone addiction was found to be significantly higher among those adolescents who had ≥3 siblings, those belonging to nuclear families, and among late-onset users (≥16 years). Late-onset users (adjusted odds ratio {aOR}: 3.398; 95% CI: 1.307-8.833) and ≥3 siblings (aOR: 1.980; 95% CI: 1.141-3.437) were independent predictors of mobile phone addiction. The mean time spent on mobile phones was significantly higher among those with addiction but no significant gender difference was found between time spent on phones and addiction. Conclusion: The high prevalence of mobile phone addiction found in our study is an indication of the potential public health concern posed by mobile phone use among adolescents in urban settings. Hence, it is essential to limit the access to mobile phones for important utility purposes for adolescents.
Stochastic Economic Dispatch of Integrated Transmission and Distribution Networks Using Distributed Approximate Dynamic Programming

With lots of distributed renewable generators and storage devices integrated into distribution networks, the coordinate economic dispatch (ED) between transmission and distribution networks is needed. A stochastic ED algorithm based on distributed approximate dynamic programming for integrated transmission and distribution networks considering the uncertain renewable generator outputs is proposed. First, the convex power flow models of transmission and distribution networks are proposed. Then, the reservoir levels of pumped-storage hydro stations and the remaining stored electric energy of battery storage stations are deemed as the storage quantities, while the renewable generator outputs are deemed as the exogenous stochastic variables to establish the stochastic storage model for the stochastic ED problem of integrated transmission and distribution networks. The approximate value functions represented by the sum of the piecewise linear functions of the storage quantities are constructed to decouple the multiperiod ED problem into a series of single-period ED problems. Finally, by using the alternating direction method of multipliers, each single-period ED problem of integrated transmission and distribution networks is solved in a distributed manner among transmission and distribution networks. Test results on the modified T6D7 system and a practical Shenzhen city network demonstrate the correctness and efficiency of the proposed algorithm.
Challenges in accessing maternal and child health services during COVID-19 and the potential role of social networking technologies

Background The COVID-19 pandemic causes new challenges to women and their babies who still need to access postnatal care amidst the crisis. The novel application of social network technologies (SNTs) could potentially enhance access to healthcare during this difficult time. Objectives This study describes the challenges experienced in accessing maternal and child health services by women with limited or no education during this COVID-19 pandemic and discusses the potential of SNTs to support maternal and child health amidst this crisis. Methods We administered surveys to women who had recently given birth in a rural setting and interviewed a purposively selected subset to ascertain their experiences of accessing maternal and child health services during the COVID-19 pandemic. Our analysis involved descriptive analysis of quantitative data using STATA 13 to describe study participants’ characteristics, and content analysis of qualitative data to derive categories describing maternal health challenges. Results Among 50 women, the median age was 28 years (interquartile range 24–34), 42 (84%) completed upper primary education. Access to the health facility was constrained by transport challenges, fear of contracting COVID-19, and delays at the facility. Due to the COVID-19 crisis, 42 (84%) women missed facility visits, 46 (92%) experienced financial distress, 43 (86%) had food insecurity, and 44 (88%) felt stressed. SNTs can facilitate remote and timely access to health services and information, and enable virtual social connections and support. Conclusion SNTs have the potential to mitigate the challenges faced in accessing maternal and child health services amidst the ongoing COVID-19 pandemic.
Disentangling Memristive and Memcapacitive Effects in Droplet Interface Bilayers Using Dynamic Impedance Spectroscopy

The underlying principles for generating intelligent behavior in living organisms are fundamentally different from those in traditional solid‐state circuits. Biomimetic neuromorphic equivalents based on biological membranes offer novel implementation of tunable plasticity and diverse mechanisms to control functionality. Here, dynamic electrochemical impedance spectroscopy (dEIS) to probe diphytanoylphosphatidylcholine (DPhPC) droplet interface bilayers (DIBs) to better understand the differences in molecular level structure/dynamics that give rise to hysteretic loops and neuromorphic, memelement behaviors in lipid bilayers in response to electrical biasing is used. Importantly, this system does not have ion‐conducting channels and is, therefore, not expected to show memristive behavior. Surprisingly, both memristive and memcapacitive behaviors by measuring the time‐dependent complex impedance of DPhPC DIBs are detected. It is shown that nonlinear memristance can originate from structural changes in the bilayer, affecting its dielectric properties. This novel dEIS application allows for the simultaneous analysis of the system's changing memristive and memcapacitive properties, which originate from different molecular restructuring processes. Moreover, and importantly, access to this type of information increases the number of neuromorphic processes supported simultaneously in a single two‐terminal device.
Interaction of Cry1 Gene Polymorphisms and Dominant Food Patterns on Obesity: A Cross-Sectional Study

Background: Evidence suggests that there is some relationship between circadian clock gene variants and obesity. However, there are few examinations supporting this observation in human subjects. This study was aimed to investigate the interaction between Cry1 circadian gene polymorphism and major dietary patterns on obesity measurements. Methods: Healthy overweight and obese women aged 18–53 years old were recruited from health centers in Tehran, Iran by a multistage cluster random sampling method (n = 377). Major dietary patterns were elicited after assessing the intake of 16 food groups using a valid and reliable 147-item food frequency questionnaire (FFQ). Anthropometric measurements were performed for each and every participant. Body composition was analyzed using bioelectrical impedance analysis (BIA). Socio-demographic and physical activity data were also collected by a validated Farsi demographic questionnaire and the international physical activity questionnaire (IPAQ). The Cry1 rs2287161 polymorphism were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Generalized linear models were used for interaction analysis. Results: Two major dietary patterns, including healthy and unhealthy dietary pattern (HDP and UDP, respectively) were determined using factor analysis. Our study showed a significant higher weight (P = 0.003), body mass index (BMI) (P = 0.042), hip circumference (P = 0.052), and body fat mass (P = 0.028) in carriers of C allele compared with G allele. Moreover, a significant gene-diet interaction was observed between being a carrier of C allele and BMI (P = 0.099 for CC genotype; P = 0.1 for CG genotype) and fat mass (P = 0.1 for CG genotype). Conclusions: The current study suggests a significant interaction of Cry1 rs2287161 gene polymorphisms in people following a healthy dietary pattern on BMI and fat mass among carriers of C allele compared to carriers of G allele.
Improvement of look ahead based on quadratic approximation for crystal structure prediction

ABSTRACT Crystal structure prediction based on first-principles calculations is usually time-consuming since a lot of candidate structures have to be locally optimized. Look Ahead based on Quadratic Approximation, which is one of the selection-type algorithms we previously developed, can control the optimization priority of the candidates. It can efficiently reduce the computational costs in many cases, however, it has been found to be inefficient in some data sets. In the present study, we proposed an improved score of Look Ahead based on Quadratic Approximation, where the stress term is added to overcome the drawbacks of the previous score. Crystal structure prediction simulations by this improved algorithm are performed to investigate the searching efficiency for typical materials such as Si, Al2O3, NaCl, and SrCO3. These results demonstrate that this improved algorithm reduces the searching cost to less than 40% with respect to random search in most cases. The introduction of the stress term makes this algorithm more robust and versatile. GRAPHICAL ABSTRACT
Study on Valve-shut off Scheme of Dimensional Heat Supplying Network Based on GIS

The valve-shut off scheme and arithmetic of dimensional heat supplying network in fault condition were studied by the method of breadth-first search,based on its special characteristic.Then,the realization method of valve-shut off scheme in MAPGIS was discussed and the program was made.Finally,the application in GIS for heat supplying network was introduced combining a project.The results showed that integration of fault analysis in heat supplying network and GIS can effectively solve the problems of large workload,slow velocity,bad veracity and lack of science in fault analysis.
Self-Assembly of Nanoparticles in Dispersion and at Fluid-Fluid Interfaces

Self-Assembly of Nanoparticles in Dispersion and at Fluid-Fluid Interfaces Kjersta L. Larson-Smith Chair of the Supervisory Committee: Assistant Professor Danilo C. Pozzo Department of Chemical Engineering Self-assembly is a fundamental mechanism by which structures form in materials. This mechanism has given rise to many new technological advances in applications as diverse as structural composites, medicine, alternative energy sources and information technology. Many current approaches to self-assembly have shown promise, but each has its own unique limitations. The over-arching goal of this study is to develop self-assembly processes for nanoparticles that are simple, scalable and cost effective. In this dissertation we examine the mechanism for self assembly of nanoparticles in dispersion and at fluid-fluid interfaces. A simple method is presented for the synthesis of amphiphilic gold nanoparticle surfactants that self-assemble into clusters of controllable structure. The technique is based on the control of particle clustering through sequential functionalization of the surface with thiol terminated polyethylene glycol to sterically stabilize particles in water and short alkane-thiols that render the particles amphiphilic. These nanoparticle surfactants are surface active and form rafts at the air-water interface and stable nanoparticle clusters in dispersion. The clusters are also reminiscent of traditional micelles with an adjustable aggregation number that is controlled via modification of the grafting density of polymer on the nanoparticle surface. The nanoparticle surfactants are also shown to be highly effective emulsifying agents due to their amphiphilicity and they adsorb strongly to oil-water interfaces with controllable inter-particle separation distances. Both the clusters and colloidosomes that can be formed with these particles exhibit tunable shifts in plasmon resonance, enhancing the near-infrared optical absorption and making them useful for a wide range of applications.
Bayesian Hyperparameter Optimization and Ensemble Learning for Machine Learning Models on Software Effort Estimation

—In recent decades, various software effort estimation (SEE) algorithms have been suggested. Unfortunately, generating high-precision accuracy is still a major challenge in the context of SEE. The use of traditional techniques and parametric approaches is largely inaccurate because they produce biased and subjective accuracy. Meanwhile, none of the machine learning methods performed well. This study applies the AdaBoost ensemble learning method and random forest (RF), on the other hand the Bayesian optimization method is applied to determine the hyperparameters of this model. The PROMISE repository and the ISBSG dataset were used to build the SEE model. The developed model was comprehensively compared with four machine learning methods (classification and regression tree, k-nearest neighbor, multilayer perceptron, and support vector regression) under 3-fold cross validation (CV). It can be seen that the RF method based on AdaBoost ensemble learning and bayesian optimization outperforms this approach. In addition, the AdaBoost-based model assigns a feature importance rating, which makes it a promising tool in software effort prediction.
Managing Positive and Negative Trends in Sales Call Outcomes: The Role of Momentum

Existing research treats sales performance as a series of discrete, independent events rather than a series of sales attempts with intertemporal spillover across these attempts. This research examines whether there are systematic short-term trends (“momentum”) in sales performance. To do so, the authors use the clumpiness approach to examine the existence of sales momentum in a high-frequency call-level data set obtained from two call centers of a large European firm. They further investigate the effect of positive (negative) momentum, or the positive (negative) deviation from the long-term expected performance on subsequent sales performance. Exploiting the differences in the social environment of the call centers, the authors find that the social working environment mitigates the harmful effect of negative momentum and sustains positive momentum. Further, they demonstrate that calls made midday, early-week, and late-week boost performance by mitigating the adverse effects of negative momentum. The findings suggest that monitoring sales performance can help managers detect momentum and use timely interventions to enhance sales productivity. Managers can also leverage momentum by creating a more social working environment to optimize overall salesperson performance.