Miracle-dz/UniFAR-FTUs huggingface dataset:
An evaluation of effort estimation supported by change impact analysis in agile software development

In agile software development, functionality is added to the system in an incremental and iterative manner. Practitioners often rely on expert judgment to estimate the effort in this context. However, the impact of a change on the existing system can provide objective information to practitioners to arrive at an informed estimate. In this regard, we have developed a hybrid method, that utilizes change impact analysis information for improving effort estimation. We also developed an estimation model based on gradient boosted trees (GBT). In this study, we evaluate the performance and usefulness of our hybrid method with tool support and the GBT model in a live iteration at Insiders Technologies GmbH, a German software company. Additionally, the solution was also assessed for perceived usefulness and understandability in a study with graduate and post‐graduate students. The results from the industrial evaluation show that the proposed method produces more accurate estimates than only expert‐based or only model‐based estimates. Furthermore, both students and practitioners perceived the usefulness and understandability of the method positively.
Reflective thinking in school: a systematic review

Everything around us changes rapidly and to adapt to these constantly changing conditions and to understand the meaning of our life in the society in which we live, we must reflectively and consciously think about our actions in each given scenario. A school is a miniature form of society where learners are exposed to situations where they need to find solutions for every problem faced. No faultless solution and conclusions can be arrived at without a carefully employed reflective thinking process. In this context, the present study reviewed 19 intervention studies on reflective thinking in schools published between 2010 and 2021 and presents a brief summary. Various theories on reflective thinking, approach of educationists on reflective thinking of students and the relation between reflective thinking and students’ academic performance, are extensively analyzed. The findings of the study reveal that there are a few generally accepted theories of reflective thinking; reflection is a useful learning strategy and reflective thinking is an essential characteristic of academic excellence. This study recommends future research with a wider scope to accommodate more theoretical perspectives and wide-ranging databases.
Beyond pGALS: the need for a multifaceted musculoskeletal decision-making tool (‘pGALSplus’) in community-based clinical practice

Abstract Musculoskeletal (MSK) problems in children are common, and health-care professionals must identify those requiring onward referral. Paediatric gait, arms, legs and spine (pGALS) is an MSK assessment to discern abnormal joints. We aimed to identify MSK assessments to add to pGALS (pGALSplus) to facilitate decision-making in the context of exemplar conditions representing a spectrum of MSK presentations, namely JIA, mucopolysaccharidoses, muscular dystrophy and developmental co-ordination disorder. A literature review identified 35 relevant articles that focused on clinical assessments [including questionnaire(s), physical examination and functional tests] used by health-care professionals in the context of the exemplar conditions. We provide a description of these assessments and the rationale regarding how they, or components of such tools, might be useful within pGALSplus. This process provides a foundation for further work to develop and validate pGALSplus.
Repeated dose administration toxicity studies - Use of t-tests in multiplicity data analysis

— In conducting repeated dose administration toxicity (RDAT) studies with rats and mice, a minimum of three dose groups and one control group are normally set for determing NOEL/NOAEL (no-observed effect level/no-observed adverse effect level) of the test item. For comparison of data among the groups, initially, the data are analysed by analysis of variance (ANOVA). If ANOVA shows a signifi - cant difference, then groups means are compared by a multiple comparison range test (MCRT). Howev - er, in RDAT studies, at the end of the long duration of the test substance administration, the distribution of the data obtained varies considerably among the groups and the number of animals decreases due to mortality/morbidity, especially in the high-dose groups. Increased variance in the distribution of the data and decreased animals in one or more groups may result in an insignificant ANOVA, though the low-dose group may show a marked difference compared to the control. Dunnett's multiple comparison test is com - monly used to compare each treatment group with the control group. However, Dunnett's test has a lower ability to detect significant differences than the t -test, and its detection power decreases with the increase in the number of groups. Therefore, we recommend the t -test, by-passing ANOVA, which has a high detectable significant difference in the two-group test. In addition, the application of the t -test eliminates the need to select an MCRT. However, the final judgment of the adverse effects may be made based on the toxicological relevance in consideration of the statistical analysis results.
Online Teaching Effectiveness of Nurse Instructors: A Systematic Literature Review

The online teaching effectiveness of nurse instructors builds a systematic literature review that aimed to identify and critically assess available research publications for identifying online teaching effectiveness in nursing education. The review process undergoes five stages namely: framing of questions for review, identifying relevant work, data screening, assessing the quality of studies, and interpretation of findings. The articles were thematically analyzed. The articles' patterns were observed and grouped accordingly. The themes were subsequently aggregated and organized into a thematic map. The thematic map served as the basis for the generation of item statements. The three main themes that emerged based on the thematic analysis of the systematic literature review were: active learning, instructor-learner connection, and modern teaching. Keywords: Nurse instructors; Online education; Online teaching, Online teaching effectiveness
Rapid Assessment for Emergency Infrastructures in Responding Flood related to Mining Activity: Case Study of Banjarasri and Kedungbanteng Flood, Sidoarjo, Indonesia

Flooding has become the most frequent and impactful disaster events in the Indonesian cities. The Indonesian disaster board also noted that flood events has increasing pattern for today and future time. One of the strategic ways to decrease the impact of flooding is delivering the emergency infrastructures to the needs immediately. The faster delivery makes less impact of disaster events. Therefore, this paper synthesizes a rapid model of assessment from the full assessment of emergency infrastructure need in Sidoarjo Flooding 2020. During the synthesize, the model can reduce duration of assessment from 148 days to four days only. Within the time, eight main steps include finding relevant emergency infrastructures, determining emergency infrastructures needed, assessing emergency infrastructures needed and confirming the actual emergency infrastructure needed. Those eight steps should be taken by encouraging more FGD and in-depth interviews for key relevant stakeholders, guided by specific checklist and filling the gap forms. Strict forms and guidelines provided to help the process in achieving four days assessment period. Consequently, the authorities can use the output assessment for emergency infrastructure program delivery starting from the fifth days after the events.
Network Traffic Prediction based on Diffusion Convolutional Recurrent Neural Networks

By predicting the traffic load on network links, a network operator can effectively pre-dispose resource-allocation strategies to early address, e.g., an incoming congestion event. Traffic loads on different links of a telecom is know to be subject to strong correlation, and this correlation, if properly represented, can be exploited to refine the prediction of future congestion events. Machine Learning (ML) represents nowadays the state-of-the-art methodology for discovering complex relations among data. However, ML has been traditionally applied to data represented in the Euclidean space (e.g., to images) and it may not be straightforward to effectively employ it to model graph-stuctured data (e.g., as the events that take place in telecom networks). Recently, several ML algorithms specifically designed to learn models of graph-structured data have appeared in the literature. The main novelty of these techniques relies on their ability to learn a representation of each node of the graph considering both its properties (e.g., features) and the structure of the network (e.g., the topology). In this paper, we employ a recently-proposed graph-based ML algorithm, the Diffusion Convolutional Recurrent Neural Network (DCRNN), to forecast traffic load on the links of a real backbone network. We evaluate DRCNN's ability to forecast the volume of expected traffic and to predict events of congestion, and we compare this approach to other existing approaches (as LSTM, and Fully-Connected Neural Networks). Results show that DCRN outperforms the other methods both in terms of its forecasting ability (e.g., MAPE is reduced from 210% to 43%) and in terms of the prediction of congestion events, and represent promising starting point for the application of DRCNN to other network management problems.
Global noise score indicator for classroom evaluation of acoustic performances in LIFE GIOCONDA project

Abstract The LIFE GIOCONDA is an ongoing project that aims to provide an innovative methodology to the authorities for supporting the environment & health policies by involving the young people in the decision-making processes. The project suggests a web platform able to relate air and noise pollution data in the schools with the students’ pollution awareness. GIOCONDA aims to enhance the awareness of students, teachers and local administrations on the noise issues in schools, presenting suitable tools to improve the public participative processes. This paper presents a new method that has been developed within the Project. It aims to evaluate the acoustic performances of a classroom and to suggest the use of a global indicator based on a group of acoustic parameters compared with their limit values. Whit the new method the comparison between different classrooms or different schools becomes possible, together with a homogeneous evaluation of the priority for planning noise mitigation actions. Several noise measurement campaigns have been performed to characterize the students’ exposure in eight Italian schools. The results are useful to describe the acoustic performances of classrooms.
A Categorical Representation Language and Computational System for Knowledge-Based Robotic Task Planning

Classical planning representation languages based on first-order logic have preliminarily been used to model and solve robotic task planning problems. Wider adoption of these representation languages, however, is hindered by the limitations present when managing implicit world changes with concise action models. To address this problem, we propose an alternative approach to representing and managing updates to world states during planning. Based on the category-theoretic concepts of C-sets and double-pushout rewriting (DPO), our proposed representation can effectively handle structured knowledge about world states that support domain abstractions at all levels. It formalizes the semantics of predicates according to a user-provided ontology and preserves the semantics when transitioning between world states. This method provides a formal semantics for using knowledge graphs and relational databases to model world states and updates in planning. In this paper, we conceptually compare our category-theoretic representation with the classical planning representation. We show that our proposed representation has advantages over the classical representation in terms of handling implicit preconditions and effects, and provides a more structured framework in which to model and solve planning problems.
First record of Soybean Stem Fly Melanagromyza sojae (Zehntner, 1901) (Diptera, Agromyzidae) in Uruguay confirmed by DNA barcoding

Colloquially known as Soybean Stem Fly, Melanagromyza sojae (Zehntner, 1901) (Diptera, Agromyzidae) is an oligophagous pest of plants in the family Fabaceae, including Uruguay’s biggest commodity crop, Soybean (Glycine max (L.) Merr. ). To our knowledge, this is the first scientific record of M. sojae in Uruguay, and we confirm its identity through using DNA barcoding. Characteristic damage to host plants and immature stages of M. sojae were confirmed in Dolores and Colonia, Uruguay.
Object tracking via background subtraction for monitoring illegal activity in crossroad

In the field of intelligent transportation system a great number of vision-based techniques have been proposed to prevent pedestrians from being hit by vehicles. This paper presents a system that can perform pedestrian and vehicle detection and monitoring of illegal activity in zebra crossings. In zebra crossing, according to the traffic light status, to fully avoid a collision, a driver or pedestrian should be warned earlier if they possess any illegal moves. In this research, at first, we detect the traffic light status of pedestrian and monitor the crossroad for vehicle pedestrian moves. The background subtraction based object detection and tracking is performed to detect pedestrian and vehicles in crossroads. Shadow removal, blob segmentation, trajectory analysis etc. are used to improve the object detection and classification performance. We demonstrate the experiment in several video sequences which are recorded in different time and environment such as day time and night time, sunny and raining environment. Our experimental results show that such simple and efficient technique can be used successfully as a traffic surveillance system to prevent accidents in zebra crossings.
Quantifying Uncertainties of Ground‐Level Ozone Within WRF‐Chem Simulations in the Mid‐Atlantic Region of the United States as a Response to Variability

Understanding forecast uncertainties and error growth dynamics is a prerequisite for improving dynamical prediction of meteorology and air quality. While predictability of meteorology has been investigated over the past few decades, the uncertainties in air quality simulations are less well known. This study explores the uncertainties in predicting ground‐level ozone (O3) in the Mid‐Atlantic region of the United States during June 2016 through a series of simulations using WRF‐Chem, focusing on the sensitivity to the meteorological initial and boundary conditions (IC/BCs), emissions inventory (EI), and planetary boundary layer (PBL) scheme. The average uncertainty of ground‐level maximum 8‐hr average O3 mixing ratio (MD8‐O3) was most sensitive to uncertainties in the IC/BCs, while uncertainty in the EI was of secondary importance, and was least sensitive was to the use of different PBL schemes. Updating the NO emissions in the EI had the greatest influence on the accuracy, with an estimated decrease of 0.59 ppbv/year in the root‐mean‐square error and an average decrease of 0.63 ppbv/year in the values of modeled MD8‐O3. Our study suggests using perturbations in IC/BCs may lead to a more dispersive ensemble of O3 prediction than using different PBL schemes and/or different EI. However, considering the combined uncertainties from all three sources examined are still smaller than the averaged root‐mean‐square errors of predicted O3 against observations, there are apparent other sources of uncertainties not studied that need to be considered in future ensemble predictions of O3.
A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10−3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10−3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.
Lasso Logistic Regression , GSoft and the Cyclyc Coordinate Descent Algorithm . Application to Gene Expression Data

Statistical methods generating sparse models are of great value in the gene expression field, where the number of covariates (genes) under study moves about the thousands, while the sample sizes seldom reach a hundred of individuals. For phenotype classification, we propose different lasso logistic regression approaches with specific penalizations for each gene. These methods are based on a generalized soft–threshold (GSoft) estimator. We also show that a recent algorithm for convex optimization, namely the cyclic coordinate descent (CCD) algorithm, provides with a fast way to solve the optimization problem posed in GSoft. Results are obtained for simulated and real data. The leukemia and colon datasets are commonly used to evaluate new statistical approaches, so they come in useful to establish comparisons with similar methods. Furthermore, biological meaning is extracted from the leukemia results, and compared with previous studies. In summary, the approaches presented here give rise to sparse, interpretable models, competitive with similar methods developed in the field.
Building the Virtual Dancefloor: Delivering and Experiencing House Music Events in the Time of COVID-19

The COVID-19 pandemic and its consequent social lockdowns necessitated an immediate cessation of events, replaced entirely by virtual events—a concept present in the existing events literature, but one not fully conceptualised. This article explores the virtual event experience during the pandemic through the lens of a case study, Love to Be events, a long-established house music brand which produced a series of successful virtual events during the lockdowns and beyond. This research also offers discussion on the potential for virtual events—in contrast to what this research terms ‘venued events’—to become a notable and profitable element of the events industry in the longer term. This article presents a substantial literature review considering multiple relevant areas, centring on the event experience and virtual event potential. This article also presents primary qualitative data in the form of a semi-structured interview with Marc Dennis and Tony Walker, DJs and founders of Love to Be, conducted in Autumn 2020 via Zoom, and analysed using a thematic analysis as well as a comparison with other pandemic virtual events. The original contribution to the events studies literature is two-fold; firstly, a novel conceptualisation of event experience during this historic moment, documenting the practical and philosophical nature of events at that time. Secondly, by synthesising primary and secondary data, it offers theoretical principles on how virtual events may develop to become a notable and profitable element of the industry. This article concludes with suggestions for future research, in the hope that this exploratory research will stimulate further studies.
Robust Output Regulation in Discrete-Time Singular Systems With Actuator Saturation and Uncertainties

This brief studies robust output regulation in discrete-time singular systems with actuator saturation and matched uncertainties. The singularity of dynamical equations makes the design procedure more complex due to the appearance of both algebraic and difference equations in such systems. In addition to this, the actuator saturation constraint along with uncertain terms complicates the design problem. The idea for robust output regulation is to add an additional control term to the composite nonlinear feedback (CNF) controller which guarantees the output regulation of the uncertain closed-loop singular system with an ultimately bounded tracking error. In this regard, a theorem is given which guarantees the output regulation even if the saturation takes place. The accuracy of the theoretical results is tested through simulations for a practical example.
The HIF-1α and mTOR Pathways Amplify Heterotopic Ossification

Fibrodysplasia ossificans progressiva (FOP; MIM# 135100) is an ultra-rare congenital disorder caused by gain-of-function point mutations in the Activin receptor A type I (ACVR1, also known as ALK2) gene. FOP is characterized by episodic heterotopic ossification (HO) in skeletal muscles, tendons, ligaments, or other soft tissues that progressively causes irreversible loss of mobility. FOP mutations cause mild ligand-independent constitutive activation as well as ligand-dependent bone morphogenetic protein (BMP) pathway hypersensitivity of mutant ACVR1. BMP signaling is also a key pathway for mediating acquired HO. However, HO is a highly complex biological process involving multiple interacting signaling pathways. Among them, the hypoxia-inducible factor (HIF) and mechanistic target of rapamycin (mTOR) pathways are intimately involved in both genetic and acquired HO formation. HIF-1α inhibition or mTOR inhibition reduces HO formation in mouse models of FOP or acquired HO in part by de-amplifying the BMP pathway signaling. Here, we review the recent progress on the mechanisms of the HIF-1α and mTOR pathways in the amplification of HO lesions and discuss the future directions and strategies to translate the targeting of HIF-1α and the mTOR pathways into clinical interventions for FOP and other forms of HO.