Assessment Planning Research Articles

Hindari Nomophobia pada Anak dengan Bermain Sambil Belajar

The emergence of cell phones/smartphones has made many children more fun and busy with the features on these devices, individuals prefer interaction via social media networks rather than having to meet face to face. The emergence of this smartphone also has a new effect on user behavior, namely smartphone addiction behavior or a person's dependence on the continuous use of a smartphone without considering the negative impact. Cellphone/smartphone addiction is a form of attachment or addiction to cell phones that may become a social problem such as withdrawal, and difficulties in performing daily activities or as a disorder of impulsive control over one's self. This is also experienced by the people in the Kedai Durian Village. Children in this community think that playing smartphones for long periods of time is a natural thing to do. Therefore, the assistance process will be carried out in PKL 2 activities using the groupwork method with stages consisting of Intake, Assessment and Intervention Planning, Member Selection, Group Development, Evaluation and Termination. The focus of the completion that will be carried out is by getting used to doing positive activities such as drawing, painting, making handicrafts. This is done as a strategy to get rid of smartphone addiction. This goal was declared successful because the children had started to be interested in doing positive activities by drawing with their friends instead of playing on smartphones.

Applying molecular genetic data at different scales to support conservation assessment of European Habitats Directive listed species: A case study of Eurasian otter in Austria.

Evaluating intraspecific genetic structure and diversity is fundamental to assessing a species' conservation status, but direct incorporation of such information into legal frameworks such as the EU's Habitats Directive is surprisingly rare. How genetic structure aligns with EU member state boundaries or biogeographic regions may be very important in designing management plans or achieving legislative goals. The Eurasian fish otter experienced a sharp population decline during the 20th century but is currently re-expanding in several countries. The species is listed under Annex II and IV of the European Habitats Directive, and member states are obliged to assess the species separately across different biogeographic regions. We genotyped 2492 otter spraints across four provinces in Austria, collected between 2017 and 2021. A total of 384 different genotypes were identified, supporting densities along river habitats from 0.1 to 0.47 otters per river km (mean: 0.306), with a resampling-based simulation supporting limited density overestimation at survey lengths of 20 km or more. Three distinct genetic clusters were revealed, two of them presumably reflecting two relict populations whereas the source of the third cluster is unknown. The geographic extent of the three clusters does not coincide with provincial or biogeographic boundaries, both relevant for assessment and management within existing national or European legislative frameworks. We advocate more consideration of genetic structure in the assessment and conservation management planning of species listed in the European Habitats Directive.

Institutionalizing Community-Based Research

Institutionalizing Community-Based Research

Planning Benefit-Risk Assessments Using Visualizations.

A Benefit Risk Assessment Plan (BRAP) describes the assessments planned to determine whether the benefits of an investigational drug outweigh the risks. The plan can have two sections, one with timelines for aligning resources with decision milestones and the other for pre-specifying assessments for decision milestones. Regulatory guidance recommends a proactive planning process over an ad-hoc process. However, very little has been published about proactive plans themselves. This article works through a hypothetical example visualizing a series of assessments across the drug development lifecycle. Based on a regulatory framework, the planning process starts with assessing the medical condition and current treatment options. These early assessments bring out major considerations in assessing the investigational drug.

Defining Body Mass Index Using Weight and Length for Gestational Age in the Growth Assessment of Preterm Infants at Birth.

The objectives of this study were to describe (1) body mass indexes (BMIs) using weight and length for gestational age (GA) classifications, and (2) the additional information BMI, as a measure of body proportionality, provides for preterm infant growth assessment and care plans at birth. Birth weight, length, and BMI of 188,646 preterm infants (24-36 weeks gestation) admitted to U.S. neonatal intensive care units (Pediatrix Clinical Data Warehouse, 2013-2018) were classified (Olsen curves) as small, appropriate, or large for GA (SGA < 10th, AGA 10-90th, LGA > 90th percentile for GA, respectively). The distribution for the 27 weight-length-BMI combinations was described. At birth, most infants were appropriate for weight (80.0%), length (82.2%), head circumference (82.9%), and BMI (79.9%) for GA. Birth weight for GA identified approximately 20% of infants as SGA or LGA. Infants born SGA (or LGA) for both weight and length ("proportionate" in size) were usually appropriate for BMI (59.0% and 75.6%). BMI distinguished disproportionate weight for length in infants with SGA or LGA weight at birth (58.3%, 49.9%). BMI also identified 11.4% of AGA weight infants as small or large for BMI ("disproportionate" in size) at birth; only using weight for GA missed these underweight/overweight for length infants. The unique, additional information provided by birth BMI further informs individualized preterm infant growth assessment by providing an assessment of an infant's body proportionality (weight relative to its length) in addition to the routine assessment of weight, length, and head circumference for GA and may better inform care plans and impact outcomes. · Most preterm infants were born AGA for all growth measures.. · AGA weight infants may be under- or overweight for length.. · BMI distinguished body disproportionality in SGA/LGA infants.. · Recommend BMI assessed along with weight, length and head.. · Further research on BMI in preterm infants is needed..

Evaluation of Nocturnal Symptoms in Chinese Parkinson's Disease Patients Based on the PDSS-2 Scale: A Multicenter Cross-Sectional Study.

Nocturnal symptoms have a significant effect on the quality of life in Parkinson's disease (PD) patients. This study aimed to investigate the prevalence and associated factors of nocturnal symptoms in Chinese PD patients. This multicenter cross-sectional study included 1,500 patients with primary PD from 18 centers in China was carried out between February 2019 and February 2020. Questionnaires including Parkinson's disease sleep scale 2 (PDSS-2), Parkinson's disease questionnaire 8 (PDQ-8), Beck depression inventory (BDI), and generalized anxiety disorder scale 7 (GAD-7) were used to assess nocturnal symptoms, quality of life, depression, and anxiety. Among 1,500 Chinese PD patients, 576 (38.4%) reported nocturnal symptoms. Of them, 59.2% were older than 65 years. The PDQ-8 total score was higher in patients with nocturnal symptoms (p < 0.01). Moderate and severe depression was reported more often in patients with nocturnal symptoms (p < 0.01), and the occurrence and severity of anxiety were higher as well (p < 0.01). Longer disease duration and higher Hoehn-Yahr (HY) stage were independently associated with nocturnal symptoms (p < 0.01). Education level, depression, disease course, HY stage, and nocturnal symptoms were related to the quality of life in Chinese PD patients (p < 0.01). Our study found that 38.4% of Chinese PD patients have nocturnal symptoms, even in early and mid-stage PD. Nocturnal symptoms were associated with worse quality of life and higher incidences of depression and anxiety. Nocturnal symptoms should be included in the assessment and care plan, especially in patients with longer disease courses and higher HY stages.

Demographics moderated the association of symptom burden with falls and fall-related outcomes

ObjectivesTo examine whether (1) prior-year symptom burden predicted later-year falls and fall-related outcomes and (2) demographics moderated the longitudinal effects of symptom burden on falls and fall-related outcomes among community-dwelling older adults. MethodsWe used 2011–2018 National Health and Aging Trends Study data that included 9,060 community-dwelling older adults (contributed 34,327 observations). Falls and fall-related outcomes included self-reported falls, multiple falls, fear of falling (FOF), and FOF limiting activity. Symptom burden was defined as the presence of pain, insomnia, breathing difficulty, depressive symptoms, anxiety, and fatigue, and calculated the number of symptoms (range from 0 to 6). Binomial logistic regression was used to examine the associations between symptom burden and falls and fall-related outcomes and the moderation effects of demographic factors. ResultsThe majority of the sample were aged between 65 and 79 years old (57.7%), non-Hispanic White (70.5%), and female (58.4%). Each additional symptom was associated with an increased risk of falls (Adjusted Odds Ratio [AOR]: 1.13, 95% CI: 1.10–1.15), multiple falls (AOR: 1.15, 95% CI: 1.12–1.18), FOF (AOR: 1.20, 95% CI: 1.18–1.23), and FOF limiting activity (AOR: 1.24, 95% CI: 1.20–1.28). Age, race/ethnicity, education, and living arrangement statistically significantly moderated the relationships between symptom burden and falls and fall-related outcomes. ConclusionsSymptom burden predicted falls, multiple falls, FOF and FOF limiting activity, and demographics may differentially modify this risk. Individually tailored symptom assessment and management plans should be incorporated into fall risk assessment and interventions for community-dwelling older adults living.

Improving personalised care and support planning for people living with treatable-but-not-curable cancer

People living with treatable-but-not-curable (TbnC) cancer encounter cancer-related needs. While the NHS long-term plan commits to offering a Holistic Needs Assessment (HNA) and care plan to all people diagnosed with...

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Electricity authorities need capacity assessment and expansion plans for efficiently charging the growing Electric vehicle (EV) fleet. Specifically, the distribution grid needs significant capacity expansion as it faces the most impact to accommodate the high variant residential EV charging load. Utility companies employ different scheduling policies for the maintenance of their distribution transformers (hereinafter, XFR). However, they lack scenario-based plans to cope with the varying EV penetration across locations and time. The contributions of this paper are twofold. First, we propose a customer feedback-based EV charging scheduling to simultaneously minimize the peak load for the distribution XFR and satisfy the customer needs. Second, we present a deep reinforcement learning (DRL) method for XFR maintenance, which focuses on the XFR’s effective age and loading to periodically choose the best candidate XFR for replacement. Our case study for a distribution feeder shows the adaptability and success of our EV load scheduling method in reducing the peak demand to extend the XFR life. Furthermore, our DRL-based XFR replacement policy outperforms the existing rule-based policies. Together, the two approaches provide a complete capacity planning tool for efficient XFR maintenance to cope with the increasing EV charging load.

The Interacting Multiple Model Smooth Variable Structure Filter for Trajectory Prediction

An autonomous vehicle would benefit from being able to predict trajectories of other vehicles in its vicinity for improved safety. In order for the self-driving car to plan safe trajectories, paths of nearby vehicles are required to be predicted for risk assessment, decision making, and motion planning. In this study, a trajectory prediction algorithm based on the Interacting Multiple Model (IMM) estimation strategy is proposed to predict paths involving lane-changing, lane-keeping, and turning motion. More specifically, the Interacting Multiple Model estimation technique is used with models defined in curvi-linear coordinates to predict a vehicle’s trajectory based on prior behavioral maneuvers. The road geometry is used to help facilitate behavior identification and prediction. Moreover, the combination of a more recently developed estimation technique known as the Generalized Variable Boundary Layer-Smooth Variable Structure Filter and the Interacting Multiple Model Estimator is applied to track, identify behaviors, and predict trajectories of a vehicle. The performance of this technique is compared with a Kalman Filter based formulation using synthetic and experimental data. This model-based strategy is also compared with machine learning-based strategies for trajectory prediction.

The role of phreatomagmatism in the formation of complex monogenetic volcanic systems in a low-lying coastal plain

Monogenetic volcanoes are characterised worldwide by a single eruptive phase that is short-lived, with small eruptive volumes that produce a wide range and complexity of landforms. Investigating the evolution in eruptive styles through the active history of a volcano is key to understanding the role of phreatomagmatism in the formation of these complex monogenetic systems. The classification of the complexity of a monogenetic volcanic centre is of great significance to understanding both past volcanoes and the hazards of future volcanoes. Complexity can be defined by the number of volcanic structures created, the presence of multiple types of eruptions, and the number of conduits involved. In this paper we reconstruct the volcanic history of the Waitomokia Volcanic Complex, located in the southern lowlands of the active monogenetic Auckland Volcanic Field (AVF), New Zealand. Activity began with deposition of an initial tuff ring, followed by vent migration and establishment of a maar with a surrounding tuff ring. Four smaller tuff rings developed on the north and northeastern rim of the maar. This was followed by the noteworthy deposition of complex intercalated transitional deposits from at least two tuff rings and magmatic deposits from a scoria cone. Three scoria cones were then built within the maar, followed by minor effusion of short lava flows. Waitomokia highlights the prospect of having a complex system with ten or more landforms being created in different areas active at the same time, with associated eruptive styles and mechanisms. The possibility of a complex eruption, where several styles may occur simultaneously from distinct vents, should be considered in hazard assessment and crisis management plans.

Process evaluation of paediatric fellowship training programs at a University Hospital in Pakistan

BackgroundFellowship programs offer training in a subspecialty focusing on distinct and advanced clinical/academic skills. This advanced postgraduate training allows physicians, who desire a more specialized practice, to further develop clinical, academic, research, and leadership/administrative skills. The Aga Khan University (AKU) is one of the few institutes offering paediatric sub-specialty training in Pakistan. We aimed to evaluate the current Paediatric fellowship programs at AKU.MethodsProcess evaluation of six paediatric fellowship programs (cardiology, neurology, endocrinology, critical care medicine, neonatology, and infectious disease) was conducted from September 2020 to April 2021 by senior clinicians and medical educationists. Evidence was collected through document review (using existing postgraduate medical education program information form), observation of teaching and learning support, and focused group discussions/interviews with program faculty and fellows were conducted. A review of the evaluation report was done as part of this study. This study received an exemption from the ethical review committee. The quantitative data were analyzed using SPSS (22.0) while the reports of discussion with fellows and friends underwent content analysis.ResultsAll fellowship programs met the criteria for having a robust competency-based fellowship curriculum as per the institutional and national guidelines. Formative assessment in the form of continuous evaluation was found to be integrated into all the fellowship programs, however, most of the programs were found to lack a summative assessment plan. Fellows in training and program faculty were satisfied with the opportunities for mentorship, teaching, and learning. Thematic analysis of the discussion reports with faculty and fellows revealed three key themes including, program aspects translating into strengthening the training, gaps in the training program in delivering the expectations, and making ways to reach par excellence.ConclusionsThe process evaluation of paediatric fellowship programs provided an opportunity to holistically review the current strengths and quality of the training in individual programs along with the unmet needs of the trainees. This will help the program stakeholders to prioritize, align and allocate the resources to further enhance the quality of training and outcome of individual fellowship programs to ensure wider impacts at a regional, national, and international health system level.

Assessing Learning Outcomes in Higher Education: From Practice to Systematization

Accountability systems in higher education ensure that academic programs meet learning outcomes and address student cognitive and affective development. The learning outcome assessment is one of the most important elements to enhance and assure the quality of programs. In this paper, we present a “tour” of how an assessment cycle is performed in a national/public university in Qatar. The study draws on “good” practices and how a higher education institution systemizes the process of assessing learning. The approach demonstrates how learning is connected to program objectives and the curriculum through a rational, logical, conceptual, consensual, and interpretable process. Employing specification tables known as curriculum and assessment plan matrices, with tools being assessments and rubrics, learning outcomes measures can be reached. The approach as well as system calls for an institutional structure and organization which place responsibilities on all stakeholders involved in academic programs delivery. The futuristic approach and practice posit a reconceptualization of how students will be assessed, more likely a paradigm shift necessitating a change in practice and trend in which students’ success (achievement) can be based on the learning outcomes attained.

Linkage Analysis of SKL, KI, KD, IPK, Materials, Learning Processes and Assessment Plans Includes Materials About, SKL, KI, SD, K4, KD and GPA Determination, Division of Learning Materials and Learning Plans

This paper aims to analyze the relationship between SKL, KI, KD, GPA, material, learning process and assessment plan including materials about SKL, KI, SD, K4, KD and GPA determination, division of learning materials and lesson plans. The development of writing materials using the literature study method from related reference sources in order to enrich the scientific treasures in analyzing the interrelationship of SKL, KI, KD, GPA, materials, learning processes and assessment plans includes materials about SKL, KI, SD, K4, KD and determination of GPA, division of learning materials and lesson plans. Based on this study, it was found that the importance of applying the linkage analysis of SKL, KI, KD, GPA, materials, learning processes and assessment plans includes materials about SKL, KI, SD, K4, KD and determination of GPA, division of learning materials and lesson plans.

In-house validation of an LC-MS method for the multiplexed quantitative determination of total allergenic food in chocolate.

Mass spectrometry has been widely accepted as a confirmatory tool for the sensitive detection of undeclared presence of allergenic ingredients. Multiple methods have been developed so far, achieving different levels of sensitivity and robustness, still lacking harmonization of the analytical validation and impairing comparability of results. In this investigation, a quantitative method has been validated in-house for the determination of six allergenic ingredients (cow's milk, hen's egg, peanut, soybean, hazelnut, and almond) in a chocolate-based matrix. The latter has been produced in a food pilot plant to provide a real and well-characterized matrix for proper assessment of method performance characteristics according to official guidelines. In particular, recent considerations issued by the European Committee for Standardization have been followed to guide a rigorous single-laboratory validation and to feature the main method performance, such as selectivity, linearity, and sensitivity. Synthetic surrogates of the peptide markers have been used both in native and labelled forms in matrix-matched calibration curves as external calibrants and internal standards, respectively. A two-order of magnitude range was investigated, focusing on the low concentration range for proper assessment of the detection and quantification limits (LOD and LOQ) by rigorous calibration approach. Conversion factors for all six allergenic ingredients have been determined for the first time to report the final quantitative information as fraction of total allergenic food protein (TAFP) per mass of food (µgTAFP/gfood), since such areporting unit isexploitable in allergenic risk assessment plans. The method achieved good sensitivity with LOD values ranging between 0.08 and 0.2 µgTAFP/gfood, for all ingredients besides egg and soybean, whose quantitative markers reported a slightly higher limit (1.1 and 1.2 µgTAFP/gfood, respectively). Different samples of chocolate bar incurred at four defined concentration levels close to the currently available threshold doses have been analyzed to test the quantitative performance of the analytical method, with a proper estimate of the measurement uncertainty from different sources of variability. The sensitivity achieved resulted in compliance with the various threshold doses issued or recommended worldwide.

Artificial intelligence in orthodontics and orthognathic surgery: a bibliometric analysis of the 100 most-cited articles

BackgroundThe application of artificial intelligence (AI) in orthodontics and orthognathic surgery has gained significant attention in recent years. However, there is a lack of bibliometric reports that analyze the academic literature in this field to identify publishing and citation trends. By conducting an analysis of the top 100 most-cited articles on AI in orthodontics and orthognathic surgery, we aim to unveil popular research topics, key authors, institutions, countries, and journals in this area.MethodsA comprehensive search was conducted in the Web of Science (WOS) electronic database to identify the top 100 most-cited articles on AI in orthodontics and orthognathic surgery. Publication and citation data were obtained and further analyzed and visualized using R Biblioshiny. The key domains of the 100 articles were also identified.ResultsThe top 100 most-cited articles were published between 2005 and 2022, contributed by 458 authors, with an average citation count of 22.09. South Korea emerged as the leading contributor with the highest number of publications (28) and citations (595), followed by China (16, 373), and the United States (7, 248). Notably, six South Korean authors ranked among the top 10 contributors, and three South Korean institutions were listed as the most productive. International collaborations were predominantly observed between the United States, China, and South Korea. The main domains of the articles focused on automated imaging assessment (42%), aiding diagnosis and treatment planning (34%), and the assessment of growth and development (10%). Besides, a positive correlation was observed between the testing sample size and citation counts (P = 0.010), as well as between the time of publication and citation counts (P < 0.001).ConclusionsThe utilization of AI in orthodontics and orthognathic surgery has shown remarkable progress, particularly in the domains of imaging analysis, diagnosis and treatment planning, and growth and development assessment. This bibliometric analysis provides valuable insights into the top-cited articles and the trends of AI research in this field.

BIGQA: Declarative Big Data Quality Assessment

In the big data domain, data quality assessment operations are often complex and must be implementable in a distributed and timely manner. This article tries to generalize the quality assessment operations by providing a new ISO-based declarative data quality assessment framework (BIGQA). BIGQA is a flexible solution that supports data quality assessment in different domains and contexts. It facilitates the planning and execution of big data quality assessment operations for data domain experts and data management specialists at any phase in the data life cycle. This work implements BIGQA to demonstrate its ability to produce customized data quality reports while running efficiently on parallel or distributed computing frameworks. BIGQA generates data quality assessment plans using straightforward operators designed to handle big data and guarantee a high degree of parallelism when executed. Moreover, it allows incremental data quality assessment to avoid reading the whole dataset each time the quality assessment operation is required. The result was validated using radiation wireless sensor data and Stack Overflow users’ data to show that it can be implemented within different contexts. The experiments show a 71% performance improvement over a 1 GB flat file on a single processing machine compared with a non-parallel application and a 75% performance improvement over a 25 GB flat file within a distributed environment compared to a non-distributed application.

Machine learning-assisted optimal schedule of underground water pipe inspection

There are over 2.2 million miles of underground water pipes serving the cities in the United States. Many are in poor conditions and deteriorate rapidly. Failures of these pipes could cause enormous financial losses to the customers and communities. Inspection provides crucial information for pipe condition assessment and maintenance plan; it, however, is very expensive for underground pipes due to accessibility issues. Therefore, water agencies commonly face the challenge to 1) decide whether it is worthwhile to schedule expensive water pipe inspections under financial constraints, and 2) if so, how to optimize the inspection schedule to maximize its value. This study leverages the physical model and data-based ML (ML) models for underground water pipe failure prediction to shed light on these two important questions for decision making. Analyses are firstly conducted to assess the value of water pipe inspection. Results by use of a physical-based failure model and Monte Carlo simulations indicate that by inspecting pipe’s condition, i.e., assessment of pipe’s erosion depth, the uncertainty of water pipe failure prediction can be narrowed down by 51%. For optimal inspection schedule, an artificial neural network (ANN) model, trained with historical inspection data, is evaluated for its performance in forecasting the future pipe failure probability. The results showed that a biased pipe failure prediction can occur under limited rounds of inspection. However, incorporating more rounds of inspection allows to predict the pipe failure conditions over its life cycle. From this, an optimal inspection plan can be proposed to achieve the maximum benefits of inspection in uncertainty reduction. A few salient results from the analyses include 1) the optimal schedule for inspection is not necessarily equal in the time interval, 2) by setting the goal of uncertainty reduction, an optimal inspection schedule can be obtained, where ML (ML) model augmented by continuously training with inspection data allows to reliably predict water pipe failure conditions over its life cycle. While this study focuses on underground pipe inspection, the general observations and methodology are applicable to optimize the inspection of other types of infrastructure as well.

Frac-Hit Prevention Countermeasures in Shale Gas Reservoirs with Natural Fractures

The development of natural fractures (NFs) in shale gas reservoirs is conducive to improving the productivity of shale gas wells. However, NF development leads to high-frequency frac hits between the infill and parent wells, which critically restricts its efficiency. To elucidate the large contribution of hydraulic fractures (HFs) and NFs in frac hits during the production and the development of NF-developed shale gas reservoirs, such reservoirs in the WY area of western China are taken as an example. A total of 197 frac hits well events in this area are systematically classified via the frac-hit discrimination method, and the effects of different factors on HF- and NF- dominated frac hits are classified and studied. Combined with the correlation analysis method and the chart method, the main controlling factors affecting the two types of frac hits are determined, and the corresponding frac-hit prevention countermeasures are proposed. The research demonstrates that (1) the distribution and development of NFs are crucial to production after frac hits. NFs and HFs in the WY area cause 51% and 49%, respectively, of the frac hits. (2) The main controlling factors in NF-dominated frac hits are the approximation angle, fracture linear density, and horizontal stress difference, whereas they are net pressure in fractures, horizontal stress difference, and liquid strength in HF-dominated frac hits. Sensitivity analysis shows that the NF activation difficulty coefficient fluctuates between −35.1% and 47.6%, and the maximum hydraulic fracture length fluctuates between −43.5% and 25.29%. (3) The corresponding frac-hit prevention countermeasures are proposed for the two types of formation mechanisms from different approaches, including frac-hit risk assessment and path planning, production well pressurization and stress diversion, and infill-well fracturing parameter optimization. This paper not only provides a reference for exploring the formation mechanism of frac hits in fractured shale gas reservoirs but also a theoretical basis for the corresponding frac-hit prevention countermeasures.

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