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Teachers play important roles in identifying and promoting gifted students. An open question is: Which student characteristics do teachers use to evaluate whether a student is gifted or not? We used data from a representative sample of Dutch primary school teachers (N = 1,304) who were asked whether or not they thought the students (N = 26,720) in their class were gifted. We investigated students' cognitive and noncognitive attributes as well as demographic factors that might be relevant for this judgment. In sum, the findings revealed that teachers considered students to be gifted when, in comparison with their peers, students were superior in cognitive domains, especially with respect to academic achievement, scored higher on openness to experience and lower on agreeableness, were male, were younger, and came from families with higher parental education. These findings are discussed in relation to research on teacher nominations, teachers' representations of giftedness and gifted students, and theoretical conceptions of giftedness.

Increasingly, the melting of Arctic ice due to global warming has provided opportunities for commercial shipping between Asia and Europe. Given the vulnerability of the Arctic environment, especially due to emissions of short-lived pollutants from shipping activities, a more effective propulsion system with a comprehensive control strategy is required to reduce fuel consumption, thus potentially mitigating the impacts of shipping activities on the northern sea route (NSR). In this paper, a shipboard DC hybrid system powered by a combination of diesel generator sets and batteries is proposed and analysed in terms of its application on a ship in the NSR. The specific fuel consumption and various losses in the power sources were analysed to develop an efficiency-optimisation control strategy for the proposed DC hybrid power system. To evaluate the performance of the hybrid power system with the proposed optimisation control strategy, lab-scale experiments have been conducted in the Shanghai Marine Diesel Engine Research Institute to compare the proposed system with a conventional hybrid system. The experimental results indicate that the proposed DC hybrid power plant with the energy optimisation control contributes a 5.35% fuel saving compared with the DC fixed-speed diesel electric configuration during a scaled-down NSR scenario.

Navigation is a challenging problem in the area of underwater unmanned vehicles, due to the significant electronmagnetic wave attenuation and the uncertainties in underwater environments. The conventional methods, mainly implemented by acoustic devices, suffer limitations such as high cost, terrain effects and low refresh rate. In this paper, a novel low-cost underwater visual navigation method, named Integrated Visual Odometry with a Stereo Camera (IVO-S), has been investigated. Unlike pure visual odometry, the proposed method fuses the information from inertial sensors and a sonar so that it is able to work in context-sparse environments. In practical experiments, the vehicle was operated to follow specific closed-loop shapes. The Integrated Visual Odoemtry with Monocular Camera (IVO-M) method and other popular open source Visual SLAMs (Simultaneous Localisation and Mappings), such as ORB-SLAM2 and VINS-Mono, have been used to provide comparative results. The cumulative error ratio is used as the quantitative evaluation method to analyse the practical test results. It is shown that the IVO-S method is able to work in underwater sparse-feature environments with high accuracy, whilst also being a low cost solution.

Different cross-domain trajectories in the development of students' ability self-concepts (ASCs) and their intrinsic valuing of math and language arts were examined in a cross-sequential study spanning Grades 1 through 12 (n = 1,069). Growth mixture modeling analyses identified a "Moderate Math Decline/Stable High Language Arts" class and a "Moderate Math Decline/Strong Language Arts Decline" class for students' ASC trajectories. Students' intrinsic value trajectories included a "Strong Math Decline/Language Arts Decline Leveling Off," a "Moderate Math Decline/Strong Language Arts Decline," and a "Stable Math and Language Arts Trajectories" class. These classes differed with regard to student characteristics, including gender, family background, and math and reading aptitudes. They also resulted in different high school math course enrollments, career aspirations, and adult careers.

Position effects (PE) cause decreasing probabilities of correct item responses towards the end of a test. We analysed PEs in science, mathematics and reading tests administered in the German extension to the PISA 2006 study with respect to their variability at the student- and school-level. PEs were strongest in reading and weakest in mathematics. Variability in PEs was found at both levels of analysis. PEs were stronger for male students, for students with a migration background (science and mathematics), and for students with a less favourable socio-economic background (reading). At the school level, PEs were stronger in lower school tracks and in schools with a high proportion of students with a migration background. The relationships of the test scores with the covariates partly reflected the covariates' relationships with PEs. Our findings suggest that PEs should be taken seriously in large-scale assessments as they have an undesirable impact on the results.

The seafaring occupation will soon evolve as human operators transition to a more supervisory role for autonomous systems onboard. Therefore, gaining a greater understanding of the mindset that officers have towards the world of autonomy will aid the maritime industry by developing a baseline for future navigational training. This paper examines the perceptions and attitudes of 100 navigational seafaring participants of varying navigational ranks and levels of seagoing experience. The aim of the study was to identify the perceptions and self-conscious trust that current seafarers have towards automated and future autonomous systems. Participants were issued a situational judgement test comprising of three questions, allowing them to assess and respond to a hazardous scenario. The results of the study found that seafarers are receptive towards the introduction of autonomous shipping. Furthermore, the participants showed an awareness of what autonomous shipping would mean for the maritime industry. However, concerns remain about the responsibility and safety of the vessel in the event of the introduction of an unmanned vessel. Moreover, when comparing opinions and trust levels among the cohort of ranks, it was found that participants of a higher rank had a similar outlook towards autonomy to that of the less experienced groups.

Underwater Vehicles have become more sophisticated, driven by the off-shore sector and the scientific community’s rapid advancements in underwater operations. Notably, many underwater tasks, including the assessment of subsea infrastructure, are performed with the assistance of Autonomous Underwater Vehicles (AUVs). There have been recent breakthroughs in Artificial Intelligence (AI) and, notably, Deep Learning (DL) models and applications, which have widespread usage in a variety of fields, including aerial unmanned vehicles, autonomous car navigation, and other applications. However, they are not as prevalent in underwater applications due to the difficulty of obtaining underwater datasets for a specific application. In this sense, the current study utilises recent advancements in the area of DL to construct a bespoke dataset generated from photographs of items captured in a laboratory environment. Generative Adversarial Networks (GANs) were utilised to translate the laboratory object dataset into the underwater domain by combining the collected images with photographs containing the underwater environment. The findings demonstrated the feasibility of creating such a dataset, since the resulting images closely resembled the real underwater environment when compared with real-world underwater ship hull images. Therefore, the artificial datasets of the underwater environment can overcome the difficulties arising from the limited access to real-world underwater images and are used to enhance underwater operations through underwater object image classification and detection.

To understand, mitigate and reduce the detrimental effects on human health and the environment from exhaust gas emissions from ships it is necessary to be able to estimate the quantity and location of these ship emissions in time.Currently, the two most commonly used ship emission assessment methods sit on opposite ends of the spectrum – the top-down approach, which provides low resolution yet efficient aggregated results however is unable to account for specific shipping activities, and the bottom-up vessel-by-vessel approach, which provides near-instantaneous ship emissions production at a high resolution – yet is data and time intensive.To address the market gap for a ship greenhouse emission estimation method that hybridises the best of both the bottom-up and top-down methods the novel Ship Emissions Assessment (SEA) method is proposed as an innovative hybrid solution.It is a cost effective and resource efficient method, presenting spatial ship emissions utilising widely accessible data, and it is precise – fulfilling the requirements needed to evaluate ship emissions reduction measures.Novel SEA method is the first in its endeavour to replace Automatic Identification System (AIS) Vessel-based raw data allocation, by using rapid analyses of readily available ship track density data and average voyage information. It combines obtained average voyage distance with voyage average speed to estimate ship activity for emission assessments - saving costs by reducing time and reliance on complex computations, especially when many ships need to be analysed simultaneously.Using the novel SEA method, a series of containerships from geographically diverse ports were sampled and assessed for emissions with comparative results confirming the representations equivalent to the detailed and data demanding bottom-up method.Subsequently, the novel SEA method was applied to containership traffic calling into the Port of Trieste, in the northern Adriatic Sea, where it demonstrated the ability to estimate and quantify historic emissions for the preceding 12 months while taking into account seasonal port traffic variations.The novel SEA method showed to be an efficient, inexpensive and accurate, easy-to-use emission assessment tool based on widely accessible data. It can be used in day-to-day shipping operations by a variety of stakeholders including port operations managers, regional traffic operators, and those non-industry, while providing the required level of technical accuracy. In comparison, existing methods are not as time and cost effective, user-friendly, nor based on easy to interpret and readily accessible data.The novel SEA method enables further global research of ship emissions, and for regional and international policy makers to effectively manage the measures needed to reach greenhouse gas emission reduction targets.

Enrichment programs provide learning opportunities for a broader or deeper examination of curricular or extracurricular topics and are popular in gifted education. Herein, we investigated the effectiveness of a statewide extracurricular enrichment program for gifted elementary school children in Germany. The program implemented a "grass roots" strategy by which local units developed and offered the enrichment courses, which spanned a broad array of topics. The courses targeted different outcomes, including students' cognitive abilities, school achievement, interests, creativity, self-control, self-concept, and social competencies. We compared third-grade students attending the enrichment program (N =423) with nonattending third-grade students (N = 2,328) by means of a propensity score analysis. Specifically, we controlled for potential selection effects and estimated the average causal effect of the enrichment program for children attending the program. The findings revealed positive program effects on academic achievement but not on the other targeted outcomes.

Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equipment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately.

The local refining of crude oil has become a lucrative but disturbing business in the Niger Delta region of Nigeria. Deep inside the forest of the Niger Delta camps are built and used for the local refining of crude oil. The economic benefits this brings to the refiners are clear, however the host communities are severely hit by the activities of the ‘local crude oil refiners’. Farmlands have been destroyed and fishing settlements evacuated as a result of pollution of the rivers and estuaries, with loss of lives and properties. This research investigates the impact of the activities of the local refiners on their host communities' farmlands, rivers and estuaries. A quantitative research method was adopted in this study through the administration of questionnaires to local stakeholders (chiefs, the youth, farmers, traders, fishermen/fisherwomen and residents of affected communities). Quantitative data was collected from three communities in the Niger Delta with local refineries and the data was analysed using descriptive and inferential (Chi Square and Correlation) methods. The study demonstrates the high impact of the activities of the refiners on farmlands and fishing areas of the host communities and makes recommendations to all stakeholders in the upstream and downstream sectors of the petroleum industry. In addition, recommendations are made to the Nigerian government, on ways to address the impact of local refining on host communities.

The impact of response styles such as extreme response style (ERS) on trait estimation has long been a matter of concern to researchers and practitioners. This simulation study investigated three methods that have been proposed for the correction of trait estimates for ERS effects: (a) mixed Rasch models, (b) multidimensional item response models, and (c) regression residuals. The methods were compared with respect to their ability of recovering the true latent trait levels. Data were generated according to a unidimensional model with only one trait, a mixed Rasch model with two populations of ERS and non-ERS, and a two-dimensional model incorporating a trait and an ERS dimension. The data were analyzed using the same models as well as linear regression where the trait estimate is regressed on an ERS score and the resulting residual is considered the corrected trait estimate. Over all conditions, the two-dimensional model achieved the best trait recovery, though the difference to the unidimensional model was rather small. Mixed Rasch models were in general inferior to the other correction methods. When the trait and ERS showed no to weak correlations, ERS appeared to have a minor impact on trait estimation.

Data from competence tests usually show a number of missing responses on test items due to both omitted and not-reached items. Different approaches for dealing with missing responses exist, and there are no clear guidelines on which of those to use. While classical approaches rely on an ignorable missing data mechanism, the most recently developed model-based approaches account for nonignorable missing responses. Model-based approaches include the missing propensity in the measurement model. Although these models are very promising, the assumptions made in these models have not yet been tested for plausibility in empirical data. Furthermore, studies investigating the performance of different approaches have only focused on one kind of missing response at once. In this study, we investigated the performance of classical and model-based approaches in empirical data, accounting for different kinds of missing responses simultaneously. We confirmed the existence of a unidimensional tendency to omit items. Indicating nonignorability of the missing mechanism, missing tendency due to both omitted and not-reached items correlated with ability. However, results on parameter estimation showed that ignoring missing responses was sufficient to account for missing responses, and that the missing propensity was not needed in the model. The results from the empirical study were corroborated in a complete case simulation.

Rationale: Sepsis often leads to long-term functional deficits and increased mortality in survivors. Postacute rehabilitation can decrease long-term sepsis mortality, but its impact on nursing care dependency, health care use, and costs is insufficiently understood. Objectives: To assess the short-term (7-12 months postdischarge) and long-term (13-36 months postdischarge) effect of inpatient rehabilitation within 6 months after hospitalization on mortality, nursing care dependency, health care use, and costs. Methods: An observational cohort study used health claims data from the health insurer AOK (Allgemeine Ortskrankenkasse). Among 23.0 million AOK beneficiaries, adult beneficiaries hospitalized with sepsis in 2013-2014 were identified by explicit codes from the International Classification of Diseases, Tenth Revision. The study included patients who were nonemployed presepsis, for whom rehabilitation is reimbursed by the AOK and thus included in the dataset, and who survived at least 6 months postdischarge. The effect of rehabilitation was estimated by statistical comparisons of patients with rehabilitation (treatment group) and those without (reference group). Possible differential effects were investigated for the subgroup of ICU-treated sepsis survivors. The study used inverse probability of treatment weighting based on propensity scores to adjust for differences in relevant covariates. Costs for rehabilitation in the 6 months postsepsis were not included in the cost analysis. Results: Among 41,918 6-month sepsis survivors, 17.2% (n = 7,224) received rehabilitation. There was no significant difference in short-term survival between survivors with and without rehabilitation. Long-term survival rates were significantly higher in the rehabilitation group (90.4% vs. 88.7%; odds ratio [OR] = 1.2; 95% confidence interval [95% CI] = 1.1-1.3; P = 0.003). Survivors with rehabilitation had a higher mean number of hospital readmissions (7-12 months after sepsis: 0.82 vs. 0.76; P=0.014) and were more frequently dependent on nursing care (7-12 months after sepsis: 47.8% vs. 42.3%; OR=1.2; 95% CI=1.2-1.3; P,0.001; 13-36 months after sepsis: 52.5% vs. 47.5%; OR=1.2; 95% CI=1.1-1.3; P,0.001) compared with those without rehabilitation, whereas total health care costs at 7-36 months after sepsis did not differ between groups. ICUtreated sepsis patients with rehabilitation had higher short- and long-term survival rates (short-term: 93.5% vs. 90.9%; OR= 1.5; 95% CI=1.2-1.7; P,0.001; long-term: 89.1% vs. 86.3%; OR=1.3; 95% CI=1.1-1.5; P,0.001) than ICU-treated sepsis patients without rehabilitation. Conclusions: Rehabilitation within the first 6 months after ICU- and non-ICU-treated sepsis is associated with increased long-term survival within 3 years after sepsis without added total health care costs. Future work should aim to confirm and explain these exploratory findings. [ABSTRACT FROM AUTHOR]

Teachers play important roles in identifying and promoting gifted students. An open question is: Which student characteristics do teachers use to evaluate whether a student is gifted or not? We used data from a representative sample of Dutch primary school teachers (N = 1,304) who were asked whether or not they thought the students (N = 26,720) in their class were gifted. We investigated students' cognitive and noncognitive attributes as well as demographic factors that might be relevant for this judgment. In sum, the findings revealed that teachers considered students to be gifted when, in comparison with their peers, students were superior in cognitive domains, especially with respect to academic achievement, scored higher on openness to experience and lower on agreeableness, were male, were younger, and came from families with higher parental education. These findings are discussed in relation to research on teacher nominations, teachers' representations of giftedness and gifted students, and theoretical conceptions of giftedness. [ABSTRACT FROM AUTHOR]