Listiani Listiani, Ágnes Hódi and Marianne Nikolov
In recent years, there has been an increase in research on English as a foreign language (EFL) learners’ engagement with teachers’ feedback; however, little is known about how students engage with feedback combining written and audio feedback. Previous research has primarily focused on a single mode of feedback addressing specific writing issues, although findings have indicated the potential of combined modes of feedback (CMF) for addressing a broader range of writing problems across different levels. The purpose of this study was to address this gap by examining how 23 low-proficient university students behaviorally engaged with their teacher’s CMF in an EFL writing class in Indonesia. Datasets included students’ initial and final drafts of their descriptive and narrative tasks and their teacher’s CMF (audio and written). The findings revealed that successfully used feedback was more frequent than partially and unused feedback. The level of behavioral engagement varied across the language features addressed in the teacher’s feedback and the error categories also varied between the two writing tasks. Students used several strategies, including Revision, No Revision, Deletion, Substitution, and Addition. These strategies generally concerned micro level errors, which did not require extensive understanding and knowledge to implement the feedback. This article discusses the study’s pedagogical implications, limitations, and potential directions for future research.
Martin Åhlén, Suzanna Törnroth, Åsa Wikberg-Nilsson
This article introduces the Design Sensibility Approach—a sensorial and embodied process for making sense of possible futures. The approach is applied through a case study on speculative energy design in the home, conducted and adapted within a participatory workshop held at a regional art hall in Northern Sweden. It unfolds in four phases—Imagine, Make, Explore, and Reflect—across a broader timeline comprising pre-workshop, active workshop, and post-workshop stages. During the workshop, participants were invited to engage with their senses through a series of activities designed to prompt reflection on their own future energy imaginaries, which they materialized using a MakeTools kit. The results reveal three themes: emotional responses elicited from embodied experiences with energy; energy as a lifestyle; and critique of the political landscape surrounding resource extractivism in Northern Sweden. These findings inform the research question: How might the human senses be leveraged to create stronger emotional connections with future domestic energy products and systems? The article concludes by proposing concrete applications of the Design Sensibility Approach at individual, community, and governance levels, highlighting its ethical and inclusive dimensions as areas for future development.
The examination of land-use change simulations across a range of scenarios represents a pivotal research avenue for the advancement of sustainable development analysis. Nevertheless, the extant research merely categorises all building land in a land-use classification into a single category, which is unable to provide a detailed analysis of the dynamic internal spatial form of the city. This paper analyses 17 LCZ land-use types in Guiyang City in 2013 and 2022, and reclassifies them into 7 RLCZ land-use types based on the height of urban building sites. It also proposes three possible scenarios of BAU, WLC, and SPC in 2040 and simulates their land-use changes using the PLUS model. The results demonstrate that (1) the size of low-rise buildings in Guiyang has declined significantly over the past decade; (2) built-up land within cities is significantly affected by drivers such as night-time lighting, topography, elevation, and roads; (3) the SPC scenario emphasises a development pattern of land intensification and a focus on high density in urban built space. It also protects the stability of the ecosystem. The scenario can provide informative suggestions for spatial pattern changes in rapidly developing cities such as Guiyang.
Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting management, precise fertilization, and yield prediction. In the karst mountainous of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats, and uneven plant growth. This study took a tobacco planting area in Guizhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds, and shadow masking, this area was classified into eight habitats. The U-Net model was trained using different habitat datasets. The results show that (1) the overall precision, recall, F1-score, and Intersection over Union (IOU) of tobacco plant information extraction were 0.68, 0.85, 0.75, and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can blur images, reducing the plant-identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.
The new industrial era has brought new opportunities and chances for the entire business development. Smart machines, artificial intelligence, cloud computing, the Internet of Things, big data are taking over many jobs and roles, thus leaving room for the development of new skills and abilities. The rapid technological development in terms of automation and digitization has made machines replace human work. In this sense, it is a matter of time when technology will replace traditional accountants. (Management) accountants who want to adapt and survive in the digital world have to improve their offer and change the focus from data calculation to interpretation of results and business management. Thus, by applying new digital information technology tools, management accounting can provide quality information for determining the costs of products and services, performance measurement, planning and control, strategic and operational decision-making and the like. The general objective of this paper is to review the potential impact of digital information technologies on the usefulness of cost accounting systems and organizational performance in healthcare institutions in the Republic of Serbia with the help of statistical analysis of the relationship between the selected variables. The results of the analysis show that digital technologies have a great impact on the usefulness of the cost accounting system. Also, the largest number of respondents pointed out that improved IT systems have a positive effect on increasing organizational performance.
The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, which involves selecting aggregate gradation and binder content, is time-consuming and labor-intensive. To accelerate the traditional mix design procedure, this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning (ML) and a meta-heuristic algorithm. Specifically, ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity (Gmb) and air void (VV)) and both mixture component properties and mixture proportion, based on a dataset collected from literature with 660 mixture designs. Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization (MOGWO) algorithm, an automatic asphalt mix design was proposed to pursue three goals, including VV, cost, and CO2 emission. The results indicated that least squares support vector regression (LSSVR) and eXtreme gradient boosting (XGBoost) achieved the highest prediction accuracies (correlation coefficient: 0.92 for VV and 0.96 for Gmb). The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs. cost vs. CO2 emission. Compared to the traditional laboratory design, the optimal mixture with VV of 4% achieves a cost saving of 2.46% and a reduction of 4.03% in carbon emission. The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.
Nitrogen reduction reaction (NRR) and nitrate reduction reaction (NO3−RR) provide a potential sustainable route by which to produce ammonia, a next-generation energy carrier. Many studies have been conducted over the years, mainly emphasizing material design and strategies to improve catalytic performance. Despite significant achievements in material design and corresponding fundamental knowledge, the produced ammonia is still very limited, which makes it prone to bias. The presence of interferants (e.g., cations and sacrificial reagents), the pH of the solution, and improper analytical procedure can lead to the over or underestimation of ammonia quantification. Therefore, the selection of the appropriate ammonia quantification method, which meets the sample solution condition, along with the proper analytical procedures, is of great importance. In this review, the state-of-the-art ammonia quantification method is summarized, emphasizing the advantages, limitations, and practicality for NRR and NO3−RR studies. Fundamental knowledge of the quantification method is introduced. Perspective on the considerations for selecting the suitable quantification method and for performing the quantification process is also provided. Although non exhaustive, this focused review can be useful as a guide to design the experimental setup and procedure for more reliable ammonia quantification results.
Enrique González-Plaza, David García, Jesús-Ignacio Prieto
Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models to estimate the maximum power in Stirling engines with a kinematic drive mechanism. The models are based on regression or ANN techniques, using data from 34 engines over a wide range of operating conditions. To facilitate the generalisation and interpretation of results, all models are expressed by dimensionless variables. The first group models use three input variables and 23 data points for correlation construction or training purposes, while another 66 data points are used for testing. Models in the second group use eight inputs and 18 data points for correlation construction or training, while another 36 data points are used for testing. The three-input models provide estimations of the maximum brake power with an acceptable accuracy for feasibility studies. Using eight-input models, the predictions of the maximum indicated power are very accurate, while those of the maximum brake power are less accurate, but acceptable for the preliminary design stage. In general, the best results are achieved with ANN models, although they only employ one hidden layer.
Engineering machinery, tools, and implements, Technological innovations. Automation
AbstractIn computer vision, convolution and pooling operations tend to lose high-frequency information, and the contour details will also disappear with the deepening of the network, especially in image semantic segmentation. For RGB-D image semantic segmentation, all the effective information of RGB and depth image can not be used effectively, while the form of wavelet transform can retain the low and high frequency information of the original image perfectly. In order to solve the information losing problems, we proposed an RGB-D indoor semantic segmentation network based on multi-scale fusion: designed a wavelet transform fusion module to retain contour details, a nonsubsampled contourlet transform to replace the pooling operation, and a multiple pyramid module to aggregate multi-scale information and context global information. The proposed method can retain the characteristics of multi-scale information with the help of wavelet transform, and make full use of the complementarity of high and low frequency information. As the depth of the convolutional neural network increases without losing the multi-frequency characteristics, the segmentation accuracy of image edge contour details is also improved. We evaluated our proposed efficient method on commonly used indoor datasets NYUv2 and SUNRGB-D, and the results showed that we achieved state-of-the-art performance and real-time inference.
Infrastructure monitoring, inspection, repair, and replacement in space is crucial for continued usage and safety, yet it is expensive, time-consuming, and technically very challenging. New robotics technologies and artificial intelligence algorithms are potentially novel approaches that may alleviate such demanding operations using existing or novel sensing technologies. Space structures must necessarily be very light weight due to the high costs of placing robots in space. Several methods are proposed and compared to control highly flexible space robotics, where a key challenge is the presence of flexible resonant modes at frequencies so low as to reside inside typical feedback controller bandwidths. Such conditions imply the very action of sending control signals to the ultra-light weight robotics will cause structural resonance. Implementations of incrementally increasing order are offered, achieving an over ninety percent performance improvement in trajectory tracking errors, while improvement using unshaped methods merely achieve a twenty-four percent improvement in direct comparison (where the only modification is the proposed control methodology). Based on superior performance, single-sinusoidal trajectory shaping is recommended, with a corollary benefit of preparing future research into applying deterministic artificial intelligence whose current instantiation relies on single-sinusoidal, autonomous trajectory generation.
Reproducible open science with FAIR data sharing principles requires research to be disseminated with open data and standardised metadata. Researchers in the geographic sciences may benefit from authoring and maintaining metadata from the earliest phases of the research life cycle, rather than waiting until the data dissemination phase. Fully open and reproducible research should be conducted within a version-controlled executable research compendium with registered pre-analysis plans, and may also involve research proposals, data management plans, and protocols for research with human subjects. We review metadata standards and research documentation needs through each phase of the research process to distil a list of features for software to support a metadata-rich open research life cycle. The review is based on open science and reproducibility literature and on our own work developing a template research compendium for conducting reproduction and replication studies. We then review available open source geographic metadata software against these requirements, finding each software program to offer a partial solution. We conclude with a vision for software-supported metadata-rich open research practices intended to reduce redundancies in open research work while expanding transparency and reproducibility in geographic research.
Objective To find out the causes of anterior femoral notching in 3D printed patient-specific patient-specific instrumentation (PSI) assisted total knee arthroplasty (TKA). Methods A retrospective analysis was carried out on the consecutive cases undergoing PSI assisted TKA in the Peking University International Hospital from January 2019 to September 2021. The clinical data of those having anterior femoral notching were collected. 3D CT scanning was performed on the knee joint after intraoperative osteotomy and the intraoperative bone fragments. Rapidform software (Version 2006) was used to reconstruct 3D images and to perform image registration and comparison analysis with the preoperatively planned 3D models. The anterior femoral notching depth was measured, and the differences in the thickness of bone fragments between preoperatively planned and intraoperative bone cutting in order to analyze the causes of the anterior femoral notching. Results A total of 86 consecutive cases (94 knees) were included and 17 cases (18/94, 19.1%) of them had anterior femoral notching. The causes of anterior femoral notching were summarized into 3 categories: abnormal position of the PSI (10 cases, 83.3%), intraoperative reduction of the femoral prosthesis size (2 cases, 16.7%), and overextension of the femoral prosthesis (1 case, 8.3%). Conclusion Abnormal femoral PSI position, intraoperative reduction of femoral prosthesis size, and preoperative femoral prosthesis overextension design are the main causes of anterior femoral notching after PSI assisted knee arthroplasty.
Aymen M. Madkhali, Ahmad Hassn Ghzwani, Hesham M. Al-Mekhlafi
This cross-sectional study aimed to assess the performances of a rapid diagnostic test (RDT)—the AllTest Malaria p.f./p.v., microscopy, and nested polymerase chain reaction (PCR) for diagnosing <i>Plasmodium falciparum</i> malaria in 400 febrile patients from a low-transmission region (Jazan) in southwestern Saudi Arabia. Diagnostic performance of all three methods was compared using microscopy and nested PCR as reference methods. Overall, 42 (10.5%), 48 (12.0%), and 57 (14.3%) samples were found positive by microscopy, RDT, and PCR, respectively. With PCR as reference method, the RDT showed higher sensitivity (79% vs. 71.9%), similar specificity (99.1% vs. 99.7%), and better NLR (0.20 vs. 0.27) and area under the curve (89.0% vs. 85.8%) than microscopy. The sensitivity of RDT and microscopy decreased as age increased, and false negatives were associated with low parasite density. In addition, the sensitivity of RDT and microscopy was higher in non-Saudi than in Saudi participants. Against microscopy, both RDT and PCR showed high sensitivity (83.3% vs. 97.6%), specificity (96.4% vs. 95.5%), and NPVs (98.0% vs. 99.7%), but reduced PPVs (72.9% vs. 71.9%), respectively. The results showed that the performance of the AllTest Malaria p.f./p.v RDT was better than that of microscopy in diagnosing <i>P. falciparum</i> malaria among febrile patients in the Jazan region when nested PCR was used as the reference. However, further studies are required to assess malaria diagnostic methods among asymptomatic individuals in the region.
Nurhikmah H., Hamsu Abdul Gani, Muh. Putra Pratama
et al.
Teachers still use a semi-conventional evaluation process. Namely, the exam process is carried out by utilizing social media to send students' exam results manually on paper. This kind of evaluation process is less effective and efficient, both in terms of time, the method used and examining exam results which takes a long time and is inaccurate. This study aims to develop a Computer Based Test based on Android. This research method is Research and Development, which is focused on developing a Computer Based Test based on smartphones, especially Android. The development model used refers to the Alessi and Trollip development model. The research subjects were 30 students and 1 mathematics teacher. The instrument used to collect data is a questionnaire. Data collection techniques used are observation, interviews, questionnaires or questionnaires, tests, and documentation. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The results of the study were the results of validation by material experts obtained an average score of 3.7 (very valid). By media, experts obtained an average score of 3.8 (very valid) so that the Computer Based Test could be tested in the field to determine the practicality where the practicality level met criteria with efficient results. This means that it can be stated that the Android-based Computer Based Test is efficient in the evaluation/exam process on mathematics subjects. Based on the feasibility test of the Computer Based Test, it can be concluded that the use of the Computer Based Test is feasible to be used as a medium for evaluating learning in mathematics subjects.
The potential ability of the human brain to represent an artificial limb as a body part (embodiment) has been inspiring engineers, clinicians, and scientists as a means to optimise human-machine interfaces. Using functional MRI (fMRI), we studied whether neural embodiment actually occurs in prosthesis users' occipitotemporal cortex (OTC). Compared with controls, different prostheses types were visually represented more similarly to each other, relative to hands and tools, indicating the emergence of a dissociated prosthesis categorisation. Greater daily life prosthesis usage correlated positively with greater prosthesis categorisation. Moreover, when comparing prosthesis users' representation of their own prosthesis to controls' representation of a similar looking prosthesis, prosthesis users represented their own prosthesis more dissimilarly to hands, challenging current views of visual prosthesis embodiment. Our results reveal a use-dependent neural correlate for wearable technology adoption, demonstrating adaptive use-related plasticity within the OTC. Because these neural correlates were independent of the prostheses' appearance and control, our findings offer new opportunities for prosthesis design by lifting restrictions imposed by the embodiment theory for artificial limbs.