Class Project Research Articles

Clinical Characteristics of Anti-Synthetase Syndrome: Analysis from the CLASS project.

Anti-synthetase syndrome (ASSD) is a rare systemic autoimmune rheumatic disease (SARD) with significant heterogeneity and no shared classification criteria. We aimed to identify clinical and serological features associated with ASSD that may be suitable for inclusion in the data-driven classification criteria for ASSD. We utilized a large, international, multi-center "Classification Criteria for Anti-synthetase Syndrome" (CLASS) project database, which includes both ASSD patients and controls with mimicking conditions, namely SARDs and/or interstitial lung disease (ILD). The local diagnoses of ASSD and controls were confirmed by project team members. We employed univariable logistic regression and multivariable Ridge regression to evaluate clinical and serological features associated with an ASSD diagnosis in a randomly selected subset of the cohort. Our analysis included 948 ASSD cases and 1077 controls. Joint, muscle, lung, skin, and cardiac involvement were more prevalent in ASSD than in controls. Specific variables associated with ASSD included arthritis, diffuse myalgia, muscle weakness, muscle enzyme elevation, ILD, mechanic's hands, secondary pulmonary hypertension due to ILD, Raynaud phenomenon, and unexplained fever. In terms of serological variables, Jo-1 and non-Jo-1 anti-synthetase autoantibodies, antinuclear antibodies with cytoplasmic pattern, and anti-Ro52 autoantibodies were associated with ASSD. In contrast, isolated arthralgia, dysphagia, electromyography/MRI/muscle biopsy findings suggestive of myopathy, inflammatory rashes, myocarditis, and pulmonary arterial hypertension did not differentiate between ASSD and controls or were inversely associated with ASSD. We identified key clinical and serological variables associated with ASSD, which will help clinicians and offer insights into the development of data-driven classification criteria for ASSD.

Low-dimensional projection of reactivity classes in chemical reaction dynamics using supervised dimensionality reduction.

Transition state theory (TST) provides a framework to estimate the rate of chemical reactions. Despite its great success with many reaction systems, the underlying assumptions such as local equilibrium and nonrecrossing do not necessarily hold in all cases. Although dynamical systems theory can provide the mathematical foundation of reaction tubes existing in phase space that enables us to predict the fate of reactions free from the assumptions of TST, numerical demonstrations for large systems have been yet one of the challenges. Here, we propose a dimensionality reduction algorithm to demonstrate structures in phase space (called reactive islands) that predict reactivity in systems with many degrees of freedom. The core of this method is the application of supervised principal component analysis, where a coordinate transformation is performed to preserve the dynamical information on reactivity (i.e., to which potential basin the system moves from a region of interest) as much as possible. The reactive island structures are expected to be reflected in the transformed, low-dimensional phase space. As an illustrative example, the algorithm is scrutinized using a modified Hénon-Heiles Hamiltonian system extended to many degrees of freedom, which has three channels leading to three different products from one stable potential basin. It is shown that our algorithm can predict the reactivity in the transformed, low-dimensional coordinate system better than a naïve coordinate system and that the reactivity distribution in the transformed low-dimensional space is considered to reflect the underlying reactive islands.

Students create prize‐winning app to keep everyone safer on campus

Four students from the University of Nevada, Reno, for their senior project in computer science class, created an app designed specifically to help women feel safer on campus. Kathleen Scafidi, Anna Desorcy, Michael Munakash, and Kurtis Bertauche were all computer science majors when they created the app Ur Next Route.

Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices.

Long-range axonal projections of diverse classes of neocortical excitatory neurons likely contribute to brain-wide interactions processing sensory, cognitive and motor signals. Here, we performed light-sheet imaging of fluorescently labeled axons from genetically defined neurons located in posterior primary somatosensory barrel cortex and supplemental somatosensory cortex. We used convolutional networks to segment axon-containing voxels and quantified their distribution within the Allen Mouse Brain Atlas Common Coordinate Framework. Axonal density was analyzed for different classes of glutamatergic neurons using transgenic mouse lines selectively expressing Cre recombinase in layer 2/3 intratelencephalic projection neurons (Rasgrf2-dCre), layer 4 intratelencephalic projection neurons (Scnn1a-Cre), layer 5 intratelencephalic projection neurons (Tlx3-Cre), layer 5 pyramidal tract projection neurons (Sim1-Cre), layer 5 projection neurons (Rbp4-Cre), and layer 6 corticothalamic neurons (Ntsr1-Cre). We found distinct axonal projections from the different neuronal classes to many downstream brain areas, which were largely similar for primary and supplementary somatosensory cortices. Functional connectivity maps obtained from optogenetic activation of sensory cortex and wide-field imaging revealed topographically organized evoked activity in frontal cortex with neurons located more laterally in somatosensory cortex signaling to more anteriorly located regions in motor cortex, consistent with the anatomical projections. The current methodology therefore appears to quantify brain-wide axonal innervation patterns supporting brain-wide signaling.

Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices

Long-range axonal projections of diverse classes of neocortical excitatory neurons likely contribute to brain-wide interactions processing sensory, cognitive and motor signals. Here, we performed light-sheet imaging of fluorescently labeled axons from genetically defined neurons located in posterior primary somatosensory barrel cortex and supplemental somatosensory cortex. We used convolutional networks to segment axon-containing voxels and quantified their distribution within the Allen Mouse Brain Atlas Common Coordinate Framework. Axonal density was analyzed for different classes of glutamatergic neurons using transgenic mouse lines selectively expressing Cre recombinase in layer 2/3 intratelencephalic projection neurons (Rasgrf2-dCre), layer 4 intratelencephalic projection neurons (Scnn1a-Cre), layer 5 intratelencephalic projection neurons (Tlx3-Cre), layer 5 pyramidal tract projection neurons (Sim1-Cre), layer 5 projection neurons (Rbp4-Cre), and layer 6 corticothalamic neurons (Ntsr1-Cre). We found distinct axonal projections from the different neuronal classes to many downstream brain areas, which were largely similar for primary and supplementary somatosensory cortices. Functional connectivity maps obtained from optogenetic activation of sensory cortex and wide-field imaging revealed topographically organized evoked activity in frontal cortex with neurons located more laterally in somatosensory cortex signaling to more anteriorly located regions in motor cortex, consistent with the anatomical projections. The current methodology therefore appears to quantify brain-wide axonal innervation patterns supporting brain-wide signaling.

Data-driven portfolio management for motion pictures industry: A new data-driven optimization methodology using a large language model as the expert

Portfolio management is one of the unresponded problems of the Motion Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor, it is essential to predict the box office of each project. Moreover, for an accurate box office prediction, it is critical to consider the effect of the celebrities involved in each MPI project, which was impossible with any precedent expert-based method. Additionally, the asymmetric characteristic of MPI data decreases the performance of any predictive algorithm. In this paper, firstly, the fame score of the celebrities is determined using a large language model. Then, to tackle the asymmetric character of MPI’s data, projects are classified. Furthermore, the box office prediction takes place for each class of projects. Finally, using a hybrid multi-attribute decision-making technique, the preferability of each project for the distributor is calculated, and benefiting from a bi-objective optimization model, the optimal portfolio is designed. To validate our approach, we conducted experiments using a dataset of movies released in the United States from 1980 to 2020 and employed the proposed approach to predict box office performance. Our results demonstrate that the proposed methodology significantly improves prediction accuracy and provides a robust framework for effective portfolio management.

Students create prize‐winning app to keep everyone safer on campus

Four students from the University of Nevada, Reno, senior project in computer science class created an app designed specifically to help women feel safer on campus. Kathleen Scafidi, Anna Desorcy, Michael Munakash, and Kurtis Bertauche were all computer science majors when they created the app Ur Next Route.

Augmented Reality untuk Meningkatkan Keterampilan Menulis, Berbicara, Keterampilan Berpikir Kritis dalam Pembelajaran Bahasa Indonesia Kelas V SD Xyz Bintaro

Students need to learn thoroughly on the writing, speaking and critical thinking skills in order to adapt with the development of technology and information. This study aims to analyze the differences between 2 classes that implemented 2 different means in learning Bahasa Indonesia for grade 5 students, between control class that use lecturing method and the experiment class that use Augmented Reality. The research subject were the fifth graders of XYZ school in Bintaro area. There were 28 students from control group and 27 students from the experiment group with a total of 55 students in all. This research period was conducted in 4 months from late January in 23 January 2024 to 10 May 2024. This study used a weak experimental design method with a static-group pretest-posttest design model. The research data was collected from the observation of the students performance during class discussion and project presentation which used a rubric that has been validated and is a reliable instrument. The result of the statistical analysis showed differences in the control class pretest and control class posttest, in the experimental class pretest and experimental class posttest, also the nGain result for both classes in writing, speaking and critical thinking skills. The average nGain for control class in writing, speaking and critical thinking skills respectively are; 0,17; 0,21; 0,26 While the average nGain for the experiment class in writing, speaking and critical thinking skills is 0,41; 0,31; 0,27 respectively. Based on the result, the ngain result for critical thinking skills in experiment class showed low result using 0,3 as the significance value. This result means the use of Augmented Reality was less effective in critical thinking skills but effective in writing and speaking skills.

The Influence of Implementing the Project Based Learning Model on Students’ Geography Learning Motivation at SMA N 1 Tanjung Mutiara Agam Regency

This research is motivated by the problem of low motivation to study geography, no concentration and low activity during learning, there is no student feedback when learning takes place. The purpose of this study was to find out and analyze data that had the effect of applying the Project Based Learning learning model on increasing students' motivation to learn Geography at SMA N 1 Tanjung Mutiara, Agam Regency. The type of research used was a quasi-experimental research, the research design used was a non-equivalent Control Group Design. The population of all class X students of SMA N 1 Tanjung Mutiara, Agam Regency who were registered in the 2022/2023 school year consisted of 6 classes and the sampling in this study was conducted by random sampling, the instruments in this study were warm and the data analysis techniques were descriptive and inferential statistical analysis. Research results: Is there an effect of applying the Project Based Learning learning model to increasing students' motivation to learn Geography at SMA N 1 Tanjung Mutiara, Agam Regency, research. Based on the descriptive analysis of the experimental class (Project based learning) which obtained a percentage value of 84% in the High category while students in the control class (Conventional) obtained a percentage value of 78% with High. Based on the independent sample t-test regarding student questionnaire answers, it can be concluded that the sig value is 0.002 < 0.05 which states that the coefficient value is significant because tcount > ttable = 3.298 > ttable 1.670, thus it can be interpreted that H0 is rejected and Ha is accepted. so, there is an influence of the project based learning learning model on student learning motivation in the subject of Geography at SMA N 1 Tanjung Mutiara, Agam Regency.

A computational materials science paradigm for a Course-based Undergraduate Research Experience (CURE)

Abstract Course-based Undergraduate Research Experiences (CUREs) bring the excitement of research into the classroom to improve learning and the sense of belonging in the field. They can reach more students, earlier in their studies, than typical undergraduate research. Key aspects are: students learn and use research methods, give input into the project, generate new research data, and analyze it to draw conclusions that are not known beforehand. CUREs are common in other fields but have been rare in materials science and engineering. I propose a paradigm for computational material science CUREs, enabled by web-based simulation tools from nanoHUB.org that require minimal computational skills. After preparatory exercises, students each calculate part of a set of closely related materials, following a defined protocol to contribute to a novel class dataset which they analyze, and also calculate an additional property of their choice. This approach has been used successfully in several class projects. Graphical abstract

From local bacterial communities to energy: application of microbial fuel cells in laboratory experimentation.

It is well known that bacterial communities are an essential component to maintain the balance of terrestrial ecosystems due to the functions and services performed by microorganisms in the environment. The research seeking on alternative energy sources has shown that bacterial communities can bioconvert the chemical energy of an organic substrate into electrical energy, within devices known as microbial fuel cells. For this reason, this class project allows students of Biotechnology, Environmental Science, and Microbiology to apply the appropriate methodology to develop a class project throughout an environmental bacterial community capable of generating electrical energy.

Linear sections of determinantal varieties

Denote Mmn(k) to be the set of all m n matrices over k, and PMmn(k) to be its affine space. Let Dm,n,r PMmn(k) be the subvariety consisting of the projective classes of all m n matrices with rank less than or equal to r. We study the minimal integer k > 0 such that for any linear subspace L of dimension k in PMmn(k), Dm,n,r L = . We get some partial results about this problem. Specially, when m = n = r + 1, we solve this problem for k = R, C and Q.

Teacher Capability in Developing Independent Curriculum Learning Tools

The Merdeka Curriculum is the latest innovation that takes time for teachers to adjust their teaching methods. This study aims to analyze teachers' ability to develop learning tools in accordance with the Merdeka Curriculum at SD Negeri 32 Cakranegara. The focus of this research is on grade 4A and 4B teachers, using observation, documentation, and interview methods to collect data regarding teachers' competencies and their learning designs. The results showed that SDN 32 Cakranegara has implemented various concepts from Merdeka Belajar. Teachers demonstrate skills in designing learning tools such as teaching modules, Learner Worksheets (LKPD), media, teaching materials, and evaluations, in accordance with the required stages and components. However, in its implementation, there are challenges such as arranging the Flow of Learning Objectives (ATP) to be more organized and systematic. In addition, teachers also have difficulties in preparing the Learning Implementation Plan (RPP) and choosing appropriate assessments to evaluate learning. The limitations of student books, teachers' lack of ability and readiness to use varied learning methods and media, as well as limited technology skills, are also obstacles. The wide range of teaching materials and the difficulty in determining appropriate class projects in grade IV are also challenges. To overcome this, it is recommended that teachers seek additional references on learning assessment and attend training regularly to improve their competence.

Identification of Sources of Complexity in Various Types of Building Projects in India Based on Design and Performance Attributes

Building projects as sub-sect of construction projects are complex and different in design and performance attributes. The prominent classes of building projects are high-rise residential buildings, hospitals, hotels, general offices, retail buildings/malls, educational institutions, and warehouses. In this study, building projects have been classified based on their end use. Complexities vary between construction projects and within various types of building projects, as well as across regions and countries. Understanding the sources of complexity is imperative to strategize their successful development. This research paper attempts to identify the sources of complexities using the design and performance attributes of seven prominent building types. A survey was conducted through experienced architects and construction professionals (the mean years of experience being approximately 13.88 years) over 34 different attributes or parameters under six categories. These attributes have been shortlisted from past research work by various authors, and the pilot study for this project. The Kruskal-Wallis Test was used to calculate the mean ranking and to confirm that the medians of each building type were not the same when compared against each attribute, irrespective of the level of complexity. Using data analysis and the mean ranking scores as a representation of relative complexity, primary sources of complexity in each class of buildings have been identified and listed. The results indicate that hospitals (as a building typology) are relatively the most complex building facilities in terms of design and performance attributes, showing the highest mean ranking towards 16 attributes (with only one shared ranking). Hospitals are followed by retail/malls (10 with one shared ranking), hotels (3), high-rise residential buildings (4), educational institutions (2), general offices (0), and warehouses (0). Strategies to manage these attributes have been proposed for each building typology so that the risks emanating from the complexities of these building projects can be mitigated and effectively managed at the design stage.

Shaping Future Leaders: Exploring Challenges and Strategies in Character Education at Madrasah Tsanawiyah

This research aims to gain a deep understanding of character education management and the supporting and inhibiting factors in the implementation of Character Education Management at Madrasah Tsanawiyah Pesantren Mizanul Ulum. This research employs a qualitative approach with a case study design. Data collection is conducted through in-depth interviews with various stakeholders, including the school principal, teachers, and parents. Participatory observation is also carried out to understand the daily dynamics in the madrasah. Thematic analysis is applied to identify supporting and inhibiting factors in character education implementation. The results reveal that full involvement of the pesantren community such as regular meetings with parents and open classes for parents, integrated curriculum such as material development and collaborative projects in class, and active character guidance are the main supporting factors. Conversely, challenges from external environments, resource limitations, and technological impacts emerge as inhibiting factors. Wise and creative management steps in overcoming resource limitations, coupled with a prudent technological approach, have proven effective in maintaining focus on shaping students' characters.

Artificial Intelligence (AI) is Not A Writing Gods, So Why Do Post-Graduate Students Believe It?

This study was aimed at understanding graduate students' preferences in applying AI when dealing with their class papers and projects in Indonesia. It also sought to understand how students who have used AI in previous writing feel about such experience. This study adopted a phenomenological research method in order to elicit in-depth insights into the concerns of the students. The participants included graduate students from four universities in Indonesia. These data were gathered using semi-structured interviews of 30 students who had experience using AI for their academic writing. Guided by understanding the decision-making process, perceived benefits, and drawbacks of AI, and overall experiences, interview questions were prepared. One-way thematic analysis was conducted with the interview data. Students seemed to view AI applications as only helping with formatting and editing tasks, as most of them would like to have the opportunity to do the major work by themselves for better learning. Another underlying strong theme emerging here is related to AI overdependency and unequal access to it. The results offer insights into the respective areas that can be used by educators and institutions to provide a balance between the rising AI in use and support for independent learning within academics.

Exploring student perspectives on generative artificial intelligence in higher education learning

This study examined the perspectives of Ghanaian higher education students on the use of ChatGPT. The Students’ ChatGPT Experiences Scale (SCES) was developed and validated to evaluate students’ perspectives of ChatGPT as a learning tool. A total of 277 students from universities and colleges participated in the study. Through exploratory factor analysis, a three-factor structure of students' perspectives (ChatGPT academic benefits, ChatGPT academic concerns, and accessibility and attitude towards ChatGPT) was identified. A confirmatory factor analysis was carried out to confirm the identified factors. The majority of students are aware of and recognize the potential of Gen AI tools like ChatGPT in supporting their learning. However, a significant number of students reported using ChatGPT mainly for non-academic purposes, citing concerns such as academic policy violations, excessive reliance on technology, lack of originality in assignments, and potential security risks. Students mainly use ChatGPT for assignments rather than for class or group projects. Students noted that they have not received any training on how to use ChatGPT safely and effectively. The implications for policy and practice are discussed in terms of how well-informed policy guidelines and strategies on the use of Gen AI tools like ChatGPT can support teaching and improve student learning.

Assessment and Analysis of Reverberation Time in Hyder-Burks Arena at Tennessee Technological University

This study addresses the issue of long reverberation times in large-scale arenas, specifically the HyderBurks Arena (HBA) at Tennessee Technological University. HBA primarily hosts cattle shows and agricultural events. The current multi-speaker sound system in the facility ensures audibility, but the long reverberation time accumulates noise that impairs communication and causes dissatisfaction. The objectives of this work conducted as a student project in an undergraduate noise control class include measuring the reverberation time, analyzing the data, researching noise reduction methods, and proposing feasible plans that consider cost, achievability, and ease of implementation. The current reverberation time measured in the arena is approximately 2.313 seconds, posing challenges for speechapplications. The aim was to reduce this to a range of 1.32 to 1.02 seconds. The study employed a Larson Davis Sound Advisor 831C sound level meter to measure the reverberation time at various points in the arena. Despite low-frequency analysis challenges, critical frequencies affecting speech intelligibility were identified. The study also highlights potential issues with the arena’s sound system and the need for a synchronized, centralized speaker system. The assessment revealed that areas near the concrete walls without sound absorption have the maximum T60, indicating the need for treatment. Recommendations include ceiling treatment to reduce the reverberation time and enhance speech intelligibility. The proposed plan, considering cost-effectiveness and financial feasibility, aims to improve the atmosphere and experience for HBA users.

A few projective classes of (non-Hausdorff) topological spaces

A class of topological spaces is projective (resp., ω-projective) if and only if projective systems of spaces (resp., with a countable cofinal subset of indices) in the class are still in the class. A certain number of classes of Hausdorff spaces are known to be, or not to be, (ω-) projective. We examine classes of spaces that are not necessarily Hausdorff. Sober and compact sober spaces form projective classes, but most classes of locally compact spaces are not even ω-projective. Guided by the fact that the stably compact spaces are exactly the locally compact, strongly sober spaces, and that the strongly sober spaces are exactly the sober, coherent, compact, weakly Hausdorff (in the sense of Keimel and Lawson) spaces, we examine which classes defined by combinations of those properties are projective. Notably, we find that coherent sober spaces, compact coherent sober spaces, as well as (locally) strongly sober spaces, form projective classes.

Unconditional optimal [formula omitted]-norm error estimate and superconvergence analysis of a linearized nonconforming finite element variable-time-step BDF2 method for the nonlinear complex Ginzburg–Landau equation

In this paper, a linearized fully discrete scheme combining the variable-time-step two-step backward differentiation formula (VSBDF2) in time and the nonconforming finite element methods (FEMs) in space is constructed and analyzed for the nonlinear complex Ginzburg–Landau equation. A novel convergence analysis approach is proposed, which shows that the H1-norm error of this scheme can reach optimal order in space and sharp second-order convergence in time. The key ingredients of our analysis are temporal–spatial error splitting approach, a new positive definiteness property of discrete orthogonal convolution (DOC) kernels with respect to complex sequences, an energy projection operator and a discrete Laplace operator Δh related to the nonconforming element space, and a relationship between Δh and classical L2 projection operator in complex space. Furthermore, through a specific treatment of the error equation and the combination technique of interpolating operator and projection operator, we prove that the proposed scheme has the global superconvergence result of order O(τ2+h2) in H1-norm for the first time, which further improves the efficiency of numerical computation. Here τ and h are the maximum time and spatial step sizes, respectively. It should be pointed out that all these error results are rigorously established under mild assumptions of the time step, and get rid of the usual grid ratio constraints between temporal and spatial step sizes in linearized scheme. At last, numerical experiments are provided to verify the theoretical analysis.