Reviewerly/exHarmony huggingface dataset:
Fuzzy Conjoint Need Assessment Method for Model Development Justification

Authors:
raw_author_name: Ahmad Termimi Ab Ghani
raw_affiliation_string: Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia
raw_author_name: Mohd Lazim Abdullah
raw_affiliation_string: Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia

Abstract: This paper proposes a Fuzzy Conjoint Need Assessment Method (FCNAM) procedure for model development justification. The failure to select the analysis method and neglect the data when completing the needs assessment, before developing a model, leads to incorrect implementation of the justification. There is a misunderstanding in the requirements analysis, and certain elements that should be considered in the model's design and development may be overlooked. Therefore, as an alternative method in the model development justification procedure, the fuzzy conjoint analysis method together with the triangular fuzzy number is used as a data analysis medium in the need assessment survey. As an implementation and evaluation of the FCNAM procedure, a need assessment survey involving 53 mathematics teachers was conducted to justify the need for the development of a student's mathematics problem-solving ability (SMPSA) measurement model. This procedure is proven to comply with the guideline need assessment, which is to successfully confirm gaps related to mathematics problem-solving ability, describe the perspective of teachers as users and practitioners of the proposed model and synthesize the level of development needs of the model involved. This paper contributes knowledge about the application of the triangular fuzzy number-based conjoint method in perception surveys and introduces a more effective model development justification procedure.
Carbon-derived Black Soldier Fly (BSF) Larvae for Adsorption of Methylene Blue Dye: Characterization, Kinetic and Thermodynamic Studies

Authors:
raw_author_name: Mukhamad Nurhadi
raw_affiliation_string: Department of Chemical Education, Universitas Mulawarman, Kampus Gunung Kelua, Samarinda, 75119, East Kalimantan, Indonesia
raw_author_name: Muhammad Akbar
raw_affiliation_string: Department of Chemical Education, Universitas Mulawarman, Kampus Gunung Kelua, Samarinda, 75119, East Kalimantan, Indonesia

Abstract: The most effective substance for effectively adsorbing organic dyes is activated carbon (AC). We looked at the possibility of using Black Soldier Fly (BSF) larvae (Hermetia illucens Genome, MG) as a source for AC in this study. The two unique materials known as MG(300) and MG(500) were produced as a result of the larvae being heated for two hours at 300 and 500°C, respectively. These MG materials were further activated (MGO) using hydrogen peroxide (H2O2), and subsequently utilized for the adsorption of methylene blue (MB) dye. The objectives of this research include: (1) Investigation of the cabonization temperature and activation effects of the MG and MGO towards adsorption capacity; (2) Study of the physico-chemical properties of the adsorbents; (3) Exploration of the adsorptivity of MG and MGO in different parameters, including initial dye concentration, contact time, and temperature; (4) Examination of the kinetic and thermodynamic findings for the MG and MGO towards adsorption capacity. To analyze the obtained carbon, various analytical methods, including FTIR, WDXRF, SEM, and BET, were used. The equilibrium and kinetics of MB dye's adsorption behaviour on the AC were carefully investigated. It's interesting to note that within the first 10 minutes of contact time, all adsorbents demonstrated quick MB adsorption. The dye removal percentage (%) and adsorption capacities (mg×g-1) were measured at 65.7% and 6.33 mg×g⁻¹ for MG(300), 76.5% and 11.07 mg×g⁻¹ for MG(500), 91.1% and 16.41 mg×g⁻¹ for MGO(300), and 96.1% and 17.13 mg×g⁻¹ for MGO(500). Furthermore, the kinetics of dye adsorption using the pseudo-first-order (PFO) and pseudo-second-order (PSO) models were conducted, whereby PSO is the best fit with R2 in the range of 0.949 to 0.999.
Characterizing non-Markovian off-resonant errors in quantum gates

Authors:
raw_author_name: Ken Xuan Wei
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: Emily Pritchett
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: David M. Zajac
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: David C. McKay
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
raw_author_name: Seth Merkel
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA

Abstract: As quantum gates improve, it becomes increasingly difficult to characterize the remaining errors. Here we describe a class of coherent non-Markovian errors---excitations due to an off-resonant drive---that occur naturally in quantum devices that use time-dependent fields to generate gate operations. We show how these errors are mischaracterized using standard quantum computer verification and validation techniques that rely on Markovianity and are therefore often overlooked or assumed to be incoherent. We first demonstrate off-resonant errors within a simple toy model of $Z$ gates created by the ac Stark effect, then show how off-resonant errors manifest in all gates driven on a fixed-frequency transmon architecture, a prominent example being incidental cross-resonance interaction driven during single-qubit gates. Furthermore, the same methodology can access the errors caused by two-level systems, showing evidence of coherent, off-resonant interactions with subsystems that are not intentional qubits. While we explore these results and their impact on gate error for fixed-frequency devices, we note that off-resonant excitations potentially limit any architectures that use frequency selectivity.
Exploration of Unggan weaving in Minang culture: An ethnomathematics study

Authors:
raw_author_name: Kusno Kusno
raw_affiliation_string: Pendidikan Matematika, Universitas Muhammadiyah Purwokerto
raw_author_name: Sri Supiyati
raw_affiliation_string: Universitas Hamzanwadi, West Nusa Tenggara

Abstract: Unggan weaving, an original cultural product of the Sijunjung people, especially in Unggan Village, is widely used as employee, traditional, and school uniforms once a week. However, the use of Unggan weaving in mathematics learning has yet to be widely explored. This research aims to reveal the mathematical elements of Unggan weaving as a learning vehicle more appropriate to students' culture. This research is qualitative research with an ethnographic approach. Data collection methods use in-depth interviews, documentation, and ethnographic notes. The interview subjects were Ernita, owner of Unggan Weaving (R-1), and Masniati, an employee of Unggan Weaving (R-2). Data analysis uses the results of interpretation and translation of the phenomena found, as well as based on the results of the informant's conception of the meaning and the informant's original language regarding the focus, which is combined verbally with the researcher's language after in-depth understanding through focus group discussions (FGD). The research results show that Unggan woven fabric contains mathematical elements of geometric transformation (translation, reflection, dilation, symmetry) and arithmetic number patterns. The results of this research can be recommended as material for developing school mathematics teaching materials based on local wisdom.
Computational thinking on concept pattern number: A study learning style Kolb

Authors:
raw_author_name: Ratni Purwasih
raw_affiliation_string: Universitas Pendidikan Indonesia, West Java
raw_author_name: Turmudi Turmudi
raw_affiliation_string: Universitas Pendidikan Indonesia, West Java
raw_author_name: Jarnawi Afgani Dahlan
raw_affiliation_string: Universitas Pendidikan Indonesia, West Java
raw_author_name: Naufal Ishartono
raw_affiliation_string: University Malaya, Kuala Lumpur

Abstract: This research aimed to determine how the number pattern concept's computational thinking characteristic picture was reviewed from the Kolb model's learning style. The research method used in this study is qualitative descriptive. The research was conducted at one of the state's small schools in Bandung. The research subjects consisted of 29 students in the ninth grade. One of the 29 study issues is selected with assimilator learning styles. The data-gathering techniques used are questionnaire tests, test instruments, and interviews. Angket is used to group subjects into four groups of learning style types. The test instrument was used to describe the computational thinking characteristics of high school students on the concept of number patterns, and the interview was used to strengthen the test summary results of the subject. The results of this study show that the characteristics of computational thinking that each type of learning style dominates are different. Computational thinking students with an assimilator learning style in solving mathematical problems of number patterns can solve issues by involving decomposition, pattern identification, abstraction and generalization, and algorithms. They can generalize patterns using accurate, thorough, complete, and systematic problem-solving strategies.
Deep Attributes: Innovative LSTM-based seismic attributes

Authors:
raw_author_name: G Roncoroni
raw_affiliation_string: Università di Trieste, Dipartimento di Matematica e Geoscienze

Abstract: Summary Seismic attributes are derived measures from seismic data that help characterize subsurface geological features and enhance the interpretation of subsurface structures: we propose to exploit the hidden layers of Long-Short Time Memory neural network predictions as possible new reflection seismic attributes. The idea is based on the inference process of a neural network, which in its hidden layers stores information related to different features embedded in the input data and which usually are not considered. Neural network applications typically ignore such intermediate steps because the main interest lies in the final output, which is considered as the exclusive exploitable feature of the process. On the contrary, here we analyse the possibility to exploit the intermediate prediction steps, hereafter referred as “Deep Attributes” because they are produced by a deep learning algorithm, to highlight features and emphasize characteristics embedded in the data but neither recognizable by traditional interpretation, nor evident with classical attributes or multi-attribute approaches. Nowadays, classical signal attributes are numerous and used for different purposes; we here propose an original strategy to calculate attributes previously never exploited, which are potentially complementary or a good alternative to the classical ones. We tested the proposed procedure on synthetic and field 2-D and 3-D reflection seismic data sets to test and demonstrate the stability, affordability and versatility of the entire approach. Furthermore, we evaluated the performance of deep attributes on a 4-D seismic dataset to assess the applicability and effectiveness for time-monitoring purposes and comparing them with the sweetness attribute.
Improved free energy calculations using multi topology ensemble of expanded ensemble

Authors:
raw_author_name: Wei Tse Hsu
raw_affiliation_string: Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA

Abstract: Protein binding interactions are difficult to adequately sample using computational methods due primarily to the relatively high degree of conformational flexibility of proteins. Our group has developed a novel method termed multi-topology ensemble of expanded ensemble (MTEEXE) which allows for expanded ensemble calculations to be run in parallel for iterative relative free energy calculations in GROMACS and to allow conformational swapping between the parallel transformations. These iterative transformations can be applied to any systems with a common back bone or central substructure. We have applied this method to calculate the relative binding free energy of acyl-ACP to the ketoacyl ACP synthase FabF for acyl chain lengths varying from 4 to 16 carbons. This system features both a flexible protein-protein binding interface and a highly flexible acyl chain which is covalently attached to ACP. The degree of conformational flexibility in our system poses a significant challenge to traditional computational free energy methods. We have also applied this method to multi-site perturbations of a small molecule core similar to multisite lambda dynamics in the CHARMM infrastructure, but without the need for modification of the MD code. We outline a method of setting up topologies in order to ensure separability of the partition function for each iterative transformation and provide instructions on how to perform inter-replicate coordinate modifications in order to generalize our proposed method. We demonstrate that MTEEXE not only increases efficiency of running iterative transformations, but also increases conformation sampling, especially when transforming bulky groups like the acyl chain, to produce more accurate and reproducible free energy estimates in GROMACS.
Marangoni spreading on liquid substrates in new media art

Authors:
raw_author_name: Eliot Fried
raw_affiliation_string: Mechanics and Materials Unit, Okinawa Institute of Science and Technology Graduate University , Onna, Okinawa, 904-0495 , Japan

Abstract: Abstract With the advent of new media art, artists have harnessed fluid dynamics to create captivating visual narratives. A striking technique known as dendritic painting employs mixtures of ink and isopropanol atop paint, yielding intricate tree-like patterns. To unravel the intricacies of that technique, we examine the spread of ink/alcohol droplets over liquid substrates with diverse rheological properties. On Newtonian substrates, the droplet size evolution exhibits two power laws, suggesting an underlying interplay between viscous and Marangoni forces. The leading edge of the droplet spreads as a precursor film with an exponent of 3/8, while its main body spreads with an exponent of 1/4. For a weakly shear-thinning acrylic resin substrate, the same power laws persist, but dendritic structures emerge, and the texture of the precursor film roughens. The observed roughness and growth exponents (3/4 and 3/5) suggest a connection to the quenched Kardar–Parisi–Zhang universality class, hinting at the existence of quenched disorder in the liquid substrate. Mixing the resin with acrylic paint renders it more viscous and shear-thinning, refining the dendrite edges and further roughening the precursor film. At larger paint concentrations, the substrate becomes a power-law fluid. The roughness and growth exponents then approach 1/2 and 3/4, respectively, deviating from known universality classes. The ensuing structures have a fractal dimension of 1.68, characteristic of diffusion-limited aggregation. These findings underscore how the non-linear rheological properties of the liquid substrate, coupled with the Laplacian nature of Marangoni spreading, can overshadow the local kinetic roughening of the droplet interface.
Trained image analysis techniques for characterizing cell phenotype in electron microscopy images of mouse thrombi

Authors:
raw_author_name: Omar Faruque
raw_affiliation_string: University of Arkansas for Medical Sciences, Little Rock, AR, USA

Abstract: The thrombus formation following a puncture wound is integral to vascular healing. However, the mechanism(s) underlying blood platelet recruitment and their activation is still poorly understood. Platelet differentiation is fundamental to sealing leaks at sites of vascular injury during wound healing. Investigating platelet activation and distribution within thrombi can elucidate the mechanism behind clot formation. Furthermore, the application of deep learning to characterization of morphology-based cell phenotypes of tens of thousands of cells within a single thrombus, can be generalizable to other tissue types. Montages of 500–1,000 high-resolution TEM images of mouse femoral artery and jugular vein thrombi cross sections were our study models for mapping platelet activation states. After individual platelet auto-segmentation using Amira software, each platelet was assigned to one of five activation states: discoid (least activated), loosely adherent, tightly adherent, degranulated, and procoagulant (most activated). A trained machine-learning model was used to produce a heat-map localizing different platelets types. Remarkably, the thrombi formed in the femoral artery and jugular vein samples showed considerable consistency in organization across samples, which indicated that staged platelet activation resulted in a discoid platelet tethering zone that was pushed progressively outward as platelets converted from one activation state to another. Our analysis supports a model that we term “capture and activate,” in which the initial high platelet activation is directly linked to the exposed adventitia. All subsequent tethering of discoid platelets is to loosely adherent platelets that convert to tightly adherent platelets, and self-limiting, intravascular platelet activation over time is the result of decreased signaling intensity. In the future, we hope that this analysis technique can be expanded to other image processing applications for cellular characterization, as well as 3D imaging modalities.
High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Authors:
raw_author_name: Qianli Ma
raw_affiliation_string: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Chong Luo
raw_affiliation_string: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
raw_author_name: Xiangtian Meng
raw_affiliation_string: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
raw_author_name: Weimin Ruan
raw_affiliation_string: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
raw_author_name: Huanjun Liu
raw_affiliation_string: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Abstract: Soil is the foundation of sustainable agricultural development. Soil organic matter (SOM) is a key indicator for characterizing soil degradation, and remote sensing has been applied in SOM prediction. However, the differences in SOM prediction from different remote sensing data and the ability to combine multi-source and multi-phase remote sensing data for SOM prediction urgently need to be explored. The following research employed Landsat-8, Sentinel-2, and Gaofen-6 satellite data, utilizing a random forest algorithm to establish a SOM prediction model. It aimed to explore the variations in SOM prediction capabilities among these satellites in typical black soil regions. Additionally, the study involved creating multi-phase synthetic images for SOM prediction using Landsat-8 and Sentinel-2 images captured during three years of bare soil periods. Finally, the research examined the ability to combine three satellites to construct high spatiotemporal remote sensing images for SOM prediction. The results showed that (1) using Landsat-8 and Sentinel-2 to extract the principal components of the three-year bare soil period to construct the multi-phase synthetic image for SOM prediction, higher prediction accuracies can be obtained compared with the single-phase images. (2) The highest accuracy can be obtained using multi-phase synthetic images and high spatial resolution images to construct high spatiotemporal remote sensing images and perform SOM prediction (R² is 0.65, RMSE is 0.67%, MAE is 0.42%). (3) Simultaneously, high spatiotemporal remote sensing images can reach 2 m spatial resolution to reveal the spatial heterogeneity of SOM. The causes of SOM spatial anomalies can be determined after analysis combined with soil degradation information. In subsequent research, SOM prediction should focus more on multi-sensor collaborative prediction.
Does the Manufacturing Industry Also Need Gamification? – A Gamification Design Practice for CNC Machine Tool Skill Training

Authors:
raw_author_name: Tian-shu Li
raw_affiliation_string: Beijing Information Science & Technology University, China

Abstract: In CNC (Computer Numerical Control) vocational colleges, the current traditional pedagogy has become conflicting with the learning preferences of the “Generation Z”, resulting in their low sense of competence, low learning autonomy, and insufficient recognition of industry identification. Thus, gamification design provides novel ideas for addressing these issues. Based on the theories of self-determination theory and motivational affordances, this study analyzes the sore points and study needs of students who are majoring in CNC technology at vocational colleges. Hence, according to the 12 dimensions of motivational affordances, the “needs-affordances-features” framework and the course content of CNC technology, a set of gamification design strategies for CNC machine tool skill training is proposed. Consequently, a smartphone-based interactive design practice is completed. Following this design practice, a user experience test is executed, and a positive result is obtained. Theoretically, this study explores the feasibility of modified gamification design processes that combine the motivational affordance model and the “needs-affordances-features” framework. Practically, the integration of gamification design into CNC machine tool skill training is not only a bold attempt to boost the learning motivation of students, but also a practical research foundation for the innovative development and transformation of CNC technology vocational education.