Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Q-Factor for AM-SLM Cavity Based Resonators Using Surface Roughness Models

This research delves into losses of X-band cavity resonators manufactured using additive manufacturing-selective laser melting (AM-SLM) compared to the standard subtractive manufacturing milling technology. Measured losses are benchmarked in terms of resonator (quality) <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor. The measured data is further modelled using the Groiss and one-ball Huray models taking into account the implications of surface roughness and electrical conductivity. The unloaded <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor is derived from frequency-dependent scattering (<inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula>) parameters obtained from measurements and full-wave simulations. Surface roughness was found to impact the <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor significantly and the resonant frequency marginally. Cavities based on AM-SLM technology exhibit higher roughness compared to milling and lowers the <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor. A fusion of both manufacturing methods by milling AM-SLM cavity walls demonstrates an augmented <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor compared to a directly printed cavity. In the study it was also found that the Groiss model tends to overestimate the <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor owing to AM-SLM's rougher surface, while the one-ball Huray model furnishes precise projections by establishing a link between surface roughness and powder particles. Electrical conductivity's influence on <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor was also investigated, showing negligible impact with increased surface roughness. Further, side walls of the AM-SLM cavity were more susceptible to surface roughness, compared to the cavity front walls due to higher surface current density. This study underscores the significance of analyzing surface roughness and electrical conductivity in AM-SLM cavity resonators and highlights the suitability of the one-ball Huray model for accurate <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-factor prediction of microwave structures with rough surfaces.
Extension et érosion tectonique dans un prisme d'accrétion : l'exemple du Prisme Hikurangi (Nouvelle-Zélande)

The discovery of extensional structures of Miocene to Pliocene age on the emerged ridge of the Hikurangi accretionary prism led us to recognize a long period of «tectonic erosion» between two periods of accretion along the active margin. The discontinuous evolution of the Hikurangi Prism is probably due to a major change of plate kinematics in the SW Pacific. This modifies the conditions of trench feeding with detrital sediments and therefore the ability of accretionary prism construction
A Review Paper on Understanding the Various Methods and Challenges of Metal 3D Printing

A component of additive manufacturing (AM), which has transformed the industrial sector by producing complex and specialized parts, is metal 3D printing. As it advances in future it is crucial to know and gain knowledge on understanding the various methods and challenges associated with metal 3D printing. This review paper aims to provide a brief review on the various methods used in metal 3D printing along with the challenges faced during the process. The many techniques include the Power Bed Fusion (PBF) process, which uses selective laser melting (SLM) and electron beam melting (EBM), in addition to Directed Energy depositing techniques, which use laser metal deposition (LMD) and electron beam freeform fabrication (EBFF). Furthermore, the paper addresses the challenges encountered during metal 3D printing from material selection to post-processing. This review provides valuable insights and knowledge to researchers, engineers and industry professionals with the knowledge and valuable insights to advance the field further. It enables the realization of its full potential in different industries including aerospace, automotive, and healthcare.
A Study of Challenges and Limitations to Applying Machine Learning to Highly Unstructured Data

In recent years, there has been a lot of curiosity about the use of machine learning algorithms to analyze unstructured data, including social media posts and videos. The limitations and difficulties posed by these technologies must be overcome in order to guarantee accurate and trustworthy results, despite the promise they hold. This study examines the restrictions and difficulties associated with using machine learning algorithms on unstructured data. We identify the main difficulties in working with unstructured data, such as the lack of standardization and the difficulty in handling noise and bias, through a literature review and case studies. We then look at methods for overcoming these obstacles, such as feature engineering, pre-processing techniques, and ensemble methods. The results of this study have implications for both theoretical and applied work in machine learning, particularly in the fields of feature engineering, model selection, and data cleaning. This paper's goal is to provide a thorough understanding of the constraints and difficulties associated with using machine learning algorithms on unstructured data, as well as to contribute to the creation of best practices for handling these data type
Paleoproterozoic Granite Porphyry in Southwestern Margin of North China Craton and its Geological Significance

The granite porphyry in southern section of Liupanshan and eastern section of Longxian Guguan Zhen was considered asMesozoic-Cenozoic high-K calc-alkaline granites that record Paleoproterozoic magma activities. The granite porphyry contains high levelof SiO2(71.14%~73.33%), alkaline-rich(Na2O+K2O=7.61~8.70), K-high(K2O/Na2O=1.21~2.52,with an average of 2.03), fallinginto the range of metaluminous to weakly peraluminous(A/CNK=0.93~1.09,with an average of 1.0). The intense enrichment in LREEasymmetrical right-inclined rare earth element pattern, and the obvious negative Eu anomaly(δEu=0.44~0.47) suggest that the rocksare enriched in LILE(K, Rb, Ba, Th) and depleted in the high field strength elements(Ti, Nb, Ta). They are characterized by crustalderived A-type granites in geochemistry. Zircon U-Pb LA-ICP-MS dating of the magmatic genetic zircon yields an age of 1846±32 Ma,which should represent the emplacement age of the granite porphyry, suggesting that the granite porphyry was formed inPaleoproterozoic, not Mesozoic-Cenozoic as previously thought. The calcirudites of Beiguoshan Formation in Upper Ordovician wereactually deposited on the granite porphyry of Paleoproterozoic. This study shows that the southwestern margin of North China Craton inPaleoproterozoic was in an extensional tectonic environment. It may provide direct petrological evidence for Paleoproterozoic aulacogenof Helan mountain that extends southward to the southern section of Liupanshan.
The Use of Sarcasm in ESP Classes

Sarcasm can be defined as the use of words that mean the opposite of what the speaker actually says, especially to insult the recipient, to show irritation or to amuse the others. Sarcastic remarks are funny and constructive when they are mutually understood within a genuine relationship. Sarcasm has the potential to catalyze creativity for those who produce and receive it. A positive message delivered in a friendly tone - „You did a great job” and a negative message delivered in a patronizing or deprecating tone - “Your answers are far beyond all expectations” - are congruent.
Estimating Flooding Level Through the Brazilian Coast Using Reanalysis Data

ABSTRACT Gomes da Silva, P.; Dalinghaus, C. ; González, M. , Gutiérrez, O. , Espejo, A. , Abascal, A.J., and Klein, A.H. F. , 2016. Estimating flooding level through the Brazilian coast using reanalysis data. In: Vila-Concejo, A. ; Bruce, E. ; Kennedy, D.M. , and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1092 - 1096. Coconut Creek (Florida), ISSN 0749-0208. This paper presents an estimative of coastal flooding level values distribution along the Brazilian coast by using reanalysis data and by simulating wave propagation with Snell approximation. The coast was divided into 24 zones and flooding levels series were calculated for each one. Mean and extreme distribution show higher flooding levels occurring in northern coast (3 m higher than beaches in central coast) followed by the southern coast (1 m higher than central coast). High values obtained on north region were associated with greater tidal elevations (maximum values > 3 m) while higher values in south were related to greater wave heights and higher storm surge elevations (∼2 m and >7 m respectively). A comparison between beach types were also conducted and demonstrate higher values of flooding levels during reflective conditions (0.3 m higher for a specific beach condition), as a result of the runup model applied. Although simplifications were used to calculate this large scale variation of the flooding level along the coast, the results partially presented here can be of large usefulness in pre-design phases of coastal projects and in preliminary risk analysis.
A Target Position Estimation Method Based on Beam Feature Matching of DSAs

The research on DSAs position estimation method has become a research hotspot in recent decades. However, its random sparse characteristics lead to extremely narrow beam main lobe, so the target search is very difficult. Against these issues, a DSAs target estimation method based on beam feature matching is proposed. The method performs matching search for array beam feature information to realize target estimation. This method effectively reduces the computational complexity compared with existing methods. The simulation results show that the proposed method can reduced the computational complexity effectively for large-scale DSAs.
Enhancement in the Electrocatalytic and Optoelectronic Performance of Cost-Effective Counter Electrode VO2 for Dye-Sensitized Solar Cell (DSSC)

Dye-sensitized solar cells (DSSCs) have garnered significant attention in the scientific community for more than two decades due to their cost-effectiveness, convenient manufacturability, little toxicity, and straightforward preparation methodology. In this study, we present a cost-effective alternative to the platinum electrode for DSSCs, which serves as the counter electrode. The utilization of vanadium oxide nanoparticles as counter electrodes (CEs) in DSSCs has been the subject of research due to its enhanced stability, cost-effectiveness, and favorable photovoltaic characteristics. The device has been fabricated in configuration of fluorine-doped tin oxide (FTO)||TiO2||ruthenium (II) dye (N719)||iodide—triiodide electrolyte||VO2 (counter electrode)||FTO and investigate their photovoltaic performance. The utilization of X-ray diffraction (XRD) analysis has provided insights into the crystalline properties of VO2, indicating that it exists in a crystalline phase with a crystalline size measuring 43.19 nm and a lattice strain of 1.68 × 10−3. The utilization of a field emission scanning electron microscope (FESEM) that is equipped with an energy dispersive X-ray spectrum reveals a dense microstructure characterized by a uniform distribution of vanadium (V) and oxygen (O) across the whole surface. The Raman spectroscopic examination of VO2 reveals the existence of many Raman bands, thereby confirming the presence of the monoclinic phase. Cyclic voltammetry measurements were employed to investigate the catalytic activity of the CE toward the electrolyte. The photovoltaic performance of the manufactured device was examined by I–V measurement, revealing a notable open circuit voltage (Voc) and efficient power conversion efficiency when compared to the other materials that were evaluated.
Machine learning music emotion recognition based on audio features

Music is a kind of language expressing emotion, different structures and content contain different emotions. For the music emotion classification problem, a novel music emotion recognition strategy is proposed using audio features and machine learning. The developed scheme includes four stages: preprocessing, feature extraction, feature selection, and classification. As a first step, the signals in the data set are stably continuous by framing and windowing in processing. Secondly, a 1x140 size feature is derived from signal to profile the detailed music emotion. In the third step, the extracted features are selected and pre-weighted based on the feature weighting algorithms (ReliefF), max-relevance and min-redundancy (mRMR). In the final step, the data are led to the convolutional neural network and the long short-term memory (CNN-LSTM) combination model to recognize music emotions. The experimental results approve that the proposed CNN-LSTM based on ReliefF & mRMR (RM-CNN-LSTM) feature processing method could obtain higher identification accuracy, and all indicators of the proposed model improve significantly.
Big data usability text mining of publicly available YouTube electronic health record (EHR) tutorials

This study seeks to harness the power of big data through text mining the content of publicly available YouTube tutorials related to Electronic Health Record (EHR) systems. Most information about EHR systems is proprietary and hidden to the public. Nevertheless, understanding the capacity of EHRs from YouTube tutorials can offer insights to the public into health informatics and EHR functionality and usability for their patient care.We established a search strategy of the top three EHR vendors in North America (i.e., Oracle-Cerner, Meditech, and Epic Systems) from YouTube containing metadata and transcripts of EHR-related tutorials. Text mining techniques across a three-phased approach included establishing a search strategy to select the YouTube videos, auto-coding (from Voyant Tools) and manual coding (via standard usability heuristics), and analysis. The EHR components or applications in the tutorials were selected based on search strategy of total volume and quality of videos related to patient registration, scheduling, problem list, documentation, medication/order, and discharge/administration. The tutorials were analyzed for keyword frequencies, thematic patterns, and overall usability in the information presented via video transcriptions. Thematic patterns were established by heuristic categories of navigation, visibility, content, and complex interactions of learnability and memory.Preliminary findings suggest big data text mining led to analyzing 1,788,126 words via YouTube transcripts across 2280 videos. Oracle (Cerner) had a total of 799,653 words in the 1,066 videos. Meditech has 608 videos with 454,968 words. Epic has 466 videos with 436,598 words. There was a good range of content quality from medium to high, with some tutorials offering comprehensive step-by-step workflows like for medication/orders. The heuristics of content and navigation categories suggested that the tutorials covered enough material and word frequencies to be educational towards experiential learning of EHRs.The study underscores the significant role of using YouTube in disseminating knowledge about complex systems like EHRs to the public. Text mining of large, big data corpus can reveal insights into the quality and nature of online tutorials of more targeted and effective educational resources.
Long-read sequencing-based transcriptomic landscape in longissimus dorsi and transcriptome-wide association studies for growth traits of meat rabbits

Rabbits are an attractive meat livestock species that can efficiently convert human-indigestible plant biomass, and have been commonly used in biological and medical researches. Yet, transcriptomic landscape in muscle tissue and association between gene expression level and growth traits have not been specially studied in meat rabbits. In this study Oxford Nanopore Technologies (ONT) long-read sequencing technology was used for comprehensively exploring transcriptomic landscape in Longissimus dorsi for 115 rabbits at 84 days of age, and transcriptome-wide association studies (TWAS) were performed for growth traits, including body weight at 84 days of age and average daily gain during three growth periods. The statistical analysis of TWAS was performed using a mixed linear model, in which polygenic effect was fitted as a random effect according to gene expression level-based relationships. A total of 18,842 genes and 42,010 transcripts were detected, among which 35% of genes and 47% of transcripts were novel in comparison with the reference genome annotation. Furthermore, 45% of genes were widely expressed among more than 90% of individuals. The proportions (±SE) of phenotype variance explained by genome-wide gene expression level ranged from 0.501 ± 0.216 to 0.956 ± 0.209, and the similar results were obtained when explained by transcript expression level. In contrast, neither gene nor transcript was detected by TWAS to be statistically significantly associated with these growth traits. In conclusion, these novel genes and transcripts that have been extensively profiled in a single muscle tissue using long-read sequencing technology will greatly improve our understanding on transcriptional diversity in rabbits. Our results with a relatively small sample size further revealed the important contribution of global gene expression to phenotypic variation on growth performance, but it seemed that no single gene has an outstanding effect; this knowledge is helpful to include intermediate omics data for implementing genetic evaluation of growth traits in meat rabbits.
“MENEROBOS BATAS” NELAYAN INDONESIA DI PERAIRAN AUSTRALIA: PERMASALAHAN DAN PROSPEK

Tulisan ini mengkaji isu nelayan tradisional Indonesia di perairan Australia yang telah berulangkali menimbulkan persoalan dalam hubungan bilateral kedua negara. Konvensi Hukum Laut (UNCLOS) dan nota kesepakatan Indonesia-Australia atau MoU Box 1974 sesungguhnya telah memberikan jaminan hukum atas hak penangkapan ikan tradisional oleh nelayan-nelayan tradisional Indonesia di perairan Australia yang telah mereka lakukan secara turun menurun sejak sekitar abad 17. Namun fakta empiris memperlihatkan adanya praksis berbeda. Nelayan-nelayan tradisional Indonesia tersebut masih menghadapi hambatan dalam melaksanakan hak perikanan tradisionalnya akibat perbedaan penafsiran hak penangkapan ikan tradisional dan perubahan kawasan area yang disepakati dalam MoU Box oleh pihak Australia. Dengan menggunakan perspektif dari Indonesia, kajian ini dimaksudkan untuk memahami persoalan tersebut sebagai bahan pijakan untuk mencari solusi atas permasalahan tersebut. Persoalan ini perlu mendapatkan perhatian serius dari pemerintah. Tidak saja untuk kepentingan menjaga hubungan bilateral Indonesia-Australia, tetapi yang jauh lebih penting adalah untuk memberikan jaminan keamanan dan kesejahteraan nelayan-nelayan tradisional kita. Kata Kunci : Indonesia, Australia, UNCLOS, MoU Box, nelayan tradisional.
Osteoporosis guidelines on TCM drug therapies: a systematic quality evaluation and content analysis

Objective The aims of this study were to evaluate the quality of osteoporosis guidelines on traditional Chinese medicine (TCM) drug therapies and to analyze the specific recommendations of these guidelines. Methods We systematically collected guidelines, evaluated the quality of the guidelines using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool, and summarized the recommendations of TCM drug therapies using the Patient-Intervention-Comparator-Outcome (PICO) model as the analysis framework. Results and conclusions A total of 20 guidelines were included. Overall quality evaluation results revealed that four guidelines were at level A, four at level B, and 12 at level C, whose quality needed to be improved in the domains of “stakeholder involvement”, “rigor of development”, “applicability” and “editorial independence”. Stratified analysis suggested that the post-2020 guidelines were significantly better than those published before 2020 in the domains of “scope and purpose”, “stakeholder involvement” and “editorial independence”. Guidelines with evidence systems were significantly better than those without evidence systems in terms of “stakeholder involvement”, “rigor of development”, “clarity of presentation” and “applicability”. The guidelines recommended TCM drug therapies for patients with osteopenia, osteoporosis and osteoporotic fracture. Recommended TCM drugs were mainly Chinese patent medicine alone or combined with Western medicine, with the outcome mainly focused on improving bone mineral density (BMD).
FM-Fusion: Instance-Aware Semantic Mapping Boosted by Vision-Language Foundation Models

Semantic mapping based on the supervised object detectors is sensitive to image distribution. In real-world environments, the object detection and segmentation performance can lead to a major drop, preventing the use of semantic mapping in a wider domain. On the other hand, the development of vision-language foundation models demonstrates a strong zero-shot transferability across data distribution. It provides an opportunity to construct generalizable instance-aware semantic maps. Hence, this work explores how to boost instance-aware semantic mapping from object detection generated from foundation models. We propose a probabilistic label fusion method to predict close-set semantic classes from open-set label measurements. An instance refinement module merges the over-segmented instances caused by inconsistent segmentation. We integrate all the modules into a unified semantic mapping system. Reading a sequence of RGB-D input, our work incrementally reconstructs an instance-aware semantic map. We evaluate the zero-shot performance of our method in ScanNet and SceneNN datasets. Our method achieves 40.3 mean average precision (mAP) on the ScanNet semantic instance segmentation task. It outperforms the traditional semantic mapping method significantly.
Video‐based consensus annotations for learning: A feasibility study

Video‐based learning is increasingly common in higher education; however, the video players available make limited use of logged interaction data to support and guide students' viewing. In this work in progress, we explore the feasibility of aggregating students' annotations of videos (e.g., highlights, notes, and tags) to identify “hot spots,” which can signal areas of interest to subsequent learners. We conducted a deployment study with 315 undergraduate students using ViDeX, a video player designed for active viewing. We logged students' use of ViDeX for four months, and then aggregated and graphed their annotations across 13 instructional videos. Our results show that consensus annotations—the video content that has received attention from many students— may be a feasible, data‐driven way to flag information for use by subsequent learners.