Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Plastic Pollution on Ecosystem Services, Sustainable Development Goals, and Need to Focus on Circular Economy and Policy Interventions

Plastic pollution is ubiquitous in terrestrial and aquatic ecosystems. Plastic waste exposed to the environment creates problems and is of significant concern for all life forms. Plastic production and accumulation in the natural environment are occurring at an unprecedented rate due to indiscriminate use, inadequate recycling, and deposits in landfills. In 2019, the global production of plastic was at 370 million tons, with only 9% of it being recycled, 12% being incinerated, and the remaining left in the environment or landfills. The leakage of plastic wastes into terrestrial and aquatic ecosystems is occurring at an unprecedented rate. The management of plastic waste is a challenging problem for researchers, policymakers, citizens, and other stakeholders. Therefore, here, we summarize the current understanding and concerns of plastics pollution (microplastics or nanoplastics) on natural ecosystems. The overall goal of this review is to provide background assessment on the adverse effects of plastic pollution on natural ecosystems; interlink the management of plastic pollution with sustainable development goals; address the policy initiatives under transdisciplinary approaches through life cycle assessment, circular economy, and sustainability; identify the knowledge gaps; and provide current policy recommendations. Plastic waste management through community involvement and socio-economic inputs in different countries are presented and discussed. Plastic ban policies and public awareness are likely the major mitigation interventions. The need for life cycle assessment and circularity to assess the potential environmental impacts and resources used throughout a plastic product’s life span is emphasized. Innovations are needed to reduce, reuse, recycle, and recover plastics and find eco-friendly replacements for plastics. Empowering and educating communities and citizens to act collectively to minimize plastic pollution and use alternative options for plastics must be promoted and enforced. Plastic pollution is a global concern that must be addressed collectively with the utmost priority.
Partial-Power Post-Regulated LLC Resonant DC Transformer

This article presents a partial-power postregulated LLC resonant converter. In the proposed topology, the input power is first transferred to the secondary side of the transformer through the LLC-DC transformer converter to achieve galvanic isolation and voltage conversion functions. Then, most of the power is directly transmitted to the load side, and only a small portion of the power is converted to the output side by the partial-power processing converter. Therefore, unlike the two-stage solution, the converter proposed in this article achieves output voltage regulation while minimizing the efficiency loss caused by the two-stage conversion. The operating principle of the proposed converter is analyzed, and the system prototype is constructed. Experimental result validates the correctness of the analysis and proves the feasibility of the proposed converter.
Recurrent knowledge tracing machine based on the knowledge state of students

Knowledge tracing (KT) aims to closely trace the knowledge level of students during their learning. KT is often implemented by intelligent tutoring systems (ITS) to predict the student performance, and schedule the individual learning plan for each student. However, most existing KT models cannot predict the knowledge forgetting well due to the lack of temporal information and some KT models for knowledge forgetting prediction are not accurate enough. To resolve these issues, this paper proposes a recurrent knowledge tracing machine (RKTM), which temporally enriches the encoding of knowledge tracing machine (KTM) and difficulty, student ability, skill, and student skill practice history (DAS3H) with the knowledge state of students. RKTM consists of two major components, including the tracing component and the predicting component. The tracing component temporally traces the knowledge state of a student, while the predicting component captures the interaction between the current learning scenario and the current knowledge state of that student and provides accurate prediction of student performance. Experiments show that the proposed RKTM well outperforms KTM, DAS3H, and some other models.
Neural‐network‐based adaptive finite‐time output constraint control for rigid spacecrafts

This article proposes a neural‐network‐based adaptive finite‐time output constraint control scheme for attitude stabilization of rigid spacecrafts. First, a novel singularity‐free terminal sliding mode variable is constructed and an auxiliary function is developed in the controller design to avoid the singularity problem. Then, an adaptive neural control law is designed to approximate the lumped uncertainty of spacecraft system including inertia uncertainties and external disturbances. Furthermore, a novel finite‐time prescribed performance function is constructed for characterizing the convergence rate and steady state of the spacecraft attitude, such that the attitude can be maintained within a prescribed small region in finite time. Finally, the finite‐time stability of the whole closed‐loop system is analyzed by rigorous theoretical proofs, and comparative simulations are given to show the effectiveness and superiority of the proposed scheme.
On the Physical Origin of the Semiannual 1 Component of Surface Air Temperature over 2 Mid-latitude and Subpolar Oceans

With the understanding that seasonal cycle of the temperature are forced principally by the annually evolving solar irradiance, many previous studies have defined seasonal cycle of surface air temperature (SAT) as the sum of yearly-period sinusoidal component and its harmonics, especially semiannual component. In mid-latitude and subpolar regions, the ratio between the semiannual and annual components of solar irradiance is negligibly small but that of the SAT over oceans is not, which remains to be understood. In this study, a simple energy budget model including main energy sources and sinks of oceanic mixed layer is designed to understand this puzzle. It is revealed that, when the oceanic mixed layer is prescribed as a layer of constant depth, the phase and amplitude of the modeled SAT is not consistent with that of the observation. However, when the annually changing heat capacity of the oceanic mixed layer is included, both the amplitude and phase of the modeled SAT share these of the observed SAT, proving that the semiannual component of SAT over mid-latitude and subpolar oceans is a result of the heat capacity-varying oceanic mixed layer in response to annually evolving solar irradiance.
The Spatial Agglomeration and Industrial Network of Strategic Emerging Industries and Their Impact on Urban Growth in Mainland China

Strategic emerging industries (SEIs) are an important industrial policy to promote innovation, develop advanced manufacturing, and upgrade the economy in China. The research explores the impact of SEIs on urban economic growth in mainland China, from agglomeration externalities and network externalities. The results show that SEIs have a significant impact on growth, and network externalities are generally more important than agglomeration externalities. Although the agglomeration of large-scale listed enterprises in the city promotes growth, too many large enterprises in the cluster, that is, the lack of small and medium-sized supporting enterprises, will harm urban growth. Meanwhile, a city with a higher degree of centrality has a larger GDP, but the strength has a negative impact.
Numerical Studies of Disperse Three-Phase Fluid Flows

The dynamics of a three-phase gas–liquid–liquid multiphase system is examined by direct numerical simulations. The system consists of a continuous liquid phase, buoyant gas bubbles, and smaller heavy drops that fall relative to the continuous liquid. The computational domain is fully periodic, and a force equal to the weight of the mixture is added to keep it in place. The governing parameters are selected so that the terminal Reynolds numbers of the bubbles and the drops are moderate; while the effect of bubble deformability is examined by changing its surface tension, the surface tension for the drops is sufficiently high so they do not deform. One bubble in a “unit cell” and eight freely interacting bubbles are examined. The dependency of the slip velocities, the velocity fluctuations, and the distribution of the dispersed phases on the volume fraction of each phase are examined. It is found that while the distribution of drops around a single bubble in a “unit cell” is uneven and depends on its deformability, the distribution of drops around freely interacting bubbles is relatively uniform for the parameters examined in this study.
A Review on Inclusion of Agriculture Subject in the High Schools of Bhutan: Perspective and Challenges

Inclusion of Agriculture subject in the curriculum plays a very vital role in the country where the economic is fully dependent on agriculture. Agriculture education provides an alternative to career ladder for those students who are interested in taking farming in the future. In Bhutan inclusion of Agricultural subject became a part of school’s curriculum under the joint initiative of Agriculture and Education ministry from 2013 onwards. Agriculture inclusion in the curriculum of the school brings both the perspective and the challenges to the learners and also to the teachers. The main aim of introducing the curriculum is to make children learn through practical teaching and prepare them to value the dignity of work. But there are also many challenges faced by the student during the learning period that demotivates the children in opting agriculture as their vocational subject. This study aims to find out the challenges encountered by the students of IX-XII in taking up the optional Agricultural subject and also explore some of the benefit gained by the students in taking it to make it worthy in taking up their optional subjects.
Personality, exercise addiction and orthorexia. A research contribution

Background : The objectives of the study were to verify the presence of exercise addiction and orthorexia in the population identified, as well as the relationship between these two variables, and to investigate whether alexithymia and impulsivity are personality traits associated with sport addiction or orthorexia. Methods : The sample, consisting of 140 subjects (between 17 and 52 years old), who practiced bodybuilding, crossfit, calisthenics, powerlifting, or who attended fitness courses, were given: a personal data sheet, the Barrat Impulsiveness Scale-11, the exercise Dependence Scale-Revised, the Toronto Alexithymia Scale-20 and the Bratman Test. Results : The percentage of subjects with sports addiction is 13% and part of the sample reports symptoms of orthorexia. There is a relationship between exercise addiction and orthorexia, and there are traits of impulsivity and alexithymia in dependent subjects or at risk of addiction. An association was found between alexithymia and impulsivity. Conclusions : Reading the results, it is clear that the obsession with a healthy lifestyle can manifest itself not only in the food area, but also in the sports field, and that impulsiveness and alexithymia are associated with addiction to sports, probably because this represents a strategy to stem dysregulated emotional states.
Eco-Friendly UPLC-MS/MS Quantitation of Delafloxacin in Plasma and Its Application in a Pharmacokinetic Study in Rats

A novel UPLC-MS/MS assay was developed for rapid quantification of delafloxacin (a novel fluoroquinolone antibiotic in plasma samples by one step sample cleanup procedure. Delafloxacin (DFX) and internal standard (losartan) were separated on a UPLC BEH C18 column (50 × 2.1 mm; 1.7 μm) by using gradient programing of a mobile phase containing 0.1% formic acid in acetonitrile and 0.1% formic acid in water. The quantification was performed by a using triple-quadrupole mass detector at an electrospray ionization interface in positive mode. The precursor to the product ion transition of 441.1 → 379.1 for the qualifier and 441.1 → 423.1 for the quantifier was used for DFX monitoring, whereas 423.1 → 207.1 was used for the internal standard. The validation was performed as per guidelines of bioanalytical method validation, and the evaluated parameters were within the acceptable range. The greenness assessment of the method was evaluated by using AGREE software covering all 12 principles of green analytical chemistry. The final score obtained was 0.78, suggesting excellent greenness of the method. Moreover, Deming regression analysis showed an excellent linear relationship between this method and our previously reported method, and it is suitable for high-throughput analysis for routine application. The proposed method was effectively applied in a pharmacokinetic study of novel formulation (self-nanoemulsifying drug delivery systems) of DFX in rats.
Performance analysis of set partitioning formulations on the rule extraction from random forests

Öz Random Forests is a widely used machine learning algorithm for classification and regression problems from different domains. Although they are generally accurate, their interpretability is low compared to their building blocks: single decision trees. Using the fact that each member of a Random Forest is a decision tree, we propose different set partitioning formulations to extract interpretable if-then rules from Random Forests. Our experiments on well-known classification and regression datasets show that the original set partitioning model formulation significantly reduces the number of rules while keeping the accuracy at acceptable levels. We also propose a modification to the problem's objective function, which aims to reduce the number of extracted rules further. We observe a further reduction in the number of extracted rules while the accuracy values stay nearly the same. Although the set partitioning problem is NP-hard, we obtain optimal results for most datasets within twenty minutes. Rastgele Ormanlar farklı alanlardaki sınıflandırma ve regresyon problemleri için sıklıkla kullanılan bir yapay öğrenme algoritmasıdır. Yüksek başarım göstermelerine rağmen, yapıtaşları olan karar ağaçlarına kıyasla yorumlanabilirlikleri oldukça düşüktür. Her bir üyesinin bir karar ağacı olduğu gerçeğinden yola çıkarak, Rastgele Ormanlardan yorumlanabilir eğer-ise tipinde kurallar çıkarmak için farklı küme bölüntüleme formülasyonları öneriyoruz. Literatürde sıklıkla kullanılan sınıflandırma ve regresyon veri setleri üzerinde yaptığımız deneylerin sonuçları göstermektedir ki orijinal küme bölüntüleme model formülasyonu, başarımı kabul edilebilir seviyelerde tutarak kural sayısını önemli ölçüde düşürebilmektedir. Çıkarılan kural sayısını daha da düşürebilmek için problemin amaç fonksiyonuna bir değişiklik öneriyoruz. Bu değişiklikle birlikte, çıkarılan kural sayısında daha da düşüş gözlemlerken başarımın aynı seviyelerde kaldığını gözlemliyoruz. Küme bölüntüleme problemi NP-zor olmasına rağmen, çoğu veri seti için yirmi dakika içinde en iyi çözümü buluyoruz.
Analisis pelaksanaan pendidikan jarak jauh pada pelajaran bahasa Inggris di Kalimantan Barat

Penelitian ini merupakan penelitian analitik terhadap penerapan Pendidikan Jarak Jauh dalam Pembelajaran Bahasa Inggris di Kalimantan Barat. Penelitian ini bertujuan untuk menganalisis pelaksanaan pendidikan jarak jauh dalam pembelajaran bahasa Inggris di Kalimantan Barat, dengan ketentuan, (1) apa saja kesulitan yang dihadapi oleh guru bahasa Inggris dalam melaksanakan Pembelajaran Jarak Jauh? (2) platform online apa yang menurut guru bahasa Inggris paling cocok untuk diterapkan dalam pelaksanaan Pendidikan Jarak Jauh ini? ; dan (3) platform online apa yang menurut guru bahasa Inggris dapat diterapkan dalam Blended Learning? Metode penelitian deskriptif ini adalah kualitatif. Teknik pengumpulan data yang digunakan dalam penelitian ini adalah kuesioner online yang dilakukan dengan menggunakan platform google form untuk mengumpulkan data tentang pelaksanaan Pendidikan Jarak Jauh dalam pelajaran bahasa Inggris tahun 2020. Hasil kuesioner akan dijabarkan dalam bentuk data grafik tabulasi. Hasil penelitian menunjukkan bahwa platform WhatsApp paling banyak digunakan dalam Pembelajaran Jarak Jauh ini. Kendala yang paling banyak dihadapi guru adalah sinyal internet yang tidak stabil, kurangnya fasilitas, kesulitan keuangan, dan kurangnya kesadaran dan disiplin siswa untuk mengikuti pembelajaran online ini. Media online yang dianggap dapat digunakan untuk blended learning adalah Google Form dan YouTube. This research is an analytical study of the implementation of Distance Learning in English Language Learning in West Kalimantan. This study aims to analyze the implementation of distance education in English lessons in West Kalimantan, with the provisions, (1) what are the difficulties encountered by English teachers in implementing Distance Learning? (2) what online platform do English teachers think is the most suitable to be applied in the implementation of this Distance Education? and (3) what online platforms do English teachers think that can be applied in Blended Learning? The method of this descriptive research is qualitative. The data collection technique used in this study is an online questionnaire which is carried out using the google form platform to collect data about the implementation of Distance Education in English lessons in 2020. The results of the questionnaire will be described in the form of a data tabulation graph. The results showed that platform WhatsApp was the most widely used in this Distance Learning. The obstacles that most teachers had to deal with were an unstable internet signal, lack of facilities, financial difficulties, and lack of students ‘awareness and discipline to take part in this online learning. Online media that are considered to be used for blended learning are Google Form and YouTube
Sparse Representation Based Inverse Halftoning with Boosted Dictionary1

Halftoning image is widely used in printing and scanning equipment, which is of great significance for the preservation and processing of these images. However, because of the different resolution of the display devices, the processing and display of halftone image are confronted with great challenges, such as Moore pattern and image blurring. Therefore, the inverse halftone technique is required to remove the halftoning screen. In this paper, we propose a sparse representation based inverse halftone algorithm via learning the clean dictionary, which is realized by two steps: deconvolution and sparse optimization in the transform domain to remove the noise. The main contributions of this paper include three aspects: first, we analysis the denoising effects for different training sets and the redundancy of dictionary; Then we propose the improved a sparse representation based denoising algorithm through adaptively learning the dictionary, which iteratively remove the noise of the training set and upgrade the quality of the dictionary; Then the error diffusion halftone image inverse halftoning algorithm is proposed. Finally, we verify that the noise level in the error diffusion linear model is fixed, and the noise level is only related to the diffusion operator. Experimental results show that the proposed algorithm has better PSNR and visual performance than state-of-the-art methods.
External Fixation Characteristics Drive Cost of Care for High-Energy Tibial Plateau Fractures

Objective: To determine the total cost for a 30-day episode of care for high-energy tibial plateau fractures and the aspects of care associated with total cost. Design: Time-driven activity-based costing analysis. Setting: One Level 1 adult trauma center. Patients: One hundred twenty-one patients with isolated, bicondylar tibial plateau fractures undergoing staged treatment were reviewed. Primary Outcome: Total cost. Results: A total of 85 patients were included and most sustained Schatzker VI fractures (n = 66, 77%). All patients were treated with biplanar external fixation before definitive fixation. A total of 26 patients (31%) were discharged to skilled nursing facilities, and 37 patients (43%) were not discharged between procedures. Total cost for a 30-day episode of care was $22,113 ± 4056. External fixation components ($5952, 26.9%), length of hospital stay ($5606, 25.4%), discharge to skilled nursing facility (SNF) ($3061, 13.8%), and definitive fixation implants ($2968, 13.4%) contributed to the total cost. The following were associated with total cost: patient discharged to SNFs (P < 0.001), patient remaining inpatient after external fixation (P < 0.001), days of admission for open reduction internal fixation (ORIF) (P = 0.005), days spent with external fixation (P < 0.001), days in a SNF after ORIF (P < 0.001), and external fixation component cost (P < 0.001). Conclusions: External fixation component selection is the largest contributor to cost of a 30-day episode of care for high-energy bicondylar tibial plateau fractures. Reduction in cost variability may be possible through thoughtful use of external fixation components and care pathways. Level of Evidence: Economic analyses Level IV. See Instructions for Authors for a complete description of levels of evidence.
On minimal positive basis for polyhedral cones in R n

For polyhedral cones in the Euclidean space, we present its conic dimension, which is invariant under linear isomorphisms that is sensitive to the number of generators of this cone, and the related notion of conic basis. We may interpret these two notions as versions of the definitions of linear dimension and linear basis for linear subspaces in the setting of polyhedral cones. We establish a conic version of the rank-nullity theorem that, in this case, is an inequality involving the conic dimensions of both the cone and its image under a linear map. We use this conic rank-nullity inequality to establish both a decomposition and a union of conic basis, involving the lineality space of the cone. We introduce the signature of a polyhedral cone and establish some results on the injectivity of a linear map and the preservation of the signature of a polyhedral cone under linear maps. In particular, we show that a linear map that acts injectively on the linear span of a polyhedral cone preserves its signature.
Molecular Characterization of a Natural Mutant of Feline Calicivirus in China

During epidemiological surveillance of Feline calicivirus (FCV) isolates in Shanghai, China, a natural mutant of FCV, designated SH1909, was successfully isolated from a stray cat. The complete genome sequence of SH1909 was determined in this study. Sequence comparison and analysis showed that thirteen unique aa residues substitutions and single-aa insertion of N or Y were observed in SH1909, which indicated that SH1909 was a novel and natural mutant of FCV. Interestingly, phylogenetic analysis based on LC-VP1 showed SH1909 could be clustered into an independent evolutionary branch with some Chinese isolates and was more distantly related to vaccine strains, indicating its potential to escape from vaccine-elicited immunity. According to the predicted B-cell epitopes of LC-VP1, amino acid mutation sites and positive selective sites, peptide C (aa sites: 445–460) and, peptide D (aa sites: 425–440) located in the hypervariable regions of LC-VP1, may result in the decreased immunological protection. Moreover, amino acid sites 439 and 449 may be responsible for the potential immune escape of SH1909. This study provides an important insight into genetic variations of FCV and vaccine development.