Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein interactions with osseointegrable titanium implants

This review aims to present the mechanisms of protein interactions with titanium dental implant surfaces. Methods: the analyses were based on searches of scientific articles available in English and Portuguese in PubMed (MEDLINE), Bireme (LILACS), Scielo, Web of Science and Google Scholar. Results: titanium dental implant treatments success rates (95-98%) are mainly due to the biocompatibility of titanium oxide on the implant surface, surgical techniques adopted, good implants manufacturing processes and biomechanical knowledge of the systems. Studies in past decades has empirically developed implant surfaces with significant changes in morphologies, roughness, wettability, surface energy, chemical composition, and chemical groups density or deposited molecules. These changes promoted better protein adsorption, osteoblast adhesion, and changes in the mechanisms involved in osseointegration. Thus, the time to put the implant in function has been reduced and the success rates have increased. In the osseointegration process, at the nanoscale, there is no contact between the bone and the implant surface, but there is the formation of a protein anchorage between the periosteum and the implant with an interface formed by proteins. In all the reactions between the body and the implant surface, the activities of fibronectin and integrin are essential, since they are responsible for transmitting information to the cell for its differentiation, adhesion and mobility. Conclusion: thus, the analyses of protein-implant interactions are indispensable for a better understanding of the performance of osseointegrated dental implants.
Additives in Nanocrystalline Tin Dioxide: Recent Progress in the Characterization of Materials for Gas Sensor Applications

Tin dioxide has huge potential and is widely studied and used in different fields, including as a sensitive material in semiconductor gas sensors. The specificity of the chemical activity of tin dioxide in its interaction with the gas phase is achieved via the immobilization of various modifiers on the SnO2 surface. The type of additive, its concentration, and the distribution between the surface and the volume of SnO2 crystallites have a significant effect on semiconductor gas sensor characteristics, namely sensitivity and selectivity. This review discusses the recent approaches to analyzing the composition of SnO2-based nanocomposites (the gross quantitative elemental composition, phase composition, surface composition, electronic state of additives, and mutual distribution of the components) and systematizes experimental data obtained using a set of analytical methods for studying the concentration of additives on the surface and in the volume of SnO2 nanocrystals. The benefits and drawbacks of new approaches to the high-accuracy analysis of SnO2-based nanocomposites by ICP MS and TXRF methods are discussed.
Tropical Precipitation Extremes

AbstractClassifying tropical deep convective systems by the mesoscale distribution of their cloud properties and sorting matching precipitation measurements over an 11-yr period reveals that the whole distribution of instantaneous precipitation intensity and daily average accumulation rate is composed of (at least) two separate distributions representing distinctly different types of deep convection associated with different meteorological conditions (the distributions of non-deep-convective situations are also shown for completeness). The two types of deep convection produce very different precipitation intensities and occur with very different frequencies of occurrence. Several previous studies have shown that the interaction of the large-scale tropical circulation with deep convection causes switching between these two types, leading to a substantial increase of precipitation. In particular, the extreme portion of the tropical precipitation intensity distribution, above 2 mm h−1, is produced by 40% of .
Challenges in Imaging Analyses of Biomolecular Condensates in Cells Infected with Influenza A Virus

Biomolecular condensates are crucial compartments within cells, relying on their material properties for function. They form and persist through weak, transient interactions, often undetectable by classical biochemical approaches. Hence, microscopy-based techniques have been the most reliable methods to detail the molecular mechanisms controlling their formation, material properties, and alterations, including dissolution or phase transitions due to cellular manipulation and disease, and to search for novel therapeutic strategies targeting biomolecular condensates. However, technical challenges in microscopy-based analysis persist. This paper discusses imaging, data acquisition, and analytical methodologies’ advantages, challenges, and limitations in determining biophysical parameters explaining biomolecular condensate formation, dissolution, and phase transitions. In addition, we mention how machine learning is increasingly important for efficient image analysis, teaching programs what a condensate should resemble, aiding in the correlation and interpretation of information from diverse data sources. Influenza A virus forms liquid viral inclusions in the infected cell cytosol that serve as model biomolecular condensates for this study. Our previous work showcased the possibility of hardening these liquid inclusions, potentially leading to novel antiviral strategies. This was established using a framework involving live cell imaging to measure dynamics, internal rearrangement capacity, coalescence, and relaxation time. Additionally, we integrated thermodynamic characteristics by analysing fixed images through Z-projections. The aforementioned paper laid the foundation for this subsequent technical paper, which explores how different modalities in data acquisition and processing impact the robustness of results to detect bona fide phase transitions by measuring thermodynamic traits in fixed cells. Using solely this approach would greatly simplify screening pipelines. For this, we tested how single focal plane images, Z-projections, or volumetric analyses of images stained with antibodies or live tagged proteins altered the quantification of thermodynamic measurements. Customizing methodologies for different biomolecular condensates through advanced bioimaging significantly contributes to biological research and potential therapeutic advancements.
The analysis of sources of ambient light extinction coefficient in summer time of Beijing city

Using the measured concentrations of ambient particles and gaseous species in the summer of 1999, the averaged contributions of light extinction are calculated by Mie model. It is found that the total extinction coefficient is 0.388? km~(-1). The major component of b_(ext) is b_(sp) (70.5% of b_(ext)), followed by b_(ap) (22.3% of b_(ext)), b_(sg) (3.6% of b_(ext)) and b_(ag) (3.5% of b_(ext)). The light extinction is mainly from particles, which accounts for more than 90% of total extinction coefficient.
Multiannual and Seasonal Variability of Water Masses and Fronts Over the Uruguayan Shelf

Abstract Seasonal (autumn and spring) and multiannual (1991, 1995, 1996, 1997, 1998, and 2001) variability in the composition of water masses on the Uruguayan shelf were analyzed. The main difference between seasons was the presence of Tropical Water (TW) and a relative dominance of Subtropical Water (STW) over Subantartic Water (SAW) in autumn, whereas spring was characterized by a dominance of SAW and the absence of TW. Changes in the composition of water masses, surface temperature distributions, and front positions and strengths are discussed in regards to biological implications. Our results reveal seasonal changes in the frontal zone position and suggest an enhaced migration during El Niño Southern Oscillation (ENSO) warm and cold episodes, reaching higher latitudes during the former (e.g., autumn of 1998). During cold episodes, the position of the frontal zone reached lower latitudes, with prevailing cold waters over the shelf. Higher frontal strength was observed during ENSO episodes, mainly because of the haline component of CW at the surface. The combination of highly variable freshwater inputs and the occurrence of shelf convergences produced a distinctive hydrographic system with a high degree of seasonal and interannual variation and an evident impact on ecological processes.
Covid, Work-from-Home, and Securities

We consider whether traders are more likely to commit securities violations when trading at home, a new form of working induced by the Covid pandemic. We examine data pre-and post-Covid, during which some traders were unexpectedly forced to work at home. The data indicate the presence of both a treatment and a selection effect, where work at home exhibits fewer misconduct cases. Work at home is associated with fewer cases of trading misconduct, although no difference in communications misconduct. The economic significance of working from home on trading misconduct is large for both the treatment and selection effects.
Class-Variational Learning With Capsule Networks for Deep Entity-Subspace Clustering

The progression of deep clustering techniques in the recent years emphasizes the need for unsupervised representation learning methods that build lower-dimensional embeddings within expressive latent feature spaces. An important performance factor for such techniques constitutes the representational capacity of the used neural network technology. Although Capsule Networks (CapsNet)s are predestinated for the task of deep clustering through their rich entity representations and inter-layer dynamics related to clustering, CapsNets are to date rarely explored in this context. The main challenge for enabling unsupervised representation learning with CapsNets results from the required differentiation of the output capsules to encompass data-intrinsic classes. This paper proposes a novel end-to-end framework denominated as Class-Variational Learning (CVL) which utilizes an asymmetric autoencoder consisting of a CapsNet encoder and a non-capsular decoder network for facilitating entity-subspace clustering. To the best of our knowledge CVL represents the first approach which accomplishes a class-to-capsule specialization of the output capsules without external supervisory signals. As unique characteristic, CVL forms an equivariant latent space with continuous transitions between data-intrinsic classes. This means a crucial gain in the explainability of the constructed inference mechanism, since the class-discriminative equivariant space is linearly navigable and fully accessible by a human actor. Despite our CVL model does currently not lead to competitive accuracies compared to the state-of-the-art deep clustering techniques, CVL opens promising perspectives on the use of CapsNets as the basis for deep clustering which hopefully motivates future research in this field.
Interpretation Planning and its Role in Sus- tainable Tourism and Visitor Management at Geoheritage Sites

This paper is concerned with how and why we should plan our communication with visitors about our heritage, and how this can help the sustainable development and management of Geoparks. Communication with the public about natural, cultural and historical heritage can take place in a number of ways. This paper is concerned particularly with the role that site interpretation can play in the development of tourism and the management of heritage resources, with particular reference to Geoparks. This paper is a companion to a paper by my colleague David Revell, How to get excited about rocks: Designing environmental interpretation that inspires and motivates visitors and enhances the visitor experience, published elsewhere in this journal and also presented at the 3 International Forum on Geoheritage.
Foundational Competencies in Educational Measurement

This article presents the consensus of an National Council on Measurement in Education Presidential Task Force on Foundational Competencies in Educational Measurement. Foundational competencies are those that support future development of additional professional and disciplinary competencies. The authors develop a framework for foundational competencies in educational measurement, illustrate how educational measurement programs can help learners develop these competencies, and demonstrate how foundational competencies continue to develop in educational measurement professions. The framework introduces three foundational competency domains: Communication and Collaboration Competencies; Technical, Statistical, and Computational Competencies; and Educational Measurement Competencies. Within the Educational Measurement Competency domain, the authors identify five subdomains: Social, Cultural, Historical, and Political Context; Validity, Validation, and Fairness; Theory and Instrumentation; Precision and Generalization; and Psychometric Modeling.
Effectiveness of Mindfulness-Based Intervention (MBI) on Reducing Work Stress

Workplace stress is the result of an individual's interaction with their job tasks, which can create tension and imbalance both physically and psychologically. This impacts emotions, cognitive abilities, and the mental state of an employee. This study was conducted to determine the effectiveness of Mindfulness-Based Intervention in reducing workplace stress through a meta-analysis method by reviewing relevant literature from 10 international research journals related to the study variables. The total number of participants in this study was 1,119 individuals divided into two groups: the experimental group (n=560) and the control group (n=559). Based on statistical calculations using mean (M), standard deviation (SD), and the number of participants (n), the effect size value obtained from a random-effect model was determined. Thus, it was sought to establish whether Mindfulness-Based Intervention is an effective treatment for reducing workplace stress and to what extent this effectiveness is. The research results indicated a random effect model value of -0.536 (95% CI = (-0.8208 to -0.2506). The p-value was found to be <0.001 with an inconsistency value (I^2) of 80.6%. Based on the results of the meta-analysis, it can be concluded that Mindfulness-Based Intervention has a medium effect on workplace stress. This suggests that Mindfulness-Based Intervention is less successful in reducing workplace stress and would be more effective if used in conjunction with other types/methods of intervention.Stres kerja adalah hasil dari interaksi individu dengan tugas pekerjaannya, yang bisa menciptakan ketegangan dan ketidakseimbangan baik secara fisik maupun psikologis. Hal ini berdampak pada emosi, kemampuan berpikir, serta kondisi mental seorang pekerja. Penelitian ini dilakukan untuk mengetahui efektifitas Mindfulness Based Intervention untuk menurunkan stres kerja dengan menggunakan metode meta-analisis melalui reviu literatur dari 10 jurnal penelitian international yang relevan dengan variabel penelitian. Jumlah partisipan pada penelitian ini 1.119 orang yang dibagi dalam dua kelompok yakni kelompok eksperimen (n=560 orang) dan kelompok kontrol (n=559 orang). Berdasarkan perhitungan statistik dengan menggunakan data mean (M), standar deviasi (SD), dan jumlah partisipan (n) akan menemukan nilai effect size yang diperoleh dari random effect. Dengan demikian, nantinya akan ditemukan bahwa apakah Mindfulness Based Intervention merupakan treatment yang memiliki efektifitas untuk menurunkan stres kerja dan seberapa besar nilai efektifitas yang diberikan. Hasil Penelitian menunjukkan nilai random effect model yaitu sebesar -0,536 (95% CI = (-0,8208 sampai -0,2506). Hasil dari nilai p Value = <0.001 dengan nilai inkonsitensi (I^2) = 80,6 %. Berdasarkan hasil uji meta analisis dapat disimpulkan mindfulness-based intervention memiliki efek medium terhadap stres kerja. Hal ini menunjukkan bahwa, mindfulness-based intervention kurang berhasil dalam menurunkan stres kerja, sehingga akan lebih efektif jika salah satu jenis/ metode intervensi digunakan bersamaan dengan intervensi lain.
A Parent Authentication System for the Security of Pre-basic School Pupils

The safety of pupils is of uttermost concern to school authorities and especially parents in this age of rampant kidnappings and other malicious activities by unscrupulous people within the human ecosystem. In this paper, a robust security system, referred to as a Parent Authentication System (PAS) is designed and implemented using bi-modal authentication and/or verification mechanisms for the safety of Pre-Basic School pupils. The primary module for authentication and verification is Fingerprint (i.e. biometrics) with an alternative module of a Password which, can only be used if the primary module fails or rejects the biometrics of a particular user. The Agile System Development Life Cycle Prototyping model was leveraged to design the system; all key components responsible for the functioning of the system have been appropriately implemented and integrated resulting in a very successful and robust system. The developed system was deployed and tested at Garu Senior High School in the Upper East Region of Ghana. During the test implementation, It was affirmed that students with issues such as scratches and dirt on their fingers were denied access and were not able to be authenticated and verified. However, after three attempts via this primary authentication module, they were able to use the alternative module, which gave them the opportunity to be authenticated and verified. As a result, a full implementation of this system will make sure only rightful parents pick up their wards from school. This system can also be deployed in other areas for attendance monitoring or personality tracking.
Spatial-Temporal Variation Characteristics of Summer Precipitation in Sichuan,1961-2007

Based on the precipitation data of 119 stations from 1961to 2010,the temporal and spatial variations of summer precipitation in Sichuan were analyzed by using the EOF,REOF linear trend,Mann-Kendall analysis and Morlet wavelet analysis.The analyzed results are as follows:The summer precipitation presents a downward tendency during the 47years.Abrupt changes appeared in 1962,1982and 2000,respectively.There were changing periods of 22and 6-8years.Spatially,the summer precipitation in Sichuan can be subdivided into the following four regions:east Sichuan basin,mid-west Sichuan basin,southwest Sichuan mountain region and western Sichuan plateau.Summer precipitation was increasing in east Sichuan basin and western Sichuan plateau,was decreasing in mid-west Sichuan basin and more stationary,perhaps with a little increasing,in southwest Sichuan mountain region.In the four regions,there were unique annual and decadal variations in summer precipitation,with changing periods of 23,16,14,12,8and 6years.
The Fragility of Success: Repositioning Mauritian Development in the Twenty-First Century

Mauritius is often considered a ‘success story’ to be read for salutary purposes by other small island developing states (SIDS). While it does share broadly similar attributes with many other SIDS, and acts in unison with other SIDS in international fora, local histories, cultures, geography and location invariably lead to significant differences in developmental trajectories. This paper presents an assessment of Mauritian history in order to explore the contemporary threats and opportunities that face the island in its contemporary quest to transform the island into Maurice Ȋle Durable. Rather than offering Mauritius as a guide to other SIDS, it presents a useful case study of the tension between establishing social equity and carving out a functional role within the global economy.
Impact of Different Tillage Method on Changing of Soil Organic Carbon in Semi-arid Area

In order to discuss the existence forms and the conversion process of the soil organic carbon,and the relationship on different soil organic carbon components under the different tillage method in loess plateau semi-arid area,the soil in different tillage condition had be analyzed by chemical method.The results showed that no-till can increase the content of soil organic carbon.Leaving crops straw affects soil total organic carbon(TOC),no-till with straw cover(NTS)can significant improve the content of TOC on the surface of the soil,TOC content can be improve 20.77% in semi-arid area after return straw for 17 months.Conventional tillage with no straw(T)and conventional tillage with straw incorporated(TS)can destroy soil construction,go against accumulation of soil organic carbon.There is no significant discrepancy in soil sodium pyrophosphate-extractable carbon(SPPC)among different soil treatments.There is big range of variation on the rate of SPPC in TOC,from 23.70% to 50.60%. NTS has the highest SPPC content,NT has the lowest SPPC content,that is no-till with straw cover is benefit to improve the soil physical chemistry property,but only no-till,doesn't return straw go against the accumulation of the soil organic matter.The content of hot water-extractable carbohydrate(HWCC)accumulation is NTSNTTST,that is no-till with straw cover benefit to the active organic matter accumulation,to improve the soil physical chemistry property,to provide lots of energy resource to soil microorganism and to benefit soil development.
Robotic Volatile Sampling for Early Detection of Plant Stress: Precision Agriculture Beyond Visual Remote Sensing

Global agriculture is challenged to provide food for a human population that is larger than ever before and still increasing. This is accompanied by the need to reduce the large global impacts of agriculture while increasing yields. Early identification of plant stress enables fast intervention to limit crop losses and optimized application of pesticides and fertilizer to reduce environmental impacts. Current image-based approaches identify plant stress responses hours or days after the stress event, usually only after substantial damage has occurred and visual cues become apparent. In contrast, plant volatiles are released seconds to hours after stress events and can quickly indicate both the type and severity of stress. An automatable and nondisruptive sampling method is needed to enable the use of plant volatiles for monitoring plant stress in precision agriculture. In this work, we detail the development of a plant volatile sampler that can be deployed and collected with an uncrewed aerial vehicle (UAV). The effect of sampling flow rate, horizontal distance to volatile source, and overhead downwash on collected volatiles is investigated, along with the deployment accuracy and retrieval successes with manual flight. Finally, volatile sampling is validated in outdoor tests. The possibility of robotic collection of plant volatiles is a first and important step toward the use of chemical signals for early stress detection and opens up new avenues for precision agriculture beyond visual remote sensing.