Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Functional Treatments for Modified Wood

Wood modification is now widely recognized as offering enhanced properties of wood and overcoming issues such as dimensional instability and biodegradability which affect natural wood. Typical wood modification systems use chemical modification, impregnation modification or thermal modification, and these vary in the properties achieved. As control and understanding of the wood modification systems has progressed, further opportunities have arisen to add extra functionalities to the modified wood. These include UV stabilisation, fire retardancy, or enhanced suitability for paints and coatings. Thus, wood may become a multi-functional material through a series of modifications, treatments or reactions, to create a high-performance material with previously impossible properties. In this paper we review systems that combine the well-established wood modification procedures with secondary techniques or modifications to deliver emerging technologies with multi-functionality. The new applications targeted using this additional functionality are diverse and range from increased electrical conductivity, creation of sensors or responsive materials, improvement of wellbeing in the built environment, and enhanced fire and flame protection. We identified two parallel and connected themes: (1) the functionalisation of modified timber and (2) the modification of timber to provide (multi)-functionality. A wide range of nanotechnology concepts have been harnessed by this new generation of wood modifications and wood treatments. As this field is rapidly expanding, we also include within the review trends from current research in order to gauge the state of the art, and likely direction of travel of the industry.
Integration of Audio video Speech Recognition using LSTM and Feed Forward Convolutional Neural Network

In the current scenario, audio visual speech recognition is one of the emerging fields of research, but there is still deficiency of appropriate visual features for recognition of visual speech. Human lip-readers are increasingly being presented as useful in the gathering of forensic evidence but, like all human, suffer from unreliability in analyzing the lip movement. Here we used a custom dataset and design the system in such a way that it predicts the output for the lip reading. The problem of speaker independent lip-reading is very demanding due to unpredictable variations between people. Also due to recent developments and advances in the fields of signal processing and computer vision. The task of automating the lip reading is becoming a field of great interest. Here we use MFCC techniques for audio processing and LSTM method for visual speech recognition and finally integrate the audio and video using feed forward neural network (FFNN) and also got good accuracy. That is why the AVSR technique attract a great attention as a reliable solution for the speech detection problem. The final model was capable of taking more appropriate decision while predicting the spoken word. We were able to get a good accuracy of about 92.38% for the final model.
An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

Currently, localization in distributed massive MIMO (DM-MIMO) systems based on the fingerprinting (FP) approach has attracted great interest. However, this method suffers from severe multipath and signal degradation such that its accuracy is deteriorated in complex propagation environments, which results in variable received signal strength (RSS). Therefore, providing robust and accurate localization is the goal of this work. In this paper, we propose an FP-based approach to improve the accuracy of localization by reducing the noise and the dimensions of the RSS data. In the proposed approach, the fingerprints rely solely on the RSS from the single-antenna MT collected at each of the receive antenna elements of the massive MIMO base station. After creating a radio map, principal component analysis (PCA) is performed to reduce the noise and redundancy. PCA reduces the data dimension which leads to the selection of the appropriate antennas and reduces complexity. A clustering algorithm based on K-means and affinity propagation clustering (APC) is employed to divide the whole area into several regions which improves positioning precision and reduces complexity and latency. Finally, in order to have high precise localization estimation, all similar data in each cluster are modeled using a well-designed deep neural network (DNN) regression. Simulation results show that the proposed scheme improves positioning accuracy significantly. This approach has high coverage and improves average root-mean-squared error (RMSE) performance to a few meters, which is expected in 5G and beyond networks. Consequently, it also proves the superiority of the proposed method over the previous location estimation schemes.
Nipping Diseases in the Bud? Ethical and Social Considerations of the Concept of ‘Disease Interception’

Abstract ‘Disease interception’ describes the treatment of a disease in its clinically inapparent phase and is increasingly used in medical literature. However, no precise definition, much less an ethical evaluation, has been developed yet. This article starts with a definition of ‘disease interception’ by distinguishing it from other preventions. It then analyses the ethical and social implications of the concept in light of the four principles of medical ethics by Beauchamp and Childress. The term ‘disease interception’ refers to a form of secondary prevention applied in a short interception window intended to prevent a preclinical disease from developing further. We propose the definition ‘early and targeted secondary prevention by treatment’. The ethical evaluation of the concept shows that while it promises to be beneficial, it raises a number of ethical and social challenges regarding patient autonomy and justice. In order to ensure decision-making that respects patient autonomy, commercially motivated metaphors such as ‘disease interception’ should make way for precise definitions. Future research should not only focus on how to detect clinically inapparent diseases but also on the ethical question, when this is justifiable and what consequences it has for the individual and society as a whole.
The digital twin combined with real-time performance measurement in lean manufacturing

This chapter examines the advantages that a digital twin and real-time performance measurement brings to lean manufacturing. The lean manufacturing approach improves operational performance and provides sustainable value by omitting activities that do not add value. One way to promote continuous improvement is to visualize lean processes in real time, for example, by utilizing a digital twin with the desired functionalities. To explore the benefits of utilizing a digital twin, an illustrative case is presented that describes the application of a lean strategy to facilitate the maintenance services process in real time with a digital twin. Chapter reveals that a digital twin combined with real-time performance measurement improves process monitoring and control, positively impacts the quality of leadership and the customer relationship, enables worker engagement in the lean process, increases collaboration and integration among different levels, and facilitates communication. The chapter should prove useful for anyone considering a digital-twin implementation.
Dual inhibitors of epidermal growth factor receptor and topoisomerase IIα derived from a quinoline scaffold

Based on the quinazoline bearing EGFR inhibitors, a series of thirty four compounds having a quinoline scaffold were synthesised and evaluated in vitro for EGFR kinase inhibitory activity. A structure–activity relationship study revealed that 2,4-bis(arylamino) substituted quinolines possessed better anti-EGFR kinase activity. Compounds 3f and 3m emerged as potent EGFR kinase inhibitors (200 and 210 nM, respectively) and showed excellent anticancer activity at the micromolar level against a panel of cancer cell lines comparable to erlotinib. Furthermore, representative compounds inhibited the human topoisomerase IIα selectively and catalytically, did not intercalate with DNA, increased intracellular ROS concentration (except 3m) and altered the mitochondrial membrane potential of the cancer cells. Cell cycle analysis and annexin-V staining in a lung cancer cell line showed that the compounds delayed cell cycle progression by inducing cell cycle arrest and subsequent apoptosis at the G1 phase. The facts were further corroborated through molecular modeling studies.
Forecasting with strategic transport models corrected for endogeneity

The correction of endogeneity is a problem in strategic transport modelling; the question remains on how to make appropriate forecasts in this case. We propose a variation of the classical Control Function (CF) method, called Control Function Updated (CFU), which considers updating the endogeneity correction using information from the future equilibria. The proposed method is assessed using Monte Carlo simulation for a strategic transport model affected by three endogeneity sources, examining the equilibrium results for various future scenarios. We compare the CFU method by doing nothing and with the classical CF approach. The forecasts are evaluated in terms of recovering the true (simulated) travel times and two indices of fit. Results show that the endogenous (do nothing) model produces large biases in simulated travel times and poor goodness-of-fit measures that steeply worsen with time in future scenarios. The corrected models perform much better and, in particular, the new CFU approach shows statistically significant improvements over the classical approach in all scenarios tested.
New observations of NO2 in the upper troposphere from TROPOMI

Abstract. Nitrogen oxides ( NO x ≡ NO + NO 2 ) in the NOx -limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT, and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1 ∘ × 1 ∘ by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free-tropospheric NO2 determined by comparison to Pandora total columns at high-altitude free-tropospheric sites at Mauna Loa, Izana, and Altzomoni and MAX-DOAS and Pandora tropospheric columns at Izana. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20–30 pptv over remote oceans to >80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud-top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent ( R=0.6 –0.8) with an existing coarser spatial resolution (5 ∘ latitude × 8 ∘ longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also due to altitude differences in TROPOMI and OMI cloud products and NO2 retrieval algorithms. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.
Heritage-based tribalism in Big Data ecologies: Deploying origin myths for antagonistic othering

This article presents a conceptual and methodological framework to study heritage-based tribalism in Big Data ecologies by combining approaches from the humanities, social and computing sciences. We use such a framework to examine how ideas of human origin and ancestry are deployed on Twitter for purposes of antagonistic ‘othering’. Our goal is to equip researchers with theory and analytical tools for investigating divisive online uses of the past in today’s networked societies. In particular, we apply notions of heritage, othering and neo-tribalism, and both data-intensive and qualitative methods to the case of people’s engagements with the news of Cheddar Man’s DNA on Twitter. We show that heritage-based tribalism in Big Data ecologies is uniquely shaped as an assemblage by the coalescing of different forms of antagonistic othering. Those that co-occur most frequently are the ones that draw on ‘Views on Race’, ‘Trust in Experts’ and ‘Political Leaning’. The framings of the news that were most influential in triggering heritage-based tribalism were introduced by both right- and left-leaning newspaper outlets and by activist websites. We conclude that heritage-themed communications that rely on provocative narratives on social media tend to be labelled as political and not to be conducive to positive change in people’s attitudes towards issues such as racism.
Improvement of the optical and photocatalytic properties of ZnAl2O4: 1% La3+, x% Pb2+ nanoparticles synthesized by citrate sol-gel route

Samples of pure zinc aluminate (ZnAl2O4) and doped both with lead (Pb2+) at different ratios (0, 0.5, 1, 1.5, 2 and 2.5% mol) and a constant amount of lanthanum (La: 1% mol), were prepared by the citrate sol-gel technique, and then annealed at 900°C for 2h. In order to study the structural, optical and thermal properties; different characterization methods were used, such as: powder X-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDX), differential scanning calorimetry (DSC), TGA, Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. The Analyzes by XRD revealed the presence of the cubic single phase ZnAl2O4 for all samples, with a crystallites size between 19 and 25 nm. These results were confirmed using FTIR, Raman spectroscopy and SEM. Also, photocatalytic study for different samples of ZnAl2O4 shows that they can be used like as photocatalyst and good adsorbents for degradation of Hexamethyl crystallized violet dye in aqueous solution.
In Search for the Adaptive Self: A Model of Adaptive Self-Concept

The main aim of this article is to present a descriptive social-cognitive model of the adaptive self-concept (ASC) which integrates knowledge concerning the relationship between two aspects of the self—self-awareness and self-knowledge—and optimal functioning. We propose that adaptive self-awareness is moderately frequent, non-ruminative, focused on inner states, and motivated by curiosity. Adaptive self-knowledge is defined as accurate, complex, integrated and consisting of easily accessible self-beliefs, both abstract and concrete. The broader context for the ASC model is discussed, including its regulatory and interpersonal functions and factors which influence ASC development. The limitations of the model are discussed and suggestions are made for future investigations.
Evaluation of housekeeping gene expression stability in carnation (Dianthus caryophyllus)

ABSTRACT The real-time quantitative reverse transcription PCR (RT-qPCR) is widely used for gene expression analysis, owing to its advantages of high specificity, sensitivity and repeatability. Suitable reference gene is an absolute prerequisite for accurate normalisation. In this study, three computational statistical methods before performing RT-qPCR, including geNorm, NormFinder and BestKeeper was used to integrated expression stability evaluations of 12 frequently-used reference genes in Dianthus caryophyllus across different experimental conditions. The results showed that the expression stability of candidate genes varies greatly in different sample pools. The expression of TIP41 (TIP41-like family protein) and UBQ10 (ubiquitin10) was relatively stable under different experimental conditions, while CYP (cytochrome P450) and TUA (a tubulin) could act as reliable internal controls in different tissues. The reliable reference genes under treatment of hormone, heavy metal, salt, heat, cold, flooding and drought were EF1α (elongation factor 1 alpha)/TIF5A (translation initiation factor 5A), UBQ10/18S (18S Ribosome RNA), UBQ10/CYP, TUB (β-tubulin gene)/TIP41 (TIP41-like family protein), TIF5A/EF1α, TUB/TIP41 and TIP41/PP2A (protein phosphatase 2A). Some common housekeeping genes, such as ACT (actin gene) and GAPDH (glyceraldehyde-3-phosphate dehydrogenase), exhibited unstable expression patterns. This was the first systematic study on the stability of internal reference genes of selection of reference genes in Carnation.
Köppen Climate Zones in China Over the Last 21,000 Years

This study investigates the spatiotemporal evolution of Köppen climate zones in China over the past 21,000 calendar years based on a set of transient simulations from TraCE‐21ka using version 3 of the Community Climate System Model (CCSM3). The model results reproduce the modern distribution of Köppen climate zones observed by the Climatic Research Unit (CRU) TS4.03 dataset reasonably well. More than half of the national land has experienced changes in climate types, which is characterized by the warmer and drier trend in major climate types from the last glacial maximum (LGM) to the present. This evolution mainly includes the emergence and northward extension of tropical climates in far south of China, slight expansion of arid climates toward surrounding regions, northward invasion of temperate climates at the expense of cold climates, and retreat of polar climates to the higher altitudes of the central Tibetan Plateau. Temperature and precipitation have played different roles in the transformations of various Köppen climate types. Temperature has been dominant throughout the entire period, and precipitation has become increasingly important since the late Holocene. The comparison between the reconstructed vegetation, as estimated from pollen records, and simulated vegetation inferred from Köppen climate types displays consistency in the aforementioned change trends over China.
Using Graph Networks to Quantify the Cumulative Importance of Riparian Wetlands within a Watershed

Wetlands provide many valuable ecosystem functions including nutrient cycling and retention, sediment capture, flood reduction, carbon storage, and habitat for water-dependent plant and wildlife species. The alteration of landscapes and the deterioration of upstream wetlands have been determined to be detrimental to downstream stream and watershed health. The position of the wetland in the landscape and its quality and size can significantly change the influence it has on stream condition. This research tests the efficacy of graphed networks created from the terrestrial-wetland-stream landscape to quantify the cumulative benefits of riparian wetlands within a watershed. We tested a combination of network parameters such as node degree, betweenness centrality, and the integral index of connectivity. Graphed networks are created by nodes that are connected by edges. Nodes were defined as stream reaches that extend out to the riparian landscape and edges as the stream confluences that connect them. Nodes were weighted by their capacity to perform ecosystem functions and the opportunity for such functions. We found that the network-based approach can quantify the impact of riparian wetland loss revealing that some riparian losses within the watershed were inherently worse than others at reducing connectivity and cumulative wetland function within the watershed. Incorporating these network metrics into wetland assessments can quantify the cumulative influence of geographic position, wetland function and size on overall wetland benefits within the watershed. This new approach can be applied to watershed planning efforts to assist managers with identifying wetlands for protection, enhancement, and re-establishment.
Mask Material Filtration Efficiency and Mask Fitting at the Crossroads: Implications during Pandemic Times

ABSTRACT The COVID-19 pandemic triggered the widespread use and need for respirators and face masks for the healthcare workers and public. In this study, several generally available respirators and mask designs were fit tested, and their materials were evaluated for filtration efficiency using 250 nm polystyrene latex particles. Efficiency testing was performed for 2 and 0.5 h at low (2.6 L min-1) and high (7.4 L min-1) airflows, respectively, using ~17.4 cm2 material area. As expected, all N95 and KN95 respirators passed the fit test, and their materials showed efficiencies >95% for the entire experiment at both airflows. Of the three air filters used in the 3D-printed Montana masks, only the HEPA filter had a filtration efficiency >95% at both airflows. Regardless of the insert material, the Montana mask failed all fit tests. Homemade duckbill masks made of Halyard H600 sterilization wrap and WypAll X80 reusable wipe also failed the fit test, and both filter materials had an average filtration efficiency <95% at high airflows. To explain the filtration efficiency results, the structure and composition of all filter materials were determined using FE-SEM, and IR and Raman spectroscopy. In conclusion, when highly efficient materials are used in masks that do not fit the users properly, the potential of these materials to protect the users from aerosols is compromised. Therefore, the mask design is as important as the filtration efficiency of the mask material.
THE ROLE OF FAMILY OWNERSHIP TOWARD CORPORATE SOCIAL RESPONSIBILITIES: GOVERNANCE AS A MODERATING VARIABLE in INDONESIA

This study examines the financial implications of corporate social responsibility (CSR) in family firms in Indonesia and the role of corporate governance (CG) in improving the CSR performance of family-controlled firms. Family firms, on the one hand, are known as firms that only care about families, are run privately and ignore wider CSR. On the other hand, it is precisely because it really maintains the "name" so that the family firms will strive to do CSR well to its stakeholders. Corporate governance is measured by using the Stochastic Frontier Approach (SFA). The research sample consists of 102 firm years. The data analysis method uses moderated regression analysis (MRA). The results of this study show that family ownership in manufacturing companies in Indonesia increases CSR disclosure, regardless of whether the stakeholders come from internal or external parties. The results also show that corporate governance, both effectively and efficiently, has no effect on CSR disclosure in general in family firms. However, effective corporate governance can strengthen the relationship between family ownership and CSR disclosure if it is based on internal and external stakeholders. Meanwhile, efficient corporate governance is proven to be unable to enhance the influence of family ownership on CSR disclosure, both in general and in separated way between internal and external stakeholders.