DeBERTa-GRU: Sentiment Analysis for Large Language Model

Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the text.Sentiment analysis is essential in business and society because it impacts strategic decision-making.Sentiment analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance correlations.The execution time increases due to the sequential processing of the sequence models.However,the calculation times for the Transformer models are reduced because of the parallel processing.This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations.In particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment analysis.Using the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics correctly.The proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations.The BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security threats.Meanwhile,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of trust.The decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing mechanisms.In this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their causes.Additionally,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security threats.Finally,we discuss the challenges posed by BGP security problems and outline prospects for future research.

Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent integration.The sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and processing.However,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion terminals.Simultaneously,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart grid.In response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these issues.The scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT terminals.It then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy states.This comprehensive approach reduces the impact of subjective factors on trust measurements.Additionally,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious terminals.This,in turn,enhances the security and reliability of the smart grid environment.The effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation experiments.Notably,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

Variations in Surface Urban Heat Island and Urban Cool Island Intensity:A Review Across Major Climate Zones

The climate has an impact on the urban thermal environment,and the magnitude of the surface urban heat island(SUHI)and urban cool island(UCI)vary across the world’s climatic zones.This literature review investigated:1)the variations in the SUHI and UCI intensity under different climatic backgrounds,and 2)the effect of vegetation types,landscape composition,urban configuration,and water bodies on the SUHI.The SUHI had a higher intensity in tropical(Af(tropical rainy climate,Köppen climate classification),Am(tropical monsoon climate),subtropical(Cfa,subtropical humid climate),and humid continental(Dwa,semi-humid and semi-arid monsoon climate)climate zones.The magnitude of the UCI was low compared to the SUHI across the climate zones.The cool and dry Mediterranean(Cfb,temperate marine climate;Csb,temperate mediterranean climate;Cfa)and tropical climate(Af)areas had a higher cooling intensity.For cities with a desert climate(BWh,tropical desert climate),a reverse pattern was found.The difference in the SUHI in the night-time was greater than in the daytime for most cities across the climate zones.The extent of green space cooling was related to city size,the adjacent impervious surface,and the local climate.Additionally,the composition of urban landscape elements was more significant than their configuration for sustaining the urban thermal environment.Finally,we identified future research gaps for possible solutions in the context of sustainable urbanization in different climate zones.

Computational Analysis for Computer Network Model with Fuzziness

A susceptible,exposed,infectious,quarantined and recovered(SEIQR)model with fuzzy parameters is studied in this work.Fuzziness in the model arises due to the different degrees of susceptibility,exposure,infectivity,quarantine and recovery among the computers under consideration due to the different sizes,models,spare parts,the surrounding environments of these PCs and many other factors like the resistance capacity of the individual PC against the virus,etc.Each individual PC has a different degree of infectivity and resis-tance against infection.In this scenario,the fuzzy model has richer dynamics than its classical counterpart in epidemiology.The reproduction number of the developed model is studied and the equilibrium analysis is performed.Two different techniques are employed to solve the model numerically.Numerical simulations are performed and the obtained results are compared.Positivity and convergence are maintained by the suggested technique which are the main features of the epidemic models.

Structural, elastic, and electronic properties of topological semimetal WC-type MX family by first-principles calculation

Recently, the non-centrosymmetric WC-type materials(i.e., MoP, ZrTe, TaN, etc) have attracted extensive interest due to the discovery of their topological properties.By means of the first-principles calculations, here we have investigated the structural, thermodynamic, elastic, and electronic properties of the WC-type MX compounds(TiS, TiSe, TiTe, ZrS, ZrSe,ZrTe, HfS, HfSe, and HfTe).Among these nine compounds, five of them(TiS, ZrS, ZrSe0.9, ZrTe, and Hf0.92 Se) have been experimentally synthesized to crystallize in the WC-type structure and other four members have never been reported.Our calculations demonstrated that they are all structurally, thermodynamically, and dynamically stable, indicating that all of them should be possibly synthesized.We have also derived their elastic constants of single crystalline and their bulk and shear moduli in terms of the R.Hill approximations.Furthermore, in similarity to ZrTe, all these compounds have been theoretically derived to be topological semimetals.Whereas TiS is unique because of the coexistence of the Dirac nodal lines(DNLs) and sixfold degenerate nodal points(sixfold DNPs), the other eight members are revealed to exhibit coexisted Weyl nodes(WPs) and triply degenerate nodal points(TDNPs).Their electronic and topological properties have been further discussed.

Optimizing Row Spacing to Ameliorate the Productivity of Spring Sugarcane (Saccharum officinarum L.)

One of the main reasons behind reduced cane yield is pathetic method of planting. Planting method and row spacing are the most important yield contributing factors in sugarcane. A field experiment was carried out in order to determine quality and yield of sugarcane in various spatial arrangements. Treatments are 180 cm spaced trenches with triple row strips;180 cm spaced trenches with alternate row strips;120 cm spaced trenches with double row strips and 60 cm spaced furrow with single row. Perusal of data revealed that 3.6%, 13.4%, 15%, 15.3% more cane diameter (cm), cane length (cm), stripped cane yield (t·haˉ1), sugar yield (t·haˉ1) were obtained from 180 cm spaced trenches with triple row strips as compared to conventional planting method i.e. 60 cm spaced furrows. While the number of millable canes mˉ2, polarity %, cane juice purity %, cane juice %, commercial cane sugar % and cane sugar recovery % remained non-significant by different planting techniques.

Silicon Mediated Arsenic Reduction in Rice by Limiting Its Uptake

This study was planned to examine the effects of exogenous silicon supply on growth parameters and arsenic accumulation level in rice. The experiment was conducted in the wire house of Saline Agriculture Research Centre, Institute of Soil and Environmental Science, University of Agriculture Faisalabad. The study was comprised of treatments viz: control;(100 μM Arsenic);(200 μM Arsenic);(5 mM Silicon);(5 mM Silicon + 100 μM Arsenic) and (5 mM Silicon + 200 μM Arsenic). Results revealed that maximum shoot fresh weight, shoot dry weight, root fresh weight and root dry weight were observed in (5 mM Si) solution. In the same way, maximum number of tillers was also recorded in (5 mM Si) solution;while silicon application failed to alleviate arsenic concentration of rice genotype.

Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan

Anthropogenic activities and natural processes are continuously altering the mountainous environment through deforestation, forest degradation and other land-use changes. It is highly important to assess, monitor and forecast forest cover and other land-use changes for the protection and conservation of mountainous environment. The present study deals with the assessment of forest cover and other land-use changes in the mountain ranges of Dir Kohistan in northern Pakistan, using high resolution multi-temporal SPOT-5 satellite images. The SPOT-5 satellite images of years 2004, 2007, 2010 and 2013 were acquired and classified into land-cover units. In addition, forest cover and land-use change detection map was developed using the classified maps of 2004 and 2013. The classified maps were verified through random field samples and Google Earth imagery(Quick birds and SPOT-5). The results showed that during the period 2004 to 2013 the area of forest land decreased by 6.4%, however, area of range land and agriculture land have increased by 22.1% and 2.9%, respectively. Similarly, barren land increased by 1.1%, whereas, area of snow cover/glacier is significantly decreased by 21.3%. The findings from the study will be useful for forestry and landscape planning and can be utilized by the local, provincial and national forest departments; and REDD+ policy makers in Pakistan.

Synthesis of silver and gold nanoparticles from leaf of Litchi chinensis and its biological activities

Objective: To synthesize and isolate silver and gold nanoparticles from Litchi chinensis leaf methanolic extract, and to evaluate its comparative biological activities including muscles relaxant, analgesic, anti-inflammatory and antidiarrheal. Methods: The gold and silver nanoparticles were synthesized by dissolving methanolic extract in gold chloride and silver nitrate solution separately which were confirmed by colour change and UV-Vis spectroscopy, and pellets were collected through centrifugation. Biological activities of the extract were conducted on BALB/c mice through various standard methods and the data were subjected to One-way ANOVA. Results: The colorless gold chloride solution changed to purple soon after the addition of plant extract, demonstrating that the reaction took place and gold ions were reduced to gold nanoparticles, while colorless silver nitrate solution changed to light and dark brown that was indicative of silver nanoparticles. The muscles relaxant activity showed that silver nanoparticles were more effective than gold nanoparticles and methanolic extract in traction test. The analgesic activity showed that silver and gold nanoparticles showed highest percentage decrease in acetic acid induced writhing at the doses of 50, 100 and 150 mg/kg b.w. The highest anti-inflammatory activity was produced by gold nanoparticles followed by silver nanoparticles, while low activity was observed in methanolic leaf extract. Only the crude methanolic extract showed significant antidiarrheal activity as compared to the standard drug atropine sulphate, while antidiarrheal activities of gold and silver nanoparticles were non-significant. Conclusions: The present work concludes that isolated silver and gold nanoparticles from leaf methanolic extract shows strong muscles relaxant, analgesic and anti-inflammatory activities while crude methanolic extract possesses good antidiarrheal activity.

Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan

Landsat-8 spectral values have been used to map the earth’s surface information for decades.However,forest types and other land-use/land-cover(LULC)in the mountain terrains exist on different altitudes and climatic conditions.Hence,spectral information alone cannot be sufficient to accurately classify the forest types and other LULC,especially in high mountain complex.In this study,the suitability of Landsat-8 spectral bands and ancillary variables to discriminate forest types,and other LULC,using random forest(RF)classification algorithm for the Hindu Kush mountain ranges of northern Pakistan,was discussed.After prior-examination(multicollinearity)of spectral bands and ancillary variables,three out of six spectral bands and five out of eight ancillary variables were selected with threshold correlation coefficients r2<0.7.The selected datasets were stepwise stacked together and six Input Datasets(ID)were created.The first ID-1 includes only the Surface Reflectance(SR)of spectral bands,and then in each ID,the extra one ancillary variable including Normalized Difference Vegetation Index(NDVI),Normalized Difference Water Index(NDWI),Normalized Difference Snow Index(NDSI),Land Surface Temperature(LST),and Digital Elevation Model(DEM)was added.We found an overall accuracy(OA)=72.8%and kappa coefficient(KC)=61.9%for the classification of forest types,and other LULC classes by using the only SR bands of Landsat-8.The OA=81.5%and KC=73.7%was improved by the addition of NDVI,NDWI,and NDSI to the spectral bands of Landsat-8.However,the addition of LST and DEM further increased the OA,and Kappa coefficient(KC)by 87.5%and 82.6%,respectively.This indicates that ancillary variables play an important role in the classification,especially in the mountain terrain,and should be adopted in addition to spectral bands.The output of the study will be useful for the protection and conservation,analysis,climate change research,and other mountains forest-related management information.

The Nutrient Expert decision support system improves nutrient use efficiency and environmental performance of radish in North China

Excessive fertilization has led to nutrient use inefficiency and serious environmental consequences for radish cultivation in North China.The Nutrient Expert(NE)system is a science-based,site-specific fertilization decision support system,but the updated NE system for radish has rarely been evaluated.This study aims to validate the feasibility of NE for radish fertilization management from agronomic,economic,and environmental perspectives.A total of 46 field experiments were conducted over four seasons from April 2018 to November 2019 across the major radish growing regions in North China.The results indicated that NE significantly reduced N,P_(2)O_(5),and K_(2)O application rates by 98,110,and 47 kg ha^(-1) relative to those in the farmers’practice(FP),respectively,and reduced N and P_(2)O_(5) inputs by 48 and 44 kg ha^(-1),respectively,while maintaining the same K_(2)O rate as soil testing(ST).Relative to FP and ST,NE significantly increased radish yield by 2.7 and 2.6 t ha^(-1)(4.2 and 4.0%)and net returns by 837 and 432 USD ha^(-1),respectively.On average,NE significantly improved the agronomic efficiency(AE)of N,P,and K(relative to FP and ST)by 42.4 and 31.0,67.4 kg kg^(-1) and 50.9,and 20.3 and 12.3 kg kg^(-1);enhanced the recovery efficiency(RE)of N,P,and K by 11.4 and 7.0,14.1 and 7.5,and 11.3 and 6.3 percentage points;and increased the partial factor productivity(PFP)of N,P,and K by 162.9 and 96.8,488.0 and 327.3,and 86.9 and 22.4 kg kg^(-1),respectively.Furthermore,NE substantially reduced N and P_(2)O_(5) surpluses by 105.1 and 115.1 kg ha^(-1),respectively,and decreased apparent N loss by 110.8 kg ha^(-1) compared to FP.These results indicated that the NE system is an effective and feasible approach for improving NUE and promoting cleaner radish production in North China.

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the text.To accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that,we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

Space-Time Cluster Analysis of Accidental Oil Spills in Rivers State, Nigeria, 2011–2019

Oil spills cause environmental pollution with a serious threat to local communities and sustainable development.Accidental oil spills can be modelled as a stochastic process where each oil spill event is described by its spatial locations and incidence-time and hence allow for space-time cluster analysis.Spacetime cluster analysis can detect space-time pattern distribution of oil spills which can be useful for implementing preventive measures and evidence-based decision making.This study aims to detect the space-time clusters of accidental oil spills in Rivers state,Nigeria through the Space-time Scan Statistic.The Space-time Scan Statistic was applied under the permutation model to the oil spill data(each for sabotage and operational oil spills)collected at Local Government Area(LGA)-level during the period from 2011 to 2019.The results show that the sabotage oil spill clusters have covered most of the LGAs in the southern part of the state at the start of the study period and then in 2018–2019,it moved to the west covering a single LGA.The operational oil spill clusters covered two neighboring LGAs in the south.In addition,the temporal cluster of sabotage oil spills was seen in 2019 and operational oil spills in 2011–2012.The sabotage oil spills show an increasing trend with the maximum in 2019 while the operational oil spills show a decreasing trend with the minimum in 2019.These findings assist in more effective decision-making for combating the environmental problems and controlling the future spill incidence in the cluster-regions.

An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data

Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies.The state-of-the-art method for this kind of problem is the Space-time Scan Statistics(SaTScan)which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson orGaussian counts.Addressing this problem,an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution.However,it is based on the population counts data which are not always available in the least developed countries.In addition,the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales,where the catchment area for each hospital/pharmacy is undefined.We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts.The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts.The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods:Multi-EigenSpot and SaTScan.The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.

The Carbon Sinks and Mitigation Potential of Deodar (<i>Cedrus deodara</i>) Forest Ecosystem at Different Altitude in Kumrat Valley, Pakistan

Forest carbon monitoring and reporting are critical for informing global climate change assessment. The regional estimates of forest carbon attached greater attention, to assess the role of forest in carbon mitigation. Here using field inventory, we examined the carbon sink and mitigation potential of monospecific Deodar forest in the Kumrat valley, of Hindu Kush Himalaya, Region of Pakistan, at a different elevation. The elevation of monospecific Deodar forest ranges from 2300 to 2700 m (a.s.l). We divided the forest into three elevation classes (that is 2300 - 2400 m (EI) 2400 - 2500 m (EII) and 2500 - 2700 m (EIII) a.s.l respectively). In each elevation class, we laid out 09 sample plots (33*33 m2) for measuring carbon values in living tree biomass (LT), soil (SC), litter, dead wood, cone (LDWC) and understory vegetation (USV). Our results showed that the carbon density at EI was 432.37 ± 277.96 Mg·C-1, while the carbon density at EII and EIII was 668.35 ± 323.94 and 1016.79 ± 542.99 Mg·C-1 respectively. Our finding revealed that the carbon mitigation potential of the forest increases with increasing elevation. Among the different elevation classes, EIII stored significantly higher carbon due to the dominance of mature, old age, larger trees, and the minimum anthropogenic disturbance, whereas EI stored statistically lower carbon because of maximum anthropogenic disturbance, which resulted in the removal of mature and over-mature trees. Furthermore, our correlation analysis between tree height and carbon stock and basal area and LT carbon, underlines that the basal area is the stronger predictor of LT carbon estimation than height. Overall our results highlight that deodar forest stored 716.94 ± 462.06 Mg?C·ha-1. However, the rehabilitation, preservation and sustainable management of disturb forest located at a lower elevation could considerably improve carbon mitigation potential.

Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data centers.Modern TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow rerouting.In this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance deterioration.We look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a solution.Simulations are used to assess the proposed NUFTCP.Our findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

Non-covalent interaction of atomically dispersed dual-site catalysts featuring Co and Ni nascent pair sites for efficient electrocatalytic overall water splitting

The scarcity of highly effective and economical catalysts is a major impediment to the widespread adop-tion of electrochemical water splitting for the generation of hydrogen.MoS_(2),a low-cost candidate,suffers from inefficient catalytic activity.Nonetheless,a captivating strategy has emerged,which involves the en-gineering of heteroatom doping to enhance electrochemical proficiency.This investigation demonstrates a successful implementation of the strategy by combining ultrathin MoS_(2) nanosheets with Co and Ni dual single multi-atoms(DSMAs)grown directly on 2D N-doped carbon nanosheets(CoNi-MoS_(2)/NCNs)for the purpose of improving hydrogen evolution reaction(HER)and oxygen evolution reaction(OER).With the aid of a dual-atom doped bifunctional electrocatalyst,effective water splitting has been achieved across a broad pH range in electrolytes.The double doping of Co and Ni strengthens their interactions,thereby altering the electromagnetic composition of the host MoS_(2) and ultimately leading to improved electrocat-alytic activity.Additionally,the synergistic effect between NCNs and MoS_(2) nanosheets provided efficient electron transport channels for ions and an ample surface area with open voids for ion diffusion.Con-sequently,the CoNi-MoS_(2)/NCNs catalysts demonstrated exceptional stability and activity,producing low degree overpotentials of 180.5,124.9,and 196.4 mV for HER and 200,203,and 207 mV for OER in neu-tral,alkaline,and acidic mediums,respectively,while also exhibiting outstanding overall water-splitting performance,durability,and stability when used as an electrolyzer at universal pH.

Enhancing environmental quality in the United States by linking biomass energy consumption and load capacity factor

This study analyzes the impact of biomass energy,financial development,and economic growth on environmental quality using the novel Fourier autoregressive distributed lag(ARDL)approach on annual data for the period 1965–2018 in the United States(USA).The study analyzes the impact of related variables on the load capacity factor(LCF)as well as on indicators of environmental degradation such as carbon dioxide emissions and ecological footprint.The LCF is one of the most comprehensive environmental indicators to date,encompassing both biocapacity and ecological footprint.In this regard,this study contributes to the environmental economics literature by examining,for the first time,the impact of biomass energy on the LCF.The results of the cointegration test show that there is only a long-run relationship between the LCF and the independent variables.According to the Fourier ARDL results,biomass energy improves the environmental quality,while financial development has no effect on the LCF.Moreover,the increase in per capita income reduces the LCF.Furthermore,since the income elasticity is larger in the long run than in the short-run,the environmental Kuznets curve is validated.Therefore,the United States government should encourage the use of biomass and investment in this form of energy.

Preparation of epoxy resin/rare earth doped aluminate nanocomposite toward photoluminescent and superhydrophobic transparent woods

A translucent wooden substrate with long-lasting phosphorescence,high photostability and durability,tough surface,ultraviolet protection,high optical transmittance,and superhydrophobicity was developed.This long-lasting phosphorescent wooden substrate is able to continue emitting light for extended time periods.Lignin-modified wood(LMW)was immobilized with a solution of epoxy resin(ER)and rare-earth doped aluminate(REDA)phosphor nanoparticles(NPs).For an improved dispersion of pigment,REDA was synthesized in a nanoscale particle size,and characterized by transmission electron microscopy(TEM)to indicate a particle size of 8-14 nm.The crystal structure of REDA nanoparticles was also proved by X-ray diffraction(XRD).For an improved production of long-persistent phosphorescent colo rless woods,REDA must be well-dispersed in MAA without aggregation.Absorption and emissio n,as well as decay and lifetime spectra were explored.The morphologies of the wooden substrates with different ratios of REDA were investigated by scanning electron microscopy(SEM),X-ray fluorescence(XRF)analysis,Fourier transform infrared spectra(FT-IR),elemental mapping,and energy-dispersion Xray(EDXA).The phosphorescent woods show changes in color from colorless to green under ultraviolet(UV)irradiation,and to yellowish-green in the dark,as proved by the colorimetric parameters of the CIE Lab system.The afterglow wood samples display an absorbance band at 365 nm and two phosphorescent bands at 431 and 520 nm.Improved UV shielding,photostability,and hydrophobicity were explored.With increasing REDA ratio,both static contact and slide angles are found to improve in the ranges of147.6°-163.6°and 9°-14°,respectively.The long-lasting photoluminescence is optimized at a REDA ratio of 8%.The present strategy shows a large-scale production approach of multiple functional woods for many potential applications,such as smart glow in the dark windows and safety signs.