In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O models from the mul-ti-preprocessing PLSR 5 range method could be used for most applications, including research, whereas the C and H models from GA-PLSR performance is fair and applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving by 4.1839%, 8.1842%, 3.7587%, and 35.9404%, respec-tively. Therefore, it can be considered a reliable and non-destructive alternative method for rap-idly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for a robust application.

A non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; and ash content) for a variety of chipped and ground biomasses was investigated through a combination of thermogravimetric analysis (TGA) and near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). In this study, the thermogravimetric method was employed as a tool to determine the proximate analysis data of biomass samples through TG and DTG curves, which were generated by tracking changes in biomass mass loss over time or temperature. NIRS non−destructively scanned the chipped biomass in diffuse reflectance while ground biomass using transflectance mode, covering the wavenumber range of 3594.87 to 12,489.48 cm−1. The PLSR−based models: Full−PLSR, genetic algorithm (GA)−PLSR, successive projection algorithm (SPA)−PLSR, the spectral multi−preprocessing (MP) PLSR− 5 range method, and the MP PLSR−3 range method were developed. Model performance was assessed, compared, and selected based on the coefficient of determination in the prediction set (R2P), root mean square error in the validation set (RMSEP), and the ratio of prediction to deviation (RPD). Based on the comprehensive analysis of model performance, MC and FC models of chip biomass have demonstrated satisfactory performance which makes it applicable cautiously in any application, including research. The optimum models for MC and FC in chip biomass were constructed using GA−PLSR with 2nd derivative and Full−PLSR with a constant offset, resulting in R2P values of 0.8654 and 0.8773, RMSEP values of 0.85% and 2.12%, and RPD values of 2.9 and 3.0, respectively. Other parameters such as MC and FC in ground biomass as well as VM and ash content in both chip and ground biomass were found suitable for rough screening. The sensitivity of the selected model was assessed by calculating the limit of quantification (LOQ), and the results indicated that the calculated LOQ values for VM in both chip and ground biomass and FC in chip biomass are lower than the minimum reference values used during model development, signifying a high level of sensitivity. However, for the remaining parameters, the LOQ values surpass the established minimum reference value, suggesting the model could not predict the future samples with reference value lower than the calibration range. Nevertheless, enhancing the models by incorporation an ample number of representative biomass samples and consistently validating the models with unknown samples is imperative to ensure accurate predictions.

The ultimate analysis parameters including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass was rarely found to be predicted by nondestructive tests until to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near infrared (NIR) raw spectra of non-wood and wood samples from fast growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5−range, and MP 3−range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples was non-wood samples of 65-67% and wood samples was 33-35%, and the model performance were evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2P), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation values (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA−PLSR, yielding R2P, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR−5 range method with R2P of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR−3 range method, resulting in R2P, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of H model provided R2P, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N, exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for robust model but also they must provide their trend line characteristics in the scatter plot i.e. correlation coefficient (R), slope and intercept (same slope and slope approached to 1 and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope and intercept to obtain the better optimized model were studied. The results show that the different species affected model performance of each parameter prediction in a different manner and by scatter plot analysis which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time of the effect is studied by the principle of scatter plot.

Climate change affects both human and natural systems. Afghanistan ranked globally on the top of highly vulnerable countries to the adverse effects of climate change. The agricultural communities of Afghanistan is highly affected by climate change. Understanding farmers’ real experiences on changing climate become crucial in planning the future adaptation strategies. This study assessed the farmers' perception of climate change and its impacts on farming communities. Primary data were collected through face-to-face interviews conducted with 120 household heads. Additional qualitative data were collected by conducting 4 Focus Group Discussions (FGDs), 4 Historical Timeline Calendars (HTCs), 18 Key Informant Interviews (KII), and sketches of 4 Crop Calendars. The study reveals that climate indicators have varied and changed. The farmers express their experiences of decreasing snowfall in winter and annual rainfall in spring and summer, which led to the intensity and frequency of drought and water shortages for agriculture and rangelands in the upper and lower part of the valleys. The temperatures in winter and summer have increased and led to earlier snow melting, earlier blooming, flowering, and greening of the plants. These changes affected both positively and negatively. There is a half-month new opportunity for cultivation and increased earlier animal ranching in the rangelands. These findings can be valuable inputs for developing effective and efficient adaptation strategies.

Nepal, primarily an agricultural country, heavily relies on agricultural residue and fuelwood for daily energy requirements. In 2022, total energy consumption was 640 PJ, with traditional sources accounting for 64.17%, and fuelwood comprising 58.53% of total fuel consumption. The estimated potential supply of agricultural residue is 26 million tonnes, yielding about 442 million GJ of energy in 2021. Biomass trading often emphasizes volume or weight, necessitating a rapid and non-destructive assessment of energy properties for mutual benefit, aiding in the identification, management, and utilization of biomass sources. In this study, 200 biomass samples were collected in two batches (126 and 74 samples) from various locations in Nepal. Using Partial Least Squares Regression (PLSR), a model was developed correlating higher heating value (HHV) from a bomb calorimeter and spectral data from Fourier Transform near-infrared spectroscopy (FT-NIRS) (3595 – 12,489 cm-1) sensor. PLSR models incorporated raw spectra, eight preprocessing techniques, the multi-preprocessing five-range method, and a genetic algorithm. Outliers in the first batch were identified, and the first batch divided into an 80% calibration set and a 20% validation set, while the second batch was designated as an unknown sample set. The optimum PLSR model, utilizing first derivative preprocessing, improved accuracy by 6.77%, with coefficients of determination in the calibration set, validation set, and unknown set as 0.9694, 0.9578, and 0.8089, respectively. Root mean square errors were 132.4790 J/g, 189.4800 J/g, and 360.8845 J/g for the calibration set (RMSEC), validation set (RMSEP), and unknown set (RMSEUN), respectively. The prediction to deviation ratio (RPD) for the validation set and unknown set was 4.9 and 2.4, respectively. The cross validation model of combined sample data of every sets showed the R2CV of 0.95 and RPDCV of 4.6 indicating the model could serve as a reliable and swift non-destructive alternative for evaluating biomass HHV using NIRS and ready for updating for industrial use. However, incorporating a larger number of representative samples is crucial to enhance accuracy and develop a more comprehensive global model for predicting biomass HHV.

Distinguishable annual growth rings produce in Pinus roxburghii are an asset to find out the age of individual tree. This paper aimed to determine the age of P. roxburghii through coring samples and test the relationship with forest production. The biomass estimated, girths measured at two different sections and heights measured which allowed to determine the rate of tapering of the stand. The regression analysis was performed to test the relationship between various variables. The mean age of the P. roxburghii stand was found to be 23.97 (~24 years). The result showed the significant (p<0.05) positive correlation coefficient has been seen between age with girth at breast height, biomass, volume and carbon stock. However, no significant (p>0.05) correlation (r = 0.08) was found between age and height of the stand. In contrast, a correlation between diameter at breast height (DBH) was significant (p<0.05) and positive with volume, biomass, but no significant (p>0.05) correlation (r = 0.14) found between DBH and height of the stand. However, height has a significant (p<0.05) positive correlation with biomass. The mean biomass was 375 kg and mean annual increment (MAI) was 15 kg per tree. Rate of tapering of the studied stand predicted to be 3 cm diameter decreased with trunk height running at 100 cm from base to upward of P. roxburghii stand and vice versa. Result suggests that height-age relation is very weak whereas age, DBH, biomass and carbon has a significant correlation signifies that time-based forests' production and potential production estimation can be obtained in a relatively accurate way by utilizing the age of stand. The time-based forest production analysis is pioneer work in Nepal. The study affirms the tree ring count in P. roxburghii would be a credible and accurate method to determine the age of standing trees.

The increased adoption of information and communication technology for smart grid applications will require innovative cyber-physical system (CPS) testbeds to support research and education in the field. The groundbreaking CPS testbeds with realistic and scalable platforms have progressively gained interest in recent years, with electric power flowing in the physical layer and information flowing in the network layer. However, CPSs are critical infrastructures and not designed for testing or direct training, as any misbehaving in an actual system operation could cause a catastrophic impact on its operation. Based on that, it is not easy to efficiently train professionals in CPSs. Aiming to support the advancement and encourage the training of industry professionals, this paper proposes and develops a complete testbed with commercial tools. The testbed can reliably replicate the performance of smart grid systems and the main potential cyber threats that electric grids may face. The complex interdependencies between the cyber and physical domains are discussed in detail, and different case scenarios are presented, providing insightful guidelines for key features and design decisions for future smart grid testbeds.

Herein, we describe precisely on covalent modification of pure magnesium (Mg) surface and applied to induce in vitro osteogenic differentiation. A new concept, chemical bonding method is proposed for developing stable chemical bonds on Mg surface through serial assembly of bioactive additives including ascorbic acid (AA) and bovine serum albumin (BSA). The coating with such potential materials shows strong integrity to the Mg and could suitable for cell-interface interaction with the host tissue during implantation in bone tissue repair. The physicochemical and electrochemical properties of surface modified Mg assess how these nanoscales layered of biomolecules could demonstrate the significance improvement in chemical stability of coating. The modified Mg-OH-AA-BSA exhibits better anti-corrosion behavior with high corrosion potential (Ecorr ~ ‒ 0.96 V) and low corrosion current density (Icorr ~ 0.2 µA cm-2) as compared to pure Mg (Ecorr ~ ‒1.46 V, Icorr ~ 10.42 µA cm-2). Outer layer of BSA on Mg protects fast degradation rate of Mg which is the consequence of strong chemicals bonds between amine groups on BSA with carboxylic groups on AA. Collectively, the results suggest that surface modified Mg provides strong bio-interface and enhances the proliferation and differentiation of pre-osteoblast (MC3T3-E1) cells through protein-lipid interaction. Owing to this fact, the cost-effective and scalable covalent functionalization of Mg surface inherits biological advantage in orthopedic and dental implants with long term stability.

The guthi, an ancient institution deeply embedded in Nepal's social, cultural, and economic fabric, has long overseen the maintenance of temples, shrines, festivals, and heritage sites since the Lichchhavi era (400-750). Since 1960, however, this system has been challenged by government land reforms, which has impacted its influence on sustainable heritage conservation. Nevertheless, there's compelling evidence that land nationalization and the guthi's declining authority have harmed local heritage conservation. Nevertheless, the guthi system has endured, serving as an important informal means of heritage conservation and highlighting the adaptability of traditional institutions. These community-based trusts, originally established for social and religious purposes, have played a pivotal role in preserving cultural practices, monuments, and temples for future generations. This article aims to demonstrate the central role of the guthi as the cornerstone of Nepal's efforts to conserve and manage both tangible and intangible cultural treasures. Using observational techniques, case studies, and a qualitative approach, it explores the historical evolution of heritage conservation through the lens of the guthi, taking into account subjective, valued, and constructed realities. The paper concludes by urging policymakers to recognize the significant contribution of indigenous systems such as the guthi in achieving sustainable heritage conservation. Embracing such traditional strategies can mitigate multifaceted problems and effectively preserve Nepal's cultural heritage through the guthi.

Admission and discharge diagnoses of in-hospital patients are often in discord. Incorrect admission diagnoses are related to increased cost of care and patient safety. Additionally, due to the seasonality of many conditions, this discord may vary across the year. In this paper, we used medical claims data to develop a methodological framework that examines these differences, for Medicare beneficiaries. We provide examples for pneumonia, a condition with seasonal implications, and aneurysm, where early detection can be life-saving. Following a Bayesian approach, our work quantifies and visualizes with time series plots the degree that any clinical condition is correctly diagnosed upon admission. We examined differences in weekly intervals, during a calendar year. The mean length of stay and hospital charges were furthermore compared between matching and non-matching {admission, discharge Dx} pairs, and 95% confidence intervals of the difference of means were estimated. We applied Statistical Process Control methods and then visualized differences, for the hospital charges and the length of stay, per week, with time series plots. Our methodology and the visualizations underline the importance of a rigorous and non-delayed diagnostic process upon admission since there are significant implications in terms of hospital outcomes and cost of care.

Planning regulations determine a substantial part of buildings but their constraints are usually not included in the setup of a BIM model or used explicitly for design guidance. This is symptomatic of wider tendencies and ingrained biases that emphasize tacit knowledge and assume that information in a project starts from scratch – an assumption that runs contrary to predesign information ordering practices, as well as to the findings of creativity studies. In terms of process control, it negates important possibilities for feedforward. The paper describes an exercise in which students collected planning regulations for a particular plot of land and modelled their constraints in BIM. The results suggest that converting such information into a foundation for design is constructive and feasible, even for novices, and produce a bandwidth view of project information that acknowledges pre-existing information and includes it in the BIM workflow. They also verify that, for a large part of Dutch housing, this foundation amounts to a considerable and binding part of the design that guides subsequent decisions.

This academic paper explores the synergistic potential of Agroforestry systems and Climate-Smart Agriculture (CSA) in enhancing climate change adaptation strategies. Focusing on Australia and Pacific Island Countries, the study delves into the pivotal role of these approaches in mitigating the impacts of changing climate variables on agricultural production. By analysing important climate variables, their potential alterations, and their subsequent effects on crop production, the paper advocates for a holistic and forward-thinking approach to sustainable agriculture in the face of climate change.

Acid lime [Citrus aurantifolia (Christm.) Swingle] is a fruit crop, enriched with high commercial value and is cultivated in 60 out of 75 districts representing all geographical landscapes of Nepal. Lack of high yielding cultivars is probably one of the main reason for its extremely reduced productivity which warrants a deep understanding of genetic diversity in existing germplasm. Hereby, we aim to access the genetic diversity of acid lime germplasm cultivated at 3-different ecological gradients of eastern Nepal employing PCR-based Inter-Simple Sequence Repeats markers (ISSR). Altogether, 21 polymorphic ISSR markers were used to assess the genetic diversity in 60 acid lime cultivars sampled from different geographical locations. Analysis of binary data matrix was performed on the basis of bands obtained, scoring of the data was done accordingly, and principal coordinate analysis and phenogram were constructed using different computer algorithms. ISSR profiling yielded 234 amplicons, of which 87.18% were found to be polymorphic. The number of amplified fragments ranged from 7-18 with amplicon size ranging from 250-3200 bp. The NTSYS based Cluster analysis using UPGMA algorithm taking Dice Similarity coefficient separated 60 accessions into 2-major and 3-minor clusters. The genetic diversity analysis revealed the highest for Terai and the lowest for High-hill zone. Cluster I comprised of accessions from High-hill and Mid-hill regions revealing the close genetic relationship, whereas cluster II comprised of accessions from all three agro-ecological zones and the exotic varieties. Furthermore, our results revealed the accessions harvested from different geographical gradients were not genetically distinct, but highest diversity was observed in Terai accessions in comparison to the regions belonging to the High and Mid-hills. Thus, our data indicate that the ISSR provides a better option for evaluating the genetic diversity of Nepalese Acid Lime cultivars and furnished significant information, assisting parental selection in current and future breeding programs and germplasm conservation which ultimately may help to provide a technological breakthrough for the farmers of the developing country like Nepal.

The global food system has three recognized challenges: (a)increasing the availability of food consumption, (b) reducing food loss, and (c) reducing food waste. The increasing demand for food consumption, increasing quantity of food loss, and corresponding increase in food waste are resulting in serious health, aesthetic, social, economic, and environmental problems due to a lack of appropriate planning and management. Despite its importance, there is no clear, concise, and comprehensive definition of food consumption, loss, and waste. Generally, food consumption, food loss, and food waste are dealt with separately. This article presents a logically constructed ontological framework of food consumption, loss, and waste. It gives equal importance to all three aspects of global food management. The systematic ontological framework is general, and the analysis can be applied to any country. The framework deconstructs the combinatorial complexity of the problem and explicates the pathways to manage the consumption, loss, and waste. The ontological framework encapsulates 19*8*7*4*5*6 = 25,536 possible components of the challenge. A critical analysis based on available data using the framework will help to develop strategies to deal with the problem. It can help to discover the gaps and to find ways to bridge the gaps. It is novel to conceptualize food consumption, loss, and waste together.

This study investigates the efficiency of BEAM (Biologically Enhanced Agriculture Management) inoculant, derived from compost produced under Dr. David Johnson-Su's composting technique, in promoting plant growth. A winter mix of pasture comprising Oats, Tetila, unhulled Couch, and Haifa was used as the test crop. Seeds were soaked in BEAM inoculant, water (control), and a competitor product 'Companion.' Results revealed significant enhancements in germination rates, foliage growth, and root development in the BEAM-treated group compared to controls. Further research aims to identify the microbial components of BEAM and explore its potential as a suppressant for fusarium species in orchards.

Data Extraction is a technique is called as clustering which is used to retrieve data either from the files or data bases or both. This paper focuses on the performance evaluation parameters of the clustering algorithms based on different parameters or conditions or constraints and parameters which are used to perform the clustering process to get the clusters on the data sets. Therefore best clusters are retrieved when best parameters or conditions or constraints or preferences which are applied on the data sources for the clustering process. These parameters or conditions or constraints are opted by the user called as user preferences.

Abstract: Building Information Modelling (BIM) methodology has been empowering the quality of the construction activity in all sectors: multidisciplinary designs development; construction planning and monitoring; building management and maintenance. A BIM environment aggre-gates several disciplines and different professional skillsets and in order to control and improve the quality of a BIM project, a BIM manager is required. The BIM manager has the responsibility to coordinate all tasks involved in a building design and associated activities usually workout over the project documents. This professional can access to the distinct discipline models, located in a shared platform, and request for amendments if inconsistencies are detected. The topic of the present study is illustrated with three building cases were distinct specific projects, disciplines and tasks were elaborated: collaboration between disciplines (architecture, structures and con-struction); structural analyses and reinforcement details; quantity take-off of materials and cost estimation; construction scheduling and simulation. Although there are still limitations in the implementation of BIM methodology in all sectors and stages, within the construction industry, BIM has been bringing an important improvement in the quality of a building design, reflected in the quality of the final product. The present study put in evidence the BIM manager role in pro-jects that aggregates several disciplines and experts, bringing an important contribution in the quality of a building design. BIM methodology is a current demand in the construction industry supported on advanced technology and in an adequate management of projects.

Difference in two quantum states defined for a point in space time is represented by space time curvature and the superposition of these quantum states will affect the momentum of mass existed at that point. Analogy of the same with Einstein’s General relativity field equation and Einstein’s gravitational constant , indicated an equation containing ‘Hubble’s constant H , Einstein’s gravitational constant ‘H’ and Siva’s constant ‘K’. Its output is a quadratic equation. Solution to this quadratic equation predicted a fundamental force particle. This solution gives the diameter of that particle. From the fundamental principles, the diameter of this force particle gives the coupling constant of that fundamental force. Prediction and review of this particle may provide us new insights in to quantum physics and standard model of particle physics. From the first principles, it is discussed that this particle can be claimed as a force carrying particle of a fundamental force related to dark matter. This work has provided simple explanation to reconcile quantum nature of space time with smooth space time of General Relativity. This has demanded the necessity to introduce a new postulate in quantum mechanics which is beyond Hilbert space and Dirac notation. A new perspective of uncertainty principle in quantum mechanics has been elaborated.

Specifically this work is based on the concept of ‘dark energy’ as a phenomenal effect of expansion through which a theoretical value of Hubble’s constant has been introduced. It is a constant that is based on a postulate that the square root of ratio of the Siva’s constant ‘K’ and Hubble’s constant ‘H’ is exactly equal to the diameter of the neutral hydrogen atom. That is the, exact theoretical value of Hubble’s constant. This is always constant .However, space-time conversions and the changes in the velocity of light will affect the distance and velocity in Hubble’s equation. Deviation factors in velocity, distance and the Hubble’s constant have been calculated separately which are affected by change of velocity of light with expansion of space time. The observations are related to red shift. So the change factor in terms of Hubble’s constant has been calculated with respect to red shift. Thus a factor which affects the theoretical Hubble’s constant has been calculated to find the exact value of Hubble’s constant that satisfies the experimental results. The present theoretical value of Hubble’s constant has been calculated. The experimental results by plank, ACT (Atacama Cosmology Telescope), WMAP (Wilkinson Microwave Anisotropy Probe) etc. experiments are at par with the theoretical result. Thus the ontology of space time introduced in this theoretical work supposed to be correct and leads to a new theory of origin of universe for which a brief description has been provided.

Interactive Planning Support Systems (PSS) implemented on a maptable are deemed suitable to support participatory planning processes. Through their interactive nature and user-friendly interface they are supposed to facilitate exchange of knowledge between stakeholders, consensus building among them, group learning processes, and thereby strengthen participation. We analyze in this systematic review, based on 16 case studies using interactive PSS, how such PSS have contributed to the goal of strengthening stakeholder participation. Results show that tools and applications have become more sophisticated in recent years and the goals of the studies changed from collaboratively designing interventions to observing and understanding how the application of such tools contributes to improved plan outcomes and group based learning. However, many case studies lack a proper framework and operationalization for investigating the impacts of the tools and applications on participation. Consequently, impacts on participation are assessed rather incidentally based on implicit assumptions and often no distinction is made between the different aspects of participation. In conclusion, further theoretical studies conceptualizing impacts of interactive PSS on participation are needed as well as empirical studies testing these impacts in real world case contexts with various groups of stakeholders.

This research work uses a simplified approach to combine location information from beacons propagation signal interaction with a mobile device with local building information to give real-time location and guidance to a user inside a building. This is an interactive process with visualisation information that can help user’s orientation inside unknown buildings and the data stored from different users can provide useful information about users movements inside a public building. Beacons installed on the building at specific pre-defined position emit signals that give a geographic position with an associated imprecision, related with Bluetooth’s range. This uncertainty is handled by building layout and users’ movement in a developed system that maps users’ position, gives guidance and store user movements. This system is based on an App (Find Me!) for Android OS (Operating System) which captures the Bluetooth Low Energy (BLE) signal coming from the beacon(s) and shows, through a map, the location of the user ‘s smartphone and guide him to the desired destination. Also, the beacons can deliver relevant context information. The application was tested by a panel of new and habitual campus users against traditional wayfinding alternatives yielding navigation times about 30% smaller, respectively.

Nickel based superalloys finds extensive usage in manufacturing of intricate part shapes in gas turbine, aircraft, submarine, and chemical industries owing their excellent mechanical property and heat resistant abilities. However, machining of such difficult-to-machine alloys up to the desired accuracy and preciseness is a complex task owing to a rapid tool wear and failure. In view of this, present work proposes an experimental investigation and optimization of process parameters of the cryogenic assisted turning process during machining of Nimonic C-263 super alloy with a multilayer CVD insert. Taguchi’s L-27 orthogonal array is used plan the experiments. Effect of input parameters viz. cutting speed (N), cutting feed (f), depth of cut (d) are studied on responses viz. surface roughness (SR), nose wear (NW) and cutting forces (F) under hybrid cryogenic (direct+indirect) machining environment. A scanning electron microscope (SEM) analysis is carried out to explore the post-machining outcomes on the performance measures. The multiple responses are converted in to single response and ranked according to Taguchi based gray relational grade (TGRG). Feed rate (f) is found to be the most influential parameter from the analysis of variance of GRG. The means of GRG for each level of process parameters are used to improve the optimal process parameters further. Finally, the confirmative experiment is performed with these optimal set of process parameters which showed an improvement of 9.34% in the value of GRG. The proposed work can be beneficial to choose ideal process conditions to enhance the performance of turning operation.

Residential satisfaction with post-disaster housing is crucial for the success and sustainability of a resettlement project. However, little attention has been given to this aspect in the permanent houses developed after the 2015 Gorkha earthquake. To bridge this gap, the study examined factors affecting residential satisfaction among poor displaced households resettled in the Panipokhari Integrated Settlement of Nepal. Data were collected through a questionnaire survey, interviews, and field observation. Internal consistency and reliability were assessed using Cronbach's Alpha test, while satisfaction was measured by mean satisfaction scores. The relative importance index ranked the factors, and Spearman's rank correlation coefficient analyzed their relationship with overall residential satisfaction. The study identified several crucial fac-tors influencing residential satisfaction, including the design and layout of the house, space for modification, provision of a kitchen garden and cattle shed, thermal comfort, completeness of the house, provision of a hearth, and spaces for rituals and cultural events. These findings shed light on the resettlement decisions of affected populations and provide valuable insights for policymakers, implementers, and researchers aiming for successful and sustainable resettlement outcomes.

The corrosion inhibition behavior of cetyl pyridinium chloride (CPC) on mild steel (MS) in 0.5 M H₂SO₄ solution was examined using the weight loss method and potentiodynamic polarization method. The experimented MS surface was characterized by Fourier Transform Infrared Spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), and Atomic Force Microscopy (AFM). Corrosion rate and corrosion efficiency was calsulated via weight loss and potentiodynamic polarization methods where the corrosion rate in 0.5 M H₂SO₄ , 0.000192 M, and 0.0077 M of CPC solution was 12.09×10^-3, 8.23×10^-3, 6.63×10^-5 (mm/Year), respectively and the inhibition efficiency of 94.42% and 98.54%. FESEM studies demonstrated the corrosion attack on MS surface. Also, the EDX showed the presence of nitrogen of CPC. Thus, CPC acts as an important corrosion inhibitor.

Highway alignment is an essential part of the highway planning and design phase. Highways are impacted by existing projects and surrounding context. The isolation of geo-technical analysis from highway planning and design also delays the planning process. This study therefore proposes a model that integrates building information modelling (BIM) and geographic information science (GIS) capabilities to facilitate the planning and design process. Semantic web technologies are used to integrate BIM and GIS data on a semantic level. The proposed model also helps to identify geohazards by providing geological analysis. The visualization of the proposed project can help reduce design errors and miscommunication, which, in turn, reduces project risk. In addition, the model facilitates highway alignment optimization by incorporating visualization, simulation, and analysis into the planning and design phase. The proposed model provides future opportunities for project professionals to have organized, reliable and dynamic ways to manage the project during construction.

The paper attempts to explore the relationship between federalism and rural development. Federalism is a division of power, responsibility and accountability to bring the administrative and political power closer to the ground and essentially to increase the good governance. On the other hand, rural development is a complex and multidimensional issue- especially much demanding for least developed and developing countries. A descriptive and qualitative approach was carried out to study the complex relationship between rural development and federalism. Similarly, a SWOT analysis was carried out to have a better understanding of the relationship. The study found that there is significant potentiality for accelerated development of rural landscape in federalism if carefully executed. However, on the other hand, federalism may pose several risks on rural development and may restrict the development pace if not executed with appropriate care and understanding. Therefore, the study concludes that cooperation and coordination among the federal structures are crucial for better development of the rural economy.

In the scientific community, it is difficult to find a consensus on defining BIM. Just as the acronym BIM is developed in different ways, it is also understood in different ways. Depending on its understanding, different definitions emerge. It is defined differently by organizations and standards, still differently by academics. Many years of academic discourse on the subject have failed to produce a solution. Despite the fact that the acronym BIM has already done its work for the construction industry, it still stirs up excitement. There is still no clear definition, as the view of BIM varies from one perspective to another. This article attempts to sort out the definitions cited so far by important organizations and key academics. The review was based on deep literature study has attempted to be inclusive and consistent. The question still remains open: do we need a single, correct definition of BIM? The aim of the article is to try to answer this question, open up a renewed discussion and come to a satisfactory consensus. BIM can be identified with an activity, product or system. The article breaks down the definitions of BIM, identifies six key attributes of BIM, presents the evolution of the understanding of BIM and proposes new definitions in a narrow and broad approach.

Current bridge inspection practices rely on paper-based data acquisition, digitization, and multiple conversions in between incompatible formats to facilitate data exchange. This practice is time-consuming, error-prone, cumbersome, and leads to information loss. One aim for future inspection procedures is to have a fully digitized workflow that achieves loss-free data exchange, which lowers costs and offers higher efficiency. On the one hand, existing studies proposed methods to automatize data acquisition and visualization for inspections. These studies lack an open standard to make the gathered data available for other processes. On the other hand, several studies discuss data structures for exchanging damage information through out different stakeholders. However, those studies do not cover the process of automatic data acquisition and transfer. This study focused on a framework that incorporates automatic damage data acquisition, transfer, and a damage information model for data exchange. This enables inspectors to use damage data for subsequent analyses and simulations. The proposed framework shows the potentials for a comprehensive damage information model and related (semi-)automatic data acquisition and processing.

Saponins are a diverse group of naturally occurring plant secondary metabolites present in a wide range of foods ranging from grains, pulses, green leaves to sea creatures. They consist of a hydrophilic sugar moiety linked to a lipophilic aglycone, resulting in an amphiphilic nature and unique functional properties. The amphiphilic structures enable saponins to exhibit surface-active properties, forming stable foams and complexes with various molecules. In the context of food applications, saponins are utilized as natural emulsifiers, foaming agents, and stabilizers. They contribute to texture and stability in food products and have potential health benefits, including cholesterol-lowering and anticancer effects. Saponins possess additional bioactivities that make them valuable in the pharmaceutical industry as anti-inflammatory, antimicrobial, antiviral, and antiparasitic agents to name a few. Saponins can demonstrate cytotoxic activity against cancer cell lines and can also act as adjuvants, enhancing the immune response to vaccines. Their ability to form stable complexes with drugs further expands their potential in drug delivery systems. However, challenges such as bitterness, cytotoxicity, and instability under certain conditions need to be addressed for effective utilization of saponins in foods and related applications. In this paper, we have reviewed chemistry, functionality, and application aspects of saponins from various plant sources and have summarised the regulatory aspects of food application of Quillaja saponins. Further research to explore the full potential of saponins in improving food quality and human health has been suggested. It is expected that this article will be a useful resource for researchers in food, feed, pharmaceutical and material science.

One of the biggest challenges in modern physics is how to unify gravity with quantum theory. There is an absence of a complete quantum theory of gravity, and conventionally it is thought that the effects of quantum gravity occur only at high energies (Planck scale). Here we suggest that certain novel quantum effects of gravity can become significant even at lower energies and could be tested at laboratory scales. We also suggest a few indirect effects of dark energy that can show up at laboratory scales. Using these ideas, we set observational constraints on radio recombination lines of the Rydberg atoms. We further suggest that high-precision measurements of Casimir effects for smaller plate separation could also show some manifestations of the presence of dark energy.

Fraudulent transactions pose a significant threat to financial institutions and e-commerce platforms.Machine learning models, trained on historical labeled data (fraudulent vs. legitimate transactions), are often employed to identify and prevent fraud. However, real-world datasets frequently exhibit class imbalance, where fraudulent transactions (minority class) are significantly outnumbered by legitimate transactions (majority class). Machine learning models may perform poorly as a result of this imbalance, underestimating fraud and favouring the majority class. This paper proposes a novel approach to address class imbalance and improve fraud detection accuracy. We explore the implementation of FROST (Feature space RObust Synthetic saTuration) oversampling, a technique specifically designed to generate synthetic samples for the minority class. The FROST function leverages the k-nearest neighbors (KNN) algorithm and a user-defined amplification factor (m) to create synthetic data points that closely resemble existing minority class instances. We integrate the FROST-enhanced oversampling technique into the machine learning pipeline for fraud detection. The paper evaluates the effectiveness of this approach compared to traditional oversampling methods and analyzes its impact on classification accuracy metrics.

Water reuse is critical to national development, sustenance, and survival in this era of climate, demographic, and social changes. There is no national systemic approach to address the challenge systematically. The paper presents a framework and a method to develop a national research strategy for water reuse. It presents an ontology of water reuse strategy that encapsulates the combinatorial complexity of the problem clearly, concisely, and comprehensively. Subsequently, it discusses the method to use the framework to develop a national strategy, adapt it through feedback and learning, and ultimately effect a revolutionary change in the strategy for water reuse.

This article delves into the pivotal role that biodiversity of soil microbes plays in bolstering tree health and overall agroecosystem productivity. With the advent of regenerative agricultural practices, there has been a resurgence of interest in understanding the complex interactions between soil microbiota and plant roots, particularly through processes like rhizophagy. Rhizophagy, the digestion of living root cells by soil microorganisms, stands out as a fundamental mechanism in nutrient cycling and overall tree vitality. By examining the impact of enhanced microbial diversity on tree health, we shed light on the practical applications, such as the utilization of the BEAM Inoculant product, which has demonstrated remarkable efficacy in augmenting soil health and, consequently, tree Vigor

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.

contaminated sites located in each school district. Previous studies showed that various environmental factors, such as exposure to toxic substances negatively impacts human health depending on their concentration and occurrence. Therefore, the study aims to use Geographical Information System (GIS) technology and secondary data on IEP numbers of each school district, chemical occurrence in contaminated sites, and demographic data to conduct a correlational analysis. The secondary data obtained from school districts and contaminated sites from Iowa Department of Natural Resources Facility Explorer were populated in ArcMap 10.5 (a GIS software) for generating maps and data to conduct statistical analysis. The contaminants were categorized into metals, polyaromatic hydrocarbons (PAH), and solvents with weighing factors as 1, 0.5, and 0.25. A total of 1 Superfund site and 39 CERCLA sites were identified as contaminated sites for this study. The majority of contaminants were heavy metals such as lead, arsenic, mercury, and cadmium. The mean toxic score of all contaminated sites combined was 13.4 (sd 14.4). The correlational analysis between the IEP numbers from each school districts and toxic scores from the contaminated sites was positive indicting that increased toxic score increased the number of students enrolled in the IEP program (F=23.7, p<0.0001). The correlational analysis between toxic score and demographics indicated some interesting results. Children under the age of 10 were living in areas with high toxic score and the showed mild correlation (p<0.00052). Similarly, higher number of black population resided in areas with high toxic score (p=0.0032) than the white population (p<0.0001). The result also indicated that children enrolled in IEP were predominantly among the black population (p<0.0001). The correlational analysis between household income and poverty percentage of people residing near the contaminated area indicated that people had low average household income (p=0.0002) and high poverty percentage (p=0.0203) residing in areas with higher toxic score. Regarding the educational status, less number of people with post graduate degree (p<0.0001) resided in areas with high toxic score and more number of people with no degree (p<0.0001) resided in areas with high toxic score. Finally, the study showed the increasing trend of eligible children enrolling in free and reduced lunch programs with increasing toxic scores (p=0.0012) and IEP levels (p=0.0416). This study emphasize on developing multiple exposure to correlate environmental factors contributing to the negative health outcome on people.

Municipal solid waste management is a growing worldwide challenge. Landfill mining is a promising technique to reduce MSW landfills. In the present study, arbitrary embankments made of different MSW fractions are analyzed to find the safety factor of their slopes. Properties of Municipal solid waste fines (MSWF), Shredded MSW, MSW Incineration Ash, and MSW having particles less than 35 mm are used in the analysis. For doing the same, finite element-based application 'Plaxis-2D' is used. The safety of slopes is assessed while the water table is inserted, and a uniform load is applied to the embankment to establish how well they will perform in critical situations. It was found that the slope made of shredded MSW is most safe due to its low density and high friction angle, while the least safe slope is made of MSW having less than 35 mm fractions.

As is known, the construction industry has one of the highest occupational accidents incidence rates among all economic sectors. Currently, Building Information Modeling (BIM) appears as a tool that addresses occupational safety issues throughout the construction life cycle projects, avoiding hazards and risks and, consequently, increasing safety. This work investigates BIM methodology and related technologies application for building safety planning in construction and demonstrates the potential of this technology for the integrated implementation of safety measures during the design phase and the construction site management. The first step consisted of a literature review on the application of BIM in safety in the design and planning phases. Following, to show the potentialities of construction simulation, a case study based on BIM 4D to prevent falls from height was developed. With BIM 4D, it is possible to follow the construction process over time, giving the construction safety technicians’, designers, supervisors and managers the capability to analyse, in each phase, the potential risks and which safety measures should be implemented. BIM can effectively integrate safety measures from the design phase to the construction and use phase and do integrated safety planning within construction planning, leading to reliable safety management throughout the construction process.

The transition from Building Information Modelling (BIM) to Heritage Building Information Modelling (H-BIM) is intended to pursue an adequate knowledge of the artefact that is to be preserved, progressively replacing the traditional methods of the restoration and structural reinforcement projects with new tools for managing both existing information and new interventions. The aim of the paper is to show the application of the H-BIM method to a stone pavement road located in the Archaeological Site of Pompeii. In detail, starting from a Laser Scanner based survey, juxtaposed by coordinated points georeferenced through a total station, points clouds were handled by means of several BIM-based tools to perform the road design process, starting from the digital elevation model (DEM) to the corridor representation. Subsequently, a visual programming application based on Python language was adopted to update corridor information by means of the object property set. As preliminary results, a tool, complete with graphical and non-graphical information, is proposed to be used in conservation, maintenance and restoration project.

Building Information Modelling (BIM) methodology have been optimizing the construction activity in all sectors: multidisciplinary designs development; construction planning and monitoring; building management and maintenance. A BIM environment aggregates several disciplines and different skillsets and in order to control and improve the quality of a BIM project, a BIM manager is required. The BIM manager has the responsibility to coordinate all tasks involved in a building design and associated activities usually workout over the project documents. The professional can access to various discipline models, located in a shared board, and request for amendments if inconsistencies are detected. The present study issue is illustrated with three building cases were distinct specific projects, disciplines and tasks were elaborated: collaboration between disciplines (architecture, structures and construction); structural analyses and reinforcement details; quantity take-off of materials and cost estimation; construction scheduling and simulation. Although there are limitations in the implementation of BIM methodology in all sectors and stages, within the construction industry, BIM have been bringing an important improvement in the quality of a building design, reflected in the quality of the final product. BIM methodology is a current demand in the construction industry supported on advanced technology and in an adequate management of projects, were the BIM manager has an important role.

This paper introduces a method for tunnel point cloud and BIM model integration and cross-section monitoring, providing information to analyse tunnel cross-sections and surrounding rock deformation, and support for tunnel maintenance and reconstruction. Three types of data are processed for the integration: laser scanning point cloud, BIM tunnel model and terrain model from oblique photogrammetry. An adaptive BIM modelling scheme is proposed for tunnels with alien structures. Precise spatial registration of the data sets is conducted by applying singular value decomposition (SVD) algorithm to calculate transformation parameters from the point cloud model to the BIM model. Since the tunnel central line has high-order derivability, a cross-section calculation method based on tangent vector is proposed to obtain the cross-sectional profile of tunnels at any mileage. The proposed method has been verified by applying it to a tunnel reconstruction project. The experiment results show that the tunnel point cloud and the BIM model were highly coincident after the integration. The developed program can effectively get the cross-section of the tunnel at any mileage, and correctly express the spatial relationship between the BIM tunnel, the point cloud of tunnel and the external mountainous terrain.

Crop productivity is directly dependent to soil fertility. High soil organic carbon (SOC) content in soil is vital as it leads to improved soil quality, increased productivity, and stable soil-aggregates. In addition, with the signing of the climate agreement, there is growing interest in carbon sequestration in landscapes. This paper looks at how SOC can be increased so that it not only contributes to reduction of CO₂, but also translates to increased food production thereby enhancing food security. This synergy between mitigation and enhancing food security is even more relevant for mountain landscapes of the Hindu Kush Himalayan (HKH) region where there remains huge potential to increase CO₂ sequestration and simultaneously address food security in the chronic food deficit villages. Soil samples were collected from seven transects each in Bajhang and Mustang and from 4 land use types in each transect. Samples of soils were taken from two depths in each plot; 0-15 cm below the soil surface and 15-30 cm below the soil surface to compare the top soil and subsoil dynamics of the soil nutrients. The lab analysis was performed to assess the soil texture, soil color, soil acidity in 'power of hydrogen' (pH), macro-nutrients as soil fertility. Secondary data was used to analyze the level of food deficit in the villages. The result shows that most of the sample soils from Mustang were clay (82.1%) which is 46 samples out of 56. The pH value of soil from Bajhang ranged from 5.29 to 9.09. The pH value of soil ranged from 5.65 to 8.81 in Mustang. SOC contents of sampled soils from Bajhang ranged from 0.20% to 7.69% with mean amount of 2.47% ± 0.17. SOC contents of sampled soils from Mustang ranged from 0.51% to 8.56% with mean amount of 2.60% ± 0.25. By land use type, forest land had the highest carbon (C) content of 53.61 t ha^-1 in Bajhang whereas in Mustang, agricultural land had the highest C content of 52.02 tons ha-1. Based on these data, we can say that there is potential for increasing SOC through improved soil health and crop production and soil. Sustainable soil management should be practiced for higher productivity. Livestock may also provide farmyard manure, which can be used to fertilize cultivated soils, which increases soil productivity. Increasing productivity would aid in increasing the access and availability of food in these mountain villages.

Weeds are among the major constraints to any crop production system, reducing productivity and profitability. Herbicides are among the most effective methods to control weeds, and reliance on herbicides for weed control has increased significantly with the advent of herbicide-resistant crops. Unfortunately, over-reliance on herbicides leads to environmental-health issues and herbicide-resistant weeds, causing human-health and ecological concerns. Crop diversification can help manage weeds sustainably in major crop production systems. It acts as an organizing principle under which technological innovations and ecological insights can be combined to manage weeds sustainably. Diversified cropping can be defined as the conscious inclusion of functional biodiversity at temporal and/or spatial levels to improve the productivity and stability of ecosystem services. Crop diversification helps to reduce weed density by negatively impacting weed seed germination and weed growth. Additionally, diversified farming systems are more resilient to climate change than monoculture systems and provide better crop yield. However, there are a few challenges to adopting a diversified cropping system, which ranges from technology innovations, government policies, farm-level decisions, climate change, and market conditions. In this review, we discuss how crop diversification supports sustainable weed management, the challenges associated with it, and the future of weed management with respect to the diversification concept.

Increasing agricultural productivity is indispensable to meet future food demand. Crop im-provement programs rely heavily on genetic diversity. The success of weeds in the ecosystem can be attributed to genetic diversity and plasticity. Weedy rice, a major weed of rice, has diverse morphology and phenology, implying wide genetic diversity. Study was conducted to genotype weedy rice accessions (n =54) previously phenotyped for herbicide tolerance and allelopathic potential using 30 SSR markers. Cultivated rice (CL163, REX) and allelopathic rice (RONDO, PI312777, PI338047) were also included in the study. Nei’s genetic diversity among weedy rice (0.45) was found to be higher than cultivated rice (0.24) but less than allelopathic rice (0.56). The genetic relationship and population structure based on herbicide tolerance and allelopathic po-tential were evaluated. Herbicide-tolerant and susceptible accessions formed distinct clusters in the dendrogram, indicating their genetic variation, whereas no distinction was observed between allelopathic and non-allelopathic weedy rice accessions. Weedy rice accession B2, which was previously reported to have high allelopathy and herbicide tolerance, was genetically distinct from other weedy rice. Results from the study will help leverage weedy rice for rice improvement programs as both rice and weedy rice are closely related, thus having a low breeding barrier.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

The performance of Yttrium-Barium Copper Oxide (YBCO) high-temperature superconductors in high-frequency applications is significantly affected by the microwave surface resistance (Rs). The paper delves into the basics, measuring methods and factors affecting the resistance (Rs) in YBCO, highlighting its high critical temperature (Tc) and low resistance, positioning it as a promising material. YBCO's compatibility with epitaxial growth and microstructure engineering offers opportunities to reduce grain boundary effects and improve Rs, making it advantageous for high-frequency electronics, communication systems, and MRI coils. This is due to its high critical current density (Jc) and exceptional Rs at practical temperatures. Challenges remain in comprehending and managing Rs in YBCO, despite its favorable characteristics. Utilizing advanced fabrication methods and incorporating nanotechnology allow for customization of YBCO-based devices. Multi-scale modeling and simulation are essential for guiding experimental work and understanding YBCO's performance in high-frequency settings. This study highlights the promise of YBCO for future high-frequency technologies and stresses the importance of more research to overcome hurdles and fully exploit its capabilities, potentially transforming superconducting devices for practical use.

Clustering is a data mining task used for the data extraction from the data bases or files. Clustering is used to find unknown groups present in the data sources like files or data bases. This paper focuses on clustering algorithms performance dependency on the parallel clustering platforms and the clustering algorithms along with their clustering criteria. The problems with the present Traditional clustering algorithms were throughput and data source size changes (scalability). So they can’t address the big data. So for handling the huge volumes of data, parallel clustering algorithms along with clustering criteria were used. For processing the big Data Parallel clustering algorithms are of two types based on computing platforms used. They were the horizontal scaling platforms and vertical scaling platforms.

Wood and plants are made of fibers which contain, in addition to cellulose, lignin and hemicelluloses. Xylan and galactoglucomannan are the dominant secondary cell wall hemicelluloses. Besides fibers’ traditional use in textile and paper industries, the fibers are important materials for the biorefinery industry and for developing biocomposites. In the former context, renewable lignocellulosic biomass is used as feedstock to sustainably produce fuels, chemicals, and polymers. On the other hand, in relation to composites, cellulose fibers which are anisotropic play an important reinforcing role in fiber-matrix interactions. However, for these and other applications, structural analysis of the fibers is important and among many analytical techniques used, Raman spectroscopy is one of the methods. However, given the structural similarity between hemicelluloses and cellulose, many of their Raman contributions overlap. While the wavenumber positions corresponding to the Raman contributions of cellulose, hemicelluloses, and lignin are known, extent to which overlapping features of hemicellulose modify the spectrum of cellulose is not yet fully understood. The present investigation focuses on this aspect by using xylan as one of the hemicelluloses. As a model system, samples with various mass ratios of microcrystalline cellulose (cotton) and xylan (birch wood) were prepared and analyzed by FT-Raman spectroscopy. The findings were then applied to interpret Raman spectra of selected xylan containing delignified plant fibers. It is hoped that insights gained would lead to better interpretation of the spectra of natural and treated plant materials.

The study investigates the effect of corporate governance characteristics on the financial performance of 124 Indian-listed companies that have undergone mergers and acquisitions during 2014–2020. It employs several performance measures, such as short-term capital market performance, long-term capital market performance, accounting-based and market-based measures, and firm-level control factors. The study found board size to be a positive and significant factor affecting short-term market performance. Further, it also documents weak linkages with other corporate governance variables like board independence, CEO duality, etc. Regarding control variables, leverage, the company's age, price-to-book ratio, and research and development expenses significantly impact the acquiring companies' financial returns. The study's findings add to our understanding of corporate governance's impact on performance in cases such as mergers and acquisitions.

Proper regulation of microtubules (MTs) is critical for the execution of diverse cellular processes, including mitotic spindle assembly and chromosome segregation. There are a multitude of cellular factors that regulate the dynamicity of MTs and play critical roles in mitosis. Members of the Kinesin-8 family of motor proteins act as MT-destabilizing factors to control MT length in a spatially and temporally regulated manner. In this review, we focus on recent advances in our understanding of the structure and function of the Kinesin-8 motor domain, and the emerging contributions of the C-terminal tail of Kinesin-8 proteins to regulate motor activity and localization.

In the present scenario of increased industrialization and transportation in the world leads to increased consumption of fossil fuels which in turns leads to depletion of fossil fuels at a faster rate. Fossil fuels combustion is the dominant source for greenhouse gases and global warming. In view of energy crisis raised in 1970’s and environmental concern, many researches are directed towards search of alternative fuels which can replace consumption of fossil fuels as well as reduce pollution. In developing countries like India which is agriculture land the best alternative fuels are biodiesel and ethanol as they are produced from renewable feedstocks like sugarcane, corn etc. and they are also less hazardous to environment because of lower emission property. Ethanol blends results in significant reduction of emissions of hydrocarbon (HC), carbon monoxide (CO) and particulates matter but increase in nitrogen oxides (NOx). The main purpose of ethanol addition is to reduce the viscosity of biodiesel blends. This paper represents significance of Compression Ratio(CR) on performance, combustion and emission of single cylinder four stroke CI diesel engine by using various compression ratios such as 17.5:1, 18.5:1 and 19.5:1. Experimental research has been conducted with four types of ethanol blends, namely E10, E20, E30 and E40. Ethanol-biodiesel mixture mixed with 2% emulsifier 1% diethyl carbonate and 1% ethyl acetate to maintain similarity and to avoid phase separation. Ethanol subjected to high compression ratio has been used to increase brake thermal efficiency (BTE). The compression ratio has been increased to improve the combustion and performance of the diesel engine.

Despite the importance of influenza vaccination for healthcare workers (HCWs), their uptake remains challenging. This study aimed to measure influenza vaccine hesitancy among HCWs in Nakhon Phanom province, Thailand. A representative cross-sectional survey was conducted at six hospitals during August – September 2020 using a self-administered questionnaire. HCWs delaying getting influenza vaccines, accepting but unsure, or refusing with doubts were categorized as hesitant. Those accepting without any doubts were classified as non-hesitant. Vaccine hesitancy determinants were identified by multivariable logistic regression analysis. A total of 338 participants (97%) completed the questionnaires; most were female nurses working at district hospitals without pre-existing chronic medical conditions. Nearly 60% of participants (197/338) exhibited influenza vaccine hesitancy, and significant factors included age above 50 years adjusted odds ratio [aOR] 3.2, 95% CI 1.3-8.5), fair knowledge of influenza/vaccination (aOR 0.4, 95% CI 0.2-0.8), and negative influence of other HCW (High level– aOR 2.3, 95% CI 1.1-4.8; Moderate level- aOR 2.1, 95% CI 1.1-4.4). Imparting updated information to the HCW combined with positive guidance from influential peers may help reduce this hesitancy.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

The construction industry, a vital sector of the global economy, is projected to grow significantly over the next 15 years. Despite its importance, this industry struggles with inefficiency, high costs, prolonged timelines, and substantial ecological impacts. These challenges have led governments to advocate for Building Information Modeling (BIM) to enhance Efficiency and sustainability through comprehensive digital representations of physical and functional characteristics. The effective integration of BIM technology hinges on the active participation of industry providers, raising the question of how these providers can positively contribute to improving BIM dimen-sions. This study investigates the potential of coopetition networks within the Portuguese Orna-mental Stone industries (OS-PT) to address this challenge. Coopetition networks, which blend competition and cooperation, foster collaborative networks among companies to enhance their re-sponse to digital supply chains. The research employs an Experimental Pilot Network facilitated by an Industrial Internet of Things (IIoT) artefact to assess the benefits of coopetition networks. The findings demonstrate that transitioning from current practices (CB.P) to coopetition network practices (CN.P) significantly improves operational Efficiency, labour productivity, and sustaina-bility. Specifically, the transition resulted in a 15.6% increase in on-time delivery performance, a 27.38% improvement in labour productivity, and a 21.8% reduction in CO₂ emissions per part produced. These improvements align with Sustainable Development Goals (SDGs) 8, 9, 11, and 13, highlighting the strategic value of coopetition in driving innovation and sustainability in the construction industry. Future research should explore the scalability of these findings across different sectors and further refine methodologies to maximize the benefits of coopetition net-works.

This study focuses on the difficulties of standardizing Building Information Modeling (BIM) Execution Plans (BEPs) in the Architecture Engineering Construction and Operations (AECO) sector. The main objective is to improve digital transformation by implementing more efficient methodologies. The task involves analyzing 36 BEP documents from different international organizations to identify similarities and differences in terms of content structure operational methods and project characteristics. The research highlights the necessity of implementing a uniform method to enhance collaboration and information sharing in BIM projects. The documents contain key elements such as project information goals roles collaboration procedures model structure and quality control. The paper presents a comprehensive framework for BEPs with the goal of resolving the discrepancies observed in existing methods that impede the efficiency of project delivery. This study utilizes scientometric analysis to examine literature published between 2020 and 2023. The aim is to track the progress of BEP standards identify key research topics and determine influential works in the field. The findings support the implementation of standardized guidelines to reduce inefficiencies and improve the effectiveness of Building Information Modeling (BIM) in large-scale construction projects.

The state of BIM adoption and implementation in facility management in Africa has been regarded as a developmental discourse gradually gaining grounds. While several studies have focused on manual review of the intellectual progression of BIM research, this confined review however understates the evolution of BIM in facility management. This approach therefore is not always representative of the picture of BIM in facility management at its formative stage. This paper aims to present a scientometric review of BIM in facility management exploring the intellectual progress made so far by authors. The state of the art of BIM in facility management research and practice and the challenges debilitating against BIM in facility management research. The review revealed a considerable amount of research endeavours limited by inadequate collaboration acorss organizations and institutions. The study also revealed the low degree of awareness amongst researchers in the AEC sector. While North America and China were leading countries due to the activity of few universities, Africa wasn’t as visible in research endeavours. The study recommended an increase in awareness drive across countries and increased government effort, joint funding for research to include gaps in practice issues.

The intricate functionality of the vertebrate retina relies on the interplay between neurotransmitter activity and calcium dynamics, offering important insights into de-velopmental processes, physiological functioning, and disease progression. Neuro-transmitters orchestrate cellular processes to shape the behavior of the retina under diverse circumstances. Despite research to elucidate the roles of individual neuro-transmitters in the visual system, there remains a gap in our understanding of the holistic integration of their interplay with calcium dynamics in the broader context of neuronal development, health, and disease. To address this gap, the present review explores the mechanisms used by the neurotransmitters glutamate, GABA, glycine, dopamine, and acetylcholine and their interplay with calcium dynamics. This con-ceptual outline is intended to inform and guide future research, underpinning novel therapeutic avenues for retinal-associated disorders.

Lytic bacteriophages have the efficacy to act and eradicate pathogenic bacteria as an attractive tool in the near future. Bacteriophages specifically kill multidrug-resistant bacteria even which have the capacity to form biofilms. The present review mainly focused on the efficacy of bacteriophages and cocktails as therapeutic agents against predominate MDR-bacteria and their biofilms which are isolated from septic wound infections. The body of evidence includes data from studies investigating bacteriophages from sewage samples as novel antibacterial and antibiofilm agents against pathogenic bacteria. The goal of this review is to present an overview on predominant bacteria from septic wound infection, the biofilm-forming capacity of bacteria, lytic effect of bacteriophages and phage cocktails with an emphasis on the application of bacteriophages against septic wound causing bacteria.

HIV incidence remains alarmingly high among female sex workers (FSWs) in low- and middle-income countries, with Uganda being no exception. The elevated risk of HIV transmission among this vulnerable population can be attributed to a complex interplay of factors, including engaging in sexual activities with multiple partners, inconsistent condom usage, substance use, and structural barriers, such as legal criminalization and pervasive stigmatization by healthcare providers. To combat this epidemic, various HIV prevention interventions have been devised, targeting these multi-faceted challenges. This study involved conducting focus groups among FSWs and healthcare providers in Uganda, with the overarching objective of informing the development of a tailored HIV prevention intervention aimed squarely at FSWs. Through these discussions, valuable insights were gained into FSWs’ perceptions of their HIV risk, their attitudes regarding HIV prevention strategies, and their recommendations regarding the content and delivery of future interventions.

Traumatic brain injury (TBI) is associated with alcohol abuse and higher ethanol sensitivity later in life. Currently, it is poorly understood how ethanol sensitivity changes with time after TBI and whether there are sex-dependent differences in the relationship between TBI and ethanol sensitivity. This study uses the fruit fly Drosophila melanogaster to investigate how TBI affects alcohol sensitivity and whether the effects are sex-specific. Our results indicate that flies have a significantly higher sensitivity to the intoxicating levels of ethanol during the acute phase post-TBI, regardless of sex. The increased ethanol sensitivity decreases as time progresses; however, females take longer than males to recover from the heightened ethanol sensitivity. Dietary restriction does not improve the negative effects of alcohol post-TBI. We found that tau mutant flies exhibit similar ethanol sensitivity to TBI flies. However, TBI increased the ethanol sensitivity of dtauKO mutants, suggesting that TBI and dTau loss-of-function have additive effects on ethanol sensitivty.

The methamphetamine epidemic continues to worsen each year and has contributed to more overdose deaths than opioids. Methamphetamine was listed in the top ten lethal drugs in 2021 in the United States. The drug has been shown to cause many health problems. One solution to this crisis is through vaccinations. Although still early in the development phase, vaccinations have been found to improve withdrawal symptoms and decrease drug seeking behavior with minimal health side effects compared to medications.

Despite a significant advance in the pathophysiological understanding of peripheral nerve damage, the successful treatment of large nerve defects remains an unmet medical need. In this article, axon growth guidance for peripheral nerve regeneration is systematically reviewed and discussed mainly from the engineering perspective. In addition, the common approach to surgery, bioengineering approaches to emerging technologies (i.e. optogenetic stimulation and magnetic stimulation) for functional recovery are discussed, with pros and cons. Alternatively, clear future perspectives of axon guidance and nerve regeneration are addressed.

Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost-efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of Building Information Modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early-stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with widespread SDO implementation. These preliminary steps of systematic review and industry survey furnish a robust knowledge foundation, enabling the proposal of an intelligent framework for automating early-stage sustainable structural design optimization (ESSDO) within the construction sector. The framework ESSDO addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both, scientific and social standpoints. Despite several documented similarities, the Coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. In contrast to the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic, to study changes in thematic foci and societal pressures dictating research over the course of a crisis.

Zero-dose children, or children who have not received any routine vaccination, are a priority population for global health policy makers as these children are at high risk of mortality from vaccine-preventable illnesses. We conducted a narrative review to identify potential interventions, both within and outside of the health sector, to reach zero-dose children. We reviewed peer-reviewed and grey literature and identified 59 relevant resources. Additionally, we interviewed six key informants to enhance synthesis of our findings. Data were organized into three priority settings: (1) urban slums, (2) remote or rural communities, and (3) conflict settings. We found that zero-dose children in the three-priority settings face differing barriers to vaccina-tion and, therefore, require context-specific interventions, such as leveraging slum health commit-tees for urban slums or integrating with existing humanitarian response services for conflict set-tings. Three predominant themes emerged for grouping the various interventions: (1) community engagement, (2) health systems strengthening and integration, and (3) technological innovations. The barriers to reaching zero-dose children are multifaceted and nuanced to each setting, there-fore, no one intervention is enough. Technological interventions especially must be coupled with community engagement and health systems strengthening efforts. Evaluations of the suggested interventions are needed to guide scale up, as the evidence-base around these interventions is rel-atively small.

This research delves into the integration of green roofs with Building Information Modeling (BIM), a pivotal advancement in sustainable urban construction. Aligning with the United Nations Sustainable Development Goals, particularly Goal 11, the study explores how this integration can address global challenges like climate change and resource depletion. Using the Dynamo Visual Programming Language within Autodesk Revit, the research develops a computational modeling approach for green roofs, focusing on their thermal and structural characteristics under varied environmental conditions. Key findings demonstrate the significant influence of substrate and drainage material combinations on green roofs’ thermal performance, highlighting the need for tailored designs based on climatic conditions. The study also emphasizes the importance of considering structural performance in both dry and saturated conditions for overall building integrity. The research identifies gaps in current practices, such as limited focus on specific green roof materials and reliance on certain software tools, suggesting the need for broader material selection and software adaptability. Future research directions include expanding material selections, exploring diverse environmental conditions, and integrating green roofs with various BIM software platforms. The study's implications extend to stakeholders in the construction industry, offering a framework for architects, urban planners, and policymakers to design and implement green roofs aligned with environmental goals. This work contributes to the field by proposing a novel approach to sustainable construction, integrating ecological design with digital technology, and setting a new benchmark in the integration of green roofs with BIM. By addressing these aspects, the research paves the way for future developments in sustainable urban construction, enhancing the efficiency, sustainability, and resilience of urban communities in line with global sustainability objectives.

Building Information Modelling (BIM) is increasingly adopted in supporting Facility Management (FM). However, in the future perspective of efficiently encouraging the management of building estates, e.g. owned by public authorities or institutions, providing an effective integration between Building Information Models and Geographic Information Systems (GIS) becomes also essential. This contribution is therefore aimed at presenting a methodological framework integrating BIM and GIS environments by mainly taking advantage of open-source tools (e.g. QGIS and Dynamo) and non-proprietary data exchange formats like the COBie (Construction-Operations Building Information Exchange) international standard. Unlike what has been mainly proposed in the literature concerning BIM-GIS integration so far, the methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform. The work led instead to the development of an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. This integrated framework is intended to enable facility managers to easily visualize in the GIS platform, through queryable 2D and 3D maps, some relevant information from BIM to efficiently manage workspaces. At the same time, this hybrid informative system allows BIM practitioners to access and manage more detailed information stored in Building Information Models. The findings of experimental applications of the proposed methodological approach to the ex-Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are finally presented and discussed.

Improving the efficiency of the supply process in prefabricated components is challenging and requires accounting for a variety of risks involved in the management of the suppliers. The purpose of this study is to present a method to account for the systematic trade-offs between several supplier alternatives. A novel framework is presented for the whole assessment of supplier alternatives by taking advantage of the information extracted from customized building information modeling (BIM) and a database required for assessment of impacts. A data library related to assessment criteria for supply alternatives is built to facilitate the storage and sharing of information. Analytic hierarchy process (AHP) is used to select the optimal supplier that is able to provide the most satisfaction for the determined criteria. The proposed framework was also illustrated by the implementation in a mega project. The study implication is that BIM-enabled supplier selection can indeed lead to more benefits and higher values for all stakeholders.

This study evaluated the potential of ERA5 reanalysis as a reference dataset for snow water equivalent (SWE) modeling in 16 Alpine basins of varying catchment sizes using the semi-distributed snow model (TOPMELT) in the Upper Adige river basin in the Eastern Italian Alps. The study aimed to identify errors in ERA5 meteorological variables and assess their impact on SWE computation from 1992 to 2019. The findings revealed that ERA5 precipitation overestimated low-intensity rainfall (drizzle problem) and underestimated high-intensity rainfall, while ERA5 temperature underestimated observations. The overestimation of low-intensity rainfall created fictitious low-intensity snowfall events, which, when combined with colder ERA5 temperature, resulted in delayed snowmelt and increased fictitious snow cover days over the study area. The Quantile Mapping (QM) technique was used to remove errors in ERA5 variables. For precipitation, a monthly correction factor accounting the calibration period (1992-2005) was considered to correct the entire period. Temperature errors were corrected individually for calibration and validation periods. The corrected ERA5 SWE simulation showed improved performance, with a decrease in fictitious snow cover days. The study also highlighted the importance of temperature correction over precipitation correction in SWE simulation, particularly for smaller basins, while considering the monthly moving mean to remove seasonality.

Brain, the most complex object known in the universe, uses few watts of power. To mimic it a nuclear power plant will be required and this power house controls the human body, single handedly. Surprisingly, “On the left side, nothing is right and on the right side there is nothing left”. Typically, there are two lateral halves of the brain: Left hemisphere and right hemisphere working distinctly. Left hemisphere is inclined towards logic; Right hemisphere is the root cause of imagination added with critical thinking. In situations like the current pandemic, COVID-19, it is the right half which tends to dominate the processing. This gives birth to mental stress, anxiety thus, aggravating the existing medical condition. Considering this pattern, a survey was conducted in Durg district of Chhattisgarh, which is one of the most hard hit epicentre of the COVID-19 second wave in India. According to this survey, it was revealed that largely women of all age groups (10-25, 26-40, 40 above) were right brained i.e. dominance of right over left hemisphere. Being more imaginative and creative thinkers they are more likely to suffer from mental issues than males. The aim of this research is to improve the mental wellbeing of the citizens in such threatening conditions. To prevent this situation awareness is a must and some stress relieving games have also been created.

The paper presents an adaptation of numerical solution of first order linear differential equation in fuzzy environment. The numerical method is re-established and studied with fuzzy concept to estimate its uncertain parameters whose values are not precisely known. Demonstrations of fuzzy solutions of the governing methods are carried out by the approaches, namely Modified Runge Kutta method and Runge Kutta Merson method. The results are compared with the exact solution which is found using generalized Hukuhara derivative (gH-derivative) concepts. Additionally, different illustrative examples and an application in industry of the methods are also undertaken with the useful table and graph to show the usefulness for attained to the proposed approaches.

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of the wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy by energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex as it involves non-convex variables and requires combinatorial relay selection indicators optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimisation techniques would be difficult to solve in complex problem environments. The proposed algorithm significantly improves the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%,27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistants to combat gender bias.

Introduction: To compare the results of patient-specific absolute dosimetry using homogeneous slab phantom and anthropomorphic heterogeneous female pelvic phantom in cervical cancer patients. Materials and method: Thirty RapidArc plans already planned on treatment planning system (TPS) for cervical cancer patients were exported on both the phantoms viz. RW3 slab phantom and AHFP and dose were calculated using an anisotropic analytic algorithm (AAA). All the plans were delivered by linear accelerator (LA) and the dose for each plan was measured by a 0.6cc ion chamber. The percentage (%) variation between planned and measured doses were calculated and analyzed.Results: In the case of slab phantom, the mean percentage variations between planned and measured doses of all rapid arc QA plans were as 1.4299 and standard deviation 0.768 (t=0.00508, ρ= 0.497982) The result is not significant at p < .05. For the AHFP phantom, the mean percentage variations between planned and measured doses of all rapid arc QA plans were as 6.890 and standard deviation 2.565 (t= 3.21604, ρ= 0.001063 <0.05), the outcome is significant. Discussion: In the case of homogenous slab phantom, there is less than a 3% difference in percentage between planned and measured doses with a standard deviation of 0.7682 (t=0.00508, p= .497982. The result is not significant at p< 0.05). The deviations of planned and measured value of dose in the AHFP phantom were found as 10.67% (maximum value), 2.31% (minimum value) and 6.89% (average value) with standard deviation 2.565 (t=3.21604, p=0.001063. The result is significant at p<0.05). Also, the percentage of variation between homogeneous slab phantoms with AHFP phantom, the t-value is -11.17016. The p-value is < .00001. The result is significant at p<0.05. We can see that the outcomes differ significantly due to the influence of heterogeneous media.Conclusion: AHFP phantom results showed more dose variability than slab homogenous phantom outcomes. Therefore, patient-specific absolute dosimetry should be performed using a heterogeneous phantom that closely resembles the actual human body in terms of both density and design.

Glacial meltwater has been lifeline of agriculture in Gilgit-Baltistan for centuries which is being effected by the climatic changes and associated challenges. The government line institutions and development agencies have been investing to sustain and further extend the cultivated areas by leveraging the available glacial meltwater source. However, approximately 13% arable land, laying mainly along river banks, has limited access to glacial meltwater. Moreover, already cultivated areas also need alternate and sustainable irrigation water supplies to insurefood security. Hence, there is need to explore more potential options to address these challenges and bringing arable land under irrigation for regional prosperity and environmental gains. The International Centre for Integrated Mountain Development (ICIMOD) and its partners piloted solutions for aforementioned issues in Gojal, District Hunza. These solutions included river water lifting through solar and Hydraulic Ram Pumps, uphill water storages and application through drip irrigation systems. In order to assess the technical and economical viabilities of these piloted systems, a semi-structured questionnaire survey, field observation visits to collect data and economic assessment studies were carried out in Morkhun and Khyber. Both piloted systems have been proven as climate resilient, adaptable, environment friendly and economically viable. The cost to benefit ratio for solar powered drip irrigation and Hydraulic Ram Pump drip irrigation systems are 2.96 and 4.21 respectively, with payback period of 9.21 and 8.25 years respectively. The drip irrigation systems at Khyber and Morkhun sites have exhibited excellent hydraulic performance with Coefficient of Uniformity 92.61 and 93 % respectively. However, the field application efficiency of drip irrigation systems estimated for Khyber and Morkhun sites are 85.24 and 88.88% which are lower than the optimal value mainly due to lesser water holding capacity. The Hydraulic Ram Pump can lift four times more water to greater height, while comparing to solar pump of approximately similar capacity and higher capital costs. Both water lifting systems have some limitations and requires local customization for sustainable use.

Iterative Stochastic decoding is an alternative to standard fixed-point decoding of Low-density parity check codes (LDPC) to reduce inter-node routing. In this paper, we propose a Flexible Field-Programmable Gate Array (FPGA)-Based Stochastic decoding (SD) hardware architecture for LDPC codes in the Fifth-Generation (5G) New Radio (NR) Standard that supports decoding of set of various code rates. This decoder’s runtime flexibility is desirable to switch a better performing code rate automatically based on the channel conditions without the extra time needed for reprogramming of FPGA. An offline design method is implemented to generate the hardware description language (HDL) code description of the decoder for the required code-rate set, which is further synthesized and integrated into the Xilinx Kintex-7 series FPGA board to determine the hardware resource utilisation and processing throughput. The Synopsys design tools were employed during both the simulation and synthesis stages, in combination with TSMC 65-nm CMOS standard cell technology, to facilitate comparative analysis. Compared with state-of-the-art designs, the proposed architecture reduces hardware utilization by up to 26% and achieved energy efficiency by 52%.

Routing a person through a traffic network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes the expected travel time. We propose to create non-aggressive adaptive routes in the middle-ground seeking the best of both these extremes. Specifically, these routes still adapt to changing traffic conditions, however we limit the number of adjustments made in the route. This improves the user experience, by providing a continuum of options between saving travel time and minimizing navigation. We design strategies to model single and multiple route adjustments, and investigate enumerative techniques to solve these models. To alleviate the intractability with handling real-life traffic data, we develop efficient algorithms with easily computable lower and upper bounds. We finally present computational experiments highlighting the benefits of limited adaptability in terms of reducing the expected travel time.

Mitochondria are constantly subjected to stressful conditions due to their unique physiology and organization. The resulting damage leads to mitochondrial dysfunction, which underlies many pathophysiological conditions. Hence, constant surveillance is required to closely monitor mitochondrial health for sound maintenance of cellular metabolism and thus, for viability. In addition to internal mitochondrial chaperones and proteases, mitochondrial health is also governed by host cell protein quality control systems. The Ubiquitin-Proteasome System (UPS) and autophagy constitute the main pathways for removal of damaged or superfluous proteins in the cytosol, nucleus, and from certain organelles such as the ER and mitochondria. Although stress-induced ubiquitin-dependent degradation of mitochondrial outer membrane proteins has been widely studied, mechanisms of intramitochondrial protein ubiquitination have remained largely elusive due to the predominantly cytosolic nature of UPS components, separated from internal mitochondrial proteins by a double membrane. However, recent research has illuminated examples of intramitochondrial protein ubiquitination pathways and highlighted their importance under basal and stressful conditions. Owing to the dependence of mitochondria on the error-prone process of protein import from the cytosol, it is imperative that the cell eliminate any accumulated proteins in the event of mitochondrial import deficiency. Apparently, a significant portion of this activity involves ubiquitination in one way or another. In the present review article, following a brief introduction to mitochondrial protein quality control mechanisms, we discuss our recent understanding of intramitochondrial protein ubiquitination, its importance for the basal function of mitochondria, metabolic implications, and possible therapeutic applications.

The present work encompasses an application-oriented perspective to the possible employment of gold nanoparticles as nanomedicine in cancer therapeutics. The rationale of the work lies in the growing needs for assessment of advanced alternative treatment of cancer employing functionalized nanoparticles as nanomedicine. Gold nanoparticles fabricated via green chemistry methods by leaves of a time-honored medicinal plant, Piper betle were ascertained for their synthesis and properties under the umbrella of characterization of nanoparticles, through various techniques like UV-vis spectroscopy, FTIR spectroscopy, X-ray diffraction, and scanning electron microscopy. The cytotoxicity assay of well-characterized gold nanoparticles was monitored against lung cancer cell line (A549) by metabolic and imaging assays. MTT assay or the metabolic assay was performed for a range of nanoparticles’ concentrations. The results were promising and proved to be a leading-edge venture, envisaging the possibility of gold nanoparticles for cancer therapeutics.

Construction 4.0 is witnessing exponential growth in Digital Twin (DT) technology developments and applications, revolutionizing the adoption of Building Information Modelling (BIM) and other emerging technologies used throughout the lifecycle of the built environment. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, DT enhances this with real-time data for cyber-physical integration, enabling live asset monitoring and better decision-making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study presents a holistic review of the existing research focusing on the critical components responsible for developing DT applications in the construction industry. The study identifies five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, nontechnical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices.

Plastics are highly beneficial for the day-to-day activities of human beings; however, their decomposition is limited due to their strong covalent bonding. The concept of degradation of these big molecules into smaller ones or monomers was attempted by several researchers in the preceding decades with limited success. Pyrolysis is one of the ideas used to convert plastics into fuel, rather than small molecules, compared to the crowded structure of polymers. Among these plastics, low-density polyethylene (LDPE) is largely used as carry bags throughout the world, and, herein, the results of catalytic pyrolysis of the conversion of LDPE into fuel are reported. Different dosages of zinc oxide (ZnO) were used as a catalyst to do the pyrolysis at a specific temperature in a batch reactor specially designed at our laboratory. 0.6 g of ZnO was found to be the optimal dosage for a 50 g waste LDPE batch to get the maximum oil yield. The yielded oil was analyzed chemically through Fourier transform infrared spectroscopy (FTIR) and Reformulyzer M4 Hydrocarbon Group Type Analyzer. Evaluation of physical and chemical exergy along with exergetic efficiency of the process was done.

Rebar quantity estimation is pivotal for determining the cost of construction projects and essential for bidding purposes. A bar bending schedule plays a crucial role by providing rebar information and bending instructions, facilitating efficient procurement. Traditional methods of bar bending schedules, which involve manual extraction from 2D (two-dimensional) drawings, are error-prone and hinder construction productivity. This study proposes a Building Information Modeling (BIM)-based algorithm that automates the generation of a Bar Bending Schedule (BBS) directly from the 3D (three-dimensional) BIM models, ensuring accuracy and eliminating manual errors. The effectiveness of the algorithm was verified by comparing the rebar quantities generated by the model against those derived from optimization calculations. The results demonstrated a mean absolute error of 0.017 and a mean absolute percentage error of 1.13%, validating the algorithm’s precision. Additionally, it achieved a remarkable 33.33% reduction in the manpower required for preparing BBSs, highlighting its potential to revolutionize construction workflow efficiency and accuracy.

The paper deals with the reverse engineering, namely the as built documentation of the actual construction. The input is data measured in field using geodetic methods. In addition to modern methods of surveying 3D objects, such as laser scanning, it is still necessary to use classic surveying using a total station. The bottle neck of the process is the creation of documentation of the construction, which is still created manually in the appropriate CAD/BIM software. The goal of the research was to find a method that would reduce the amount of manual work when drawing documentation in CAD/BIM to a minimum. The core of the solution is the use of a topology that interconnects the points measured in field. The entire procedure has 2 parts: 1. Creating a topo-logical drawing template in a suitable CAD/GIS software (digital sketch), 2. Adding geometry to this sketch and creating a drawing using topological codes. The method was verified in practice by applying it to several specific buildings in the Czech Republic. The practical application of the method demonstrated a 30% time saving, a reduction in work and error rate in the entire process.

Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

Abstract Depending upon altitudinal gradient in the Himalayas, the rate of climate change varies from lowland to upland. The Chitwan Annapurna Landscape (CHAL) is the central part of the Himalayas and covers all bioclimatic zones. Analysis of time series data (1970-2019) of temperature and precipitation was carried out in seven bioclimatic zones extending from lowland Terai to higher Himalayas. The non-parametric Mann-Kendall test was applied to determine the trend, which was quantified by Sen’s slope. Annual and decade interval average temperature, precipitation trends, and lapse rate were analyzed in each bioclimatic zone. Out of seven bioclimatic zones, four zones showed a decreasing precipitation trend (lower tropical, upper tropical, upper subtropical, and alpine bioclimatic zones)at the rate of 1.8, 1.98, 2.06, and 1.80 mm/year, and in lower sub-tropical, temperate, and lower subalpine bioclimatic zones, increasing at the rate of 0.45, 1.81 and 1.28mm/year, respectively. Precipitation did not show any particular trend at decade intervals. The average annual temperature at different bioclimatic zones clearly indicates that temperature at higher elevations is significantly increasing more than at lower elevations. In lower tropical bioclimatic zone (LTBZ), upper tropical bioclimatic zone(UTBZ), lower subtropical bioclimatic zone (LSBZ), upper subtropical bioclimatic zone(USBZ), and temperate bioclimatic zone(TBZ), the average temperature increased by 0.022, 0.030, 0.036, 0.042 and 0.051oC/year, respectively. The decade level temperature scenario revealed that the hottest decade was from 1999-2009. The average temperature was found as 24.1, 21.8, 19.7, 17.5, and 13.3oC in LTBZ, UTBZ, LSBZ, USBZ, and TBZ, respectively, and the average annual precipitation in LTBZ, UTBZ, LSBZ, USBZ, TBZ, LBZ, and ABZ was 2002.1, 2613.1, 2223.9, 3146.9, 1447.2, 952.1, and 361.7mm/year, respectively, in CHAL. With the impact of climate change site and region-specific, this information highlights the need to mitigate climate change in different bioclimatic zones.

Coaxial rotors can be found in multirotor vehicles for the added thrust compared to independent rotors while keeping similar area footprints but, performance losses should be considered. This experimental study analyzes the effects of varying motor throttle and propeller pitch values in motor-propeller systems with two to four coaxial rotors. The results show that in a two-rotor coaxial system, to lessen the adverse effects of a front rotor’s backwash and to operate at the maximum performance, only the back motor should be operated initially up to 75% duty cycle before using the front motor up to its 75% duty cycle. Additional thrust requirements should be generated from the back rotor and then from the front rotor up to their maximum duty cycles. In two, three, and four-rotor coaxial setups, total thrust output generated is 1.6, 2.1, and 2.5 times the thrust output at system thrust performance of 86%, 76%, and 66%, respectively of that of an isolated rotor. In a four-rotor coaxial setup, maximum system performance is achieved when the propeller pitch values are gradually increased from the first to the last rotor. The gradual increments in propeller pitch values also result in more uniform thrust sharing among rotors.

This study investigates how Electronic Livestock Health Recording Systems (ELHRs) facilitates the detection of disease burden and make cluster analysis by applying data analytics tools and techniques. A sample size of 18333 livestock disease cases reported from 2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for data collection. The results showed that ELHRs are important as livestock disease data preservers, saving costs, and facilitating the extraction of up-to-date and complete information. Euclidean and Manhattan distance performed well at 98%, while cosine distance measurement metrics performed poorly. Finally, with the application of the selected clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect an optimal number of disease clusters and meet the objectives of the study.

Background: Diabetic patients are more likely to experience morbidity and mortality as a result of microvascular complications such as retinopathy, neuropathy, nephropathy, and stroke. There are many synthetic anti-diabetic agents available which are expensive and have undesirable pathological effects. Thus, it is essential to look for cost-effective, natural, and safe antidiabetic agents. The aim of this study was to screen phytoconstituent and evaluate the in-vitro and in-silico α-amylase inhibitory activity of ethanolic extract of Adhatoda vasica leaves. Method: The extraction of Adhatoda vasica leaves was performed with ethanol through Soxhlet extraction process. Different concentrations (0.1 mg/ml to 1 mg/ml) of ethanolic extract, Acarbose, and Sitagliptin, were prepared and all concentrations were evaluated for α-amylase inhibitory activity through the spectrophotometric method. Molecular docking (AutodockVina 1.2.0) and toxicity profiling (SToPToX web server) studies were performed. Results: The plant extract showed highest inhibition of α-amylase (56.763±0.0035) at a concentration of 1 mg/ml. This inhibitory activity was supported by the in-silico study. Vasicoline (C5) and quercetin (C9), active constitute of plant Adhatoda vasica, showed the best binding energy of 8.3 and 8.0 Kcal/mol respectively with α-amylase enzyme (PDBID: 4W93). Toxicity study revealed the safety profile of plant extract. Conclusion: It was concluded that Adhatoda vasica leaves possess some bioactive compounds which are responsible for controlling blood glucose levels and its identification, purification, and isolation may lead to the development of newer therapeutic agent with lesser side effects.

This research suggests a multi-stakeholder framework to increase the use of fintech in Africa, which would help the continent boost its financial inclusion and reach its Sustainable Development Goals (SDGs). The study evaluates previous research and frameworks that have been created to aid in the adoption of fintech by several stakeholders in developing nations, some of which have been tested in African nations with limited success. To begin with, the study suggests the use of the World Bank’s Maximising Finance for Development (MFD) approach through the prioritization of national ownership, creation of an enabling environment for private sector investment, partnering with multilateral development banks and other stakeholders, fostering innovation and digital literacy, and focusing on cost-effective, non-government guaranteed financing. In the same vein, in line with the G20’s High-Level Principles for Digital Financial Inclusion, the adoption of financial technologies and digital financial services in Africa can be promoted through the creation of a country-specific strategy. Each government can create a regulatory environment that encourages innovation and competition, improve its digital infrastructure, increase its digital literacy and awareness, and collaborate with private sector stakeholders to expand financial inclusion. Furthermore, The Better Than Cash Alliance’s strategy to increase fintech adoption can also be implemented through partnership with businesses, international organizations, and other nations. By joining the Alliance, countries can enact rules and regulations that facilitate fintech adoption, promote awareness and education, and design and execute national digital payment infrastructure with the help of fintech companies. Finally, the mSTAR initiative suggests collaborating with USAID, in order to provide support to underrepresented populations, incorporate digital financial services, increase public-private collaboration, and educate and train policymakers, practitioners, and technologists. By implementing these strategies, African nations can accelerate the adoption of fintech solutions and increase financial inclusion.

With the advent of digitization, the integration of advanced technologies in operations and projects across all sectors is in progress. Accordingly, efforts are being made to utilize cutting-edge technologies to improve the circularity of the built environment. This study aimed to review the existing applications and limitations of building information modeling (BIM) tools for circular economy (CE) implementation from this perspective. A literature review was conducted to provide an overview of the use of BIM tools for conducting life cycle assessments, energy analysis, waste management, and formulation of material passports for buildings. It was found that BIM tools, which are available across all life cycle phases of building, have a great potential to improve the circularity of the sector. The overview provided on the use of various BIM tools may help stakeholders of the built environment understand the role of BIM for CE adoption in the sector.

Building Information Modeling (BIM) has been one of the overwhelming trends in the worldwide. In Korea, from 2012, BIM application to the public construction projects worth more than $40 million was mandated. However, despite the policy, many public owners have hesitated to apply BIM to their projects. They generally view BIM very complex to manage and difficult to understand. While there have been diverse BIM guides as a reference, most of them have developed from BIM suppliers’ perspective involving certain deficiency in meeting the public owners’ taste. The public owners have addressed the necessity for developing an owner-oriented BIM guide on which they can understand the overall BIM procurement process thereby efficiently preparing their BIM service contracts. The main objective of this study is to develop the structure and contents guidance of the new guide to be developed that follows the public business process and mitigates the deficiency. To meet the research objective, a number of domestic and international BIM guides were analyzed and workshops were operated twice with the 12 BIM experts from the Korean construction industry. As a result of the study, a four-staged guide structure was developed consisting of the BIM service Scoping, Tendering, Executing, and Wrapping-up (STEW) guides.

The integration of 3D city models with Building Information Models (BIM), abbreviated as GeoBIM, facilitates improved data support to several applications, e.g. 3D map updates, building permits issuing, detailed city analysis, infrastructure design, context-based building design, to name a few. To solve the integration, several issues need to be tackled and solved, i.e. harmonization of features, interoperability, format conversions, integration of procedures. The GeoBIM benchmark 2019, funded by ISPRS and EuroSDR, evaluated the state of implementation of tools addressing some of those issues. In particular, in the part of the benchmark described in this paper, the application of georeferencing to Industry Foundation Classes (IFC) models and making consistent conversions between 3D city models and BIM are investigated, considering the OGC CityGML and buildingSMART IFC as reference standards. In the benchmark, sample datasets in the two reference standards were provided. External volunteers were asked to describe and test georeferencing procedures for IFC models and conversion tools between CityGML and IFC. From the analysis of the delivered answers and processed datasets, it was possible to notice that while there are tools and procedures available to support georeferencing and data conversion, comprehensive definition of the requirements, clear rules to perform such two tasks, as well as solid technological solutions implementing them, are still lacking in functionalities. Those specific issues can be a sensible starting point for planning the next GeoBIM integration agendas.

The purpose of this work is to compare two fracture prediction models with real-world data. The pure Artificial Neural Network (ANN) model emphasizes regression analysis, while the hybrid model (SVM-ANN) focuses on the combination of regression and classification analysis or Support Vector Machine. The results were subsequently tested against logging data by combining the Machine Learning approach with advanced logging tools. In this context, we used electrical image logs and the dipole acoustic tool which together allowed the distinction of 404 open fractures and 231 closed fractures and, consequently the estimation of fracture porosity. The results are then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models are used to establish comprehensive results, which are subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms in fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well-log analysis do permit reliable estimation of fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data is absent and costly.

The efficiency success status of organization built environment assets management needs robust and comprehensive maintenance processes which depends on the efficiency of the contract information documents for the maintenance management within the assets life cycle and achieve positive return of the investments. Therefore, this paper highlights on the appropriate interactive approach to structuring information flow scope of the asset facilities management contract for construction projects based on GIS and BIM integration processes, sustainability standards, and As-built contractual documents of the project to support the organization owner and stakeholder to improve the asset management processes. Using expert interviews with comprehensive understand for the information type in several facility management contracts conducted in common Organization types based on reviewing available international facility management standards manual and contract of procedures for different services types, and required information flow inside international facility management firms. The study classify and build significant integrated information and data flow model contract drafting with measures and indicators of performance efficiency as guideline for monitoring, technically evaluating, and financially procedures for performance efficiency and quality of services provided in the facilities management contract for King Faisal University campus projects as a case study.

Base on building information modeling(BIM) plus with Robotic process automation (RPA) technology is rapid development in smart building operations and maintenance (O&M). RPA is a workflow-based automated execution tool that is particularly well suited for replacing manual execution of simple and repetitive tasks using computer systems. The use of RPA technology can saves man-hours and increases productivity, which is important for the automation of building operations and maintenance operations.. The BIM technology, as an advanced technology in the construction industry, it can provide new ideas and methods for building operation and maintenance based on its visualization characteristics. That’s integrating RPA with BIM technology which will enhance the digitalization and virtualization of smart building operation and management. In this article will first introduce the features of RPA and BIM technology and then discuss the technical roadmap for remote automated operation and maintenance of smart building equipment room to implemention a low-cost and high efficient building equipment room operation and maintenance solution.

Recent technological improvements have made abundant changes in construction industry. In specific, some technical applications, such as Building Information Modeling (BIM) opens up many possibilities. Some studies have articulated the use of BIM and its advantages in construction, but most of them are theoretical, not practical. This study is to provide an insight to such obstacles in BIM research. By investigating a real project that could utilize BIM in planning and construction phases, the authors try to investigate a possible outline of advantages in BIM implementation. The study area was set to a railway construction site in South Korea. The site covers a multiple railway tracks, stations, telecommunication facilities, infrastructure facilities, railway structures, and so numerous. In the site, the authors have identified 12 errors in 7 projects that could be prevented if BIM was utilized before the construction. The total upfront costs required to provide a BIM for 7 projects were $116,348. On the other hand, the total costs required to fix the errors in 7 projects were $166,486. This can be regarded as the benefits of using BIM because if BIM was implemented then the associated errors are easily replaced. Therefore, the benefit-cost ratio can be estimated as 1.32 for one-month delay and 1.36 for a three-month delay.

Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and now includes time as a fourth dimension and cost as a fifth dimension, as well as such other applications as virtual reality and augmented reality. The Internet of Things (IoT) has been increasingly applied in various products (smart homes, wearables) to enhance work productivity, living comfort, and entertainment. However, research addressing the integration of these two technologies (BIM and IoT) is still very limited, and has focused exclusively on the automatic transmission of sensor information to BIM models. This paper describes an attempt to represent and visualize sensor data in BIM with multiple perspectives in order to support complex decisions requiring interdisciplinary information. The study uses a university campus as an example and includes several scenarios, such as an auditorium with a dispersed audience and energy saving options for rooms with different functions (mechanical/electric equipment, classrooms, and laboratory). This paper also discusses the design of a common platform allowing communication among sensors with different protocols (Arduino, Raspberry Pi), the use of Dynamo to accept sensor data as input and automatically redraw visualized information in BIM, and how visualization may help in making energy-saving management decisions.

Information regarding the cost of a construction project is available to the investor and project participants in order to determine the subsequent success of a project, given that the information they collect has an impact on the decisions they make. Cost calculations, especially in the initial phase of a project, often generate large errors. This paper presents the new approach based on a combination of the Case Based Reasoning method (CBR) with the originally selected criteria for the description of a construction project (as a result of Pearson correlation coefficient and Spearman's rank correlation coefficient) and Building Information Modeling (BIM) technology. The CBR method fulfils expectations for a simple and fast system supporting the cost estimation process. It does not require any specialist knowledge, so it will be comprehensible to cost estimation practitioners. The BIM-based model gives the opportunity for the calculation of quantity take-offs and enables the use of the information contained in the BIM model in the cost estimation process. In order to prepare the model an appropriate relational database had to be developed. With extensive research, a database of 173 construction projects, including the construction of a sports field, was obtained. There were 14 variables defined originally by authors; however, only 10 (as a result of the correlation analysis) were used for the calculation. Data related to the project were collected in the BIM model. Results estimating the project’s unit price, using the CBR method, were presented and discussed. The Mean Absolute Estimate Error was used to evaluate the model.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.