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An indoor office space should not only provide adequate illuminance on horizontal planes but also cater to the physiological and psychological requirements of the occupants. This paper aims to describe a lighting simulation-based work conducted in Kolkata, India which modeled an indoor office to investigate the effects of variation in room surface reflectance combinations on user perception, mean room surface exitance (MRSE), average horizontal illuminance and overall uniformity of horizontal illuminance.

A fluorescent illumination system–based office space was modeled and retrofitted with tubular LED lamps in DIALux. Simulations were conducted for 16 different room surface reflectance combinations and a five-point Likert scale-type survey questionnaire was formulated to conduct a survey with 32 test subjects to assess the subjective preferability of each resultant light scene.

Simulation results demonstrate that the relationship between average horizontal illuminance and MRSE as well as between average horizontal illuminance and overall uniformity of horizontal illuminance, was statistically significant (p < 0.001). In the conducted survey, the resultant light scene arising out of the reflectance combination of wall:ceiling:floor = 60%:90%:20% was the most well-received one with 187 convinced agreements (“agree” and “strongly agree” responses).

This work found strong linear correlation between average horizontal illuminance and MRSE and between average horizontal illuminance and overall uniformity. A five-point Likert scale-type survey questionnaire with seven questions was formulated and validated with 32 test subjects (Cronbach’s alpha > 0.9295), which showed that the wall:ceiling:floor reflectance combination of 60%:90%:20% was the most favored choice.

Good illumination creates an aesthetic environment that may positively influence patients’ well-being and provide comfort to the hospital staff. This study aims to focus on exploring the energy efficiency of lighting and subjective perception of the lit environment in a hospital ward to assess quality indicators of ambient lighting conditions.

The existing conventional tubular fluorescent lamp–based lighting system in the surveyed patients’ ward was retrofitted with light-emitting diode (LED) luminaires to explore illumination and energy parameters. Thereafter, a software lighting model was created, simulated and analyzed. A Web-based survey with five bipolar adjective pairs in a semantic differential scale was conducted with 48 participants to record and analyze their subjective responses pertaining to the variations in lamp types and surface reflectance combinations.

The findings imply that the LED tubular lamp–based illumination was deemed more adequate compared to other lamp types and the effects of variations in room surface reflectance combinations on the participants’ responses were statistically significant at α = 0.05 level. The simulated horizontal work plane average illuminance level varied from 131 to 171 lx, mean room surface exitance (MRSE) levels remained between 30 and 90 lm/m² and overall uniformity of illuminance remained between 0.5 and 0.7.

In a hospital ward illuminated by LED tubular lamps, variations in room surface reflectance combinations for a constant luminous flux package output from the lamps may affect the subjective perception of users and the correlation between horizontal work plane average illuminance and MRSE is found to be highly linear (coefficient of determination > 0.97).

The purpose of this paper is based on implementation of Global Navigation Satellite System (GNSS) technique in civil aviation for recovery of aircraft position using Single Point Positioning (SPP) method in kinematic mode.

The aircraft coordinates in ellipsoidal frame were obtained based on Global Positioning System (GPS) code observations for SPP method. The numerical computations were executed in post-processing mode in the Aircraft Positioning Software (APS) package. The mathematical scheme of equation observation of SPP method was solved using least square estimation in stochastic processing. In the experiment, airborne test using Cessna 172 aircraft on September 07, 2011 in the civil aerodrome in Mielec was realized. The aircraft position was recovery using observations data from Topcon HiperPro dual-frequency receiver with interval of 1 second.

In this paper, the average value of standard deviation of aircraft position is about 0.8 m for Latitude, 0.7 m for Longitude and 1.5 m for ellipsoidal height, respectively. In case of the Mean Radial Spherical Error (MRSE) parameter, the average value equals to 1.8 m. The standard deviation of receiver clock bias was presented in this paper and the average value amounts to 34.4 ns. In this paper, the safety protection levels of Horizontal Protection Level (HPL) and Vertical Protection Level (VPL) were also showed and described.

In this paper, the analysis of aircraft positioning is focused on application the least square estimation in SPP method. The Kalman filtering operation can be also applied in SPP method for designation the position of the aircraft.

The SPP method can be applied in civil aviation for designation the position of the aircraft in Non-Precision Approach (NPA) GNSS procedure at the landing phase. The typical accuracy of aircraft position is better than 220 m for lateral navigation in NPA GNSS procedure. The limit of accuracy of aircraft position in vertical plane in NPA GNSS procedure is not available.

This paper is destined for people who works in the area of aviation and air transport.

The work presents that SPP method as a universal technique for recovery of aircraft position in civil aviation, and this method can be also used in positioning of aircraft based on Global Navigation Satellite System (GLONASS) code observations.

The purpose of the study is focused on implementation of Global Navigation Satellite System (GLONASS) technique in civil aviation for recovery of aircraft position using Precise Point Positioning (PPP) method in kinematic mode.

The aircraft coordinates of Cessna 172 plane in XYZ geocentric frame were obtained based on GLONASS code and phase observations for PPP method. The numerical computations were executed in post-processing mode in the RTKPOST module in RTKLIB program. The mathematical scheme of equation observation of PPP method was solved using Kalman filter in stochastic processing.

In paper, the average accuracy of aircraft position is about 0.308 m for X coordinate, 0.274 m for Y coordinate, 0.379 m for Z coordinate. In case of the mean radial spherical error (MRSE) parameter, the average value equals to 0.562 m. In paper, the accuracy of aircraft position in BLh geodesic frame were also showed and described.

The PPP method can be applied for determination the coordinates of receiver, receiver clock bias, Zenith Wet Delay (ZWD) parameter and ambiguity term for each satellite.

The PPP method is a new technique for aircraft positioning in air navigation. The PPP method can be also used in receiver autonomous integrity monitoring (RAIM) module in aircraft-based augmentation system (ABAS) system in air transport. The typical accuracy for recovery the aircraft position is about cm ÷ dm level using the PPP method.

The paper is destined for people who work in area of geodesy, navigation, aviation and air transport.

The work presents the original research results of implementation the GLONASS satellite technique for recovery the aircraft position in civil aviation. Currently, the presented research PPP method is used in precise positioning of aircraft in air navigation based on global positioning system and GLONASS solutions.

Bitcoin has indeed been universally acknowledged as an investment asset in recent decades, after the boom-and-bust of cryptocurrency values. Because of its extreme volatility, it requires accurate forecasts to build economic decisions. Although prior research has utilized machine learning to improve Bitcoin price prediction accuracy, few have looked into the plausibility of using multiple modeling approaches on datasets containing varying data types and volumetric attributes. Thus, this paper aims to propose a bitcoin price prediction model.

In this research work, a bitcoin price prediction model is introduced by following three major phases: Data collection, feature extraction and price prediction. Initially, the collected Bitcoin time-series data will be preprocessed and the original features will be extracted. To make this work good-fit with a high level of accuracy, we have been extracting the second order technical indicator based features like average true range (ATR), modified-exponential moving average (M-EMA), relative strength index and rate of change and proposed decomposed inter-day difference. Subsequently, these extracted features along with the original features will be subjected to prediction phase, where the prediction of bitcoin price value is attained precisely from the constructed two-level ensemble classifier. The two-level ensemble classifier will be the amalgamation of two fabulous classifiers: optimized convolutional neural network (CNN) and bidirectional long/short-term memory (BiLSTM). To cope up with the volatility characteristics of bitcoin prices, it is planned to fine-tune the weight parameter of CNN by a new hybrid optimization model. The proposed hybrid optimization model referred as black widow updated rain optimization (BWURO) model will be conceptual blended of rain optimization algorithm and black widow optimization algorithm.

The proposed work is compared over the existing models in terms of convergence, MAE, MAPE, MARE, MSE, MSPE, MRSE, Root Mean Square Error (RMSE), RMSPE and RMSRE, respectively. These evaluations have been conducted for both algorithmic performance as well as classifier performance. At LP = 50, the MAE of the proposed work is 0.023372, which is 59.8%, 72.2%, 62.14% and 64.08% better than BWURO + Bi-LSTM, CNN + BWURO, NN + BWURO and SVM + BWURO, respectively.

In this research work, a new modified EMA feature is extracted, which makes the bitcoin price prediction more efficient. In this research work, a two-level ensemble classifier is constructed in the price prediction phase by blending the Bi-LSTM and optimized CNN, respectively. To deal with the volatility of bitcoin values, a novel hybrid optimization model is used to fine-tune the weight parameter of CNN.

The purpose of this paper is to study the implementation of GNSS technique in aviation for recovery of aircraft’s position using Precise Point Positioning (PPP) method.

The aircraft’s coordinates in ellipsoidal frame were obtained based on GPS code and phase observations for PPP method. The numerical computations were executed in post-processing mode in the CSRS-PPP and magicPPP online services. The mathematical scheme of PPP method was development using indifference equations of Ionosphere-Free linear combination. In the experiment, airborne test using Cessna 172 aircraft on June 01, 2010 in the military airport in Deblin was realized. The aircraft’s position was determined using data from GNSS receiver (Topcon HiperPro with interval of 1 s).

In this paper, the accuracy of aircraft’s position is better than 0.07 m for CSRS-PPP service and better than 0.27 m for magicPPP service. In case of the Mean Radial Spherical Error parameter, the average value for CSRS-PPP service equals to 0.01 m, whereas for magicPPP, it is about 0.38 m. The values of vertical coordinate of Cessna 172 aircraft were also checked with results of Real Time Kinematic–On The Fly technique.

In this paper, the analysis of aircraft positioning is focused on the application of the PPP method in post-processing mode. In near real time, the PPP method still has limitations, especially in the area of ambiguity resolution and also instrumental biases (e.g. Narrow Lane Hardware Delays).

The PPP method can be applied in aviation in post-processing mode for verification of true aircraft coordinates and elimination of blunder errors from adjustment processing of GNSS observations. The Zenith Wet Delay term as a product of troposphere delay and receiver clock bias as a product of precise time transfer can be obtained in the PPP method.

The paper presents that the PPP method is an alternative solution for the recovery of aircraft’s position in aviation, and this method can be also applied in the positioning of aircraft based on GLONASS or GPS/GLONASS data.

An optimal solution method based on 2-norm is proposed in this study to solve the inverse kinematics multiple-solution problem caused by a high redundancy. The current research also presents a motion optimization based on the 2-Norm of high-redundant mobile humanoid robots, in which a kinematic model is designed through the entire modeling.

The current study designs a highly redundant humanoid mobile robot with a differential mobile platform. The high-redundancy mobile humanoid robot consists of three modular parts (differential driving platform with two degrees of freedom (DOF), namely, left and right arms with seven DOF, respectively) and has total of 14 DOFs. Given the high redundancy of humanoid mobile robot, a kinematic model is designed through the entire modeling and an optimal solution extraction method based on 2-norm is proposed to solve the inverse kinematics multiple solutions problem. That is, the 2-norm of the angle difference before and after rotation is used as the shortest stroke index to select the optimal solution. The optimal solution of the inverse kinematics equation in the step is obtained by solving the minimum value of the objective function of a step. Through the step-by-step cycle in the entire tracking process, the kinematic optimization of the highly redundant humanoid robot in the entire tracking process is realized.

Compared with the before and after motion optimizations based on the 2-norm algorithm of the robot, its motion after optimization shows minimal fluctuation, improved smoothness, limited energy consumption and short path during the entire mobile tracking and operating process.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

Motion optimization based on the 2-norm of a highly redundant humanoid mobile robot with the entire modeling is performed on the basis of the entire modeling. This motion optimization can make the highly redundant humanoid mobile robot’s motion path considerably short, minimize energy loss and shorten time. These researches provide a theoretical basis for the follow-up research of the service robot, including tracking and operating target, etc. Finally, the motion optimization algorithm is verified by the tracking and operating behaviors of the robot and an example.

This paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.

Firstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.

The model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.

A novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.

The highlights of the paper are as follows:

1. A new grey prediction model with a unified nonlinear structure is proposed.

2. The new model can be fully compatible with multiple traditional grey models.

3. The new model solves the defect of poor adaptability of the traditional grey models.

4. The parameters of the new model are optimized by PSO algorithm.

5. Cases verify that the new model outperforms other models significantly.

A new grey prediction model with a unified nonlinear structure is proposed.

The new model can be fully compatible with multiple traditional grey models.

The new model solves the defect of poor adaptability of the traditional grey models.

The parameters of the new model are optimized by PSO algorithm.

Cases verify that the new model outperforms other models significantly.

The recent surge in light emitting diode (LED) lighting retrofitted into schools in the UK is as a result of the UK Government’s 2050 zero carbon pledge. However, the benefits and consequences of LED retrofit projects for staff and enablers and stakeholder knowledge gaps about LED lighting retrofitting have not been fully explored. The aim of this research is to determine the amount of savings in cost, carbon reduction and kilowatt usage and to confirm if repayment from energy and cost savings derived from LED retrofit school projects funded through the SALIX funding option in the UK would be enough to service the loan. Thus, it examines monetary and non-monetary benefits, internal project stakeholder knowledge gaps and the consequences of LED retrofit for the staff and enablers of a large community college in the UK which is funded through the SALIX funding option.

The methodology relied on a hybrid research approach of a case secondary school through the review of literature, analysis of secondary data, focus group and questionnaire survey. The focus group consists of six key project stakeholders. The secondary data was sourced from the Project IGP [Individual Grade Proposal] and the Positive Energy Report from Zenergi, and the closed online questionnaire survey was used to sample 150 teaching staff and school enablers.

The findings show that stakeholders lack project knowledge, trust and expertise/project comprehension. This is in terms of baseline information, LED technology/management, payback modalities, management of risks and ethical issues around environmental impact. The forecasted SALIX savings were not achieved in real-time, partly because it does not take into consideration the increase in energy costs over the payback period. However, the LED retrofit creates efficiencies; drives down energy costs and energy usage; and drives carbon reduction, helping pupils’ learning, improving productivity and performance, and finally leading to a better lighting environment for the school community.

The study will help schools in the UK that intend to access SALIX finance for LED retrofits to understand the challenges and mitigate the risks. It will also help the government to understand the importance of adjusting the payback modalities to the base price when the retrofit was carried out for real-time savings to be made. The research would be useful in ensuring the proactive involvement of all the identified stakeholders in understanding the challenges and what the function entails.

The purpose of this paper is to explore the internal interaction mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth from a systematic perspective, based on which this work forecasts their future development trends.

In this study, a multivariate grey model is applied to the prediction of marine scientific research and education, industrial structure upgrading and marine economic growth. Considering the impact of the COVID-19 on marine development, this paper introduces the weakening buffer operator into MGM(1,m) and constructs the AWBO-MGM(1,m) model. To verify the validity and accuracy of the new model, this paper uses AWBO-MGM(1,m), MGM(1,m), GM(1,N), GM(1,1), back propagation neural network and linear regression models for simulation and prediction based on the data from 2010 to 2021, respectively.

From the theoretical perspective, the development of marine scientific research and education can accelerate industrial upgrading and promote marine economic growth by providing high-quality talents, promoting marine science and technology progress and reducing transaction costs; while the upgrading of marine industrial structure and marine economic growth can promote the development of marine scientific research and education by guiding social capital, enhancing talent demand and stimulating market vitality. From the empirical analysis, the AWBO-MGM(1,m) model can effectively deal with epidemic shocks and has higher fitting and prediction accuracy than the other five comparative models.

The government should pay attention to the construction of marine scientific research and education, so as to provide high-quality talents and advanced scientific research results for the high-quality development of marine economy. On the basis of using science and technology to firmly build the primary and secondary marine industries, the government should actively guide the labor, capital and other factors of production to the tertiary industry, thereby promoting the optimization and upgrading of marine industrial structure.

On the one hand, the interplay mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth is analyzed from a systematic perspective; on the other hand, the enhanced AWBO-MGM(1,m) possesses higher forecasting performance and is applicable to the systemic multivariate forecasting problem in the presence of outstanding external shocks.