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Land development in sub-urban areas is more frequent than in highly urbanized cities, causing land prices to increase abruptly and making it harder for valuers to update land values in timely manner. Apart from this, the non-availability of sufficient reliable market values forces valuers to use alternatives and subjective judgement. Land value is critical not only for private individuals but also for government agencies in their day-to-day land dealings. Thus, mass appraisal is necessary. In other words, despite the importance of reliable land value in all aspects of land administration, valuation remains disorganized, with unregulated undertakings that lack concrete scientific, legal, and practical foundations. A holistic and objective way of weighing geospatial factors through expert consultation, legal reviews, and evidence (i.e., news) will provide more realistic results than a regression-based method that does not comprehend valuation factors (i.e., physical, social, economic, environmental, and legal aspects). The analytic hierarchy process (AHP) enables these factors to be included in the model, hence providing a realistic result. The innovative land valuation model (iLVM), developed in this study, is an inclusive approach wherein experts are involved in the selection and weighing of 15 factors through the AHP. The model was validated using root mean squared error (RMSE) and compared with multiple regression analysis (MRA) through a case study in Baybay City, Philippines. Based on the results, the iLVM (RMSE = 0.526) outperformed MRA (RMSE = 1.953).

In this study, we use Artificial Neural Networks (ANN) and Multiple Regression Analysis to evaluate the prediction of two crucial self-compacting concrete properties: compressive strength and split tensile strength. It was possible to create four different datasets, each of which had different concrete mix proportions along with their respective ages in days, compressive strengths (MPa), and split tensile strengths (MPa). Separate ANN models and Regression models were trained and tested using these datasets. As a gauge of prediction accuracy, Mean Squared Error (MSE) was used to assess the performance of the models. This study offers insightful information on the application of multiple regression analysis and artificial neural networks to forecast the characteristics of self-compacting concrete using GGBS and Alccofine. Here Alccofine functions as an additive and GGBS acts as a partial substitute for cement at 0 to 60% with a fluctuation of 10%. The outcomes highlight the potential of neural networks as a tool for concrete mix design optimization and quality control since they can capture complex correlations between input variables and concrete strength.

Intensive anthropogenic activity leads to ecosystem degradation, necessitating a shift from conventional management approaches towards proactive environmental risk assessment strategies. This study presents a quantitative methodology for calculating a Comprehensive Environmental Risk Indicator (CERI) based on the analysis of types of permitted use (TPU) of land plots in the Russian Federation. The methodology comprises three stages: determining a base risk weight for each TPU, statistically weighting impact factors using multiple regression analysis (MRA), and synthesizing the final CERI. This research identifies five key impact factors: industrial pollution, biogenic and agrogenic effects, landscape and resource changes, anthropogenic and household load, and specific risks and disasters. The results demonstrate that biogenic and agrogenic effects (weight 0.450) and specific risks and disasters (weight 0.398) are the most significant factors. Industrial pollution and landscape changes were excluded from the model due to multicollinearity. The model’s R2 (0.194) confirms its statistical validity as a foundational framework for macro-level risk evaluation. Further improvements could address limitations related to cadastral data variability and the integration of localized environmental parameters. The developed CERI was integrated into geographic information systems (GIS) to visualize risk gradients across land parcels. This study concludes that the use of statistically substantiated factor weights enables objective territorial zoning, facilitating a transition from subjective expert assessments to management based on actual environmental consequences.

Predicting the value of real estate is a complex endeavor due to the abundance of subjective criteria. Objective consideration of the value-affecting criteria in real estate and regulation of decision support systems will enable the acquisition of more accurate results. In this study, analytic hierarchy process (AHP), a type of multi-criteria decision analysis (MCDA), is used to reproduce coefficients that serve as the basis for real estate valuation. A region in the Selcuklu district of Konya, Turkey was used to test the model created by AHP. Weighted criteria describing areas subjected to purchase/sale were generated by the AHP method and then validated. Additionally, a valuation model was created by the multiple regression analysis (MRA) method for comparison and performance analyses. Weighted values were transformed from AHP points and acquired from the MRA method and then joined with geographic information systems (GIS). Value maps of the study area and purchase/sale values were generated according to these newly created models. The performance comparison and value maps revealed that the AHP method is more successful than the MRA method. This study addressed the complexity of criteria issue by using the original hierarchical structure of AHP and thus contributes to the world economy by enabling the generation of more accurate estimations.

The study compares the learning satisfaction of two student groups, one takes the fully online course–Introduction to Internet of Things, and the other takes the small private online course (SPOC). In the research framework, learning satisfaction is the dependent variable, and learning engagement, learning presence, video perception, platform perception, and design perception are independent variables. This work adopts online questionnaire survey to collect data from the two student groups. As to research method, Multiple Regression Analysis (MRA) is utilized to test proposed research framework. The results of MRA show that platform perception generates students’ learning satisfaction for SPOC, while video perception and design perception generate students’ learning satisfaction for fully online course. This empirical study elucidates the factors influence learner’s satisfaction and contributes to theory and practice in the domains of online courses.

In the theory and practice of valuation, it is commonly accepted that the key feature determining the value of agricultural land is its location, both general and in a specific part (zone) of a village. The model approach used in the present study can provide the answer to the question of how to maximize the value of agricultural land as part of a conducted arrangement, agricultural works. The study used data on the market sale of agricultural parcels in 10 Polish municipalities. Each parcel was described using a set of features (parameters) that were key to its value and entered into a database. Using the database, two statistical models were built: a multiple regression analysis model (MRA) and an artificial neural network model (ANN). The studies conducted have shown that changes in such features as surface area, shape, and access to a public road were accompanied by significant changes in the market values of parcels. Another important observation was that potential decreases in the value of agricultural parcels as a result of changes in their surface areas were offset (where it was reasonable) by the elimination of their excessive elongation and providing them with an access to a public road. Based on the findings, it has been concluded that change in land value should be considered one of the effects of executed land consolidation projects (LCP), during which the parameters of agricultural parcels are subject to the biggest changes.

Biodegradation of phenol using Pseudomonas pictorum (NICM 2074) a potential biodegradant of phenol was investigated for its degrading potential under different operating conditions. The neural network input parameter set consisted of the same set of four levels of maltose (0.025, 0.05, 0.075 g/l), phosphate (3, 12.5, 22 g/l), pH (7, 8, 9) and temperature (30 degrees C, 32 degrees C, 34 degrees C) on phenol degradation was investigated and a Artificial Neural Network (ANN) model was developed to predict the extent of degradation. The learning, recall and generalization characteristic of neural networks was studied using phenol degradation system data. The efficiency of the model generated by the ANN, was tested and compared with the results obtained from an established second order polynomial multiple regression analysis (MRA). Further, the two models (ANN and MRA) were used to predict the percentage of degradation of phenol for blind test data. Performance of both the models were validated in the cases of training and test data, ANN was recommended based on the following higher coefficient of determination R (2); lower standard error of residuals and lower mean absolute percentage deviation.

From a series of 50 MK801 derivative molecules, a selected set of 44 compounds was submitted to a principal components analysis (PCA), a multiple regression analysis (MRA), and a neural network (NN). This study shows that the compounds' activity correlates reasonably well with the selected descriptors encoding the chemical structures. The correlation coefficients calculated by MRA and there after by NN, r = 0.986 and r = 0.974 respectively, are fairly good to evaluate a quantitative model, and to predict activity for MK801 derivatives. To test the performance of this model, the activities of the remained set of 6 compounds are deduced from the proposed quantitative model, by NN. This study proved that the predictive power of this model is relevant.

In order to identify the relation between revenue water (RW) ratio and key local factors in a quantifiable way, 90 effect factors were considered as regional characteristics for 79 Korean cities. Seven statistically significant effect factors were chosen through correlation analysis. Three principal components independently influencing RW ratio were extracted by principal component analysis (PCA). The 79 cities were grouped into six clusters by k-means clustering (KMC) of the factor scores of the cities. Then key local factors were identified and their impacts were quantified by multiple regression analysis (MRA) and they were justified by T-test and F-test. The approach through correlation-PCA-KMC-MRA was proved to be one of scientific ways for identification of key local factors. According to the result, it was suggested that a shorter length of distribution system, a water supply with smaller number of bigger customer meters a and gravitational supply through reservoir would be advantageous from a RW ratio's point of view.

Background Several species of the genus Ziziphus are used worldwide for their medicinal and therapeutic properties. The present study aimed to investigate the phenotypic variation of five species of the Ziziphus genus, including Z. jujuba Mill. (25 accessions), Z. mauritiana Lam. (25 accessions), Z. spina-christi L. (25 accessions), Z. nummularia L. (10 accessions), and Z. xylopyrus Willd. (10 accessions) from Markazi, Sistan-va-Baluchestan, and Khuzestan provinces, Iran. Results The investigated accessions have significant differences in terms of all the measured as revealed using analysis of variance (ANOVA, P  < 0.01). The range of fruit weight was 0.43–1.29 g in Z. jujuba , 17.85–29.87 g in Z. mauritiana , 0.94–3.44 g in Z. spina-christi , 0.93–2.02 g in Z. nummularia , and 0.91–3.02 g in Z. xylopyrus . All the measured traits showed significant and positive correlations with each other. Multiple regression analysis (MRA) results showed that fruit length, stone width, stone weight, stone length, and fruit width have significant effects on fruit weight, and thus their fluctuations have a significant effect on increasing or decreasing fruit weight. The accessions were grouped into two main clusters using hierarchical cluster analysis. The first cluster (I) included all the accessions of Z. mauritiana , while the second cluster (II) contained the accessions of the rest species forming two sub-clusters. Conclusion Based on the commercial characters, accessions no. 12, 13, 17, 23, and 24 in Z. jujuba , accessions no. 3, 9, 17, 18, 20, 22, and 23 in Z. mauritiana , accessions no. 5, 6, 8, 13, 19, 22, and 24 in Z. spina-christi , accessions no. 3, 7, and 9 in Z. nummularia , and accessions no. 2, 4, 7, and 10 in Z. oxyphylla showed the highest fruit weight and thus can be suggested as superior for cultivation or use in breeding programs due to having larger fruits.