Contribution of non-timber forest products to the livelihoods of the forest-dependent communities around the Khadimnagar National Park in northeastern Bangladesh

Non-timber forest products (NTFPs) play a significant role in the improvement of the forest-dependent people’s livelihoods around the world, strengthening protection for the sustainable use of forests. The purpose of this research was to evaluate the influence of occupational category-wise (fuelwood collectors, farmers, small-scale businessmen, day labourers, and tea estate labourers) dependency on NTFPs and the role of NTFPs on household income around the Khadimnagar National Park (KNP) in northeastern Bangladesh. In 2014, 178 purposively selected respondents from four villages (out of 22 villages around the KNP) were interviewed face-to-face using a semi-structured questionnaire. The study observed that these forest-dependent communities utilized resources of the KNP mainly for domestic energy supply, household income, and house construction. Results showed that income from NTFPs made a significant contribution to family income. Income data analysis indicated that small-scale businessmen earned relatively more income from NTFPs, followed by tea estate labourers and day labourers. The study revealed significant negative relationships of the distance of households from the forest with the amount of NTFPs collected (P ​< ​0.01) and monthly income from NTFPs (P ​< ​0.01). Positive significant relationships were found between the amount of NTFPs collected and the time spent in NTFP collection (P ​< ​0.001), as well as between monthly income from NTFPs and family size (P ​< ​0.001). The fuelwood collectors and farmers collected significantly greater amounts of NTFPs per trip (P ​< ​0.001) than other occupational categories. The households that were moderately to highly dependent on NTFPs collected significantly higher amounts of NTFPs per trip (P ​< ​0.01) than the households that were moderately dependent and less dependent on NTFPs. Community dependence on KNP’s resources, community’s appreciation of the KNP’s ecosystem services for villagers’ livelihoods, and community’s high levels of concern for forest conservation provided a foundation for the sustainable management of the KNP. The study findings will be useful for designing an effective forest management plan and policy for NTFP management and forest conservation with the active involvement of the forest-dependent people in northeastern Bangladesh

STUDY OF ANALGESIC AND ANTIDIARRHOEAL ACTIVITIES OF Sonneratia caseolaris (LINN.) LEAF AND STEM USING DIFFERENT SOLVENT SYSTEM.

The different fractions of crude ethanol extract of leaf and stem of S. caseolaris (Linn.) (Sonneratiaceae) were screened for its analgesic and antidiarrhoeal activities. The different fraction of crude extract was obtained by using four different solvent systems. The different fractions of crude extract produced significant writhing inhibition in acetic acid induced writhing in mice at dose of 250 and 500mg/kg BW comparable to the standard drug diclofenac sodium at the dose of 25mg/kg BW. When tested for its antidiarrhoeal effects on castor oil induced diarrhea in mice, it increased mean latent period and decreased the frequency of defecation significantly at the dose of 250 and 500mg/kg BW comparable to the standard drug loperamide at the dose of 50mg/kg BW. The overall results tend to suggest the analgesic and antidiarrhoeal activities of the different fractions of crude extract. Both ethyl acetate fraction of stem and chloroform fraction of leaf have significant analgesic activity. Again between the two fractions of crude ethanol extract ethyl acetate fraction of S.caseolaris stem have most significant antidiarrhoeal activity.Key words: analgesic, antidiarrhoeal, S.caseolaris, diclofenac sodium, loperamide

Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms

The concept of a microgrid system, when put in simple words, is a small scale generation and deployment of power to a small geographical area in order to avoid transmission losses and maintain an uninterrupted power supply. It has been a mandatory protocol to implement the available renewable energy sources (RES) in order to minimize the emission of harmful pollutants to the atmosphere from the combustion of the fossil fuels. Economic load dispatch (ELD) deals with the optimal sizing of the distributed energy resources (DERs) by minimizing the fuel costs. Emission dispatch does the optimal sizing of the DERs sources by minimizing the amount of pollutants released in the atmosphere. A multi-objective Combined Economic-Emission Dispatch (CEED) does the optimal DER sizing providing a compromised solution of minimizing both the fuel costs and pollutants emission. This paper performs all ELD, emission dispatch and CEED on an islanded and renewable-integrated microgrid separately using a recently developed novel Whale optimization Algorithm (WOA). Four various scenarios of load sharing among the DERs are studied. The results are then compared with other recently developed bio inspired algorithms to corroborate the effectiveness of the proposed technique. Further statistical analysis such as ANOVA test and Wilcoxon signed rank test are performed to prove the superiority of the proposed approach over the various other optimization techniques used. Keywords: Combined economic emission dispatch, Penalty factor, Microgrid, Symbiotic organisms search, Grey wolf optimization, Particle swarm optimization, Differential evolution, Whale optimization algorith

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance

A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced flyrock

Blasting is an economical technique for rock breaking in hard rock excavation. One of its complex undesired environmental effects is flyrock, which may result in human injuries, fatalities and property damage. Because previously developed techniques for predicting flyrock are having less accuracy, this paper develops a new hybrid intelligent system of extreme learning machine (ELM) optimized by biogeography-based optimization (BBO) for prediction of flyrock distance resulting from blasting in a mine. In the BBO-ELM system, the role of BBO is to optimize the weights and biases of ELM. For comparison purposes, another hybrid model, i.e., particle swarm optimization (PSO)-ELM and a pre-developed ELM model were also applied and proposed. To do so, 262 datasets including burden to spacing ratio, hole diameter, powder factor, stemming, maximum charge per delay and hole depth as input variables and flyrock distance as system output were considered and used. Many models with different combinations of training and testing datasets have been constructed to identify the best predictive model in estimating flyrock. The results indicate capability of the newly developed BBO-ELM model for predicting flyrock distance. The coefficient of determination, coefficient of persistence and root mean square error values of (0.93, 0.93 and 21.51), (0.94, 0.95 and 18.84) and (0.79, 0.85 and 32.29) were obtained for testing datasets of PSO-ELM, BBO-ELM and ELM model, respectively, which reveal that the BBO-ELM is a powerful model for predicting flyrock induced by blasting. The developed BBO-ELM model can be introduced as a new, capable and applicable model for solving engineering problems

STUDY OF ANALGESIC AND ANTIDIARRHOEAL ACTIVITIES OF Sonneratia caseolaris (LINN.) LEAF AND STEM USING DIFFERENT SOLVENT SYSTEM

The different fractions of crude ethanol extract of leaf and stem of S. caseolaris (Linn.) (Sonneratiaceae) were screened for its analgesic and antidiarrhoeal activities. The different fraction of crude extract was obtained by using four different solvent systems. The different fractions of crude extract produced significant writhing inhibition in acetic acid induced writhing in mice at dose of 250 and 500mg/kg BW comparable to the standard drug diclofenac sodium at the dose of 25mg/kg BW. When tested for its antidiarrhoeal effects on castor oil induced diarrhea in mice, it increased mean latent period and decreased the frequency of defecation significantly at the dose of 250 and 500mg/kg BW comparable to the standard drug loperamide at the dose of 50mg/kg BW. The overall results tend to suggest the analgesic and antidiarrhoeal activities of the different fractions of crude extract. Both ethyl acetate fraction of stem and chloroform fraction of leaf have significant analgesic activity. Again between the two fractions of crude ethanol extract ethyl acetate fraction of S.caseolaris stem have most significant antidiarrhoeal activity

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (Mr) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r2) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted Mr values generally yielded the same distribution and trend as the observed Mr data

Sea level rise induced impacts on coastal areas of Bangladesh and local-led community-based adaptation

Bangladesh is as a low-lying country, susceptible to various Sea Level Rise (SLR) induced impacts. Previous studies have separately explored SLR effects on Bangladesh’s coastal ecosystems and livelihoods, across multiple spatial and temporal scales. However, empirical studies acknowl- edging local population’s perceptions on the causal factors to different SLR induced physio- graphic impacts, their effects at societal scale and ongoing adaptation to these impacts of SLR have not been able to establish a causal-linkage relationship between these impacts and their potential effects. Our study explores how SLR has already impacted the lives and livelihoods of coastal communities in Bangladesh and how these have been responded by adopting different adaptative measures. We applied a qualitative community-based multistage sampling procedure, using two Participatory Rural Appraisal (PRA) tools, namely Focus Group Discussions (FGDs) and Community Meetings (CM), to collect empirical data about SLR effects on livelihoods and implemented adaptation responses. Our study found that both man-made and natural causes are responsible for different physiographic impacts of SLR, and which seem to vary between place and context. Five major SLR induced impacts were identified by coastal communities, namely: salinity increase, rising water levels, land erosion, waterlogging and the emergence of char land. Salinity increase and land erosion are the two most severe impacts of SLR resulting in the largest economic losses to agriculture. Our results highlight how coastal communities in Bangladesh perceive the impacts of SLR and the benefits of different adaptation processes set in motion to protect them, via development projects and other local interventions.info:eu-repo/semantics/acceptedVersio

Impacts of Climate Change and Adaptation Strategies for Rainfed Barley Production in the Almería Province, Spain

Mediterranean water-stressed areas face significant challenges from higher temperatures and increasingly severe droughts. We assess the effect of climate change on rainfed barley production in the aridity-prone province of Almería, Spain, using the FAO AquaCrop model. We focus on rainfed barley growth by the mid-century (2041–2070) and end-century (2071–2100) time periods, using three Shared Socio-economic Pathway (SSP)-based scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Using the paired t-test, Spearman and Pearson correlation coefficient, Root Mean Squared Error, and relative Root Mean Squared Error, we verified AquaCrop’s ability to capture local multi-year trends (9 or more years) using standard barley crop parameters, without local recalibration. Starting with a reference Initial Soil Water Content (ISWC), different soil water contents within barley rooting depth were modelled to account for decreases in soil water availability. We then evaluated the efficiency of different climate adaptation strategies: irrigation, mulching, and changing sowing dates. We show average yield changes of +14% to −44.8% (mid-century) and +12% to −55.1% (end-century), with ISWC being the main factor determining yields. Irrigation increases yields by 21.1%, utilizing just 3% of Almería’s superficial water resources. Mulches improve irrigated yield performances by 6.9% while reducing irrigation needs by 40%. Changing sowing dates does not consistently improve yields. We demonstrate that regardless of the scenario used, climate adaptation of field barley production in Almería should prioritize limiting soil water loss by combining irrigation with mulching. This would enable farmers in Almería’s northern communities to maintain their livelihoods, reducing the province’s reliance on horticulture while continuing to contribute to food security goals. QC 20240529</p

Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field