Economic Viability of Smallholder Agroforestry and Beekeeping Projects in Uluguru Mountains, Tanzania: A Cost Benefit Analysis

Agroforestry and beekeeping are widely promoted as prospective Nature- Based Income Generating Activities (NIGAs) to improve livelihoods while at the same time enhancing biodiversity conservation in degrading agro-ecologies. These activities can diversify and increase famers’ incomes and support instinctive biota and fauna resilience. However, evidence to showcase and compare their long-term benefits is scant. We use the case of Uluguru Mountains in Tanzania to evaluate and compare viability of agroforestry and beekeeping projects using the Cost Benefit Analysis (CBA) approach. The results of analysis yielded positive NPVs for both agroforestry and beekeeping projects at discount rates not higher than 8.2% and 8.5% respectively. Overall, the comparison of economic viability between agroforestry and beekeeping projects revealed that the former was relatively more profitable than the later in terms of both the NPV and Benefit Cost Ratio (BCR) criteria. However, the Internal Rate of Return (IRR) for beekeeping was slightly higher than that of agroforestry. Yet, we underscore the fact that these two projects can jointly be implemented to enhance livelihoods of farmers and support biodiversity conservation in the study area and other parts with similar agro-ecologies in developing countries. However, farmers in these agro-ecologies need to be supported by governments and non-government development partners in terms of training and inspiration to shift from orthodox farming to sustainable NIGAs.

Generalized Internal Model Robust Control for Active Front Steering Intervention

Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.

Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning

The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to control.Active disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time delays.This paper designs a proportion integral-type active disturbance rejection generalized predictive control(PI-ADRGPC)algorithm to control the distillation column system with large time delay.It replaces the PD controller in ADRC with a proportion integral-type generalized predictive control(PI-GPC),thereby improving the performance of control systems with large time delays.Since the proposed controller has many parameters and is difficult to tune,this paper proposes to use the grey wolf optimization(GWO)to tune these parameters,whose structure can also be used by other intelligent optimization algorithms.The performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method,multi-verse optimizer(MVO)tuned PI-ADRGPC and MVO tuned ADRC.The simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance.

Joint model of user check-in activities for point-of-interest recommendation

With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.

Active Power Reserve Photovoltaic Virtual Synchronization Control Technology

The photovoltaic virtual synchronous generator(PV-VSG)solves the problem of lack of inertia in the PV power-generation system.The existing PV plants without energy storage are required to participate in the power grid’s frequency modulation(FM),but existing PV-VSGs with energy storage have high requirements for coordinated control.Therefore,the active power reserve PV-VSG(APR-PV-VSG)is studied.Based on the different methods to obtain the maximum power point(MPP),the peer-to-peer and master-slave APR-PV-VSG strategies are proposed.The PV inverters are deviated from the MPP to reserve active power,which is used as the virtual inertia and primary FM power.These methods equip the PV power station with FM capability.The effectiveness of the proposed control strategies is verified by simulation results.