Search Results (13)

The Vietnamese state has advocated for the sedentarization and market integration of upland northern farmers over the past thirty years, leading to both agrarian and forest transitions. This article presents a comprehensive land use and land cover change (LULCC) analysis of two adjacent upland borderland districts, Phong Thổ and Bát Xát, in northern Vietnam, spanning two neighboring inland provinces, Lai Châu and Lào Cai. These districts are primarily home to ethnic minority farmers who are encouraged by Vietnamese state officials to not only protect forests but to also transition toward cash crop cultivation from less intensive semi-subsistence agriculture. Our LULCC maps, covering the period from 1990 to 2020, revealed a reduction in the speed by which closed-canopy forests were disappearing. During interviews, state officials were confident that this was due to a range of state policies and state-sponsored initiatives, including the promotion of tree crops and payments for forest environmental services. Our own fieldwork in the region suggests other factors are also supporting this decline in deforestation rates, rooted in ethnic minority farmer livelihood decision making. Some state officials were also able to point to factors hindering a more positive result regarding forest cover, including population pressure and new infrastructure. Interestingly, despite our positive findings on Land use and land cover change (LULCC) related to forest cover, one-third of state officials, upon reviewing our LULCC maps, firmly maintained that errors had occurred. Some even proposed that there was an actual rise in forest cover. Our study shows that these discrepancies raise compelling questions about officials’ political motivations and ongoing pressures to uphold the central state’s reforestation and agrarian transition discourses. Full article

Five countries in the Lancang–Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang–Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang–Mekong region, which is crucial for implementing effective conservation strategies. Full article

Permeability and inertial factor are the most relevant variables in the Forchheimer equation. They are important to estimate pressure drop when a fluid flow is passing through a porous media. However, in the insect-proof screens field, the existing models to date are still providing a poor predictive accuracy, in part due to they are based on 2D porosity of screens. This work provides novel models for permeability and inertial factor, which are developed from experimental data of insect-proof screens in a wind tunnel and the analytical estimation of 3D porosity. Instead of fitting models directly on observed training data values of permeability and inertial factor, we propose to focus the modelling efforts on finding models dependent on the 3D porosity through the constants in the pressure drop of Forchheimer equation. Since two screens can have the same 2D porosity but different 3D porosity, this makes also models more reliable. The combination of all these aspects has led to parametric models that overcome by far predictions by previous models in the literature. Full article

The wood-borer Bacchisa medioviolacea Breuning (Coleoptera: Cerambycidae) is identified as a major new pest of Docynia indica (Rosales: Rosaceae) orchards in the northwest mountainous provinces of Vietnam. The life cycle extends over two years (721.7 days ± 17.6 days), with overwintering as larvae. Adults emerge and disperse in summer. Females lay 6–12 eggs during an oviposition period of 2–3 days, and the incubation period ranges from 27 to 38 days. The larval and pupal periods take 554–701 days and 40–59 days, respectively. Adults survive for 12–23 days. In 2019, the damage incidence (P%) and the damage index (DI) in Yen Bai, Lao Cai, Lai Chau, and Dien Bien provinces ranged from 43.5% to 71.6% and 0.80 to 1.78, respectively. Further research on the distribution and host range of B. medioviolacea is required to help formulate a management strategy for this new orchard pest. Full article

The Chinese social credit system (SCS) is a digital sociotechnical credit system that rewards and sanctions the economic and social behaviors of individuals and companies. This article uses classic social-control theories—the shaming theory and the labeling theory—to analyze the SCS, thereby contributing to a better understanding of the Chinese social-control approach to the digital transformation of society. Our research relies not only on government documents and media reports, but also on first-hand data collected from in-depth interviews conducted in China. We found that the perceived effectiveness of the shaming and labeling mechanisms is enhanced by the design of the SCS framework and the assistance of digital technology but weakened by a lack of transparency and questionable justification criteria, as well as privacy and fairness concerns. Full article

Disability inclusive development practices require reliable data to identify people with disabilities, their barriers to participation and support needs. Although several tools are available for measuring different components of disability, it is often difficult for program teams in low resource settings, including lay community workers of community based inclusive development (CBID) programs, to collect and analyze data for program monitoring and evaluation. This paper presents the development of a digital CBID Modular Tool with automated data analysis to support routine case management processes and monitoring of a CBID program in Laos PDR. The tool was developed in different phases involving stakeholder consultations, auditing of existing tools, content development for the different modules for disability assessment and support needs, software development and testing. The tool was developed in a participatory process including people with disabilities. The tool measures needs and support requirements of people with disabilities in health, functioning, economic, education and caregiver support domains, and enables intervention planning. The content included is both context specific and universal as derived from the widely used validated tools. This unique digital CBID Modular Tool can support data collection by lay community workers and support reliable data collection to measure disability inclusion in a development program. Full article

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature. Full article

The accuracy of state-of-charge (SOC) estimation, one of the most important functions of a battery management system (BMS), is the basis for the proper operation of an electric vehicle. This study proposes a method for accurate SOC estimation. To achieve a balance between accuracy and simplicity, a second-order resistor–capacitor equivalent circuit model is applied before the algorithm is deduced, and the parameters of the established model are determined using a fitting technique. Battery state space equations are then described. A strong tracking H-infinity filter (STHF) is proposed based on an H-infinity filter (HF) and a strong tracking filter. By introducing a suboptimal fading factor, the STHF approach can use the relevant information in the estimation residual sequence to update the estimation results. To verify the robustness of this approach, battery test experiments are performed at different temperatures on lithium-ion batteries. Finally, the SOC estimation results obtained using the STHF suggest that the STHF method exhibits high robustness against the measured noises and initial error. For comparison, the estimation results of the commonly used extended Kalman filter (EKF) and HF methods are also displayed. It is suggested that the proposed STHF approach obtains a more accurate SOC estimation. Full article

For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor. Full article

State of charge (SOC) estimation is becoming increasingly important, along with electric vehicle (EV) rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN) model combining the discrete wavelet transform (DWT) method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M) algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN), L-M based BPNN (LMBPNN), L-M based WNN (LMWNN), DWT with L-M based BPNN (DWTLMBPNN) and extend Kalman filter (EKF), the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC), the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test). Full article

State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms. Full article

The state of charge (SOC) is an important parameter for batteries, especially those for electric vehicles. Since SOC cannot be obtained directly by measurement, SOC estimation methods are required. In this paper, three model-based methods, including the extended particle filter (EPF), cubature particle filter (CPF), and unscented particle filter (UPF), are compared in terms of complexity, accuracy, and robustness. The second-order resistor-capacitor (RC) equivalent circuit model is selected as the circuit model of the lithium-ion battery, and the parameters of the model are obtained by off-line identification. Then, the City test is applied to compare the performance of the methods. The experimental results show that the EPF method exhibits low complexity and fast running speed, but poor accuracy and robustness. Compared with the EPF method, the complexity of the CPF and UPF methods is relatively high, but these models offer improved accuracy and robustness. Full article

Land uses are changing rapidly in Vietnam’s upland northern borderlands. Regional development platforms such as the Greater Mekong Subregion, state-propelled market integration and reforestation programs, and lowland entrepreneurs and migrants are all impacting this frontier landscape. Drawing on a mixed methods approach using remote sensing data from 2000 to 2009 and ethnographic fieldwork, we examine how land-use and land-cover change (LULCC) has occurred across three borderland provinces—Lai Châu, Lào Cai and Hà Giang—with high proportions of ethnic minority semi-subsistence farmers. After a broad examination of regional land-use changes, we select three case studies to further analyze the underlying relationships between specific LULCC and local livelihood diversification strategies. These include specific patterns of urban growth due to a range of political decisions in Lai Châu and Lào Cai Provinces; reforestation due to non-timber forest (NTFP) product cultivation in the west of Lào Cai Province; and a stable landscape that restricts government attempts at refashioning upland livelihoods in the east of Hà Giang. Our findings point to the difficulties of completing LULCC maps for this highly heterogeneous region and the complexity of LULCC and livelihood interactions and relationships examined on the ground. Full article