Performance Analysis of Zakat-Based Empowerment to Improve Mustahik's Welfare

This study aims to analyze the performance of zakat in improving the welfare of mustahik through zakat-based empowerment programs. Mustahik's welfare can be measured in terms of material and spiritual conditions, level of human development, and level of independence of mustahik. This research is a quantitative study using a multi-stage weigh index estimation technique that functions to generate a zakat welfare index from each variable. The data used are primary and secondary data, primary data obtained from interviews, and distributing questionnaires to zakat recipients of Rumah Zakat in Malang. While the secondary data was obtained from the results of the literature study. The sampling technique used was purposive sampling to obtain data following the research objectives. The results of this study are, first, the results found that zakat can improve the material and spiritual conditions of mustahik. Based on the CIBEST model, it has been found that there is an increase in the welfare index of mustahik by 42.5%. Second, zakat-based empowerment increases the condition of the mustahik HDI by 3.1%, which means that zakat has not been able to have a major influence on the mustahik HDI. Third, the zakat-based empowerment program can increase the mustahik's level of independence by 16.8%. This index shows that mustahik already has a permanent job or business and saves after obtaining empowerment. Based on the research that has been done, it can be concluded that zakat-based empowerment can improve the welfare of mustahik. It is noted that the welfare of mustahik has increased by 21.6% from the previous condition.

Roaming Dogs in Ushuaia City, Tierra Del Fuego, Argentina. How Many and Why

Free roaming dogs (FRD) in cities represent an increasing problem. Authorities need numbers of FRDs to evaluate policies implemented and to monitor the dog population. We estimated the number of FRD in Ushuaia city, Argentina, using a photographic capture-recapture methodology. We estimated an abundance index, the power to detect changes in the index, and modeled factors that may explain the spatial distribution of FRD and their welfare status. During three surveys, covering 72 transects along streets (9.9% of the street layout of Ushuaia), we recorded 539 different FRDs. A model with individual heterogeneity in capture-recapture probability gave 12,797 FRDs (95% CI 10,979 − 15,323), reflecting a dog:human relation of 1:6, higher than the relation recommended by the WHO. The abundance index was similar between surveys (8.13 ± 1.36, 8.38 ± 1.46 and 9.55 ± 1.28 dogs/km). The difference needed to detect changes in the index is about twice the standard error of estimates. The best model explaining dogs’ abundance included only geographical location, although two neighbourhoods with 9 transects stand out with 181 different FRDs identified. Together with the good overall dogs’ welfare status, modeling suggests that the behavior of owners is the main driver for the presence of FRDs. We recommend the use of photographic capture-recapture methodologies instead of simple index estimation, due to the small additional effort required and the improved accuracy and precision obtained. We also recommend a permanent systematic design for future surveys, increase the number of survey occasions, and improve the survey process.

Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection

Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.

Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data

Wheat take-all, caused by two variants of the fungus Gaeumannomyces gramnis (Sacc.) Arx & D. Olivier, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas was reduced by more than 20% and the yield of severe cases was reduced by more than 50%. Large-scale rapid and accurate estimation of the incidence of wheat take-all plays an important role in guiding field control and agricultural yield estimation. In this study, a portable ground spectrometer was used to collect the spectral reflectance in the 350–1050 nm band range of wheat canopy after take-all infection in the wheat grain filling stage and combined with the ground disease survey data.Then a winter wheat take-all disease index estimation model was proposed based on the spectral band division interval and selected band combination. According to the normalized difference spectral index (NDSI) and the determinative coefficient of the disease index formed by any two band combinations, the spectral index band combinations corresponding to the spectral index with high correlation in each region were screened by dividing spectral intervals. Partial least-squares regression was used to establish a binary and ternary disease index calibration model. The results showed that the model based on spectral indices of ternary variables had the highest coefficient of determination. Finally, the optimal regression model of wheat take-all disease condition index composed of NDSI(R590,R598), NDSI(R534,R742) and NDSI(R810,R834) was established: Y = 134.577 − 70.301 NDSI(R590,R598) − 223.533 NDSI(R534,R742) + 51.584 NDSI(R810,R834) (R2 = 0.743, RMSEP = 0.094, df = 15), which was the most suitable model for winter wheat take-all estimation. The construction of this model can provide new method and technical support for future evaluation and monitoring of wheat take-all disease on the field.