Environmental Survey of the Distribution and Metal Contents of Pteris vittata in Arsenic-Lead-Mercury-Contaminated Gold Mining Areas along the Bone River in Gorontalo Province, Indonesia.

In this paper, we report ecological and environmental investigations on Pteris vittata in the As-Pb-Hg-polluted Bone River area, Gorontalo Province, Indonesia. The density distribution of P. vittata decreases from around the artisanal and small-scale gold mining (ASGM) site to the lower reaches of the Bone River, and it is rarely found near Gorontalo City. The maximum concentrations of As, Hg, and Pb recorded in the soil samples were 401, 36, and 159 mg kg-1, respectively, with their maximum concentrations in P. vittata recorded as 17,700, 5.2, and 39 mg kg-1, respectively. Around the ASGM sites, the concentrations of As, Pb, and Hg in P. vittata were highest in the study area. These data suggest that P. vittata, a hyperaccumulator of As, may be useful as a bioindicator for assessing environmental pollution by Pb and Hg.

Examining Preference Heterogeneity in Best-Worst Scaling: Case of Preferences for Job Opportunities in Artisanal Small-Scale Gold Mining (ASGM) Communities in Indonesia.

This research empirically examines the preferences for job-related attributes among rural villagers living close to artisanal and small-scale gold mining (ASGM) in Indonesia. Based on hypothetical scenarios in which a private company collaborates with the local government to establish a food processing industry in these villages, a questionnaire survey designed with best-worst scaling (BWS) was administered to households to determine their preferences. Additionally, the heterogeneity among the villagers was examined by applying a latent class logit (LCL) model. The main household survey was conducted in 2019 in Bone Bolango Regency, Gorontalo Province. The estimation results revealed that villagers are separated into four classes, and each class has different and unique preferences. Creating more job opportunities for society is a highly evaluated attribute; however, the preference for skill acquisition differs among groups. The results indicate that accounting for heterogeneous preferences regarding job opportunities is helpful to delink dependency on ASGM and health hazards and improve the livelihoods of rural villagers. The study yields key information to substantially reduce environmental and health hazards in the poverty-plagued ASGM community by facilitating job opportunities in Indonesia.

Optical sensing and detection in turbid water using multidimensional integral imaging.

We present an approach for optical sensing and detection in turbid water using multidimensional spatial-temporal domain integral imaging and dedicated signal processing algorithms. An optical signal is encoded using pseudorandom sequences, and an image sensor array is used to capture elemental image video sequences of light propagating through turbid water. Using the captured information, multidimensional image reconstruction followed by multi-dimensional correlation to detect the source signal is performed. We experimentally demonstrate scenarios in which turbidity causes conventional signal detection to fail, while our proposed multidimensional approach enables successful detection under the same turbidity conditions. Statistical analysis is provided to support the experimental results. To the best of our knowledge, this is the first report of using multidimensional integral imaging signal detection in turbid water conditions.

Integral imaging techniques for flexible sensing through image-based reprojection.

In this work, a 3D reconstruction approach for flexible sensing inspired by integral imaging techniques is proposed. This method allows the application of different integral imaging techniques, such as generating a depth map or the reconstruction of images on a certain 3D plane of the scene that were taken with a set of cameras located at unknown and arbitrary positions and orientations. By means of a photo-consistency measure proposed in this work, all-in-focus images can also be generated by projecting the points of the 3D plane into the sensor planes of the cameras and thereby capturing the associated RGB values. The proposed method obtains consistent results in real scenes with different surfaces of objects as well as changes in texture and lighting.

Three-dimensional integral imaging and object detection using long-wave infrared imaging.

We propose a passive three-dimensional (3D) imaging technique based on integral imaging using a long-wave infrared (LWIR) camera. 3D imaging can improve visualization and detection of objects in adverse environments, such as low light levels and the presence of partial occlusions, along with depth estimation by reconstructing the scene at the plane of the object. This is achieved by capturing multiple two-dimensional images, known as elemental images (EI), of a scene with each image having a unique perspective of the 3D objects. Moreover, LWIR imaging performs well in photon-limited environments due to detection of thermal radiation from an object rather than the reflected light. Once the EIs have been captured, image restoration is performed on the captured images. A 3D scene is then reconstructed and object detection using correlation filters and support vector machines is performed. Our experiments with human face detection show that 2D imaging may fail to detect occluded humans, whereas passive 3D imaging with LWIR could be successful. To the best of our knowledge, this is the first report of passive 3D integral imaging with LWIR for object detection, and in particular, in low light environments.

Compact and field-portable 3D printed shearing digital holographic microscope for automated cell identification.

We propose a low-cost, compact, and field-portable 3D printed holographic microscope for automated cell identification based on a common path shearing interferometer setup. Once a hologram is captured from the portable setup, a 3D reconstructed height profile of the cell is created. We extract several morphological cell features from the reconstructed 3D height profiles, including mean physical cell thickness, coefficient of variation, optical volume (OV) of the cell, projected area of the cell (PA), ratio of PA to OV, cell thickness kurtosis, cell thickness skewness, and the dry mass of the cell for identification using the random forest (RF) classifier. The 3D printed prototype can serve as a low-cost alternative for the developing world, where access to laboratory facilities for disease diagnosis are limited. Additionally, a cell phone sensor is used to capture the digital holograms. This enables the user to send the acquired holograms over the internet to a computational device located remotely for cellular identification and classification (analysis). The 3D printed system presented in this paper can be used as a low-cost, stable, and field-portable digital holographic microscope as well as an automated cell identification system. To the best of our knowledge, this is the first research paper presenting automatic cell identification using a low-cost 3D printed digital holographic microscopy setup based on common path shearing interferometry.

Cell identification using single beam lensless imaging with pseudo-random phase encoding.

In this Letter, we propose a novel compact optical system for automated cell identification. Our system employs pseudo-random encoding of the light modulated by the cells under inspection to capture the unique opto-biological signature of the micro-organisms by an image sensor and without using a microscope objective lens to magnify the object beam. The proposed instrument can be fabricated using a compact light source, a thin diffuser, and an image sensor connected to computational hardware; thus, it can be compact and cost effective. Experiments are presented using the proposed system to identify and classify various micro-objects and demonstrate proof of concept. The captured opto-biological signature pattern can be attributed to the micro-object's morphology, size, sub-cellular complex structure, index of refraction, internal material composition, etc. Using the captured signature of the micro-object, we extract statistical features such as mean, variance, skewness, kurtosis, entropy, and correlation coefficients for cell identification using the random forest classifier. For comparison, similar identification experiments were repeated with a digital shearing interferometer. To the best of our knowledge, this is the first report on automated cell identification using the proposed approach.

Are firms' voluntary environmental management activities beneficial for the environment and business? An empirical study focusing on Japanese manufacturing firms.

In this paper, to clarify whether a firm's voluntary approach to environmental protection is beneficial for both the environment and business, we analyze whether a firm's voluntary implementation of an environmental management system (EMS) simultaneously reduces its environmental impacts and improves its productivity. Using data on Japanese manufacturing firms for 2002-2008, we find empirical support for the view that the implementation of an EMS simultaneously reduces environmental impacts and improves productivity, and that a reduction in environmental impacts also improves productivity. However, in the context of this relationship, the direct effect of implementing an EMS on productivity is conditional. If various other activities designed to improve productivity implemented in response to market discipline are also taken into account, the effect of implementing an EMS is hidden by the effects of these activities. This implies that voluntary environmental management activities are merely a minor component of these activities. Therefore, the relationship between the implementation of an EMS and productivity improvement is not strong, although implementing an EMS indirectly improves productivity by reducing environmental impacts.