Skeptical theism attempts to address the problem of evil by appealing to human cognitive limitations. The causal structure of the world is opaque to us. We cannot tell, and should not expect to be able to tell, if there is gratuitous evil, that is, evil which isn’t necessary for achieving some greater good or for precluding some greater evil. At first, it seems tempting to think that the rapid development of artificial intelligence (AI) technologies might change this fact. Our cognitive limitations may no longer be a fixed point in responding to the problem of evil. But I argue that this won’t ultimately matter. The workings of any AI capable of rendering the problem of evil tractable will likely be just as opaque to us as the causal structure of the world God created. Interestingly, then, both God and a sufficiently advanced AI are alien intelligences to us. This reveals what is truly difficult, and perhaps intractable, about the problem of evil. For the evils to be defeated, we would need a relational understanding of why God permitted them. Yet such an understanding of an inscrutable God seems forever beyond our, and even our post-human descendants, ken.

Under the coupling effect of node position deviation, joint clearance and wear factors, the complex landing gear retraction mechanism suffers from low kinematic accuracy, slow retraction performance and shortened reliable life. Addressing these issues, a time-dependent reliability analysis and optimisation design method for the kinematic accuracy of the retraction mechanism is proposed, considering the uncertainty of node position deviation, initial clearance, and dynamic multi-joint wear. Initially, a wear prediction model and a dynamic model of the retraction mechanism considering node position deviation and joint clearance are established to analyse their influence on retraction accuracy and joint wear depth. Subsequent retraction testing under various working conditions is conducted to ascertain the critical failure condition and validate the simulation model. The time-dependent kinematic accuracy reliability model, accounting for the dynamic evolution of wear clearance, is then established to assess reliability variation with retraction cycles. Finally, the reliability optimisation design focusing on hole-axis matching accuracy aims to strike a balance between accuracy cost and reliability, thereby enhancing performance and prolonging operational life.

Navigation is an important skill required for an autonomous robot, as information about the location of the robot is necessary for making decisions about upcoming events. The objective of the localization technique is “to know about the location of the collected data.” In previous works, several deep learning methods were used to detect localization, but none of them gives sufficient accuracy. To address this issue, an Enhanced Capsule Generation Adversarial Network and optimized Dual Interactive Wasserstein Generative Adversarial Network for landmark detection and localization of autonomous robots in outdoor environments (ECGAN-DIWGAN-RSO-LAR) is proposed in this manuscript. Here, the outdoor robot localization dataset is taken from the Virtual KITTI dataset. It contains two phases, which are landmark detection and localization. The landmark detection phase is determined using Enhanced Capsule Generation Adversarial Network for detecting the landmark of the captured image. Then the robot localization phase is determined using Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) for determining the robot location coordinates as well as compass orientation from identified landmarks. After that, the weight parameters of the DIWGAN are optimized by Rat Swarm Optimization (RSO) algorithm. The proposed ECGAN-DIWGAN-RSO-LAR is implemented in Python. The efficiency of the proposed ECGAN-DIWGAN-RSO-LAR technique shows higher accuracy of 22.67%, 12.45 %, and 8.89% compared to the existing methods.

Exposure to adversity during the perinatal period has been associated with cognitive difficulties in children. Given the role of the nucleus accumbens (NAcc) in attention and impulsivity, we examined whether NAcc volume at age six mediates the relations between pre- and postnatal adversity and subsequent attention problems in offspring. 306 pregnant women were recruited as part of the Growing Up in Singapore Towards Healthy Outcomes Study. Psychosocial stress was assessed during pregnancy and across the first 5 years postpartum. At six years of age, children underwent structural MRI and, at age seven years, mothers reported on their children’s attention problems. Separate factor analyses conducted on measures of pre- and postnatal adversity each yielded two latent factors: maternal mental health and socioeconomic status. Both pre- and postnatal maternal mental health predicted children’s attention difficulties. Further, NAcc volume mediated the relation between prenatal, but not postnatal, maternal mental health and children’s attention problems. These findings suggest that the NAcc is particularly vulnerable to prenatal maternal mental health challenges and contributes to offspring attention problems. Characterizing the temporal sensitivity of neurobiological structures to adversity will help to elucidate mechanisms linking environmental exposures and behavior, facilitating the development of neuroscience-informed interventions for childhood difficulties.

Greenstone is commonly used to produce culturally significant items across Mesoamerica, including axes, earspools, figurines, and beads. This research characterizes the mineralogy of greenstone materials recovered from sites in the Jovel Valley, Chiapas, Mexico, to document the range of green minerals utilized by the inhabitants. Our analysis of the objects suggests that the Late Classic and Early Postclassic Maya of the Jovel Valley had access to a variety of greenstone minerals, including serpentinites, green micas, grossular, and jadeite. X-ray diffraction and X-ray fluorescence spectrometry characterization of reference materials suggests procurement of greenstone resources from the well-documented sources of the Motagua–Polochic Fault Zone, and also potentially from sources in the Chalchihuitán–Chenalhó area of Chiapas, Mexico. The Jovel Valley had access to materials over long distances through historically documented trade routes that allowed the movement of greenstone materials west from the Motagua River Valley into highland Chiapas or south from the Chalchihuitán–Chenalhó area.

Commercializing targeted sprayer systems allows producers to reduce herbicide inputs but risks the possibility of not treating emerging weeds. Currently, targeted applications with the John Deere system allow for five spray sensitivity settings, and no published literature discusses the impact of these settings on detecting and spraying weeds of varying species, sizes, and positions in crops. Research was conducted in AR, IL, IN, MS, and NC in corn, cotton, and soybean to determine how various factors might influence the ability of targeted applications to treat weeds. These data included 21 weed species aggregated to six classes with height, width, and densities, ranging from 25 to 0.25 cm, 25 to 0.25 cm, and 14.3 to 0.04 plants m-2, respectively. Crop and weed density did not influence the likelihood of treating the weeds. As expected, the sensitivity setting alters the ability to treat weeds. Targeted applications (across sensitivity settings, median weed height and width, and density of 2.4 plants m-2) resulted in a treatment success of 99.6% to 84.4%, 99.1% to 68.8%, 98.9% to 62.9%, 99.1% to 70.3%, 98.0% to 48.3%, and 98.5% to 55.8% for Convolvulaceae, decumbent broadleaf weeds, Malvaceae, Poaceae, Amaranthaceae, and yellow nutsedge, respectively. Reducing the sensitivity setting reduced the ability to treat weeds. Size of weeds aided targeted application success, with larger weeds being more readily treated through easier detection. Based on these findings, various conditions could impact the outcome of targeted multi-nozzle applications. Additionally, the analyses highlight some of the parameters to consider when using these technologies.