Delineating Field Variation Using Apparent Electrical Conductivity in an Ozark Highlands Agroforestry System

Little to no work has been conducted assessing field variability using repeated electromagnetic induction (EMI) apparent electrical conductivity (ECa) surveys in agroforestry (AF) systems within regions similar to the Ozark Highlands. The objectives of this thesis were to identify i) spatiotemporal ECa variability; ii) ECa-derived soil management zones (SMZs); iii) correlations among EMI-ECa and in-situ, sentential-site soil properties; iv) whether fewer, EMI-ECa surveys could be conducted to capture similar ECa variance as mid-monthly EMI-ECa surveys; v) correlations between ECa and forage yield, tree growth, and terrain attributes based on plant (forage and tree) species, and fertility treatments, and ECa-derived SMZs, and vi); and terrain attributes that have the largest contribution to ECa variability at a 20-year-old, 4.25-ha, AF system in the Ozark Highlands of northwest Arkansas. Between August 2020 and July 2021, 12, mid-monthly ECa surveys were conducted and soil-sensor-based volumetric water content and ECa measurement were made and soil samples for gravimetric water content, EC, and pH were collected from various soil depths at fixed locations. Fourteen terrain attributes of the AF site were obtained. Tree diameter at breast height (DBH) and tree height (TH) measurements were made in December 2020 and March 2021, respectively, and total forage yield samples were collected seven times during Summer 2018 and 2019. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged between 1.8 to 18.0 and 3.1 to 25.8 mS m-1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. Largest measured ECa occurred within the local drainage way, which has mapped inclusions with aquic soil moisture regimes, or areas of potential groundwater movement, and smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. A positive (r2 = 0.4; P \u3c 0.05) linear relationship occurred over time between PRP ECa standard deviation, with a negative linear relationship (r2 = 0.93; P \u3c 0.05) between HCP ECa coefficient of variation across season (i.e., Summer to Spring). The K-means-clustering method was used to delineate three precision SMZs that were reflective of areas with similar ECa and ECa variability. Relationships between ECa and tree properties were generally stronger within the whole-site, averaged across tree property and ECa configuration (| r | = 0.38), than the SMZs, averaged across tree property, ECa configuration, and SMZ (| r | = 0.27). The strength of the SMZs’ terrain-attribute-PRP-ECa relationships were 9 to 205% greater than that for the whole-site. Whole-site, multi-linear regressions showed that Slope Length and Steepness (LS)-Factor (10.5%), Mid-slope (9.4%), and Valley Depth (7.2%) were terrain attributes that had the greatest influence (i.e., largest percent of total sum of squares) on PRP ECa variability, whereas Valley Depth (15.3%), Wetness Index (11.9%), and Mid-slope (11.2%) had the greatest influence on HCP ECa variability. Results of this study show how ECa varies and relates to soil, plant (i.e., DBH and TH and forage yield), and terrain attributes in AF systems with varying topography that could be used to influence AF management

Delineating Field Variation Using Apparent Electrical Conductivity in an Ozark Highlands Agroforestry System

Little to no work has been conducted assessing field variability using repeated electromagnetic induction (EMI) apparent electrical conductivity (ECa) surveys in agroforestry (AF) systems within regions similar to the Ozark Highlands. The objectives of this thesis were to identify i) spatiotemporal ECa variability; ii) ECa-derived soil management zones (SMZs); iii) correlations among EMI-ECa and in-situ, sentential-site soil properties; iv) whether fewer, EMI-ECa surveys could be conducted to capture similar ECa variance as mid-monthly EMI-ECa surveys; v) correlations between ECa and forage yield, tree growth, and terrain attributes based on plant (forage and tree) species, and fertility treatments, and ECa-derived SMZs, and vi); and terrain attributes that have the largest contribution to ECa variability at a 20-year-old, 4.25-ha, AF system in the Ozark Highlands of northwest Arkansas. Between August 2020 and July 2021, 12, mid-monthly ECa surveys were conducted and soil-sensor-based volumetric water content and ECa measurement were made and soil samples for gravimetric water content, EC, and pH were collected from various soil depths at fixed locations. Fourteen terrain attributes of the AF site were obtained. Tree diameter at breast height (DBH) and tree height (TH) measurements were made in December 2020 and March 2021, respectively, and total forage yield samples were collected seven times during Summer 2018 and 2019. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged between 1.8 to 18.0 and 3.1 to 25.8 mS m-1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. Largest measured ECa occurred within the local drainage way, which has mapped inclusions with aquic soil moisture regimes, or areas of potential groundwater movement, and smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. A positive (r2 = 0.4; P \u3c 0.05) linear relationship occurred over time between PRP ECa standard deviation, with a negative linear relationship (r2 = 0.93; P \u3c 0.05) between HCP ECa coefficient of variation across season (i.e., Summer to Spring). The K-means-clustering method was used to delineate three precision SMZs that were reflective of areas with similar ECa and ECa variability. Relationships between ECa and tree properties were generally stronger within the whole-site, averaged across tree property and ECa configuration (| r | = 0.38), than the SMZs, averaged across tree property, ECa configuration, and SMZ (| r | = 0.27). The strength of the SMZs’ terrain-attribute-PRP-ECa relationships were 9 to 205% greater than that for the whole-site. Whole-site, multi-linear regressions showed that Slope Length and Steepness (LS)-Factor (10.5%), Mid-slope (9.4%), and Valley Depth (7.2%) were terrain attributes that had the greatest influence (i.e., largest percent of total sum of squares) on PRP ECa variability, whereas Valley Depth (15.3%), Wetness Index (11.9%), and Mid-slope (11.2%) had the greatest influence on HCP ECa variability. Results of this study show how ECa varies and relates to soil, plant (i.e., DBH and TH and forage yield), and terrain attributes in AF systems with varying topography that could be used to influence AF management

Feasibility Assessment of an EVA Glove Sensing Platform to Evaluate Potential Hand Injury Risk Factors

Injuries to the hands are common among astronauts who train for extravehicular activity (EVA). When the gloves are pressurized, they restrict movement and create pressure points during tasks, sometimes resulting in pain, muscle fatigue, abrasions, and occasionally more severe injuries such as onycholysis. A brief review of the Lifetime Surveillance of Astronaut Health's injury database reveals that 58% of total astronaut hand and arm injuries from NBL training between 1993 and 2010 occurred either to the fingernail, MCP, or fingertip. The purpose of this study was to assess the potential of using small sensors to measure force acting on the fingers and hand within pressurized gloves and other variables such as blood perfusion, skin temperature, humidity, fingernail strain, skin moisture, among others. Tasks were performed gloved and ungloved in a pressurizable glove box. The test demonstrated that fingernails saw greater transverse strain levels for tension or compression than for longitudinal strain, even during axial fingertip loading. Blood perfusion peaked and dropped as the finger deformed during finger presses, indicating an initial dispersion and decrease of blood perfusion levels. Force sensitive resistors to force plate comparisons showed similar force curve patterns as fingers were depressed, indicating suitable functionality for future testing. Strategies for proper placement and protection of these sensors for ideal data collection and longevity through the test session were developed and will be implemented going forward for future testing

Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem

Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models. While these models have been shown to achieve very high accuracy, this lack of understanding on how decisions are made raises concerns about potential bias in the models. This limits the ability of retailers to implement these solutions, as several high-profile legal cases have recently ruled that evidence taken from these black box methods is inadmissible in court. There is an urgent need to develop models which can achieve high accuracy while providing the necessary transparency. One way to alleviate this problem is through the use of social signal processing to add a layer of understanding in the development of transparent models for this task. To this end, we present a social signal processing model for the problem of shoplifting prediction which has been trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting model provides a high degree of understanding and achieves accuracy comparable with current state of the art black box methods

Large Language Models are Zero-Shot Reasoners

Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding "Let's think step by step" before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled Objects), without any hand-crafted few-shot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with 175B parameter InstructGPT model, as well as similar magnitudes of improvements with another off-the-shelf large model, 540B parameter PaLM. The versatility of this single prompt across very diverse reasoning tasks hints at untapped and understudied fundamental zero-shot capabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilities may be extracted by simple prompting. We hope our work not only serves as the minimal strongest zero-shot baseline for the challenging reasoning benchmarks, but also highlights the importance of carefully exploring and analyzing the enormous zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or few-shot exemplars.Comment: Accepted to NeurIPS2022. Our code is available at https://github.com/kojima-takeshi188/zero_shot_co