Would AI Stocks Estimate Be as Surprised to USDA Stocks Reports As Private Market Analysts?

The USDA survey-based Quarterly Agriculture Stocks (QAS) reports are the primary source of information regarding the relative supply of U.S. corn, soybeans, and wheat for the last fifty years. Research has examined USDA stock reports and their relevancy to the market (e.g., Isengildina-Massa et al., 2021). In addition, private industry analysts estimate expected quarterly grain stock reports before USDA releases them. Market information firms such as Bloomberg and Reuters publish a subset of these estimates a few days before the USDA reports. Previous research has found that when industry analysts have significant differences in stock expectations compared to what the USDA releases for grain stocks, market prices adjust rapidly to what the USDA found in their survey. Many media outlets and previous research attribute the differences in expectations and changes in market prices to a market surprise (e.g., Karali et al. (2020)). Market analysts, USDA officials, and researchers have offered four reasons for market surprises in the grain stocks reports. First, USDA surveys may need to account for grain in transit when surveying stocks. Second, the market often uses weight (e.g., 60 lbs per bushel) to determine supply, while survey estimates ask how much volume (e.g., bushels) is on the farm or in commercial storage. When there is a deviation in the average weight of a commodity for a season, there could be discrepancies between surveyed stocks and actual stocks by weight. Third, errors in estimating what portion of existing stocks are from old or new crop production may cause surprises in the final annual report before a change in the marketing year. For example, USDA asks in their survey how much old crop corn is on hand on September 1st, although some crops taken in by grain wholesalers can be new crops by this date. There can be discrepancies when the survey respondent must accurately segregate the new and old crop amounts. Fourth, USDA survey-based stock reports contain survey noise. Market analysts may need to account for survey noise in sequential estimates. This paper seeks to use AI methods and large datasets on grain movement to understand the primary reason market analysts are frequently surprised by USDA QAS reports. Given the recent surge in grain movement data, available grain quality data, and data on the output of significant demand sources of grain, particularly at a state level, it is possible to use advances in analyzing high dimensional data (e.g., random forest, gradient boosting) to develop an objective artificial intelligent (AI) market analyst. This paper aims to explore additional public data sources related to commodity demand and supply in the corn, wheat, and soybean markets and apply AI techniques to determine whether data analytics improves the prediction of QAS reports released by USDA for corn, soybeans, and wheat compared to market analysts estimates. Our primary research objective is to determine if AI can more accurately predict QAS estimates from USDA than the survey of Market analysts that Bloomberg and Reuters have historically provided. Our secondary objective is to decompose the surprise by the source of the surprise. In this effort, we use the Extreme Gradient Boosting ML model to predict the stock estimate of the three major commodities (Corn, Soybean, and Wheat). We used grain stocks and production by state, carry-over stock from the previous year, weekly grain loaded on trains and barges, weekly ethanol production, monthly ethanol crushed, and weekly accumulated exports, market analysts\u27 estimates from Bloomberg and Reuters from the year 2007 to the 4th quarter of 2022. We aggregated all these features every quarter to understand the estimate of stock. After accumulating all the features, we cross-checked the values with the national report of these particular years we found consistency among them. This means the features show actual values from each quarter to understand the accurate estimate of the stock. We also grouped each feature according to 10 Agricultural Regions. We found through our machine learning algorithm that production is the most important one to estimate the quarterly stock, with carry-over and accumulated exports in 2nd and 3rd most essential features of the model. We also found that ethanol production and grain exports have an inverse relation with the grain stock every quarter

Session 7: \u3cem\u3eWould AI Stocks Estimate Be as Surprised to USDA Stock Reports as Private Market Analysts?\u3c/em\u3e

Would AI Stocks Estimate Be as Surprised to USDA Stock Reports as Private Market Analysts? Keywords: Machine Learning, Random Forest, Agricultural Commodities Market, Informational Impact, Efficient Market Hypothesis. The USDA survey-based Quarterly Grain Stocks reports are the primary source of information regarding the relative supply of U.S. corn, soybeans, and wheat for the last fifty years. Previous research has examined the accuracy of the USDA stock reports and their relevancy to the market, given alternative sources of estimates (e.g., Isengildina-Massa et al., 2021). For example, private industry analysts also estimate expected quarterly grain stock reports before USDA releases their reports. Market information firms such as Bloomberg and Reuters publish a subset of these estimates a few days before the USDA reports. Previous research has found that when industry analysts have significant differences in stock expectations compared to what the USDA releases for grain stocks, market prices tend to adjust rapidly to what the USDA found in their survey. Many media outlets and previous research attribute the differences in expectations and changes in market prices to a market surprise. Karali et al. (2020) found compelling evidence that the discrepancy in USDA reports from private analysts\u27 expectations plays a vital role in explaining grain futures price movements on report days. Market analysts, USDA officials, and researchers have given four reasons for market surprises in the grain stocks reports. First, USDA surveys may fail to account for grain in transit when surveying stocks. Second, many private analysts use standard conversion rates (e.g., average test weight per bushel of reported corn) for products derived from grain inputs to estimate their expected grain stocks after a quarter. However, these conversion rates may vary because of the quality of the grain and be less (more) than what private analysts estimate. Third, errors in estimating what portion of existing stocks is from old or new crop production may cause surprises in the final annual report before a change in the marketing year. For example, USDA asks in their survey how much old crop corn is on hand on September 1st, although some crops taken in by grain wholesalers can be new crops by this date. Fourth, USDA survey-based stock reports contain survey noise. It is still being determined whether market analysts can correctly consider survey noise when reconciling their estimates versus the USDA and smooth future estimates, assuming some portion of the previous report was due to noise and survey error. What is the primary reason market analysts are frequently surprised by USDA QAS reports? Given the recent surge in grain movement data, available grain quality data, and data on the output of significant demand sources of grain, particularly at a state level, it is possible to use advances in analyzing high dimensional data (e.g., random forest, gradient boosting) to develop an objective artificial intelligent (AI) market analyst. This paper aims to explore additional public data sources related to commodity demand and supply in the corn, wheat, and soybean markets and apply AI techniques to determine whether data analytics improves the prediction of QAS reports released by USDA for corn, soybeans, and wheat. Our primary research objective is to determine if AI can more accurately predict QAS estimates from USDA than the survey of Market analysts that Bloomberg and Reuters have historically provided. Our secondary objective is to attempt to decompose the surprise into by source of surprise. We will use Random Forest ML model to predict the stock estimate of the three major commodities (Corn, Soybean, and Wheat) with all the publicly available data before the national announcement of the Quarterly Stock Report. We will compare the stock estimate provided by our AI techniques to private market analysts, which have been a critical component of information before the announcement days. Our research findings will also decompose the variables most important for explaining market surprises. Specifically, does the amount of grain in transit, changes in demand due to grain quality, or the mixing of new crops and old crops in stock estimates mainly explain the surprise? Further, our findings may determine if private analysts have problems reconciling noise in previous USDA surveys when making future estimates for future reports

Laser Powder Bed Fusion of Bimetallic Structures

Laser powder bed fusion (LPBF) is a popular additive manufacturing (AM) technique that has demonstrated the capability to produce sophisticated engineering components. This work reports the crack-free fabrication of an SS316L/IN718 bimetallic structure via LPBF, along with compositional redistribution, phase transformations and microstructural development, and nanohardness variations. Constituent intermixing after LPBF was quantitatively estimated using thermo-kinetic coefficients of mass transport and compared with the diffusivity of Ni in the austenitic Fe-Ni system. The intermixing of primary solvents (Ni and Fe) in SS316L/IN718 bimetallic structures was observed for an intermixing zone of approximately 800 µm, and their intermixing coefficient was estimated to be in the order of 10−5 m2/s based on time of 10 ms. In addition, to understand the high temperature behavior, SS316L/IN718 bimetallic structures were annealed at 850, 950, and 1050 °C, for 120, 48, and 24h respectively, followed by water quenching (WQ). Furthermore, to better understand the intermixing of individual components (Ni and Fe) and to predict the varying (maximum) temperatures in LPBF of SS316L/IN718 bimetallic structures, solid-to-solid SS316L vs IN718 diffusion couples were examined at 850, 950, and 1050 °C, for 120, 48, and 24h respectively, followed by WQ. The investigation of SS316L vs IN718 diffusion couples yielded a maximum temperature of approximately 3400 K in the LPBF of SS316L/IN718 bimetallic structures. Finally, compositional redistribution, phase transformations and microstructural development, and nanohardness variations after LPBF of SS316L/IN625 bimetallic structure were also investigated to provide a better understanding of the LPBF process via bimetallic fabrication

The plight of the Bangladeshi silk industry: An empirical investigation

In spite of having a glorious history in the sericulture industry Bangladesh still is not a bright name in silk production and export. Although the agro-climatic situation in Bangladesh greatly favors the development of silk industry, Bangladesh produces very little amount of silk products every year, whereas India, situated beside Bangladesh, is the second largest producer of sericulture. To investigate the reason behind this, a questionnaire survey has been undertaken in which only the owners or managers have been considered as representatives of the industry. A total of 21 silk enterprises was randomly sampled. Data analyses show that almost 57% of the silk enterprises have less than 40 decimal of land while only 19% have more than 100 decimal of land. These enterprises provided very limited facilities for their workers and mostly depended on imported raw materials. Owners pointed out several constraints to the development of silk industry in Bangladesh including insufficient government patronization and recommended several remedial measures including that the Bangladesh Silk Board (BSB) gives out production credit without too much conditions, adoption of modern technology, and information dissemination . It is evident that government, through BSB and BSRTI (Bangladesh Silk Research and Training Institute) has to play a crucial role to pull this industry up from the brink of destruction

The link between Facebook addiction and depression among university students: evidence from a lower‐middle income country

Background and Aims Among all the social media, Facebook is the most popular social networking site among students. That raises a chance of excessive Facebook usage being a form of addiction to hamper students' mental health. The primary goal of this study was to find the association of Facebook addiction with the depression level of university students during the COVID-19 pandemic. Method Four hundred ten university students from Bangladesh were selected randomly as samples for this investigation. In this study, the Bergen Facebook Addiction Scale and nine-item Patient Health Questionnaire were used to assess the level of Facebook Addiction and depression status of the students, respectively. Ordered probit models were employed to identify the connection between Facebook addiction and depression. Ordinary least square models were utilized further to check the robustness of the findings. Results Ordered probit results confirm that Facebook addiction increases the likelihood of having heightened depression among university students. Besides, sex, household income, and history of being infected by COVID-19 also appeared to be correlated with the depression level of the students. Conclusion Creating opportunities for students to participate in more physically demanding outdoor activities should be prioritized as it could ultimately enhance their capacity to mitigate depression. Appropriate measures must be taken to increase the number of recreational facilities on the campus for students, considering their age, gender, and preferences

Comparative Study on Callus Initiation and Regeneration Frequency of Two Salt Tolerant Rice (Oriza sativa)

The objectives of this study were to find out the in vitro callus initiation and regeneration potentiality of two salt tolerant rice cultivars, viz., BRRI dhan47, BRRI dhan53. Mature seeds were used as explants. MS media supplemented with different concentrations of 2,4-D (1.0, 2.0, 3.0, 4.0 mg/l) were used for callus induction. The highest calli frequency was 85 % for BRRI dhan47 on MS media containing 3 mg/l 2,4-D, while other variety BRRI dhan53 showed maximum frequency of 75 % callus induction on MS media containing 3 mg/l 2,4-D. For complete plant regeneration the calli of two cultivars were plated on MS media containing different concentrations of kinetin, NAA (1-Naphthalene acetic acid) and BAP (6-benzyl aminopurine). The best regeneration frequency of BRRI dhan47 was 71.42 % on MS media containing 2 mg/l kinetin, 2 mg/l BAP and 1 mg/l NAA and it was 87.50 % for BRRI dhan53 on MS media containing 2 mg/l kinetin, 1 mg/l BAP and 1 mg/l NAA

An Energy conserving routing scheme for wireless body sensor nanonetwork communication

Current developments in nanotechnology make electromagnetic communication possible at the nanoscale for applications involving body sensor networks (BSNs). This specialized branch of wireless sensor networks, drawing attention from diverse fields, such as engineering, medicine, biology, physics, and computer science, has emerged as an important research area contributing to medical treatment, social welfare, and sports. The concept is based on the interaction of integrated nanoscale machines by means of wireless communications. One key hurdle for advancing nanocommunications is the lack of an apposite networking protocol to address the upcoming needs of the nanonetworks. Recently, some key challenges have been identified, such as nanonodes with extreme energy constraints, limited computational capabilities, terahertz frequency bands with limited transmission range, and so on, in designing protocols for wireless nanosensor networks. This work proposes an improved performance scheme of nanocommunication over terahertz bands for wireless BSNs making it suitable for smart e-health applications. The scheme contains - a new energy-efficient forwarding routine for electromagnetic communication in wireless nanonetworks consisting of hybrid clusters with centralized scheduling; a model designed for channel behavior taking into account the aggregated impact of molecular absorption, spreading loss, and shadowing; and an energy model for energy harvesting and consumption. The outage probability is derived for both single and multilinks and extended to determine the outage capacity. The outage probability for a multilink is derived using a cooperative fusion technique at a predefined fusion node. Simulated using a nano-sim simulator, performance of the proposed model has been evaluated for energy efficiency, outage capacity, and outage probability. The results demonstrate the efficiency of the proposed scheme through maximized energy utilization in both single and multihop communications; multisensor fusion at the fusion node enhances the link quality of the transmission

Communication for behavioural impact in enhancing utilization of insecticide-treated bed nets among mothers of under-five children in rural North Sudan: an experimental study

Abstract Background Malaria is the leading cause of morbidity and mortality in Sudan. The entire population is at risk of contracting malaria to different levels. This study aimed to assess the effectiveness of communication for behavioural impact (COMBI) strategy in enhancing the utilization of long-lasting insecticidal nets (LLINs) among mothers of under-five children in rural areas. Methods A randomized community trial was conducted in rural area of Kosti locality, White Nile State, Sudan, among mothers of under-five children, from January 2013 to February 2014. A total of 761 mothers from 12 villages were randomly selected, 412 mothers from intervention villages and 349 were from comparison villages. Results The knowledge of mothers, in intervention villages, about malaria vector, personal protective measures (PPM) against malaria, and efficacy of LLINs was significantly increased from 86.9 to 97.3 %; 45.9 to 92 % and 77.7 to 96.1 % respectively. Knowledge about usefulness of PPM, types of mosquito nets and efficacy of LLINs was significantly higher in intervention villages compared to comparison villages (p < 0.05), (η2 = 0.64). Mothers in intervention villages increasingly perceived, post-intervention, that malaria was a serious disease (99.3 %), a preventable disease (98.8 %) and also LLINs as an effective intervention in malaria prevention (92.2 %). This resulted in an increase in the utilization rate of LLINs from 19.2 to 82.8 % in intervention villages compared to comparison villages (p < 0.05) [OR = 4.6, 95 %, CI = (3.72–5.72)], (η2 = 0.64). The average of mothers’ knowledge about malaria was increased by 64 % (η2 = 0.64), the use of LLINs was increased by 79 % (η2 = 0.79) and a positive attitude towards malaria was 2.25 times higher in intervention villages than among mothers in the comparison villages. Conclusions These results established the usefulness of COMBI strategy for increasing awareness about malaria, developing a positive perception towards malaria prevention and, increasing the utilization of LLINs

WHAT FACTORS AND HOW THEY AFFECT STRESS: EVIDENCE FROM UNIVERSITY STUDENTS

The study primarily attempted to determine the stress level of university students and identify the factors causing stress.Convenience sampling was used to collect data and Perceived Stress Scale was used to determine the stress level and the factors that affecting stress level were determined by using multiple linear regression model. Findings from the study show that most of the students (58.06%) feel medium stress. But only 16.06% students feel low stress and 26.80% students feel high stress. Some factors were identified in the study which affects the stress level such as unnecessary writing of practical notebooksand overloaded academic study (p=0.0489), uncertain job opportunities (p=.01) and ongoing education life(p=0.02)