<p>Distanspresentation.</p>

A large portion of irrigated soybean in Mississippi are planted on raised beds spaced 96.52 cm apart. There is recent interest in growing soybean in narrower rows. Previous research indicates that narrower row spacing can provide advantages over wider arrangements, including increased light interception, improved weed management and greater seed yield. Soybean was planted in 96.52 cm single rows, 96.52 cm twin rows and 50 cm rows on wide beds (200 cm) at three seeding rates. Canopy closure was monitored throughout the growing season. Soybean planted in narrow rows had consistently faster canopy closure than single rows at all site years. Similarly, there was a 10% to 13% seed yield advantage for the narrow row spacing over the single row spacing at each site year. With the introduction of novel technology, such as the twin row planter, comes equipment malfunction and/or misuse that could reduce seed yield. Producer decisions in the event of a planting/planter error can be challenging. The economic loss due to a planter error may vary by soil type due to differences in plant development. The purpose of this research is to determine the agronomic effects associated with multiple potential twin row planter errors on two distinct soil types across multiple maturity groups commonly found in Mississippi. Canopy closure of each planting error was monitored throughout the growing season. Seed yield was reduced by 9 to 18% when a whole twin row was missing compared to the untreated check at all site years.

Field studies conducted in Missouri and Mississippi, in 2017 and 2018, respectively, indicated no droplet size effect on PRE herbicide efficacy, regardless of the herbicide, weed, soil, crop residue and weather conditions during spraying. Nozzle type enhanced herbicide efficacy for one location and herbicide. The TTI60 dual fan nozzle increased pendimethalin weed control, up to 91%, in a high organic matter (OM) soil with large clods and substantial weed pressure. Pendimethalin efficacy was reduced under high OM soils (> 2%) while metribuzin efficacy was reduced under low OM (< 0.7%), low cation exchange capacity (<13.1%) soils and 12.2 mm of rain three days after application. The greenhouse studies indicated that increasing crop residue levels reduced velvetleaf control by 7%. Simulated rainfall eight days after herbicide application decreased johnsongrass dry weight reductions by 29% in comparison to two day rainfall.

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COVID-19 has led to a surge in the adoption of digital transformation in almost every sector. Digital health and well-being are no exception. For instance, now people get checkupsvia apps or websites instead of visiting a physician. The pandemic has pushed the health-care sector worldwide to advance the adoption of artificial intelligence (AI) capabilities.Considering the demand for AI in supporting the well-being of an individual, we presentthe real-life diagnosis as a digital twin(DT) diagnosis using machine learning. The MachineLearning (ML) technology enables DT to offer a prediction. Although several attemptsexist for predicting disease using ML and a few attempts through ML of DT frameworks,those do not deal with disease risk prediction. In addition, most of them deal with singledisease prediction after the occurrence and rely only on clinical test data like- ECG report,MRI scan, etc.To predict multiple disease/disease risks, we propose a dynamic machine learning algo-rithm (MLA) selection framework and a dynamic testing method. The proposed frameworkaccepts heterogeneous electronic health records (EHRs) or digital health status as datasetsand selects suitable MLA upon the highest similarity. Then it trains specific classifiers forpredicting a specific disease/disease risk. The dynamic testing method for prediction isused for predicting several diseases.We described three use cases: non-communicable disease(NCD) risk prediction, mentalwell-being prediction, and COVID-19 prediction. We selected diabetes, risk of diabetes,liver disease, thyroid, risk of stroke as NCDs, mental stress as a mental health issue, andCOVID-19. We employed seven datasets, including public and private datasets, with adiverse range of attributes, sizes, types, and formats to evaluate whether the proposedframework is suitable to data heterogeneity. Our experiment found that the proposed methods of dynamic MLA selection could select MLA for each dataset at cosine similarityscores ranging between 0.82-0.89. In addition, we predicted target disease/disease risks atan accuracy ranging from 94.5% to 98%.To verify the performance of the framework-selected predictor, we compared the accuracy measures individually for each of the three cases. We compared them with traditionalML disease prediction work in the literature. We found that the framework-selected algorithms performed with good accuracy compared to existing literature.

It has become increasingly important to introduce the Digital Twin in additive manufacturing as it is perceived as a promising step forward in its development and a vital component of Industry 4.0. Digital Twin is an up-to-date representation of a real asset in operation. The aim of this thesis is to develop a Digital Twin of a hydro test rig. Digital Twins are created by developing and simulating mathematical models, which should be integrated and validated. A downscale turbine test rig in which its runner and drafttube are replicates of the Porjus U9 turbine. This test rig is located in the John-Fieldlaboratory of the Division of Fluid and Experimental Mechanics at Luleå University of Technology (LTU). A mathematical model of the test rig has been made in the MATLAB environment Simulink. The test rig itself has components such as a Kaplan turbine, hydraulic pump, magnetic braking system, rotor, and a flow meter in a closed loop system. It is known that some test rig parameters are unknown, and so two methods have been used to optimize these parameters during the validation of the mathematical model. Optimization means finding either the maximum or the minimum of the target function with a particular set of parameters. An optimization of seven total parameters was made for the mathematical model in Simulink. The parameters were optimized using two different methods: Fmincon in MATLAB and Bayesian Optimization, a machine learning tool. Due to the fact that Fmincon could only find local minima and get stuck in that area, it could not reach the global minima. In contrast, Bayesian Optimization produced better results for minimizing the cost function and finding the global minima. / AFC4Hydro

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23892号 / 工博第4979号 / 新制||工||1777(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 田中 功, 教授 乾 晴行, 教授 中村 裕之 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Parenting behaviors have long been recognized as crucial to children’s healthy development. However, examinations of the etiology of these behaviors are less prevalent. The current study investigated the driving forces behind parental warmth and discipline, particularly whether they’re related more to traits within the parent or reactions to characteristics of the child. To explore this question, three robust factors of child temperament – effortful control, negative affectivity, and surgency/extraversion – and parent personality traits were examined in association with parent behaviors through differential parenting within twin pairs. Correlations between differences in temperament within twin pairs and differences in parenting within twin pairs showed that children with higher effortful control received more discipline from parents than their co-twin. Associations between parent behaviors and parent personality found that extraversion and agreeableness in parents were significantly related to parent warmth. Finally, by examining the heritability of temperament and comparing parenting toward monozygotic vs. dizygotic twins, this study clarified the direction of effects and genetic contributions to differential parenting behaviors, supporting previous literature that discipline acts in reaction to the child, whereas warmth is more driven by parent personality. This research begins to elucidate the causes behind parenting behaviors, allowing clinicians and parents to more effectively address the parent-child relationship to correct maladaptive parenting behaviors and encourage healthy and adaptive parenting behaviors, thus promoting positive outcomes for children.

<p dir="ltr">McAlester Army Ammunition Plant (MCAAP) was established in 1943 as the U.S Naval Ammunition Depot. In World War II, MCAAP played a crucial part in supplying ammunition for the war efforts. Today, MCAAP is home to nearly 45,000 acres of land, producing almost all the bombs for the Army, Air Force, and Navy.</p><p dir="ltr">In November of 2023, the Army launched their 15-year modernization plan for their Organic Industrial Base (OIB). The plan aims to modernize facilities, processes, and the workforce to bring the OIB into the 21^st century. The Army’s OIB consists of 17 arsenals, depots, and ammunition plants, including MCAAP.</p><p dir="ltr">This thesis optimizes the operational variables of the U.S. Air Force’s Mark-84 production process at MCAAP. Using software (AnyLogic) to construct a Digital Twin of the existing process provides insights into the current operational dynamics, enabling a deep understanding of the system’s inefficiencies. Then, utilizing this understanding and the capabilities of the Digital Twin, we offer targeted recommendations for process improvement. This study aims not only to enhance the Mark-84 production process, but also to demonstrate the transformative potential of Digital Twins in optimizing manufacturing operations.</p>

Σ3{111} coherent twin boundary (CTB) in face-centered-cubic (FCC) metals and alloys have been regarded as an efficient way to simultaneously increase strength and ductility at the nanoscale. Extensive study of dislocation-CTB interaction has been carried out by a combination of computer simulations, experiments and continuum theory. Most of them, however, are based on the perfect CTB assumption. A recent study [Wang YM, Sansoz F, LaGrange T, et al. Defective twin boundaries in nanotwinned metals. Nat Mater. 2013;12(8):697-702.] has revealed the existence of intrinsic kink-like defects in CTBs of nanotwinned copper through nanodiffraction mapping technique, and has confirmed the effect of these defects on deformation mechanisms and mechanical behavior. One of the deformation mechanisms proposed therein, i.e. general hard dislocation slip intersecting with kink line is studied here in detail by molecular dynamics (MD) simulation. Simulations are performed using copper bicrystal models with a particular focus on the interaction of a screw dislocation with 0 degree and 60 degree kinked CTBs. It is found that kink-like defects have a profound impact on screw dislocation - CTB interaction mechanisms, resulting in significant strengthening or softening effects.