Artificial intelligence to predict soil temperatures by development of novel model.

Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

Obtaining patient torso geometry for the design of scoliosis braces. A study of the accuracy and repeatability of handheld 3D scanners.

OBJECTIVE: Obtaining patient geometry is crucial in scoliosis brace design for patients with adolescent idiopathic scoliosis. Advances in 3D scanning technologies provide the opportunity to obtain patient geometries quickly with fewer resources during the design process compared with the plaster-cast method. This study assesses the accuracy and repeatability of such technologies for this application. METHODS: The accuracy and repeatability of three different handheld scanners and phone-photogrammetry was assessed using different mesh generation software. Twenty-four scans of a single subject's torso were analyzed for accuracy and repeatability based on anatomical landmark distances and surface deviation maps. RESULTS: Mark II and Structure ST01 scanners showed maximum mean surface deviations of 1.74 ± 3.63 mm and 1.64 ± 3.06 mm, respectively. Deviations were lower for the Peel 1 scanner (maximum of -0.35 ± 2.8 mm) but higher with the use of phone-photogrammetry (maximum of -5.1 ± 4.8 mm). The mean absolute errors of anatomical landmark distance measurements from torso meshes obtained with the Peel 1, Mark II, and ST01 scanners were all within 9.3 mm (3.6%), whereas phone-photogrammetry errors were as high as 18 mm (7%). CONCLUSIONS: Low-cost Mark II and ST01 scanners are recommended for obtaining torso geometries because of their accuracy and repeatability. Subject's breathing/movement affects the resultant geometry around the abdominal and anterolateral regions.

Optimization of Maillard Reaction in Model System of Glucosamine and Cysteine Using Response Surface Methodology.

Sulfur-containing amino acids play important roles in good flavor generation in Maillard reaction of non-enzymatic browning, so aqueous model systems of glucosamine and cysteine were studied to investigate the effects of reaction temperature, initial pH, reaction time, and concentration ratio of glucosamine and cysteine. Response surface methodology was applied to optimize the independent reaction parameters of cysteine and glucosamine in Maillard reaction. Box-Behnken factorial design was used with 30 runs of 16 factorial levels, 8 axial levels and 6 central levels. The degree of Maillard reaction was determined by reading absorption at 425 nm in a spectrophotometer and Hunter's L, a, and b values. ΔE was consequently set as the fifth response factor. In the statistical analyses, determination coefficients (R2) for their absorbance, Hunter's L, a, b values, and ΔE were 0.94, 0.79, 0.73, 0.96, and 0.79, respectively, showing that the absorbance and Hunter's b value were good dependent variables for this model system. The optimum processing parameters were determined to yield glucosamine-cysteine Maillard reaction product with higher absorbance and higher colour change. The optimum estimated absorbance was achieved at the condition of initial pH 8.0, 111°C reaction temperature, 2.47 h reaction time, and 1.30 concentration ratio. The optimum condition for colour change measured by Hunter's b value was 2.41 h reaction time, 114°C reaction temperature, initial pH 8.3, and 1.26 concentration ratio. These results can provide the basic information for Maillard reaction of aqueous model system between glucosamine and cysteine.

Analyzing bone remodeling patterns after total hip arthroplasty using quantitative computed tomography and patient-specific 3D computational models.

BACKGROUND: Computational models in the form of finite element analysis technique that incorporates bone remodeling theories along with DEXA scans has been extensively used in predicting bone remodeling patterns around the implant. However, majority of such studies used generic models. Therefore, the aim of this study is to develop patient-specific finite element models of total hip replacement patients using their quantitative computed tomography (QCT) scans and accurately analyse bone remodelling patterns after total hip arthroplasty (THA). METHODS: Patient-specific finite element models have been generated using the patients' QCT scans from a previous clinical follow-up study. The femur was divided into five regions in proximal-distal direction and then further divided into four quadrants for detailed analysis of bone remodeling patterns. Two types of analysis were performed-inter-patient and intra patient to compare them and then the resulting bone remodeling patterns were quantitatively analyzed. RESULTS: Our results show that cortical bone density decrease is higher in diaphyseal region over time and the cancellous bone density decreases significantly in metaphyseal region over time. In metaphyseal region, posterior-medial (P-M) quadrant showed high bone loss while diaphyseal regions show high bone loss in anterior-lateral (A-L) quadrant. CONCLUSIONS: Our study demonstrated that combining QCT with 3D patient-specific models has the ability of monitoring bone density change patterns after THA in much finer details. Future studies include using these findings for the development of a bone remodelling algorithm capable of predicting surgical outcomes for THA patients.