Analysis of Microstrip Line with Asymmetric Arch Type Cross-Sectional Structure Using Micro Pattern Transfer Printing Method.

This paper presents the manufacturing procedure and electrical properties of a microstrip line on flexible printed circuit boards (FPCBs) fabricated using the micro pattern transfer printing (MPTP) method for millimeter wave band application. The MPTP method presented herein is compared to the conventional FPCB process based on the degree of insertion loss as it pertains to the cross-sectional shape of the formed microstrip line. Electromagnetic field simulations were performed to confirm that the cross-sectional arch shape fabricated by the MPTP process reduces insertion loss in the high-frequency band. Based on the simulation, the microstrip transmission line was optimized to a width of 217 µm and a length of 30 cm, fabricated on a 50 µm thick poly-cyclohexylene dimethylene terephthalate (PCT) substrate to measure the insertion loss. The insertion loss fabricated using the MPTP method is measured as 0.37 dB/cm at 10 GHz, while the conventional FPCB is measured as 0.66 dB/cm. Through the analysis, it was confirmed that the FPCBs manufactured by the MPTP process show lower insertion loss compared to the conventional FPCBs.

Technological Advancements and Economics in Plant Production Systems: How to Retrofit?

Plant production systems such as plant factories and greenhouses can help promote resilience in food production. These systems could be used for plant protection and aid in controlling the micro- and macro- environments needed for optimal plant growth irrespective of natural disasters and changing climate conditions. However, to ensure optimal environmental controls and efficient production, several technologies such as sensors and robots have been developed and are at different stages of implementation. New and improved systems are continuously being investigated and developed with technological advances such as robotics, sensing, and artificial intelligence to mitigate hazards to humans working in these systems from poor ventilation and harsh weather while improving productivity. These technological advances necessitate frequent retrofits considering local contexts such as present and projected labor costs. The type of agricultural products also affects measures to be implemented to maximize returns on investment. Consequently, we formulated the retrofitting problem for plant production systems considering two objectives; minimizing the total cost for retrofitting and maximizing the yearly net profit. Additionally, we considered the following: (a) cost of new technologies; (b) present and projected cost for human labor and robotics; (c) size and service life of the plant production system; (d) productivity before and after retrofit, (e) interest on loans for retrofitting, (f) energy consumption before and after retrofit and, (g) replacement and maintenance cost of systems. We solved this problem using a multi-objective evolutionary algorithm that results in a set of compromised solutions and performed several simulations to demonstrate the applicability and robustness of the method. Results showed up to a 250% increase in annual net profits in an investigated case, indicating that the availability of all the possible retrofitting combinations would improve decision making. A user-friendly system was developed to provide all the feasible retrofitting combinations and total costs with the yearly return on investment in agricultural production systems in a single run.

Vapor Pressure and Toxicity Prediction for Novichok Agent Candidates Using Machine Learning Model: Preparation for Unascertained Nerve Agents after Chemical Weapons Convention Schedule 1 Update.

The recent terrorist attacks using Novichok agents and subsequent operations have necessitated an understanding of its physicochemical properties, such as vapor pressure and toxicity, as well as unascertained nerve agent structures. To prevent continued threats from new types of nerve agents, the organization for the prohibition of chemical weapons (OPCW) updated the chemical weapons convention (CWC) schedule 1 list. However, this information is vague and may encompass more than 10 000 possible chemical structures, which makes it almost impossible to synthesize and measure their properties and toxicity. To assist this effort, we successfully developed machine learning (ML) models to predict the vapor pressure to help with escape and removal operations. The model shows robust and high-accuracy performance with promising features for predicting vapor pressure when applied to Novichok materials and accurate predictions with reasonable errors. The ML classification model was successfully built for the swallow globally harmonized system class of organophosphorus compounds (OP) for toxicity predictions. The tuned ML model was used to predict the toxicity of Novichok agents, as described in the CWC list. Although its accuracy and linearity can be improved, this ML model is expected to be a firm basis for developing more accurate models for predicting the vapor pressure and toxicity of nerve agents in the future to help handle future terror attacks with unknown nerve agents.

Could Japonica Rice Be an Alternative Variety for Increased Global Food Security and Climate Change Mitigation?

The growing importance of rice globally over the past three decades is evident in its strategic place in many countries' food security planning policies. Still, its cultivation emits substantial greenhouse gases (GHGs). The Indica and Japonica sub-species of Oryza sativa L. are mainly grown, with Indica holding the largest market share. The awareness, economics, and acceptability of Japonica rice in a food-insecure Indica rice-consuming population were surveyed. The impact of parboiling on Japonica rice was studied and the factors which most impacted stickiness were investigated through sensory and statistical analyses. A comparison of the growing climate and greenhouse gas emissions of Japonica and Indica rice was carried out by reviewing previous studies. Survey results indicated that non-adhesiveness and pleasant aroma were the most preferred properties. Parboiling treatment altered Japonica rice's physical and chemical properties, introducing gelatinization of starch and reducing adhesiveness while retaining micronutrient concentrations. Regions with high food insecurity and high consumption of Indica rice were found to have suitable climatic conditions for growing Japonica rice. Adopting the higher-yielding, nutritious Japonica rice whose cultivation emits less GHG in these regions could help strengthen food security while reducing GHGs in global rice cultivation.

Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model.

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000-2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.

Effect of temperature on single- and mixed-strain fermentation of ruminant feeds.

Use of raw feedstuffs for livestock is limited by low digestibility. Recently, fermentation of feedstuffs has been highlighted as a new way to improve nutrient absorption through the production of organic acids using inoculated microorganisms, which can also play a probiotic role. However, standard procedures for feedstuff fermentation have not been clearly defined because the process is influenced by climatic variation, and an analytical standard for fermented feedstuffs is lacking. This study aimed to evaluate the microbiological and biochemical changes of feedstuffs during fermentation at temperatures corresponding to different seasons (10°C, 20°C, 30°C, and 40°C). We also investigated the effects of yeast, lactic acid bacteria (LAB), and Bacillus spp. on fermentation and determined the results of their interactions during fermentation. The viable cells were observed within 8 days in single-strain fermentation. However, when feedstuffs were inoculated with a culture of mixed strains, LAB were predominant at low temperatures (10°C and 20°C), while Bacillus spp. was predominant at high temperatures (30°C and 40°C). A significant drop in pH from 6.5 to 4.3 was observed when LAB was the dominant strain in the culture, which correlated with the concentrations of lactic acid. Slight ethanol production was detected above 20°C regardless of the incubation temperature, suggesting active metabolism of yeast, despite this organism making up a marginal portion of the microbes in the mixed culture. These results suggested that fermentation temperature significantly affects microbiological profiles and biochemical parameters, such as pH and the lactic acid concentration, of fermented feedstuffs. Our data provide valuable information for the determination of industrial standards for fermented feedstuffs.