345 GHz Band-Pass Filter Using Ultra-Precision Machining Technology

This paper presents a terahertz（THz）band-pass filter using ultra-precision machining technology based on Chebyshev filter prototype.This iris inductive window coupled waveguide filter was designed by using 8 resonant cavities with a center frequency of 345 GHz and a 7% bandwidth.The final design fulfills the desired specifications and presents the minimum insertion loss of 1.55 d B and the return loss of less than 15 d B at 345 GHz.The stop-band rejection is50 d B off the center frequency about 30 GHz,which means it has a good performance of high stop-band suppression.Compared with the recent development of THz filters,this filter possesses the characteristic of simple structure and is easy to machining.

Implementing an innovated liver ex-situ machine perfusion technology:The 2018 Joint International Congress of ILTS,ELITA and LICAGE

The 2018 Joint International Congress of ILTS,ELITA and LICAGE were held in Lisbon,Portugal on May 23–26,2018.The exciting and innovative program brought together 1144 experts in liver transplantation(LT)such as surgeons,physicians or basic scientists from 61 countries.The presentations included 110 invited speakers,181 oral presentations,and 545 posters.This editorial highlights some of the most innovative and impactful presentations in

Quantifying muskmelon fruit attributes with A-TEP-based model and machine vision measurement

In this study, we established a dynamic morphological model using the accumulated thermal effectiveness and photosynthetic active radiation （PAR） （A-TEP）, aiming to explore the relationship between muskmelon （Cucumis melo L.） fruit attributes and environmental factors. Muskmelon surface color was described by parameters of red, green, blue, hue, saturation and brightness （HSI）. Three characteristic parameters, gray level co-occurrence matrix （GLCM）, angular second moment （ASM）, entropy, contrast, and the coverage rate were used to describe the process of muskmelon fruit netting formation. ASM was not significant difference during muskmelon fruit growth. The number and deep of netting stripes gradually increased with fruit growth. Coverage rate increased rapidly for 15-30 d after pollination. The vertical and horizontal diameters of muskmelon fruit were followed a logistic curve. And root mean squared errors （RMSE） between the simulated and measured vertical and horizontal diameters were 3.527 and 4.696 mm, respectively. RMSE of red, green, blue, saturation and brightness were 0.999, 2.690, 2.992, 0.033 and 5.51, respectively, and the RMSE for entropy, contrast and coverage rates were 0.077, 0.063 and 0.015, respectively, indicating a well consistent between measured and simulated values.

Application of decision tree to selection of MTBM for adverse geological conditions

So many potential risks can be identifed for application of trenchless technology especially using microtunneling methods.Unexpected changes in ground conditions,such as encountering boulders,tree roots,ground water and man-made structures such as old foundations are the principal geotechnical risks,which affect the selection of an appropriate microtunnel boring machine.On the other hand,the performance of each microtunneling technique will differ while encountering such conditions.Hence,predicting the potential hazards provides a better safety and risk management plan.In this study,a couple of potentially hazardous situation,which are commonly associated with ground conditions,were identifed and investigated.A decision tree aid methodology was proposed based on geotechnical risk assessment for selection of proper microtunneling technique.Based on the approach the most appropriate microtunneling technique has the minimum risk level either before or after hazards mitigation measures.In order to check the effciency of the approach in practice,selection of microtunnel boring machine for Hamadan sewerage pipeline project was evaluated.Accordingly,an earth pressure balance(EPB)MTBM was selected for the project.

Research Needs and Applications of Machine Learning｡ｪPredicting Logistics Stress by Machine Learning

Machine learning is the use of computers to learn the intrinsic laws and information contained in data through algorithms to gain new experience and knowledge,in order to improve the intelligence of computers,so that they can make decisions similar to those made by humans when faced with problems.With the development of various industries,the amount of data has increased and the efficiency of data processing and analysis has become more demanding,a series of machine learning algorithms have emerged.Machine learning algorithms are essentially steps and processes that apply a large number of statistical principles to solve optimisation problems.Appropriate machine learning algorithms can be used to solve practical problems more efficiently for a wide range of model requirements.This paper presents the interim state of a dynamic disruption management software solution for logistics,using machine learning methods to study the extent to which stress is predicted based on physiological and subjective parameters,to prevent physical and mental stress on workers in the logistics industry,to maintain their health,to make them more optimistic and better able to adapt to their work,and to facilitate more accurate deployment of human resources by companies according to the real-time requirements of the logistics industry.

Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR

Near infrared spectroscopy （NIR） is now probably the most popular process analytical technology （PAT） for pharmaceutical and some other industries. However, unlike mid-IR, NIR is known to have difficulties in monitoring crystallization or precipitation processes because the existence of solids could cause distortion of the spectra. This phenomenon, seen as unfavorable previously, is however an indication that NIR spectra contain rich information about both solids and liquids, giving the possibility of using the same instrument for multiple property characterization. In this study, transflectance NIR calibration data was obtained using solutions and slurries of varied solution concentration, particle size, solid concentration and temperature. The data was used to build calibration models for prediction of the multiple properties of both phases. Predictive models were developed for this challenging application using an approach that combines genetic algorithm （GA） and support vector machine （SVM）. GA is used for wavelength selection and SVM for mode building. The new GA-SVM approach is shown to outperform other methods including GA-PLS （partial least squares） and traditional SVM. NIR is thus successfully applied to monitoring seeded and unseeded cooling crystallization processes of L-glutamic acid.