Design and fabrication of a microscale Joule -Thomson refrigerator

A simple thermodynamic, heat transfer, and fluid flow model was developed for microscale Joule-Thomson refrigerators (JT devices). For a given geometry, the model predicted that the cooling capacity of the refrigerator increased with the inlet refrigerant pressure. The effectiveness of the JT device also increased with the inlet pressure, and the heat exchanger channel length. At a constant inlet pressure, the effectiveness, and the refrigeration capacity of a given JT device increased as the aspect ratio of heat exchanger channels was increased. For nitrogen refrigerant, the model predicted that it was possible to obtain approximately 250 mW of refrigeration capacity at 82 K with 10 MPa (100 atm) of inlet pressure and a flow rate of 15.17 ml/s at standard pressure and temperature (STP). This prediction was justified by experimental values of Little (1984) who obtained 250 mW of refrigeration capacity at 83 K with 10 MPa (100 atm) of inlet pressure and a flow rate of 18 ml/s at STP. The simulation model was also used to design a novel JT device based on a layered arrangement of the evaporator, capillary, and the heat exchanger. The proposed JT device would have produced approximately 250 mW of refrigeration capacity at 100 K, for an inlet pressure of 6 MPa (60 atm). This proposed JT device was fabricated on silicon wafers using photolithography. The heat exchanger channels had a cross section of 50 x 20 μm and a length of 6 cm. The capillary channel cross section was 20 x 20 μm and its length was 6 cm. Both the length and the width of the evaporator was 30 mm, and its depth was 20 μm. Pyrex 7740 glass wafers (3 mm thick) were used to separate the evaporator from the capillary and the capillary from the heat exchanger. The heat exchanger was bonded with a top glass cover plate. Most layers were successfully bonded using the anodic bonding procedure. After bonding the evaporator to a glass wafer, subsequent anodic bonding was carried out by applying voltage from sides of each glass and silicon wafer. This bonding attempt demonstrated that the anodic bonding procedure could be used in packaging several silicon and glass wafers. The packaged device held together briefly but later separated due to poor bonding quality of the capillary and the heat exchanger. This poor bonding quality may have resulted from inadequate surface quality of silicon wafers. However, the knowledge and the experience gained in this work will be very useful in future development of JT devices

Augmenting a Statistical Translation System with a Translation Memory

In this paper, we present a translation memory (TM) based system to augment a statistical translation (SMT) system. It is used for translating sentences which have close matches in the training corpus. Given a test sentence, we first extract sentence pairs from the training corpus, whose source side is similar to the test sentence. Then, the TM system modifies the translation of the sentences by a sequence of substitution, deletion and insertion operations, to obtain the desired result. Statistical phrase alignment model of the SMT system is used for this purpose. The system was evaluated using a corpus of Chinese-English conversational data. For close matching sentences, the translations produced by the translation memory approach were compared with the translations of the statistical decoder

ITEm: Unsupervised Image-Text Embedding Learning for eCommerce

Product embedding serves as a cornerstone for a wide range of applications in eCommerce. The product embedding learned from multiple modalities shows significant improvement over that from a single modality, since different modalities provide complementary information. However, some modalities are more informatively dominant than others. How to teach a model to learn embedding from different modalities without neglecting information from the less dominant modality is challenging. We present an image-text embedding model (ITEm), an unsupervised learning method that is designed to better attend to image and text modalities. We extend BERT by (1) learning an embedding from text and image without knowing the regions of interest; (2) training a global representation to predict masked words and to construct masked image patches without their individual representations. We evaluate the pre-trained ITEm on two tasks: the search for extremely similar products and the prediction of product categories, showing substantial gains compared to strong baseline models

Gap Analysis between ERP procedures and Construction procedures

Although Enterprise Resource Planning (ERP) offers many benefits to the construction industry, construction companies still hesitate to adopt ERP systems. This may be due to long-term practiced ad-hoc behaviors in the construction industry, which do not match with the standard procedures embedded in ERP systems. Therefore, through this research, it is expected to evaluate the gap between construction procedures and ERP procedures technically. Hence, to obtain indicative data for the study, a questionnaire was designed and distributed to selected 210 individuals among contractors, subcontractors, and clients in the Sri-Lankan construction industry who have used ERP. In total, 174 completed questionnaires were returned and then statistically analyzed using Chi-Square test with the Mini tab tool. It is concluded that there is a significant gap between the construction procedures and ERP procedures in identified fields related to the construction industry. The highest significant gap exists in the field of Inventory management with Chi-Square 158.766 > 9.48. And HRM (142.366), Asset Management (130.264), Finance Management (126.267), Site Operation (103.793), Project management (53.88), Purchases (34.324), Petty cash (28.337), Estimating and Tendering (22.148), Sub-Contractor management (0.492) respectively. Ultimately with the identified gaps, a framework was established to meet the organizational processes and ERP processes

Discovery of a new source of rifamycin antibiotics in marine sponge actinobacteria by phylogenetic prediction

Phylogenetic analysis of the ketosynthase (KS) gene sequences of marine sponge-derived Salinispora strains of actinobacteria indicated that the polyketide synthase (PKS) gene sequence most closely related to that of Salinispora was the rifamycin B synthase of Amycolatopsis mediterranei. This result was not expected from taxonomic species tree phylogenetics using 16S rRNA sequences. From the PKS sequence data generated from our sponge-derived Salinispora strains, we predicted that such strains might synthesize rifamycin-like compounds. Liquid chromatography-tandem mass spectrometry (LC/MS/MS) analysis was applied to one sponge-derived Salinispora strain to test the hypothesis of rifamycin synthesis. The analysis reported here demonstrates that this Salinispora isolate does produce compounds of the rifamycin class, including rifamycin B and rifamycin SV. A rifamycin-specific KS primer set was designed, and that primer set increased the number of rifamycin-positive strains detected by PCR screening relative to the number detectable using a conserved KS-specific set. Thus, the Salinispora group of actinobacteria represents a potential new source of rifamycins outside the genus Amycolatopsis and the first recorded source of rifamycins from marine bacteria

Non-linear statistical model for the daily stream flow Prediction in the kalu river catchment in Sri Lanka

Having a long record of stream flow is very valuable in planning water resources development projects. However, in many occasions, stream flow records are available for very short periods though very long rainfall records are available. Therefore, possibility to relate rainfall over a catchment to the stream flow at its outlet will enable having a long record of stream flow. Besides prediction of stream flow using already available predicted rainfall will permit taking precautionary measures in water related disaster situations such as floods and droughts. This paper presents a research carried out to find a model to predict daily stream flow of Kalu River at Ratnapura. The model, a non-linear regression model based on Marquardt’s procedure, was developed using measured daily stream flow at Kalu River at Ratnapura and daily rainfall at eight rainfall gauging stations within the catchment above Ratnapura. Data for the period 1987-1994 were used for the calibration of the model while data for the period 1995-2000 were used for verifying it. The model was validated using Nush-Sutcliffe efficiency and pseudo R2 . Nush-Sutcliffe efficiency (78%) and pseudo R2 (85%) show the possibility of the fitted model in predicting daily stream flow of Kalu River at Ratnapura