Heterogeneously Catalyzed Methanolysis of Gmelina Seed Oil to Biodiesel

The heterogeneous catalysis of transesterification of gmelina seed oil to biodiesel is evaluated. The oil was extracted from the seeds with n‐hexane by solvent extraction and characterized to determine its physiochemical properties. Response surface methodology was applied to optimize the effect of process variables on the biodiesel yield. The base‐activated clay catalyst performed as montmorillonite clay with the characteristic property of a Brønsted acid. It has an improved surface area after activation that enhanced its catalytic activity on transesterification reaction. Under optimal conditions, the biodiesel yield was 70.1 %, thus demonstrating that the model predicted well the biodiesel production.     

Digital Extraction Column: Measurement and Modeling Techniques

The digitization of extraction columns requires a profound knowledge of the present hydrodynamics/mass transport interaction as well as appropriate measurement techniques for the detection of relevant input and target values. In this article, the different techniques for droplet size distribution as well as concentration determination are presented and new methods for online evaluation are discussed. In combination with the simulation of droplet size, holdup and solute concentration distribution, an online-capable process tool for controlling and optimizing extraction columns will be obtained.     

Computational investigation of hydraulic performance variation with geometry in gabion stepped spillways

Over recent years, there has been a clear increase in the frequency of reported flooding events around the world. Gabion structures offer one means of flood mitigation in dam spillways. These types of structures provide an additional challenge to the computational modeller in that flow through the porous gabions must be simulated. We have used a computational model to investigate the flow over gabion stepped spillways. The model was first validated against published experimental results. Then, gabion stepped spillways with four different step geometries were tested under the same conditions in order to facilitate inter-comparisons and to choose the best option in terms of energy dissipation. The results show that normal gabion steps can dissipate more energy than overlap, inclined, and pooled steps. An intensive set of tests with varying slope, stone size, and porosity were undertaken. The location of the inception point and the water depth at this point obtained from this study were compared with those from existing formulae. Two new empirical equations have been derived, on the basis of a regression analysis, to provide improved results for gabion stepped spillways.     

铁路货车车体的焊接自动化

本文综述了原苏联，原联邦德国采用焊接机器人，熔化极气体保护焊，ＣＯ２＋Ｏ２混合气体保护焊，药芯保护焊丝等焊接自动化技术和焊接新材料来生产铁路货车，减轻了劳动强度，提高了焊接生产率，保证了焊接质量，又以美国为首的经济发达国家采用铝合金制造货车日益增多，使车辆轻型化和提高运行中的耐腐蚀性及毋需定期涂装和维修。     

A NEW,AUTOMATED, MULTIANGULAR RADIOMETER INSTRUMENT FOR TOWER-BASED OBSERVATIONS OF CANOPY REFLECTANCE (AMSPEC II)

Plant photosynthesis is critical for understanding carbon cycling at landscape and global scales. While tower-based measurements of CO₂ have enhanced our knowledge of ecosystem fluxes, scaling these measurements globally is difficult. Satellite observations provide full, global coverage and hold the potential of spatially continuous measurements of ecosystem fluxes, but the requirements for modeling these fluxes from satellite-derived surface parameters are not well understood. This article describes the further development of a tower-mounted, automated, multiangular spectroradiometer system (AMSPEC II) used to study the relationships between canopy-reflectance and plant-physiological processes from multiangular observations, thereby facilitating a comprehensive modeling of the bidirectional reflectance distribution of the canopy. A Webcam permits simultaneous monitoring of phenological changes over time.     

Learning algorithms for joint resource block and power allocation in underlay D2D networks

We investigate the problem of resource block (RB) and power allocation jointly and in a distributed manner using game theoretic learning solutions, in an underlay device-to-device network where device pairs communicate directly with each other by reusing the spectrum allocated to the cellular users. We formulate the joint RB and power allocation as multi-agent learning problems with discrete strategy sets; and suggest partially distributed and fully distributed learning algorithms to determine the RB and power level to be used by each device pair. The partially distributed algorithms, viz., Fictitious Play and its variant Fading Memory Joint Strategy Fictitious Play with Inertia, achieve Nash Equilibrium (NE) of the sum-rate maximization game in a static wireless environment. The completely distributed and uncoupled Stochastic Learning Algorithm converges to pure strategy NE of the interference mitigation game in a time-varying radio environment. We provide proofs for the existence of NE and convergence of the learning algorithms to the NE. Performance of the proposed schemes are evaluated in log-normal, Rayleigh and Nakagami fading environments and compared with an existing hybrid scheme and a centralized scheme. The simulation results show that the partially distributed schemes give the same performance as the centralized scheme, and the fully distributed scheme gives similar performance as the hybrid scheme but with much reduced signaling and computation overhead.     

High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble

A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video.     

Trophic ecology of salmonine predators in northern Lake Huron with emphasis on Atlantic salmon (Salmo salar)

Pacific salmon were introduced to the Great Lakes in the 1960s and now support major recreational fishery. Population declines resulting from invasive species have prompted agencies to consider diversifying sport fisheries through stocking. Atlantic salmon are native to Lake Ontario, but a small fishery has developed in northern Lake Huron since the 1990s that appears suited to the Lake Huron food web leading to requests for increased stocking by anglers and consideration by agencies. However, no study has evaluated the trophic ecology of Atlantic salmon in relation to other salmonine predators in northern Lake Huron. In this study, we used stable isotopes of carbon (δ¹³C) and nitrogen (δ¹⁵N), along with mercury (Hg) concentrations to assess resource use, niche overlap, and contaminant accumulation in Atlantic salmon compared to select Lake Huron predators. Atlantic salmon exhibited considerable niche overlap with Chinook and coho salmon but were strongly differentiated from lake trout. In addition, we observed that Atlantic salmon had similar Hg concentrations as coho but were lower than both Chinook salmon and lake trout. Based upon the relationship between fish size, δ¹⁵N, and Hg, Atlantic salmon bioaccumulate Hg similarly to Pacific salmon but likely have lower consumptive demands than Chinook salmon. Continued attention should be placed on understanding how Atlantic salmon fit into the current Lake Huron food web in order to evaluate the long-term efficacy of the Atlantic salmon stocking program.