Packing Method Research Articles

Preparation of mixed-mode stationary phase for separation of peptides and proteins in high performance liquid chromatography

Porous silica particles were prepared by sol–gel method with some modification to get wide-pore particles. These particles were derivatized with N-phenylmaleimide-methylvinylisocyanate (PMI) and styrene by reversible addition fragmentation chain transfer (RAFT) polymerization to prepare N-phenylmaleimide embedded polystyrene (PMP) stationary phases. Narrow bore stainless steel column (100 × 1.8 mm i.d) was packed by slurry packing method. The chromatographic performance of PMP column was evaluated for the separation of synthetic peptides mixture composed of five peptides (Gly-Tyr, Gly-Leu-Tyr, Gly-Gly-Tyr-Arg, Tyr-Ile-Gly-Ser-Arg, Leucine enkephalin) and tryptic digest of human serum albumin (HAS) respectively. Number of theoretical plates as high as 280,000 plates/m were obtained for peptides mixture at optimum elution condition. Separation performance of the developed column was compared with commercial Ascentis Express RP-Amide column and it was observed that separation performance of PMP column was better than commercial column in terms of separation efficiency and resolution.

Bed structure and its impact on liquid distribution in a trickle bed reactor

AbstractTrickle bed reactors, which has been a workhorse for the process and refining industry for many decades, are progressively being challenged to provide solutions to deep processing of feedstocks. It is known that the structure of the packed bed which is formed with a certain arrangement of catalyst particles in the three‐dimensional space within the reactor modulates in an unknown fashion the flow of fluids in the trickle bed, and in turn affects the conversion and selectivity in the trickle bed. Under deep processing conditions, the impact of the bed structure in modulating the overall reactor performance in a trickle bed is not as yet established. The question begets three sequential studies: estimating and quantifying the bed structure, measuring the liquid distribution, and estimating transport parameters (that are dependent on the bed structure and liquid distribution) so that the overall performance metrics as a reactor may be quantified. This contribution relates to the second of these questions, the first being already addressed to some extent by our earlier work. The current investigation aims at quantifying the effect of structure of the packed bed on hydrodynamics of the reactor. The impact of various packing techniques is discussed along with the development of correlations for two‐phase pressure drop and dynamic liquid holdup. Liquid distribution is studied in depth for various operating parameters such as gas and liquid superficial velocities and column aspect ratio for uniform and non‐uniform packing methods. The packing devices consist of various inserts attached to a hopper which can generate packing structures having void fraction in the range of 37.2%–46.4%. The maldistribution factor and flow maps for various aspect ratio of column suggest that maldistribution rises along with the increased channeling effect along the height of the column. Uniformly packed bed were measurably less prone to maldistribution along the length than the non‐uniformly packed beds.

Achieving Success by Validation! Decontamination Protocols of Category A Medical Waste for Patients with Serious Communicable Diseases

<h3>Purpose</h3> During the 2014 Ebola outbreak, treatment centers for Ebola and special pathogens had to determine safe handling protocols for the management of category A infectious substances. There have been challenges achieving validation of adequate sterilization with autoclaving. To achieve successful inactivation requires alteration of cycle parameters (temperature, pressure, and time), and packaging methods. Successful inactivation is verified through the use of biological indicators, data loggers, and the evaluation of drain and exhaust lines. We aim to describe a successful waste sterilization process utilizing an autoclave in Emory University Hospital's biocontainment unit where we maintained appropriate safe handling waste processes. <h3>Methods & Materials</h3> Waste was differentiated by type to include patient general waste, linens, liquids, and sharps (including continuous renal replacement therapy cartridges). This helped us to determine appropriate packing methods and cycle parameters to ensure adequate inactivation by autoclaves. A biological indicator (BI) was used to monitor the efficacy of the autoclave sterilization process. Waste was double bagged with autoclave bags using the "gooseneck" method and secured with autoclave tape. This allowed for waste packaging to maintain closure of biohazard bags and contain leaks and aerosols. Following failed loads, action plans were generated until achieving successful inactivation. In order to determine the effectiveness of autoclave sterilization parameters, repeat loads were performed to ensure reliability. Once cycle specifications were finalized, each cycle type was repeated to achieve three successful runs with all passing biological indicators. <h3>Results</h3> We achieved effective inactivation of waste by altering autoclave factory settings. Following separation of waste and determine appropriate packing methods, we ran 26 total cycles achieving passing biological loads in 17 cycles. Failed loads occurred primarily in liquids and sharps where parameters were readjusted until achieving three successful cycles. <h3>Conclusion</h3> Safe handling of category A infectious waste requires validation of inactivation of waste by autoclave. Validation of autoclave parameters need to be assessed with particular attention to waste loads and packaging prior to treatment. We achieved successful inactivation of infectious medical waste with simulated patient waste loads while maintaining the integrity of waste packaging to ensure no leakage of liquid or aerosol particles occurred.

DLPacker: Deep learning for prediction of amino acid side chain conformations in proteins.

Prediction of side chain conformations of amino acids in proteins (also termed "packing") is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller.

In situ anodic electrodeposition of two-dimensional conductive metal-organic framework@nickel foam for high-performance flexible supercapacitor

Owing to their large specific surface area and well-defined porosity, metal-organic frameworks (MOFs) have long been considered as promising materials for energy storage. Unfortunately, their straightforward utilization in supercapacitors was hindered for years due to poor conductivity until the emergence of 2D conductive MOF materials. To date, several 2D conductive MOF-based supercapacitors have been reported. Nevertheless, almost all these supercapacitors were fabricated from MOF powders through a slurry coating method or dense packing method, which negatively affected their capacitor performance. Herein, we have developed an anodic electrodeposition (AED) approach to fabricate a uniformly deposited 2D conducting MOF on nickel foam and use it directly as electrodes for supercapacitors without any additives. The superior performance of the 2D conducting MOF on nickel foam in both aqueous and organic electrolytes was then disclosed by a series of electrochemical measurements, demonstrating the significant advantages of the AED approach over traditional methods.

Effect of Packaging and Portioning on the Dynamics of Water-Fat Serum Release from Fresh Pasta Filata Soft Cheese.

The aim of the present study was to analyze the impact of cheese fragmentation and packaging on the dynamics of water–fat serum released from pasta filata cheese made from cow’s milk and its mixture with sheep’s milk. The addition of sheep’s milk reduced the amount of leachate from the vacuum-packed cheeses and did not cause as much loss of gloss as in the case of cow’s milk cheeses. This was also reflected in the microscopic images of the cheese samples. Consumers showed less acceptance of cow’s milk pasta filata cheeses than cheeses made with a mixture of cow’s and sheep’s milk (they had the same fat content, acidity, hardness, and oiling-off, but better stretching). The data describing water–fat serum release from pasta filata cheese within 24 h of unpacking was modeled with the use of the feed-forward artificial neural networks, whose architecture is based on Multi-Layer Perceptron with a single hidden layer. The model inputs comprised four independent variables, including one quantitative (i.e., time) and the other qualitative ones, which had the following states: type of raw material (cow’s milk, cow-sheep’s milk), way of sample portioning (whole, quarters, slices), packing method (vacuum packed and packed in brine).

A combined forecasting and packing model for air cargo loading: A risk-averse framework

In this paper, we present a combined forecasting and optimization decision-support tool to assist air cargo revenue management departments in the acceptance/rejection process of incoming cargo bookings. We consider the case of a combination airline and focus on the passenger aircraft belly capacity. The process is dynamic (bookings are received in a discrete fashion during the booking horizon) and uncertain (for some bookings the three dimensions are not provided, while the actual belly space available for cargo is only revealed a few hours before departure). Hence, analysts base decisions on historical data or human experience, which might yield sub-optimal or infeasible solutions due to the aforementioned uncertainties. We tackle them by proposing data-driven algorithms to predict available cargo space and shipment dimensions. A packing problem is solved sequentially once a new booking request is received, predicting shipment dimensions, if necessary, and considering the uncertainty of such prediction. The booking is accepted if it results in a feasible loading configuration where no previously accepted booking is offloaded. When applied in a deterministic context, our packing method outperformed the one used by the partner airline, increasing the loaded volume up to 20%. The framework was also tested assuming unknown shipment dimensions, comparing a risk-prone and a risk-averse strategy, with the latter accounting for uncertainty in dimension predictions and the former using mean values. While the average loaded volume decreases in the risk-averse case, the number of unplanned offloadings due to under-predicted dimensions decreases from 54% to 12% of the simulated cases, hence yielding a more robust acceptance strategy.

Microstructure-based finite-difference analysis of the plastic flow in low-carbon steel

Despite extensive research, the question – what makes the fronts of plastic strain (e.g. Lüders bands) propagate or remain still has not been solved yet. Therefore, the problem is acute for modern solid mechanics and physics. In this paper, an analysis of low-carbon steel plastic flow is carried out both numerically and experimentally. A microstructure-based finite-difference analysis is employed to provide a deeper insight into the emerging features of plastic flow. Low-carbon polycrystalline steel is chosen as the basic material for this investigation. A hand-controlled iterative procedure based on the step-by-step packing method is utilized to design a representative volume element (RVE) of the low-carbon steel under study. The designed model represents a cluster of grains randomly distributed within the computational domain according to a certain law and randomly oriented with respect to the global coordinate system based on the results of EBSD study. A number of earlier reported models of plastic flow are combined and modified to study the yield plateau stage of the model samples. The mathematical model parameters are accurately calibrated against the experimental data using quite simple methods. The results of modeling satisfactorily meet the available experimental data and sufficiently complement them in terms of reproducing some regularities of plastic flow observed both at micro- and macrolevel. Relying on the numerical modeling data the conditions at the front of the Lüders bands are discussed, which shed light on a possible mechanism of propagation.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region. This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression. General health indicators are extracted from the partial discharge process. The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction. The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic. Besides, only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction. Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack, even with only 50 cycles for model fine-tuning, which can save about 90% time for the aging experiment. Thus, it largely reduces the time and labor for battery pack investigation. The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell, which can reveal the weakest cell for maintenance in advance.

The effect of texture sharpness on deformation-induced surface roughening in α-titanium

The deformation behavior of hexagonal metals is known to be strongly affected by a material texture. This paper aims at understanding the effect of a basal texture on the deformation-induced surface roughness in α-titanium. The effect of the basal texture severity on the deformation behavior is estimated for polycrystals characterized by the same grain geometry and different crystallographic orientations. For this purpose, three-dimensional polycrystalline models with the grain morphology and crystallographic orientations integrated explicitly are constructed using the step-by-step packing method. The texture severity is shown to strongly affect mesoscale roughness patterns. A more pronounced basal texture leads to suppression of the microscale surface roughness formed by displacements of individual grains perpendicular to the free surface (the orange peel pattern) and mesoscopic surface roughness formed by the displacements of grain groups. The basal texture reduces the material ability to deform perpendicular to the loading axis, while the prismatic slip gives rise to in-plane shear banding.

A Branch-and-Repair Method for 3D Bin Selection and Packing in E-Commerce

A Branch-and-Repair Method for 3D Bin Selection and Packing in E-Commerce

Patch Re-Segmentation and Packing for Dynamic Point Cloud Compression via Back-and-Forth Structure

The dynamic point cloud is widely needed in 3D vision related applications such as virtual reality and telepresence. Due to the huge amount of data, a key technology before the effective application is the dynamic point cloud compression. The state-of-the-art dynamic point cloud compression scheme, video-based point cloud compression (V-PCC), generates 2D videos with some uncorrelation due to the patch segmentation and packing process, which will affect the compression efficiency. In this paper, we propose a Packing with Patch Correlation Improvement (PPCI) algorithm to adaptively remove the uncorrelated parts between matched patches in packing for the sake of inter-prediction performance. We first propose a basic unidirectional patch re-segmentation operator to remove the uncorrelated parts of the patches in the current point cloud relative to the patches in its reference point cloud. The removed parts will be formed as new patches and added to the patch collection of the current point cloud. Then we propose a back-and-forth structure, which is a combination of several basic patch re-segmentation operators, to bilaterally remove the uncorrelated parts of matched patches in a back-and-forth (BF) unit. Furthermore, we propose a framework to adaptively decide the best length of each BF unit in a point cloud sequence. Experimental results show that our method achieves noticeable bitrate savings compared with the existing V-PCC packing methods, particularly for sequences with small motion.

Online Routing and Charging Schedule of Electric Vehicles With Uninterrupted Charging Rates

With the development of electric vehicle technology and the Internet of Things (IoT) technology, IoT-Based electric vehicles (IoEVs) will become a main public transposition in smart city. The increasing penetration of IoEV will have a great impact on the transportation network and power grid. In this work, we aim to study the routing and charging scheduling problem of IoEVs with the constraint of power limit. This paper proposes a clustered rolling framework and a mathematical model for the IoEVs optimal routing and charging problem to minimize the waiting time of customers and get a good converge ratio of the energy block with power limit. This framework can effectively deal with the multi-step pickup and delivery of each vehicle over the time horizon in a dynamic environment. We give a criterion to judge whether pickup and delivery tasks can be assigned to the IoEV. Then, we design a new rectangle packing method for IoEV charging dispatch with the constraint of power limit. Moreover, we consider that the charging process of IoEVs cannot be interrupted before IoEVs meet the electricity demand. We propose the IoEV Routing and Charging Algorithm and analyze its computational complexity. Simulation results show that IoEVs can offer the shuttle service to customers with the minimum waiting time and IoEV charging energy block has a good coverage ratio of the total energy block. Simulation results can verify the effectiveness of our proposed framework, compared with two benchmark algorithms.

Effect of Steel Fiber Volume Fraction on the Mechanical Behavior of Ultra-high Performance Concrete Composites

In order to investigate the effect of fiber volume fraction on the mechanical behavior of ultra-high performance concrete composites (UHPCC), five different volume fractions of macro steel fibers (Vf = 0.5, 1, 1.5, 2 and 2.5%) are used within identical mortar matrix. Ultra-high performance fiber reinforced concrete (UHPFRC) mix was designed to achieve a compressive strength of 155 MPa based on the particle packing method. For 12 series of UHPCC mixes, compressive strength, splitting tensile strength, flexural strength, and modulus of elasticity at 28 days are determined. Test results showed a significant improvement in splitting tensile and flexural strengths of UHPFRC with the addition of steel fibers. The maximum values of compressive, splitting tensile and flexural strengths were 155.39, 17.76, and 32.50 MPa, respectively. Stress-strain behavior of fiber-reinforced concrete composites is studied and elastic modulus values evaluated are in the range of 39.52-47.99 GPa. Empirical expressions are developed based on the test results in terms of fiber volume fraction to predict the 28-day strengths of UHPFRC. Comparing the experimental values of earlier researchers to the ones predicted by empirical equations, the average absolute error (AAE) value obtained is within 5%. The proposed model's predictions are in good agreement with the experimental values. Relationship between compressive and flexure strengths of UHPFRC isdeveloped with R2=0.99 and validated.

VVC Subpicture-Based Frame Packing for MPEG Immersive Video

The moving picture experts group (MPEG) video coding group started an immersive video coding standard project to accomplish acceptable bandwidth and decoding resources for multiple immersive videos with texture (color) and geometry (depth) information. The MPEG immersive video (MIV) coding standard provides functionality to remove inter-view redundancy and merge the residuals into videos defined as atlases. Although MIV reduces the number of videos to decode, an efficient decoder instance reduction method has not been investigated. This study proposes a frame packing implementation in MIV software. The proposed method conducts frame packing for geometry atlases to align with texture atlases, encodes them as subpicture bitstreams, and merges one texture / geometry atlas into a single bitstream with a bitstream merger. Furthermore, the proposed method provides control over the number of decoder instances without significantly increasing the computational complexity compared to existing frame packing methods. Therefore, the proposed method was accepted in MIV and will be implemented on the recent version of the reference software of MIV.

Microstructural Characterization of Borided Co-Cr-Mo Alloy

Abstract This study involves the effect of boriding powder composition on the microstructure and hardness of a CoCrMo alloy borided in a solid medium using the powder pack method. To investigate the effect of boriding powder composition, two different commercial boriding agents, Ekabor-HM and Ekabor III, were thoroughly mixed with ferrosilicon powders to form the boriding media. The CoCrMo samples were tightly packed with the Ekabor-HM and Ekabor III boriding powders in stainless steel containers to minimize oxidation. The boriding process was carried out under atmospheric conditions for 9 h in an electrical resistance furnace preheated to 1223 K. X-ray diffraction (XRD) analyses revealed that the surfaces of the borided CoCrMo alloys consisted of a bilayer composed of CoB and Co2B phases and also contained minor amounts of CrB, Mo2B5, and Mo2B. The average thickness of the boride layer in the samples borided with Ekabor HM and Ekabor III powders was 28±4.1 μm and 21±2.3 μm, while the average hardness of the boride layer was 1752±5.3 HV0.1 and 1364±3.8 HV0.1, respectively.

Microstructure-based analysis of residual stress concentration and plastic strain localization followed by fracture in metal-matrix composites

A numerical study is performed to investigate the deformation and fracture in metal matrix – ceramic particle composites using Al6061T6 and ZrC as an example of the compound materials. The step-by-step packing method is adopted for generating three-dimensional model microstructures of metal-matrix composites, with the irregular shape of particles and polycrystalline structure of the matrix being taken into account. The dynamic boundary-value problems are solved by the finite-element method. Constitutive models describing the mechanical behavior of the composite compounds include anisotropic elasticity, crystal plasticity with the strain hardening and strain fracture criterion for the matrix and isotropic elastic-brittle formulation with the stress fracture criterion for particles. A user-defined subroutine is developed to combine the anisotropic matrix and isotropic particle responses and implemented in the finite element calculations of the composite thermomechanical behavior. The interrelated plastic strain localization in the aluminum matrix and crack origination and growth in the matrix and ceramic particle are investigated under compression and tension, including the influence of the cooling-induced residual stresses. A detailed analysis is performed to evaluate the residual stress concentration in local regions of bulk tension formed under all-round and uniaxial compression of the composite due to the concave and convex interfacial asperities.

ODR-VS method for a high packing fraction of dispersed TRISO particles

The stochastic dispersion of tristructural isotropic (TRISO)-coated particles brings challenge for accurate modeling and neutron transport analysis. Existed explicit modeling methods for stochastic media, like the random sequential addition (RSA) method and discrete element method (DEM), have difficulties in building dispersed particles that do not touch each other with packing fractions higher than 38%. In this paper, we proposed a new packing method for this kind of model by combining the optimized dropping and rolling (ODR) method and the virtual surface (VS) method. The testing results for different packing fractions indicate that our modified ODR method can improve the packing efficiency hundreds of times compared with the DEM. In addition, we successfully construct a stochastic geometry with no touching particle configuration. In this model, the packing fraction achieves 40%, demonstrating that our method is capable of handling model of no contact dispersed particles with a packing fraction higher than 38%.

Sub‐bitstream packing based lightweight tiled streaming for 6 degree of freedom immersive video

The MPEG immersive video (MIV) has been designed to remove the redundancies of multi-view videos and merge the residuals into two textures and two geometry videos; therefore, four bitstreams and video decoders are required to provide six degrees of freedom (6DoF) in virtual reality (VR) in MIV. This letter proposes a sub-bitstream packing method and realisation in tiled streaming for MIV to provide a single bitstream for legacy systems that have a single decoder without an increase in the server-side storage. The proposed method divided the input videos into tiles in the compressed-domain to accomplish two goals: (i) generate a merged bitstream using the existing four bitstreams for legacy systems and (ii) improve the quality of an immersive video by disabling the deblocking filter. For a player with one or few numbers of decoders, the extracted tile sub-streams were merged by the proposed four specific mixed-quality tiling and packing methods using a motion-constrained tile set (MCTS), while complying with the high-efficiency video coding (HEVC) standard. Experimental results demonstrated that the proposed sub-stream packing method required a single decoder and exhibited 17.40% decoding time and 0.81% bjontegaard-delta rate (BD-rate) savings on Y-PSNR saving compared to the non-tiled streaming method.

Comparative response of Malaysian weedy rice (Oryza sativa) initial growth towards the allelopathic potential of Leucaena leucocephala (Lam.) de Wit and Dicranopteris linearis (Burm. f.) Underw

The infestation of weedy rice has becoming a threat which leads to competition between weedy rice and commercial rice for nutrient uptake. Weedy rice management is more dependent on the herbicides application that can contribute to various environmental problems. Thus, this study was conducted to assess the allelopathic potential of two invasive plants namely L. leucocephala and D. linearis in controlling the growth of weedy rice. The allelopathic potential was evaluated through laboratory bioassay namely the dish pack method, sandwich method and plant box method to determine the allelopathic potential on the seedling growth of two weedy rice variants collected in Peninsular Malaysia (designated as WRE and WRN) and MR220 CL2 rice variety. Highest concentration of L. leucocephala leaf litter (50 mg) inhibited the radicle elongation of MR220 CL2, WRN and WRE by 49 %, 51 % and 83 % compared to control, respectively. Through the dish pack method, the volatile compound from D. linearis leaf were found to disrupt the radicle growth of MR220 CL2, WRN and WRE by 21 %, 29 % and 25 % of the control at the nearest distance (41 mm) of the bioassay plants with the D. linearis leaf. Compared with D. linearis, the root exudate from L. leucocephala exhibited higher inhibitory effect towards the bioassay species. Overall, the findings from this study showed that the allelopathic potential of L. leucocephala was more preferable to be investigated further for sustainable weedy rice management in the future by infusing the biological approach.