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Biogrouting as a new method for ground improvement or seepage control has been developed and extensively studied in recent years. Most studies have focused on lab bench, small-diameter sand column tests. To simulate in situ conditions more closely, biogrouting tests on two 1 m3 models were carried out. One was for pure silica sand and another was for rock blocks filled with sand. A significant reduction in permeability and an increase in shear strength were achieved in both model tests after ten rounds of biogrout treatment. The mechanical characteristics of the biologically cemented sand were studied using samples cored from the 1 m3 model. The distribution of the calcium carbonate content and the shear strength within the 1 m3 model was studied. Correlations between the unconfined compressive strength and the permeability of the cemented sand and the calcium carbonate content were established. Suggestions of methods to improve the uniformity of the biogrouting treatment are made in this paper.

A soil column test was carried out to investigate the mechanism of multicomponent landfill gas transport in loess. Four gas components, namely methane (CH4), carbon dioxide (CO2), oxygen (O2) and nitrogen (N2), were considered. Based on the dusty-gas model and mass balance equation, a one-dimensional transient model for multicomponent gas transport in landfill layered cover soils was then developed. Methane oxidation in the soil is also considered in the model. The numerical model was solved by the finite-element method-based program Comsol Multiphysics v5.1. The numerical results agree well with those observed from the soil column tests. The parameter analysis shows that ordinary diffusion plays an important role in the transport of multicomponent gases. For methane and carbon dioxide, ordinary diffusion contributes to 97% of the total transport flux in the shallow zone. The effect of ordinary diffusion decreases with the increase in the depth. Advection contributes to 37% of the flux at the bottom of the cover soil. The effect of Knudsen diffusion is relatively weak, which only contributes 0.5–12% to the total flux. This is due to the relatively high gas permeability of the soil. The contribution of advection is comparable to that of diffusion when the gas permeability increases up to 3 × 10−13 m2. The maximum methane oxidation rate is found to increase by a factor of 1.8, when influent flux increases from 1 × 10−5 to 1 × 10−4 (mol/m2)/s. The numerical solution can be used for the analysis of multicomponent landfill gas transport in the soils and the design of landfill cover systems with respect to gas pollution control.

What does it mean today to study a problem from a computational point of view? We focus on parameterized complexity and on Column 16 “Graph Restrictions and Their Effect” of D.S. Johnson’s Ongoing guide, where several puzzles were proposed in a summary table with 30 graph classes as rows and 11 problems as columns. Several of the 330 entries remain unclassified into Polynomial or NP -complete after 35 years. We provide a full dichotomy for the Steiner Tree column by proving that the problem is NP -complete when restricted to Undirected Path graphs. We revise Johnson’s summary table according to the granularity provided by the parameterized complexity for NP -complete problems.

Deep-learning-based soft sensors have been extensively developed for predicting key quality or performance variables in industrial processes. However, most approaches assume that data are uniformly sampled while the multiple variables are often acquired at different rates in practical processes. This article designed a progressive transfer strategy, based on which a variational progressive-transfer network (VPTN) method is proposed for the soft sensor development of industrial multirate processes. In VPTN, the multirate data are first separated into multiple data chunks where the variables within each chunk are acquired at a uniform rate. Then, a variational multichunk data modeling framework is developed to model the multiple chunks in a unified fashion through deep variational structures. The base models, including the unsupervised ones with only partial process variables and the supervised soft sensor model share a similar network structure, such that the subsequent transfer strategy can be readily implemented. Finally, a progressive transfer learning strategy is designed to transfer the model parameters from the fastest sampled data chunk to the slowest one in a progressive manner. Thus, the knowledge from various data chunks can be sequentially explored and transferred to enhance the performance of the terminal soft sensor model. Case studies on both a debutanizer column dataset and a real coal mill dataset in a thermal power plant validate the performance of the proposed method.

Reliable column–column connection contributes to a reduction in waste materials and enables structures to be demountable after their service life. This paper demonstrates the feasibility of using a bolted-flange connection with concrete-filled steel tube (CFST) columns subjected to axial compression load. Compression capacity, ductility, axial stiffness and toughness, as well as failure modes are discussed. The specimens are grouped into two categories: group I—connection without stiffeners and group II—connections supported by stiffeners. Three thicknesses of 2, 4 and 6 mm were used to manufacture the bolted flanges and stiffeners. The results showed that the proposed connection was effective to avoid a reduction in compression capacity. The various thicknesses of bolted flanges in group I specimens had a slight influence on the axial stiffness value and that influence was doubled in group II specimens. The stiffeners played a significant role in the toughness value and this was reduced with a reduction in the thickness of the stiffeners. In addition, an improvement in ductility was noticeable in stiffened specimens. Consequently, it can be inferred that bolted-flange joints have the potential to produce reliable and economical designs for CFST columns.

Matrix completion, in essence, involves recovering a low-rank matrix from a subset of its entries. Most existing methods for matrix completion neglect two significant issues. First, in several practical applications, such as collaborative filtering, some samples may be corrupted completely. However, most of the robust algorithms consider only the condition that a few components of each column have been arbitrarily contaminated. Second, many real data are not static in nature. Nevertheless, the conventional batch-based matrix completion methods cannot efficiently deal with the out-of-sample, that is, the vector completion problem. In this article, we first provide a novel robust matrix completion model and then develop an efficient optimization method that only requires conducting one time singular value decomposition for a thin matrix per iteration. Furthermore, by exploiting the essence of online matrix completion algorithms, we develop a vector completion model which can help users predict the missing values of out of sample. Numerical comparisons with traditional batch-based and online matrix completion algorithms demonstrate the benefits of the proposed method on streaming data corrupted by column outliers. Moreover, we show that our model can be used to detect outliers from incomplete information. This advantage is validated via numerous experimental results on synthetic and real-world data.

Due to the extension of cities and industries and population growth, environmental pollution has become noteworthy. Heavy metals are pollutants produced by industrial factories. Therefore, wastewater should be purified and treated, and returned to natural water circulation. This research investigated the removal of heavy metal zinc (Zn) at high concentrations in Esfarayen Steel Industrial Complex and the performance of the integrated system including activated carbon adsorption column (as pretreatment) and reverse osmosis membrane system under different operating conditions. The variable parameters are pH (4.5, 6.5, 7, and 9), pressure (5, 7, 9, and 11 bar), and zinc concentration (30, 50, and 70 mg/l) to obtain zinc removal efficiency, turbidity dissolved solids (TDS), electrocoagulation (EC), and turbidity (TU) at constant temperature and flow rate. The results show that the integrated system efficiency at 9 bar pressure and pH of 7.5 is optimal as to water outlet quality. The removal efficiencies are 98.1%, TDS; EC, 97.4%; Zinc, 100%; and TU, 95.3%, which is desirable. Moreover, system efficiency at a high concentration of zinc is evaluated. According to the results, the integrated system is resistant to probable shocks, high concentration and has desirable efficiency with all parameters almost above 90%.

This paper presents a new holistic and efficient approach to big data analysis. We introduce a new parallel algorithm, known as ATrie Group Join (ATGJ), that integrates join, grouping and aggregation operations to accelerate big data analytical workloads in in-memory column-stores. ATGJ performs a single scan of the fact columns and uses a mixture of data and task parallelism for the optimal use of computing resources. It uses a novel technique to perform group-by and aggregation realising the grouping attributes as a tree shaped deterministic finite automation known as Aggregate Trie or ATrie. ATrie facilitates grouping of attributes and processing of data in tight loops that significantly improves the performance on modern hardware. Unlike other competing algorithms, use of ATrie avoids the creation of multiple data structures with the increasing number of dimension tables and grouping attributes. Also, we demonstrate that ATGJ performs efficiently even when the ATrie becomes bushy. We evaluated the algorithm using Star Schema Benchmark (SSBM) to show that it is significantly faster and scales better than other algorithms for the number of concurrent threads, the number of group-by attributes, the data set size and the query complexity.

The observation of failure surfaces within column-supported embankments is critical to understanding how the embankment stresses are transferred towards the column heads. In this study, finite element simulations utilising a strain-softening constitutive model, nonlocal regularisation, and the Arbitrary Lagrangian–Eulerian formulation are used to examine these failure surfaces over various embankment geometries. This methodology offers insights into the nature of the failure mechanism, the development of a plane of equal settlement, and the influence of the subsoil settlement profile. Depending on the embankment geometry, the results indicate a punching failure, inverted general bearing failure, or a localised failure develops. The transition between punching and inverted general bearing failure is found to be closely related to the establishment of a plane of equal settlement within the embankment. The height of the plane of equal settlement and the range of failure mechanisms that develop were largely insensitive to the nature of the subsoil settlement profiles simulated. These findings have implications for the practical design of efficient embankments and the effective design of future experimental studies.

The security of data is a major concern in this digital era. In general text and image data are well protected with the help of existing standards, meanwhile, securing voice communications is a challenging task. In the literature, there are some methods found using oscillators and chaotic signals for protecting direct voice communications, but they have limited functionalities due to synchronization problems between chaotic signals and oscillators. This paper gives an idea of encrypting voice signals over peer to peer communication with the help of a modified and lightweight AES algorithm. This algorithm is similar to the traditional AES algorithm in most of the aspects but does not imply the use of mix column that is used in the traditional AES algorithm. The lightweight algorithm is tested on two different Field Programmable Gate Array (FPGA) and the designs are analyzed with respect to time delay and effective utilization of resources.

RC-NVM (Row-Column-NVM) is a new architecture for emerging non-volatile memory (NVM) such as ReRAM, PCM and STT-RAM. It leverages the symmetry of crossbar structure and supports both row and column memory accesses. The new architecture is well fit for the applications with different access patterns which suffer from low efficiency and high energy consumption in traditional memory architecture. However, existing hard failure defensive techniques for emerging NVM are inappropriate for RC-NVM. Therefore, the limited device endurance makes RC-NVM ephemeral and unreliable. In order to overcome this challenge, we propose a hybrid-grained remapping defensive technique to fight against hard failures. By hybrid-grained remapping, we can effectively avoid multiple reads issue of fine-grained remapping and low utilization issue of coarse-grained remapping and significantly increase RC-NVM lifetime with little performance loss. Moreover, motion vector and remap-aware write optimizations are also proposed to further improve RC-NVM reliability and degrade performance loss and energy consumption of write operation. Evaluation shows that the hybrid-grained remapping can increase the lifetime by 61.1% compared to single coarse-grained remapping with only 5% performance loss and 7% energy increase when 10% pages are mapped out. Optimizations can further improve lifetime, performance and energy by 5%, 11.4% and 30.5% respectively.

Volcanic ash advisories are produced by specialised forecasters who combine several sources of observational data and volcanic ash dispersion model outputs based on their subjective expertise. These advisories are used by the aviation industry to make decisions about where it is safe to fly. However, both observations and dispersion model simulations are subject to various sources of uncertainties that are not represented in operational forecasts. Quantification and communication of these uncertainties are fundamental for making more informed decisions. Here, we develop a data assimilation technique which combines satellite retrievals and volcanic ash transport and dispersion model (VATDM) output, considering uncertainties in both data sources. The methodology is applied to a case study of the 2019 Raikoke eruption. To represent uncertainty in the VATDM output, 1000 simulations are performed by simultaneously perturbing the eruption source parameters, meteorology and internal model parameters (known as the prior ensemble). The ensemble members are filtered, based on their level of agreement with Himawari satellite retrievals of ash column loading, to produce a posterior ensemble that is constrained by the satellite data and its uncertainty. For the Raikoke eruption, filtering the ensemble skews the values of mass eruption rate towards the lower values within the wider parameters ranges initially used in the prior ensemble (mean reduces from 1 Tg h−1 to 0.1 Tg h−1). Furthermore, including satellite observations from subsequent times increasingly constrains the posterior ensemble. These results suggest that the prior ensemble leads to an overestimate of both the magnitude and uncertainty in ash column loadings. Based on the prior ensemble, flight operations would have been severely disrupted over the Pacific Ocean. Using the constrained posterior ensemble, the regions where the risk is overestimated are reduced potentially resulting in fewer flight disruptions. The data assimilation methodology developed in this paper is easily generalisable to other short duration eruptions and to other VATDMs and retrievals of ash from other satellites.

This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids. The core contribution is to propose a probabilistic forecast-driven strategy, modeled as a min-max-min robust optimization problem with recourse. It is solved using a Benders-dual cutting plane algorithm and a column and constraints generation algorithm in a tractable manner. A dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution is proposed to improve the results. A secondary contribution is to use a recently developed deep learning model known as normalizing flows to generate quantile forecasts of renewable generation for the robust optimization problem. This technique provides a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations. Overall, the robust approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies. The case study uses the photovoltaic generation monitored on-site at the University of Liege (ULiege), Belgium.

We provide a new mathematical framework for the classic problem of fair allocation of indivisible goods, showing that it can be formulated as the problem of finding an optimal column rearrangement of multiple matrices. Based on this formulation, we design two novel algorithms called MinCov and MinCovTarget to find optimal allocations under the newly introduced notion of minimum social inequality, and the popular notion of minimum envy. Numerical illustrations show an excellent performance of the newly developed algorithms also with respect to other allocation criteria, in particular for the maximum Nash welfare.

Using a fast dynamic loading method, six Chinese traditional style lintel-column joints were tested and studied, and seismic performance indicators of the joints were obtained. The results indicate that the seismic behavior of Chinese traditional style architecture with the lintel-column joint was superior to that of the contemporary beam-column connection, and the load-bearing capacity and dissipation energy capacity of specimens with viscous dampers were superior to those of specimens without viscous dampers. Compared with single lintel-column joints, dual-lintel-column joints exhibit higher bearing capacity stiffness and fuller hysteretic curves. The dual-lintel-column structure located in the outer eave column plays a role similar to that of the ring beam, which could enhance the overall performance of the structure. A series of damage models was used to evaluate the entire process damage for the six specimens. Furthermore, the results and proposals not only benefit the further development of Chinese traditional style structures but also have reference value for the design, construction, and research of Chinese traditional style structures.

In this study, the periodic train timetabling problem is formulated using a time-space graph formulation that exploits the properties of a symmetric timetable. Three solution methods are proposed and compared where solutions are built by what we define as a dive-and-cut-and-price procedure. An LP relaxed version of the problem with a subset of constraints is solved using column generation where each column corresponds to the train paths of a line. Violated constraints are added by separation and a heuristic process is applied to help to find integer solutions. The passenger travel time is computed based on a solution timetable and Benders’ optimality cuts are generated allowing the method to integrate the routing of the passengers. We propose two large neighborhood search methods where the solution is iteratively destroyed and repaired into a new one and one random iterative method. The problem is tested on the morning rush hour period of the Regional and InterCity train network of Zealand, Denmark. The solution approaches show robust performance in a variety of scenarios, being able to find good quality solutions in terms of travel time and path length relatively fast. The inclusion of the proposed Benders’ cuts provide stronger relaxations to the problem. In addition, the graph formulation covers different real-life constraints and has the potential to easily be extended to accommodate more constraints.

The healthcare systems affiliated with the Mental Health Research Network strive to be learning healthcare systems that identify and address evidence gaps of importance to clinicians, patients and funders. This paper describes how research guides clinical care and how clinical care guides research in the area of suicide prevention, and some of the challenges of conducting embedded research. The transformation of healthcare systems into learning healthcare systems - where science, informatics, incentives and culture align for continuous improvement - could improve clinical care and decrease delays in implementing best practices. However, it can be challenging to ensure the necessary elements are present: a well-developed infrastructure to organize and analyze health records data, a culture of shared responsibility, and an organizational philosophy promoting bidirectional learning between healthcare practice and research. Nonetheless, strengthening research-practice partnerships to accelerate the adoption, implementation and improvement of evidence-based mental healthcare is a priority for federal funding agencies and healthcare organizations. This paper discusses the benefits and challenges of establishing and maintaining engaged clinical partnerships in the area of suicide prevention.

In this European Public Health news, health is put at the centre of society as it is impacting us from birth until death. Jakab emphasizes the importance of investing in health over the life-course, whilst McKee demonstrates that the whole society impacts health. A strong, unified health care system and seeing healthy people as the cornerstone of healthy economies is the key message in the contribution by Andriukaitis, the newly elected EU Commissioner for Health and Food Safety. Making the voice of public health visible, both nationally and internationally and facilitating a strong and unified voice is what Zeegers and Barnhoorn see as the core activities of EUPHA and the EPH Conference Foundation. # President's Column {#article-title-2} A new year is a time for both reflection and anticipation. Reflection on what has gone well, or not so well in the past year and anticipation of the challenges and opportunities we will face in the year ahead. For EUPHA, we have much to celebrate from the past year and much to look forward to in 2015. In my last column, I wrote about our conference in Glasgow, where a combination of excellent organization and outstanding content created an event that will be remembered for many years. Now we are looking forward to Milan, with a programme that promises to be every bit as imaginative and informative. One of the streams will be on communication and advocacy in public health, something that I am particularly looking forward to, given its importance in making the world a better place. We have a new President Elect, Natasha Muscat, who is already making an invaluable contribution to our work, stimulating our thinking about how we can take EUPHA to ever greater heights. We also have a new website and logo, two things among many for which we are grateful …

Concrete filled double-skin steel tubular (CFDST) column comprises two inner and outer steel tubes with infill concrete between tubes. CFDST columns are used in many structural systems such as offshore structures and high rise buildings. The aim of this research is to examine the performance of composite frames composed of CFDST columns and steel beam under the influence of lateral loading. The frames were modeled and analyzed utilizing ANSYS finite element (FE) software. The linear and nonlinear behavior of steel and concrete materials and confinement effects of inner and outer steel tubes on the infill concrete were considered in the analysis. Three key parameters were considered in the present study. They are the axial load and slenderness ratios of CFDST column as well as linear stiffness ratio of the beam–column. The effects of these parameters on the behavior of the composite frames were evaluated comparatively. Load-deformation responses were achieved for various cases of the investigation. The verification of the developed FE model was evaluated by considering the analysis results with the experimental data existing in the literature. The findings attained from the FE modeling were in consonance with the experimental results. Besides, it was observed that the above parameters had a substantial influence on the load-displacement relationship and the performance of the studied composite frames.

Along the progress of the era and technological developments, making the human lifestyle cannot be separated from its use. In digital era nowadays, technology is used to make human needs easily. A website is important for the progress of an Association. One of the ways that the Association's Website can grow is by maintaining the Website. The APTISI website is designed using the WordPress CMS (Content Management System). WordPress is an open system software and designed by the PHP programming language and MySQL database. This study is aim to develop a website by increasing the credibility of the association. In order to improve the statistics on website visits, it is necessary to have innovation and creativity in viewing and posting news on the website. The APTISI website make use of probability calculations and statistical data using a monitoring tool called statcounter. This tool functions to count the number of visitors based on unique visits which can be seen in the statistics column. In addition, the use of this statcounter is useful as a standard reference for website admins to update the viewboard on a better website in the future.standard for website admins to update the appearance of a better website in the future

This paper concentrates on the impact of stochastic function perturbation (SFP) on the set stability of PBNs. The problem of robust set stability is divided into two cases, and they are respectively the perturbed column of the structure matrix does not belong to /belongs to the stabilizing set. For the first case, two necessary and sufficient conditions are obtained to guarantee that the system can keep finite-time stable unchanged and the system becomes asymptotically stable, respectively. For the second case, i.e., the perturbed column in the stabilizing set, the condition which can keep the stability of the system unchanged is obtained. Lastly, an example is provided to illustrate the obtained theoretical results.

A 30-yr time series (4 February 1990–4 February 2020) of aerosol optical depth (AOD) of the atmosphere, total precipitable water (TPW), and total column ozone has been conducted in central Texas using simple, highly stable instruments. All three parameters in this ongoing measurement series exhibited robust annual cycles. They also responded to many atmospheric events, including the historic volcanic eruption of Mount Pinatubo (1991), a record El Niño (1998), an unprecedented biomass smoke event (1998), and La Niña that caused the driest drought in recorded Texas history (2011). Reduced air pollution caused mean AOD to decline from 0.175 to 0.14. The AOD trend measured for 30 years by a light-emitting diode (LED) sun photometer, the first of its kind, parallels the trend from 20 years of measurements by a modified Microtops II. While TPW responded to El Niño–Southern Oscillation conditions, TPW exhibited no trend over the 30 years. The TPW data compare favorably with 4.5 years of simultaneous measurements by a nearby NOAA GPS (r2 = 0.78). The 30 years of ozone measurements compare favorably with those from a series of NASA ozone satellites (r2 = 0.78). In 2016, 194 comparisons of Microtops II and world standard ozone instrument Dobson 83 at the Mauna Loa Observatory agreed within 1.9% (r2 = 0.81). The paper concludes by observing that students and citizen scientists can collect scientifically useful atmospheric data with simple sun photometers that use one or more LEDs as spectrally selective photodiodes.

During the seismic event, the structure will starts yield and it cracks. This leads in the reduction of its cross-sectional area, gross moment of inertia, etc., which affects the structural stiffness. In this study, the efficiency of a building in terms of its performance level, direct economic losses, and the seismic resilience with reduction in the column stiffness was examined. A high-rise G+10 storey reinforced concrete building was considered in this study. In order to consider the cracked section to evaluate the structural safety in terms of the seismic resilience, the column section stiffness was reduced with respect to the gross moment of inertia of the section. To incorporate this reduction in the gross moment of inertia, the stiffness modifier in SAP2000 software was used. As per Indian Standard (IS: 1893-2016), the gross moment of inertia of the column can be taken as 70% for the structural analysis, hence in this study the two various cases with 70% and 100% of the gross moment of inertia of the column section was compared to find the effect of stiffness reduction in the structural functionality. Using trigonometric recovery functions, the corresponding difference in seismic resilience was evaluated. The result shows that there was a significant change in the performance level but not much dropdown in the functionality of the building and demand ductility.

Primarily, columns carry axial loads, and therefore, these are designed for compression. Other loads from snow, wind, or earthquake forces can cause bending in the columns. Columns then need to be developed for Axial Load and Bending. The cost-effective design of columns is the biggest problem when we are going to design a building. The optimal design of a building depends on the costs of all three reinforced concrete elements: beam, column, and slabs. These all should also be designed with optimal. This research work aims to find the minimum cost of RCC column subjected to two types of loadings i,e axial loading and uniaxial moments. The objective of this research work was to reduce the total price of RCC column after applying all the design guidelines according to BIS codes 456:2000 (without ductile detailing) and 13920;1993 (with ductile detailing). Generally, we cannot mark a structure with a minimum cost but it should also design according to the Indian standard codes. The cost comparison of RCC column results, taken according to BIS codes, has been carried out to check the differences in the price of both cases. We found that in the case of 4 m column length, the axial load, moment, and grade of steel values are 1200kN, 200kNm, and 500 N/mm2 respectively. The variation in cost is 11%-12% for all the phases of concrete. We also found that reinforcement detailing cost variation in 9 m and 12 m column lengths is significantly less. The cost percentage of optimal cost of RCC column is reduced when force and moment are increased. The Genetic Algorithm and Matlab software has been used in this research work.

In recent years, an increasing online shopping demand has greatly promoted the innovation and development of the fashion industry. Visual fashion analysis has become a prospective research topic in computer vision and multimedia fields. Among these studies, fashion compatibility analysis is required in many real-world applications, such as fashion recommendation, matching, and retrieval. However, learning fashion compatibility is nontrivial, not only due to the uncer- tain and sparse dependencies among fashion items but also the latent and mutual associations among multiple factors such as color, texture, style, and functionality. To address these issues, in this paper, we proposed a tripartite graph regularized latent low-rank representation method, named TGRLLR, for fashion compatibility prediction. In TGRLLR, to learn more low- dimensional and effective representations, we considered the latent low-rank representation by decomposing the original feature matrix in both the column and row directions to tackle the problem of insufficient observations. On this basis, we simultaneously exploited different regularization strategies to encode the structured correlations among features, the high-order relationships among items, and the geometrical structures of outfits for more informative representations. Extensive experiments conducted on a real-world dataset demonstrate the effectiveness of our proposed method compared with state-of-the-art methods.

The evolution of high-performance liquid chromatography is reviewed with an emphasis on the innovations that occurred in the technique in response to the sample needs. The general themes are the development and/or applications of the basic theory as catalyst for change, invention of new chromatographic modes, evolution of column technology, and the development and improvement of instrumentation. Steady progress in these areas rather than sudden change is responsible for the column chemistries, particle technology, instrumentation, and data handling tools available today.

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A new progressive solution is considered, which consists in improving the design and static calculation of the strength of the elements of the sub-column part of a pipe-concrete foundation, in simplifying the design of a sub-column made of structural concrete of increased and high strength and a thin-walled metal pipe, using it in the form of a permanent formwork as a working element of a composite structure, in reduction of labor intensity and terms of performance of works of the zero cycle. An algorithm is proposed for designing elements of a precast-monolithic reinforced concrete foundation for a column, including a pipe-concrete sub-column, its fl ange connection with a steel column from above and with a foundation slab from below. Thin-walled pipe metal is an economical casing for in-situ concrete.

We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the differential optical absorption spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows for providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1 ×1014–3 ×1014 molec. cm−2 (∼30 %–70 % in emission regimes) and originate mostly from a priori data uncertainties and spectral interferences with other absorbing species. The latter may be at the origin, at least partly, of an enhanced glyoxal signal over equatorial oceans, and further investigation is needed to mitigate them. Random errors are large (>6×1014 molec. cm−2) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of detail. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-AXis DOAS (MAX-DOAS) instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appear to be biased low during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences of less than 1×1014 molec. cm−2. A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is high.

The increasing size of modern field programmable gate arrays (FPGAs) allows for ever more complex applications to be mapped onto them. However, long design implementation times for large designs can severely affect design productivity. A modular design methodology can improve design productivity in a divide and conqueror fashion but at the expense of degraded performance and power consumption of the resulting implementation. To reduce the dominant power dissipation component in FPGAs, the routing power, methodologies have been proposed that consider data communication between modules during module formation and placement on the FPGA. Selecting proper mapping region on target FPGAs, on the other hand, is becoming a critical process because of the heterogeneous resources and column arrangements in modern FPGAs. Selecting inappropriate FPGA regions for mapping could lead to degraded performance. Hence, we propose a methodology that uses communication-aware module placement, such that modules are mapped by selecting the best shape and region on the FPGA factoring the columnar resource arrangements. Additionally, techniques for module locking and splitting have been proposed for deterministic convergence of the algorithm and for improved module placement. This methodology exhibits nearly 19% routing power reduction with respect to commercial CAD flows without any degradation in achievable performance.

Hybrid Reactive Distillation Configurations (HRDCs), with single feed/multi-side draws having both reactive and non-reactive sections, can be effectively used to get the desired selectivity in multi-reaction systems. Our previous research (Hasan et al., 2013a,b, 2014, 2015; Singh et al., 2018, 2019, 2021) was focused on finding designs of hybrid RD columns of desired selectivity for complex reaction schemes without inerts. In the present work, we extend the approach to cover a wider range of multi-reaction schemes and include systems with inerts present in feed as well as inert forming multi-azeotropes in the column. Using the concepts of attainable region and graphical simulation approach, we developed algorithms that are capable of finding at least one feasible design of hybrid RD configuration successfully that give the desired selectivity in the systems under study. Further, the effect of inert percentage in feed and volatility of inert on the design of hybrid RD columns is also investigated. The developed methodology allows us to find the maximum allowable percentage of inerts in feed to obtain a feasible design of hybrid RD column. The methodologies developed here are applicable to any number of inerts present in the feed, any number of total components, inerts of any volatility, inerts forming multi-azeotropes with any of the reactant, product/desired product. However, they are limited to single feed configurations and to the multi-reaction schemes with a maximum of three products undergoing further side reactions wherein the reactant(s) is/are of intermediate volatility.

The prefabricated frame-bridge is a new type of structure in response to the problem of difficult land acquisition in the expressway reconstruction and expansion. Considering that the pre-stressed columns are the main load-bearing components, which are difficult to be repaired after damage, so an energy dissipation member, i.e. elastoplastic column-deck joint (EC-DJ) is developed to connect the deck and column. In this paper, the impacts of EC-DJ on the failure probability of pre-stressed columns are investigated through the fragility analysis methodology. Firstly, the damage indexes under different states of EC-DJ are defined by the fiber section model. Then the optimal intensity measures (IMs) of probabilistic seismic demand models for components subjected to far-field ground motions are identified. Moreover, a full-scale experiment model is carried out to verify the correctness and applicability of the finite element model. Considering that the ductility of EC-DJ can be fully utilized to protect the column, the influence of EC-DJ on the structure is investigated accounting for different configurations of EC-DJ including steel ratio, section size, and reinforcement ratio. The results show that the improved EC-DJ can be used as “fuse member” in the structure, which can dissipate much seismic energy for the prefabricated frame-bridge so the pre-stressed columns can be protected. Furthermore, it is an economical connection with repeatability, which is convenient to be repaired after damage.

Strong orthogonal arrays enjoy more attractive space-filling properties than ordinary orthogonal arrays for computer experiments. In this paper, we propose two methods for constructing column-orthogonal nearly strong orthogonal arrays. These designs enjoy column orthogonality, inherit the attractive two-dimensional space-filling property of strong orthogonal arrays, and can accommodate twice or more number of factors than the existing strong orthogonal arrays. In addition, the proposed designs with four levels enjoy an attractive space-filling property under the maximin distance criterion. The construction methods are convenient and flexible, and the resulting designs are useful in computer experiments.

Connections are essential constituents in the behavior of a moment resisting frame. There are many different detailing schemes for steel connections, most of which involve welding. In many of these connections, it is advised to use continuity or doubler plates to improve the behavior of the panel zone. Despite enhancing the connection’s behavior, using these plates is accompanied by some drawbacks, primarily due to the welding process. What this study aims to achieve is the numerical and experimental assessment of new detail for the panel zone of the beam-to-column endplates connections. In the proposed detail, the need for welding on the column in the panel zone has been thoroughly removed. A stiffened channel profile carries out the role of continuity and doubler plates. The channel is connected to the column using nuts and bolts. First, a numerical model of the proposed panel zone detail was analyzed in ABAQUS. Then, based on the numerical results, three samples were constructed and subjected to cyclic loading in a full-scale experimental setup until degradation was seen in the hysteretic diagrams. The first model was the control sample, which was built based on the specifications of the prequalified Bolted Stiffened Extended End Plate-Four Bolt Extended Stiffened (BSEEP-4ES) connection recommended by the AISC 358-16 standard. The other two samples, which were built using a channel profile with multiple stiffeners, were used as replacements for the control sample. After obtaining and evaluating the results, it was revealed that under the applied load, all three samples satisfied the seismic criteria for the special moment resistant connection. Compared to the control sample, not only did the new details exhibit a better seismic behavior, they were also more convenient to implement.

Recent studies show that the evaluation of structures in the energy domain has attracted some researchers’ attention in the field of progressive collapse. Column buckling and failure energy were introduced as the energy-based criteria and can be obtained from the single-column displacement controlled analysis for progressive collapse assessment of steel structures. Accordingly, the structural performance following the columns removal scenarios can be evaluated by comparing the column’s internal energy to the energy criteria. In this paper, three-dimensional numerical simulations are conducted to investigate the buckling and post-buckling behavior of the box-section steel columns and derive the energy criteria. In addition to the simplified single-column model, to evaluate the role of beams on the derivation of the energy criteria, beams with different sections are explicitly considered in the finite element models according to the column location. The results show that the lack of explicit modeling of beams in the analysis, depending on the column’s permanent load, can lead to conservative and non-conservative results in deriving the energy-based criteria for progressive collapse assessment of steel structures.

The middle vessel batch distillation column is an alternative to the regular batch distillation column. This configuration allows simultaneous separation of the light fraction (accumulated at top), heavy fraction (accumulated at bottom) and the intermediate fraction (accumulated in the middle vessel) in a single column. The objective of this article is to extensively analyse and discuss different control systems for the middle vessel batch distillation column (MVBDC) and dynamically optimize the column. Three control structures such as composition level cascade, composition temperature cascade, and temperature control system, have been tested and evaluated. Further, a dynamic optimization study has been performed on the control system providing the fastest separation. The optimizer is set to minimize the total energy consumed during the process. This resulted in a decrease in batch time from 26.8 to 25.2 h and a 13.5% decrease in overall energy usage. The presented dynamic optimization technique and control system design is useful for improving the performance of the MVBDC.

This study aims to evaluate pounding effects on typical building structures considering soil-structure interaction. For this purpose, 4 different adjacent building combinations were created by using 4, 8, 12 and 16 story building models. Nonlinear time history analyses were performed for 15 different ground motion records and building row effects were taken into consideration. The results indicate that, displacement demand tend to decrease in collision direction and increase in free direction. Maximum IDR demands are increasing mostly due to impact effects. However, the biggest change is in the story drift pattern and damage mechanism. Plastic rotations are shifted between column and beam members throughout building height. This behavior is apparent for all models. Maximum roof drift demands obtained for fixed-base approach reasonably close to soil-structure interaction models in average. On the other hand, the effect of modelling is obvious for pounding cases when the maximum IDR values are evaluated. Results obtained for ground motion record show large scatter due to the nature of dynamic analysis. For this reason, it is not meaningful to make a general assessment using single or few numbers of record.

In this, the second “Games” column, we look at the possibility of developing sensor-based games. We do this from a high level, pointing out that all the pieces exist but have not yet been put together.

The use of granular column is one of the ground improvement methods used for soft soils. This method improves the foundation soils mechanical properties by displacing the soft soil with the compacted granular columns. The columns have high permeability that can accelerate the excess pore water pressure produced in soft soils and increase the undrained shear strength. When it comes to very soft soils, the use of granular columns is not of interest since these soils present no significant confinement to the columns. Here comes the encased columns that receive the confinement from the encasement materials. In this study, the influence of the column installation method on the surrounding soil and the encasement effect on the granular column performance were investigated using numerical analyses and experimental tests. The results show that numerical simulations can reasonably predict the behavior of both the encased column and the surrounding soil.

In this study, numerical simulations were carried out to investigate the separation of the helium-argon gas mixture by thermal diffusion column. This research determined the significant parameters and their effects on the process performance. Effects of feed flow rate, cut ratio, and hot wire temperature in a 950 mm height column with an inner tube of 9.5 mm radius were examined through the simulation of the thermal diffusion column. For minimizing the number of simulations and obtaining the optimum operating conditions, response surface methodology (RSM) was used. Analysis of separative work unit (SWU) values as a target function for helium-argon separation clearly showed that the maximum amount of SWU in thermal diffusion column was achieved, when hot wire temperature increased as large as technically possible, and the feed rate and cut ratio were equal to 55 Standard Cubic Centimeters per Minute (SCCM) and 0.44, respectively. Finally, the SWU value in optimum conditions was compared with the experimental data. Results illustrated that the experimental data were in good agreement with simulation data with an accuracy of about 90%.

The main objective of this paper is to design and implement High throughput, area efficient AES encryption algorithm on FPGA for security purposes. The high speed and area efficiency is achieved by designing an area efficient and high throughput structural S-Box architecture for AES encryption process. A combinational logic circuit based Rijndael Low area high speed S-Box is designed. The results compared with conventional S-Box. The speed and propagation delay of the S-Box are calculated for the designed S-Box. The proposed S-Box is used in AES encryption process. To further increase the speed of the encryption process efficient structure of Mix column is also proposed. This Mix column structure is used to achieve high throughput. The proposed S-Box and Mix Column are used in AES encryption. This proposed AES structure is implemented in FPGA Virtex 5.The results are compared with the conventional implementation.

Small-angle X-ray scattering (SAXS) of proteins in solution has become a key tool for biochemists and structural biologists, thanks especially to the availability of beamlines with high-throughput capabilities at synchrotron sources. Despite the large spectrum of scientific disciplines tackled on the SWING beamline since its opening in 2008, there has always been a strong commitment to offering state-of-the-art biological SAXS (BioSAXS) instrumentation and data reduction methods to the scientific community. The extremely reliable in-vacuum EigerX-4M detector allows collection of an unlimited number of frames without noise. A small beamstop including a diamond diode-based monitor enables measurements of the transmitted intensity with 0.1% precision as well as a q max/q min ratio as large as 140 at a single distance. The parasitic scattering has been strongly reduced by the installation of new hybrid blades. A new thermally controlled in-vacuum capillary holder including fibre-optics-based spectroscopic functionalities allows the simultaneous use of three spectroscopic techniques in addition to SAXS measurements. The addition of a second high-performance liquid chromatography (HPLC) circuit has virtually eliminated the waiting time associated with column equilibration. The easy in-line connection of a multi-angle light scattering spectrometer and a refractometer allows for an independent determination of the molecular mass and of the concentration of low-UV-absorption samples such as detergents and sugars, respectively. These instrumental improvements are combined with important software developments. The HPLC injection Agilent software is controlled by the SAXS beamline acquisition software, allowing a virtually unlimited series of automated SAXS measurements to be synchronized with the sample injections. All data-containing files and reports are automatically stored in the same folders, with names related to both the user and sample. In addition, all raw SAXS images are processed automatically on the fly, and the analysed data are stored in the ISPyB database and made accessible via a web page.

A least-squares bilinear clustering framework for modelling three-way data, where each observation consists of an ordinary two-way matrix, is introduced. The method combines bilinear decompositions of the two-way matrices with clustering over observations. Different clusterings are defined for each part of the bilinear decomposition, which decomposes the matrix-valued observations into overall means, row margins, column margins and row–column interactions. Therefore up to four different classifications are defined jointly, one for each type of effect. The computational burden is greatly reduced by the orthogonality of the bilinear model, such that the joint clustering problem reduces to separate problems which can be handled independently. Three of these sub-problems are specific cases of k-means clustering; a special algorithm is formulated for the row–column interactions, which are displayed in clusterwise biplots. The method is illustrated via an empirical example and interpreting the interaction biplots are discussed. Supplemental materials for this paper are available online, which includes the dedicated R package, .