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In this paper, a dual-layer grid comprehensive resource optimizing allocation model is proposed, which considers power quality controlling and load optimization scheduling under large-scale application of distributed electric heating. The upper-layer planning aims to minimize the active power loss of the distribution network, the minimum voltage deviation, and the minimum investment cost of the power quality control device. The capacity configuration of the management device and the number and location of the commutation switch configuration were determined. The lower layer is load optimization scheduling, with the minimum number of action switches and the minimum three-phase imbalance as the planning goals, and the decision variable is the state of the commutation switch. By co-simulation through Matlab and OpenDSS, the improved particle swarm algorithm and genetic algorithm are used for multi-objective optimization and the solution. In this way, the capacity configuration of reactive power compensation and active filter, as well as the installation position and switch state of the commutation switch are optimized and managed. Finally, taking the rural low-voltage distribution network in the Tongzhou District as an example, simulations considering the variation in the distributed electric heating penetration rate in the range of 20–80% are carried out. The calculation example results show that the proposed algorithm is effective, can effectively improve the power factor, reduces the harmonic content of the distribution network and the three-phase unbalance, and significantly improves the distribution network voltage. [ABSTRACT FROM AUTHOR]

Black silicon, which is an attractive material due to its optical properties, is prepared mainly by laser inducing in an SF6 atmosphere. Considering the effect of SF6 gas on the environment and human health, here we propose an efficient, economical, and green approach to process large-scale black silicon. In the wavelength range of 0.3–2.5 µm, the role of air could replace SF6 gas to texture black silicon by laser inducing with appropriate processing parameters. Then, to extend the working window of its excellent light-trapping status, laser-plasma shockwave cleaning was introduced to eliminate the deposition and improve the structures and morphology. The results revealed that the micro-nano structures became higher, denser, and more uniform with increasing cleaning times and deteriorating cleaning velocity, which compensated for the role of S atoms from the ambient SF6. Moreover, absorptance above 85% in the wavelength range of 0.3–15 µm was realized using our method. The effect of scanning pitch between adjacent rows on large-scale black silicon was also discussed. Our method realized the ultrahigh absorptance of large-scale black silicon fabricated in air from visible to mid-infrared, which is of significance in the field of optoelectronic devices. [ABSTRACT FROM AUTHOR]

Sulfur dioxide (SO2) is a major atmospheric pollutant and abiotic stressor. Although physiological studies on abiotic stressors have focused on fully expanded leaves, the resistance of leaf functional traits to SO2 during individual leaf development has not been studied. Thus, this study aimed to conduct SO2 static artificial fumigation experiments to evaluate changes in leaf functional traits and resistance to SO2 for three common landscape tree species (Syringa oblata Lindl. (S. oblata), Prunus cerasifera var. atropurpurea Jack. (P. cerasifera), and Ulmus pumila 'Jinye' (U. pumila)) in Changchun City and ontogeny under SO2 stress. Samples were collected on three days in autumn (1 September, 9 September, and 19 September 2019) for two different leaf stages (10 days and 40 days). In addition, remote sensing data were combined to explore the resistance mechanisms of broadleaf forests to different SO2 concentration classes during different seasons on a large scale. The results showed that the chlorophyll content, leaf temperature, green-peak reflectance, and Fv/Fm (maximal photochemical efficiency) at 10 days were significantly lower than that at 40 days, regardless of sampling date or SO2 concentration. Additionally, in general the SO2 resistance for 10 days leaves was consistently smaller than those for 40 days leaves in 3 tree species. On 9 September, 10 days leaves of the three tree species showed different leaf resistance performances under different SO2 concentrations in the order: P. cerasifera > S. oblata > U. pumila. Lastly, the extent of resistance decreased with increasing ρ(SO2) classes in different seasons, and the SO2 resistance was affected by season. We conclude that mature leaves are more resistant to SO2 stress than young leaves are. These results will provide scientific guidance on artificial plant community construction and prevention of future vegetation degradation. [ABSTRACT FROM AUTHOR]

It is the first time in China to build large-scale water intake pump house facilities on the sea.There is no case to follow for the selection of platform elevation, standard to be used, structure type and construction technology of large-scale water intake pump house structure. Based on the application in practical engineering and the construction conditions of the project, and under the standard requirements of functional, safety and implementability of structural facilities, the platform elevation, structure and foundation type were reasonably determined from the perspective of the integration of design and construction, meanwhile, the key elements for the feasibility of structural construction, the technical application and control requirements of mass concrete pouring and raked rock-socketed pile construction were further explored. The complete set of structural design and construction technology of large-scale offshore water intake platform has been proved feasible and reliable by engineering practice, which can provide beneficial reference for the construction of similar large-scale offshore water intake platform. [ABSTRACT FROM AUTHOR]

• The dynamic stiffness matrix method is used for seismograms synthesis of layered transversely isotropic half-space. • The proposed method is suitable for large-scale and high-frequency cases. • The point sources are equated to the external loads of the auxiliary surfaces. • The numerical results show a 10.91% deviation in peaks distribution and 120% of amplitude since ignoring anisotropy. Natural rocks and soils show significant anisotropic behavior owing to sedimentation and weathering, particularly apparent in the crust. However, seismograms synthesis for the anisotropic medium is scarce, especially bottlenecks for large-scale and broadband seismic wave propagation. In this paper, seismogram synthesis for a multi-layered transversely isotropic half-space due to seismic point sources is addressed by using a dynamic stiffness matrix method. The critical step of the proposed method lies in the construction of the global dynamic stiffness matrix in frequency-wavenumber domain, which avoids the accumulation of errors that appeared in the reflection and transmission matrix method. Exponential divergent terms related to wavenumber and layer thickness in dynamic stiffness matrix are extracted for fast simulating large-scale and high-frequency wavefield. The seismic point sources are equated to the external loads imposed at the introduced auxiliary surfaces, through which the dynamic stiffness matrix can be utilized to obtain the solutions. The accuracy is verified by comparing the proposed method results with three examples in the publications, involving different source types and depth scales. Two numerical examples regarding Thomsen parameters are given to illustrate the mechanism of wave propagation and ground motion in an anisotropic media. Numerical results show that the anisotropic characteristics affect the wave propagation time, waveform and wave-front energy distribution, further leading to shear wave splitting phenomena, which remarkably differ from the isotropic case. A case study using a simplified three-layered crustal structure model in Tibet elucidates that peak ground displacement and its distribution difference within the epicenter of 10 km by comparing with the equivalent isotropic model without considering anisotropic properties. The results show a 10.91% deviation in the location of the peaks, with amplitude differences of up to 120% due to a 45° dip-slip source at different frequencies (from 1 Hz to 15 Hz). [ABSTRACT FROM AUTHOR]

We present a new approach to designing concretely efficient MPC protocols with semi-honest security in the dishonest majority setting. Motivated by the fact that within the dishonest majority setting the efficiency of most practical protocols does not depend on the number of honest parties, we investigate how to construct protocols which improve in efficiency as the number of honest parties increases. Our central idea is to take a protocol which is secure for n - 1 corruptions and modify it to use short symmetric keys, with the aim of basing security on the concatenation of all honest parties’ keys. This results in a more efficient protocol tolerating fewer corruptions, whilst also introducing an LPN-style syndrome decoding assumption. We first apply this technique to a modified version of the semi-honest GMW protocol, using OT extension with short keys, to improve the efficiency of standard GMW with fewer corruptions. We also obtain more efficient constant-round MPC, using BMR-style garbled circuits with short keys, and present an implementation of the online phase of this protocol. Our techniques start to improve upon existing protocols when there are around n = 10 parties with h = 4 honest parties, and as these increase we obtain up to a 13 times reduction (for n = 400 , h = 120 ) in communication complexity for our GMW variant, compared with the best-known GMW-based protocol modified to use the same threshold. [ABSTRACT FROM AUTHOR]

Among the flooding-based time synchronization protocols in wireless sensor networks, rapid-flooding outperforms slow-flooding in terms of synchronization speed. However, sometimes it is not realistic to apply rapid-flooding in dense or low-duty cycle networks due to its operational constraint that timing messages must be forwarded immediately after being received. On the contrary, slow-flooding protocols are free from this operational constraint, but they have a fundamental limitation that their convergence time increases with the size of the network. In this letter, we prove that this limitation can be overcome by sharing the compensated skew and timing information to the next hop nodes so that they can all be synchronized in the same round. The evaluation results demonstrate that we can achieve a comparable convergence speed of rapid-flooding protocols with the slow-flooding-based mechanism and can provide better accuracy for network-wide and per-hop synchronization. [ABSTRACT FROM AUTHOR]

The main focus of the current research is the development of high-performance fiber-reinforced cementitious composites with large amounts of coarse aggregates without risking deflectionhardening response, and the evaluation of the autogenous selfhealing capability of these composites at different scales. The structural performance of cementitious composites exhibiting strain hardening should be known to be used in large-scale specimens. In addition to the studies carried out in small sizes, there is a need to examine the self-healing performances of large-scale specimens. Composite mixtures included different design parameters--namely Class F fly ash-to-portland cement ratio (FA/PC = 0.20, 0.70), aggregate-cementitious materials ratio (A/CM = 1.0, 2.0), addition/type of different fibers (for example, polyvinyl alcohol [P], nylon [N], and hooked-end steel [S] fibers), addition/type of nanomaterials (for example, nanosilica [NS] and nanoalumina [NA]) and inclusion of steel reinforcing bar in tested beams. Small-scale (80 x 75 x 400 mm [3.15 x 2.96 x 15.76 in.]) and large-scale beams (100 x 150 x 1000 mm [3.94 x 5.91 x 39.4 in.]) were produced and considered for performance comparison. Four-point bending tests were performed on different-scale beams loaded by considering different shear span-effective depth ratios (a/d) ranging between 0.67 and 2.00 and 0.67 and 2.96 for small- and large-scale beams, respectively. Autogenous self-healing evaluation was made using different-scale beam specimens subjected to 30-day further cyclic wetting-and-drying curing in terms of changes in microcrack characteristics and recovery in flexural parameters of preloaded beams. Experimental results showed that it is possible to successfully produce concrete with large amounts of coarse aggregates without jeopardizing the deflection-hardening response both at small and large scale. Autogenous self-healing is valid for small- and largescale beams in terms of crack characteristics/flexural parameters and is found to improve with the increased FA/PC, decreased A/ CM, in the presence of nanomaterials, and with the increased fiber amount (regardless of the type). Outcomes of this research are thought to be important because they show the manufacturability of deflection-hardening concrete with large amounts of coarse aggregates at large scale and validate their autogenous self-healing capabilities, which are important for the real-time applicability of such mixtures in actual field conditions. [ABSTRACT FROM AUTHOR]

This research investigates a new digital fabrication method for large-scale polychromatic glass elements. Glass elements with locally differentiated properties usually require manual labor or are limited to film applications of secondary materials that are incapable of producing material texture and relief in glass. To create mono-material glass elements for buildings with customized color, opacity, and relief present in the same glass element, this research investigates a novel robotic multi-channel printing process for industrial float glass. Mono-material polychromatic glasses do not require any additional material and can be fully recycled. This paper presents a design-to-production workflow for the construction scale within feasible cost. Investigations include kilning and material considerations, multi-channel tool and fabrication setup, tool path generation, process parameter calibration, and large-scale prototyping. The co-occurrence of locally varying opacities, colors, material textures, and relief within one glass element enabled by the presented robotic fabrication method could allow for novel optical and decorative features in facades and windows. [ABSTRACT FROM AUTHOR]

In the context of optimization, visualization techniques can be useful for understanding the behaviour of optimization algorithms and can even provide a means to facilitate human interaction with an optimizer. Towards this goal, an image-based visualization framework, without dimension reduction, that visualizes the solutions to large-scale global optimization problems as images is proposed. In the proposed framework, the pixels visualize decision variables while the entire image represents the overall solution quality. This framework affords a number of benefits over existing visualization techniques including enhanced scalability (in terms of the number of decision variables), facilitation of standard image processing techniques, providing nearly infinite benchmark cases, and explicit alignment with human perception. To the best of the authors' knowledge, this is the first realization of a dimension-preserving, scalable visualization framework that embeds the inherent relationship between decision space and objective space. The proposed framework is utilized with different mapping schemes on an image-reconstruction problem that encompass continuous, discrete, constrained, dynamic, and multi-objective optimization. The proposed framework is then demonstrated on arbitrary benchmark problems with known optima. Experimental results elucidate the flexibility and demonstrate how valuable information about the search process can be gathered via the proposed visualization framework. Results of a user survey strongly support that users perceive a correlation between objective fitness values and the quality of the corresponding images generated by the proposed framework. [ABSTRACT FROM AUTHOR]

By using drilling, high-precision 3D seismic data, data of geochemistry, logging and testing, the reservoir characteristics and accumulation conditions of the KL6-1 lithologic oilfield in the Laibei Low Uplift in the Bohai Sea are examined comprehensively. The study shows that: KL6-1 oilfield is a monolithic, high-quality, large-scale Neogene lithologic oilfield featuring shallow reservoir depth, high productivity, concentrated oil-bearing intervals, large oil-bearing area, and high reserve abundance; hydrocarbon source supply from two directions provides a sufficient material basis for the formation of large oil field; two types of “inherited structural ridge” developed under the effect of block rotation, late active faults formed by Neotectonic movement, and widely distributed contiguous sand bodies provide an efficient oil and gas transportation system for the large-scale accumulation of oil and gas; contiguous channel and lacustrine lowstand system sand bodies developed in low accommodation condition provide the basic condition for the formation of large-scale lithologic traps; deep formations structural ridge, faults (dominant migration pathways) and large-scale superimposed contiguous sand bodies constitute a “vine type” oil and gas migration and accumulation system in the Laibei Low Uplift, which is conducive to the formation of large-scale and high-abundance lithologic reservoir in this area. The successful discovery of KL6-1, 100 million ton reserve order lithologic oil field, has revealed the exploration potential of Neogene large lithologic reservoirs in Bohai Sea, expanded the exploration field, and also has certain reference significance for the exploration of large lithologic reservoirs in similar areas.

Generally, fingerprint-based indoor localization works inefficiently when deployed in a large-scale area. This is because it consumes massive resources and takes long processing time for searching the exact location in the large fingerprint database. Moreover, the changing environment can degrade overall performance. To tackle these problems, we propose an adaptive indoor localization system for a large-scale area. Our system consists of three main parts. First, our area classification algorithm is the key to overcome the problem caused by the large-scale area. It identifies an area of the user's queries whether they are outdoor or located in a specific building. Specifically, the algorithm can filter out the queries sent from outdoor or out-of-scope areas. Then, the information of this part is sent to the next part. Second, our fingerprint-based indoor localization algorithm can utilize the information from the first part by searching only the fingerprint in the specific building. This can significantly reduce searching space and processing time in order to localize the exact location. Third, our missing-BSSID detector algorithm detects the missing Basic Service Set Identifiers (BSSIDs) in the incoming query and updates a sampling database. This part is for our system to quickly adapt to the changing environment. We evaluated and deployed our system in a large-scale exhibition including 37 multi-floor buildings, covering 486,000 m2 and generating approximately 600,000 records of queries from users. In addition, we created a simulation to evaluate our system in the critically-changing environment. Our proposed system achieves high accuracy. More importantly, our area classification algorithm can significantly reduce the overall processing time compared to the previous work. Also, we showed that when applying our missing-BSSID detector algorithm to our system as well as other existing systems, the overall system performance can be significantly improved.

The design, testing and optimization of Vehicle to Everything (V2X), connected and automated driving and Intelligent Transportation Systems (ITS) and technologies requires mobility traces and traffic simulation scenarios that can faithfully characterize the vehicular mobility at the macroscopic and microscopic levels under large-scale and complex scenarios. The generation of accurate scenarios and synthetic traces requires a precise modelling approach, and the possibility to validate them against real-world measurements that are generally not available for large-scale scenarios. This limits the open availability of realistic and large-scale traffic simulation scenarios. The purpose of this paper is to present a large-scale and high-accuracy traffic simulation scenario. The scenario has been implemented over the open-source SUMO traffic simulator and is openly released to the community. The scenario accurately models the traffic flow, the traffic speed and the road’s occupancy for 9 full days of traffic over a 97 km freeway section. The scenario models mixed traffic with light and heavy vehicles. The simulation scenario has been calibrated using a unique dataset provided by the Spanish road authority and a novel learning-based and iterative traffic demand calibration technique for SUMO. This technique, referred to as Clone Feedback, is proposed for the first time in this paper and does not require a pre-calibration to generate realistic traffic demand. Clone Feedback can generate calibrated mixed traffic (light and heavy vehicles) using as input only traffic flow measurements. The results obtained show that Clone Feedback outperforms two reference techniques for calibrating the traffic demand in SUMO.

In areas with high levels of human activity, there is often an imbalance between the supply and demand of water resources. This often means that ecological water requirements (EWRs) cannot be met in the long term, which leads to a deterioration in the ecological environment. To protect the ecological environment in areas with considerable human activity, research into hierarchical EWRs is essential. This paper first defined the hierarchical EWR concept (the minimum, suitable and ideal EWR) in areas with substantial human activity, and then developed a method to calculate this requirement over a large-scale area, taking the Beijing–Tianjin–Hebei region in China as a case study. Results showed that the minimum EWR in the Beijing–Tianjin–Hebei region was 42.81 × 108 m3, the suitable EWR was 54.37 × 108 m3, and the ideal EWR was 70.01 × 108 m3. The EWR of 13 cities in the region was calculated. The water requirement in the central area of Beijing–Tianjin–Hebei was significantly higher than that of the southern or northern areas. The EWR rate was used to characterize the EWR as a proportion of the surface water resources. The results showed that the minimum, suitable and ideal EWRs for Beijing–Tianjin–Hebei were 39%, 49%, and 63%, respectively. It was calculated that the annual average ecological water supply guarantee rate of Beijing–Tianjin–Hebei from 2015 to 2019 was 46%, which generally met the minimum EWR. This paper provides technical methods and a reference for calculating the EWR over large-scale areas with considerable human activity.

Evaluating exposure to radio frequencies (RF) at population-scale is important for conducting sound epidemiological studies about possible health impact of RF radiations. Numerous studies reported population exposure to RF radiations used in wireless telecommunication technologies, but used very small population samples. In this context, the real exposure of the population at scale remains poorly understood. Here, to the best of our knowledge, we report the largest crowd-based measurement of population exposure to RF produced by cellular antennas, Wi-Fi access points, and Bluetooth devices for 254,410 unique users in 13 countries from January 2017 to December 2020. First, we present methods to assess the population exposure to RF radiations using smartphone measurements obtained using the ElectroSmart Android app. Then, we use these methods to evaluate and characterize the evolution of RF exposure. We show that total exposure has been multiplied by 2.3 in the four-year period considered, with Wi-Fi as the largest contributor. The cellular exposure levels are orders of magnitude lower than regulation limits and are not correlated to national regulation policies. The population tends to be more exposed at home; for half of the study subjects, personal Wi-Fi routers and Bluetooth devices contributed to more than 50% of their total exposure. In this work, we showcase how crowdsource-based data allow large-scale and long-term assessment of population exposure to RF radiations.

In an effort to examine the spread of large-scale cyber attacks, researchers have created various taxonomies. These taxonomies are purposefully built to facilitate the understanding and the comparison of these attacks, and hence counter their spread. Yet, existing taxonomies focus mainly on the technical aspects of the attacks, with little or no information about how to defend against them. As such, the aim of this work is to extend existing taxonomies by incorporating new features pertaining the defense strategy, scale, and others. We will compare the proposed taxonomy with existing state of the art taxonomies. We also present the analysis of 174large cyber security attacks based on our taxonomy. Finally, we present a web tool that we developed to allow researchers to explore exiting data sets of attacks and contribute new ones. We are convinced that our work will allow researchers gain deeper insights into emerging attacks by facilitating their categorization, sharing and analysis, which results in boosting the defense efforts against cyber attack.

BackgroundA number of algorithms have been proposed to predict the biological targets of diverse molecules. Some are structure-based, but the most common are ligand-based and use chemical fingerprints and the notion of chemical similarity. These methods tend to be computationally faster than others, making them particularly attractive tools as the amount of available data grows.ResultsUsing a ChEMBL-derived database covering 490,760 molecule-protein interactions and 3236 protein targets, we conduct a large-scale assessment of the performance of several target-prediction algorithms at predicting drug-target activity. We assess algorithm performance using three validation procedures: standard tenfold cross-validation, tenfold cross-validation in a simulated screen that includes random inactive molecules, and validation on an external test set composed of molecules not present in our database.ConclusionsWe present two improvements over current practice. First, using a modified version of the influence-relevance voter (IRV), we show that using molecule potency data can improve target prediction. Second, we demonstrate that random inactive molecules added during training can boost the accuracy of several algorithms in realistic target-prediction experiments. Our potency-sensitive version of the IRV (PS-IRV) obtains the best results on large test sets in most of the experiments. Models and software are publicly accessible through the chemoinformatics portal at http://chemdb.ics.uci.edu/.

Offspring generation plays an important role in evolutionary multiobjective optimization. However, generating promising candidate solutions effectively in high-dimensional spaces is particularly challenging. To address this issue, we propose an adaptive offspring generation method for large-scale multiobjective optimization. First, a preselection strategy is proposed to select a balanced parent population, and then these parent solutions are used to construct direction vectors in the decision spaces for reproducing promising offspring solutions. Specifically, two kinds of direction vectors are adaptively used to generate offspring solutions. The first kind takes advantage of the dominated solutions to generate offspring solutions toward the Pareto optimal set (PS) for convergence enhancement, while the other kind uses those nondominated solutions to spread the solutions over the PS for diversity maintenance. The proposed offspring generation method can be embedded in many existing multiobjective evolutionary algorithms (EAs) for large-scale multiobjective optimization. Experiments are conducted to reveal the mechanism of our proposed adaptive reproduction strategy and validate its effectiveness. Experimental results on some large-scale multiobjective optimization problems have demonstrated the competitive performance of our proposed algorithm in comparison with five state-of-the-art large-scale EAs. [ABSTRACT FROM AUTHOR]

Objective: To identify differences related to sex and define autism spectrum disorder (ASD) comorbidities female-enriched through a comprehensive multi-PheWAS intersection approach on big, real-world data. Although sex difference is a consistent and recognized feature of ASD, additional clinical correlates could help to identify potential disease subgroups, based on sex and age.Materials and Methods: We performed a systematic comorbidity analysis on 1860 groups of comorbidities exploring all spectrum of known disease, in 59 140 individuals (11 440 females) with ASD from 4 age groups. We explored ASD sex differences in 2 independent real-world datasets, across all potential comorbidities by comparing (1) females with ASD vs males with ASD and (2) females with ASD vs females without ASD.Results: We identified 27 different comorbidities that appeared significantly more frequently in females with ASD. The comorbidities were mostly neurological (eg, epilepsy, odds ratio [OR] > 1.8, 3-18 years of age), congenital (eg, chromosomal anomalies, OR > 2, 3-18 years of age), and mental disorders (eg, intellectual disability, OR > 1.7, 6-18 years of age). Novel comorbidities included endocrine metabolic diseases (eg, failure to thrive, OR = 2.5, ages 0-2), digestive disorders (gastroesophageal reflux disease: OR = 1.7, 6-11 years of age; and constipation: OR > 1.6, 3-11 years of age), and sense organs (strabismus: OR > 1.8, 3-18 years of age).Discussion: A multi-PheWAS intersection approach on real-world data as presented in this study uniquely contributes to the growing body of research regarding sex-based comorbidity analysis in ASD population.Conclusions: Our findings provide insights into female-enriched ASD comorbidities that are potentially important in diagnosis, as well as the identification of distinct comorbidity patterns influencing anticipatory treatment or referrals. The code is publicly available (https://github.com/hms-dbmi/sexDifferenceInASD). [ABSTRACT FROM AUTHOR]

Block coordinate descent (BCD) methods are widely used for large-scale numerical optimization because of their cheap iteration costs, low memory requirements, amenability to parallelization, and ability to exploit problem structure. Three main algorithmic choices influence the performance of BCD methods: the block partitioning strategy, the block selection rule, and the block update rule. In this paper we explore all three of these building blocks and propose variations for each that can significantly improve the progress made by each BCD iteration. We (i) propose new greedy block-selection strategies that guarantee more progress per iteration than the Gauss-Southwell rule; (ii) explore practical issues like how to implement the new rules when using "variable" blocks; (iii) explore the use of message-passing to compute matrix or Newton updates effciently on huge blocks for problems with sparse dependencies between variables; and (iv) consider optimal active manifold identification, which leads to bounds on the "active-set complexity" of BCD methods and leads to superlinear convergence for certain problems with sparse solutions (and in some cases finite termination at an optimal solution). We support all of our findings with numerical results for the classic machine learning problems of least squares, logistic regression, multi-class logistic regression, label propagation, and L1-regularization. [ABSTRACT FROM AUTHOR]

Shack extraction is a growing application area for solving contemporary geographical complexities in developing countries. Traditional ground-based surveys that provided shack counts are being overtaken by the opportunities around rapid advances in spatial data collection. The ability to automate and report high accuracies against ground truth remains key to the complex nature of the digital shack extraction matrix. This study uses 8 cm aerial footage and laser scanning data to map informal settlement areas within built environments. The research design includes a set of experiments in defining the footprint of shacks obtained through profiling sampled shack/non-shack areas. Average shack heights, behaviour of unclassified points, number of non-ground points, scan texture and intensity variations were investigated. A five-parameter signature was applied to a pit-free normalized digital surface model (NDSM) and as expected on both test and validation, proved suitable for distinct shacks identification. This approach reported a 95% large settlement detection within a 1-metre-wide boundary position tolerance, coupled with a few false positives in isolated cases of multi-storey, sheds and also complex shacks. The results are proposed for inclusion in city-wide urban planning frameworks that can also be tweaked to include social and environmental parameters. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive parameterized level set topology optimization method (APLSM) is proposed by using a bilinear basis function which is common in the finite element method (FEM) to parameterize the level set function. A CPU parallel computing strategy is applied for large-scale topology optimization problems. In the presented APLSM, the solving of an additional linear system, which is necessary for each iteration in the radial basis functions-based parametric level set method (PLSM), is completely avoided since the coefficient matrix of the system degenerates into an identity matrix when using the basis function to parameterize the level set function. In this way, the computational efficiency could be improved significantly. Furthermore, pattern repetition constraints are also imposed to design the graded hierarchical and cellular structures with high geometric complexity by using the proposed APLSM and parallel computing. Several numerical examples have been carried out for verifying the effectiveness of the proposed method. For two-dimensional examples, the computational efficiency is improved about 20% to 80% by comparison with the traditional compactly supported radial basis functions (CSRBFs)-based PLSM with different support radii. For the large-scale three-dimensional problems, the computational efficiency of the proposed method is also much higher. For a plate model meshed by more than 13 million 8-node hexahedral elements, only approximately 2 s in each iteration are consumed in all the calculation processes except for the solving of the structural static equilibrium equations, including the generating of mesh, the assembling of the element stiffness matrices, the imposing of the boundary conditions, and the updating of the level set function. [ABSTRACT FROM AUTHOR]

Producing thick films of conducting polymers by a low‐cost manufacturing technique would enable new applications. However, removing huge solvent volume from diluted suspension or dispersion (1–3 wt%) in which conducting polymers are typically obtained is a true manufacturing challenge. In this work, a procedure is proposed to quickly remove water from the conducting polymer poly(3,4‐ethylenedioxythiophene:poly(4‐styrene sulfonate) (PEDOT:PSS) suspension. The PEDOT:PSS suspension is first flocculated with 1 m H2SO4 transforming PEDOT nanoparticles (≈50–500 nm) into soft microparticles. A filtration process inspired by pulp dewatering in a paper machine on a wire mesh with apertures dimension between 60 µm and 0.5 mm leads to thick free‐standing films (≈0.5 mm). Wire mesh clogging that hinders dewatering (known as dead‐end filtration) is overcome by adding to the flocculated PEDOT:PSS dispersion carbon fibers that aggregate and form efficient water channels. Moreover, this enables fast formation of thick layers under simple atmospheric pressure filtration, thus making the process truly scalable. Thick freestanding PEDOT films thus obtained are used as electrocatalysts for efficient reduction of oxygen to hydrogen peroxide, a promising green chemical and fuel. The inhomogeneity of the films does not affect their electrochemical function. [ABSTRACT FROM AUTHOR]

Introduction: South Africa is an upper middle-income country with wide wealth inequality. It faces a quadruple burden of disease and poor health outcomes, with access to appropriate and adequate health care a challenge for millions of South Africans. The introduction of large-scale, comprehensive community health worker (CHW) programs in the country, within the context of implementing universal health coverage, was anticipated to improve population health outcomes. However, there is inadequate local (or global) evidence on whether such programs are effective, especially in urban settings.Methods: This study is part of a multi-method, quasi-experimental intervention study measuring effectiveness of a large-scale CHW program in a health district in an urban province of South Africa, where CHWs now support approximately one million people in 280,000 households. Using interviewer administered questionnaires, a 2019 cross-sectional survey of 417 vulnerable households with long-term CHW support (intervention households) are compared to 417 households with no CHW support (control households). Households were selected from similar vulnerable areas from all sub-levels of the Ekurhuleni health district.Results: The 417 intervention and control households each had good health knowledge. Compared to controls, intervention households with long-term comprehensive CHW support were more likely to access early care, get diagnosed for a chronic condition, be put on treatment and be well controlled on chronic treatment. They were also more likely to receive a social grant, and have a birth certificate or identity document. The differences were statistically significant for social support, health seeking behavior, and health outcomes for maternal, child health and chronic care.Conclusion: A large-scale and sustained comprehensive CHW program in an urban setting improved access to social support, chronic and minor acute health services at household and population level through better health-seeking behavior and adherence to treatment. Direct evidence from households illustrated that such community health worker programs are therefore effective and should be part of health systems in low- and middle-income countries. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a matrix-free double-search direction based on the updated parameter file of the double-search direction with a new mathematical formula for the gamma parameter. When comparing the numerical results of this algorithm with the standard (HWY) algorithm which given by Halilu, Waziri and Yusuf in 2020. We get very robust numerical results. The proposed algorithm is devoid of derivatives to solve large-scale non-linear problems by combining two search directions in one search direction. We demonstrated the overall convergence of the proposed algorithm under certain conditions. The numerical results presented in this paper show that the new search direction is useful for solving widespread non-linear test problems. [ABSTRACT FROM AUTHOR]

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system. • Mine required CHUIs in parallel and distributed environments. • Use HG-k-means to cluster highly relevant transactions in a group that ensures fusion. • An interactive three-phases MapReduce model with a DFM-Miner algorithm is presented. • Experiments on various large-scale datasets are conducted to achieve better performance. [ABSTRACT FROM AUTHOR]

The development of efficient and stable electrocatalysts with earth-abundant elements for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) in the same electrolyte is incontrovertibly vital in water electrolysis. However, their large-scale fabrication remains a great challenge. Here, we report a self-supported electrocatalyst in the form of Fe-doped Ni3S2 nanoparticles in-situ grown on three-dimensional (3D) conductive Fe−Ni alloy foam (Fe−Ni3S2/AF) by surface-assisted chemical vapor transport (SACVT) method. Homogeneous growth environment and scalability of SACVT method allow Fe−Ni3S2 nanoparticles uniformly growing on AF in large-scale. Fe−Ni3S2/AF exhibits high activity and durability when act as HER catalyst and OER precatalyst in alkaline media. The HER and OER overpotential at 10 mA/cm2 is considerably small, only 75 and 267 mV, respectively. Moreover, the electrolyzer assembled by Fe−Ni3S2/AF for overall water splitting exhibits a low cell voltage and high durability in long-term test. Based on experiments and theoretical calculation, the significantly enhanced activity could be originated from the incorporation of Fe, which contributed to increase the electrochemical active surface area, enhance electrical conductivity, optimize the hydrogen and H2O adsorption energy of Ni3S2 (101) surface in HER, and form active bimetallic Ni−Fe(oxy)hydroxide in OER. The excellent durability of self-supported Fe−Ni3S2/AF could be benefited from the in-situ growth of Fe−Ni3S2 nanoparticles on 3D AF, which could ensure closely mechanical adhesion between active materials and substrate, promote charge transport and increase surface area. This work provides a facile method for large-scale synthesis of electrocatalysts with high activity and long-term durability for efficient water electrolysis in alkaline media. [ABSTRACT FROM AUTHOR]

Abstract During the last years, several strategies have been made to obtain mature erythrocytes or red blood cells (RBC) from the bone marrow or umbilical cord blood (UCB). However, UCB-derived hematopoietic stem cells (HSC) are a limited source and in vitro large-scale expansion of RBC from HSC remains problematic. One promising alternative can be human pluripotent stem cells (PSCs) that provide an unlimited source of cells. Human PSCs, including embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), are self-renewing progenitors that can be differentiated to lineages of ectoderm, mesoderm, and endoderm. Several previous studies have revealed that human ESCs can differentiate into functional oxygen-carrying erythrocytes; however, the ex vivo expansion of human ESC-derived RBC is subjected to ethical concerns. Human iPSCs can be a suitable therapeutic choice for the in vitro/ex vivo manufacture of RBCs. Reprogramming of human somatic cells through the ectopic expression of the transcription factors (OCT4, SOX2, KLF4, c-MYC, LIN28, and NANOG) has provided a new avenue for disease modeling and regenerative medicine. Various techniques have been developed to generate enucleated RBCs from human iPSCs. The in vitro production of human iPSC-derived RBCs can be an alternative treatment option for patients with blood disorders. In this review, we focused on the generation of human iPSC-derived erythrocytes to present an overview of the current status and applications of this field.

Abstract Large-scale implementations of effective inquiry-based learning are rare. A European-wide initiative gave teachers access to innovative e-learning tools (ranging from virtual labs, virtual games and simulations to augmented reality applications) for lesson planning and classroom implementation. We examined 668 such implementations across 453 schools within the period of one school year. Teachers could use a platform with digital resources and tools and were encouraged to adopt five different phases of inquiry-based learning: orientation, hypothesizing, planning, analysis, and conclusion. Additionally, an integrated interface for lesson implementation tracked each students’ problem-solving competence (during the inquiry lessons), culminating in about 12,000 datasets. Every user generated an average of 22 digital inquiry-based digital scenarios, each of which required approximately 50.14 min for completion. These scenarios, using high quality resources adapted to school conditions, yielded significant learning outcomes for participating students (age: 14.4 years, gender balanced). While the PISA study identified 10% high achievers on average, we exceeded this number in our framework scoring 20–29% high achievers and 37–42% low achievers (which was close to the 45% PISA average). Offering tools to teachers, which help creating individual inquiry scenarios and monitoring students’ achievement, does not yield any insurmountable obstacles for classroom-implementation of inquiry-based lessons: Compared to the PISA study, levels of high achievers increased even if complex problem-solving competence was required.

T-junction structures play an important role in nuclear power plant systems. Research on liquid entrainment is mostly based on small-scale branch pipes (d/D ≤ 0.2) and attention paid to large-scale branch pipes (0.33

Abstract Background Community Case Management of Malaria (CCMM) has been implemented through community health workers (CHWs) in many countries. Existing studies have shown that CHWs can be viable means of implementing CCMM. However, not many studies have examined the coverage under large-scale CCMM programmes. India is a big contributor to global malaria burden. Chhattisgarh is a leading state in India in terms of malaria incidence and mortality. CCMM was implemented on a large scale through the ‘mitanin’ CHWs in rural Chhattisgarh from 2015. Under CCMM, 37,696 CHWs in 84 high-burden administrative blocks of the state were trained and equipped with rapid diagnostic tests (RDT), artemisinin-based combination therapy (ACT) and chloroquine. Methods This descriptive quantitative study assesses coverage of CCMM in detection and treatment of Malaria over three rounds of household surveys—2015, 2016 and 2018. Household-interviews covered more than 15,000 individuals in each round, using multi-stage random sampling across the 84 blocks. The main objectives were to find out the coverage in identification and treatment of malaria and the share of CHWs in them. A 15-days recall was used to find out cases of fever and healthcare sought by them. Results In 2018, 62% of febrile cases in rural population contacted CHWs. RDT, ACT and chloroquine were available with 96%, 80% and 95% of CHWs, respectively. From 2015 to 2018, the share of CHWs in testing of febrile cases increased from 34 to 70%, while it increased from 28 to 69% in treatment of malaria cases. CHWs performed better than other providers in treatment-completion and administered medication under direct observation to 72% of cases they treated. Conclusion This study adds to one of the most crucial but relatively less reported area of CCMM programmes, i.e. the extent of coverage of the total febrile population by CHWs, which subsequently determines the actual coverage of case-management in malaria. Mitanin-CHWs achieved high coverage and treatment-completion rates that were rarely reported in context of large-scale CCMM elsewhere. Close to community, well-trained CHWs with sufficient supplies of rapid tests and anti-malarial drugs can play a key role in achieving the desired coverage in malaria-management.

Resistance test is a classical method used to study ship performance. In this study, the uncertainty of large-scale ship model resistance test in actual seas is analyzed. Considering the difference between these trials and traditional test in towing tanks, this study first uses the ITTC 2014 procedure based on GUM to calculate the systematic error in the test. The parameters that affect the test accuracy are also estimated. Then, the program based on the Monte Carlo method is verified, and the differences between the two methods are compared. In this study, the uncertainty sources in the test are quantitatively analyzed, and the results will be helpful for improving the ship model test scheme in actual seas.

Abstract Aim Habitat quality and heterogeneity directly influence the distribution and abundance of organisms at different spatial scales. Determining the main environmental factors driving the variation in species abundance is crucial to understand the underlying ecological processes, and this is especially important for widely distributed species living in contrasting environments. However, the responses to environmental variation are usually described at relatively small spatial scales. Here, we studied the variation in abundance of a widely distributed mustelid, the European badger (Meles meles), across France. Location The whole metropolitan France. Methods We used (a) direct detections of 9,439 dead and living badgers, from 2006 to 2009, to estimate badger relative abundance in 703 small agricultural regions of metropolitan France and (b) a Bayesian modeling approach to identify the main environmental determinants influencing badger abundance. Results Despite a continuous distribution of badger in France, we found large variation in badger abundance between regions, explained by environmental factors. Among a set of 13 environmental variables, we demonstrated that badger abundance in lowlands (

Dates have been demonstrated to display a variety of bioactivities and are rich in polyphenols. In this work, we assessed the underlying immunomodulatory effects of date seed polyphenol extracts that had been industrially encapsulated and fabricated into commercial pills in RAW264.7 macrophages using the NF-κB and Nrf2 signaling pathways. The outcomes showed that in RAW264.7 cells, the date seed pills effectively stimulated nuclear translocation of NF-E2–related factor 2 (Nrf2) and NF-κB, along with downstream cytokines (IL-1β, TNF-α, IL-6, and IFN-γ), ROS ratios, and SOD activity. It is interesting to note that the encapsulated pills activated Nrf2 nuclear translocation more effectively than the non-encapsulated ones did. Additionally, pills at 50 µg mL−1 improved immunological responses, but pills at 1000 µg mL−1 prevented macrophages from becoming inflamed. These results showed that the immunomodulatory effects were differently impacted by commercial date seed pills, a finding which was related to the large-scale manufacturing of the pills and the incubation concentrations used. These results also shed light on a new trend of using food byproducts as an innovative supplement.

Porcine circovirus type 2d (PCV2d) is becoming the predominant PCV genotype and considerably affects the global pig industry. Nevertheless, currently, no commercial PCV2d vaccine is available. Preventing and controlling the disease caused by PCV2d is therefore based on other genotype-based vaccines. However, their production platforms are laborious, limited in expression level, and relatively expensive for veterinary applications. To address these challenges, we have developed a simple and cost-efficient platform for a novel PCV2d vaccine production process, using fed-batch E. coli fermentation followed by cell disruption and filtration, and a single purification step via cation exchange chromatography. The process was developed at bench scale and then pilot scale, where the PCV2d subunit protein yield was approximately 0.93 g/L fermentation volume in a short production time. Moreover, we have successfully implemented this production process at two different sites, in Southeast Asia and Europe. This demonstrates transferability and the high potential for successful industrial production.

Advances in genomic sequencing have recently offered vast opportunities for biological exploration, unraveling the evolution and improving our understanding of Earth biodiversity. Due to distinct plant species characteristics in terms of genome size, ploidy and heterozygosity, transposable elements (TEs) are common characteristics of many genomes. TEs are ubiquitous and dispersed repetitive DNA sequences that frequently impact the evolution and composition of the genome, mainly due to their redundancy and rearrangements. For this study, we provided an atlas of TE data by employing an easy-to-use portal ( APTE website ). To our knowledge, this is the most extensive and standardized analysis of TEs in plant genomes. We evaluated 67 plant genomes assembled at chromosome scale, recovering a total of 49,802,023 TE records, representing a total of 47,992,091,043 (~47,62%) base pairs (bp) of the total genomic space. We observed that new types of TEs were identified and annotated compared to other data repositories. By establishing a standardized catalog of TE annotation on 67 genomes, new hypotheses, exploration of TE data and their influences on the genomes may allow a better understanding of their function and processes. All original code and an example of how we developed the TE annotation strategy is available on GitHub ( Extended data).

The spatial distribution of crops is an important agricultural parameter, which is used to derive important information about crop productivity and food security. However, crop mapping on a large scale is challenging due to the low spatio-temporal information of satellite data, sparse sampling, and poor computational efficiency for massive data. To alleviate these problems, this study proposes a method based on discrete grids with machine learning to integrate GaoFen-1 and Sentinel-2 imagery. First, the proposed method fuses multi-source satellite data with similar observation characteristics to improve the spatial and temporal coverage of satellites. Second, a data augmentation technique based on a discrete grid framework was proposed to solve the problem of sparse samples. Finally, a machine learning algorithm in a discrete grid was introduced to improve processing efficiency and ensure the crop classification precision of large-scale remote sensing images. An experiment in the Sanjiang Plain area (approximately 108900 km2) of Northeast China showed that the proposed scheme benefited from a high spatio-temporal multi-source dataset and achieved good performance. Compared with a single data source, the accuracy of crop mapping using multi-source optical remote sensing data is higher, attaining up to 86 and 88 % in 2017 and 2018, respectively. Furthermore, the advantages of machine learning in discrete grids over large-scale areas are validated by evaluating the accuracy of different classifiers, which indicates the suitability of discrete grids in data augmentation and large-scale crop mapping. Finally, discrete grid technology offers a possibility for crop mapping over large-scale areas, and improves the processing efficiency of remote sensing big data. The findings in this study can contribute to studies on large-scale crop classification and serve as a reference to them.

The study aims to statistically optimize the phytase production by Penicillium oxalicum PBG30 in solid-state fermentation using wheat bran as substrate. Variables viz. pH, incubation days, MgSO 4 , and Tween-80 were the significant parameters identified through the Plackett-Burman design (PBD) that majorly influenced the phytase production. Further, central composite design (CCD) method of response surface methodology (RSM) defined the optimum values for these factors i.e., pH 7.0, 5 days of incubation, 0.75% of MgSO 4 , and 3.5% of Tween-80 that leads to maximum phytase production of 475.42 U/g DMR. Phytase production was also sustainable in flasks and trays of different sizes with phytase levels ranging from 394.95 to 475.42 U/g DMR. Enhancement in phytase production is 5.6-fold as compared to unoptimized conditions. The in-vitro dephytinization of feed showed an amelioration in the nutritive value by releasing inorganic phosphate and other nutrients in a time-dependent manner. The highest amount of inorganic phosphate (33.986 mg/g feed), reducing sugar (134.4 mg/g feed), and soluble protein (115.52 mg/g feed) was achieved at 37 °C with 200 U of phytase in 0.5 g feed for 48 h. This study reports the economical and large-scale production of phytase with applicability in enhancing feed nutrition.

Although cognitive functions are hypothesized to be mediated by synchronous neuronal interactions in multiple frequency bands among widely distributed cortical areas, we still lack a basic understanding of the distribution and task dependence of oscillatory activity across the cortical map. Here, we ask how the spectral and temporal properties of the local field potential (LFP) vary across the primate cerebral cortex, and how they are modulated during visual short-term memory. We measured the LFP from 55 cortical areas in two macaque monkeys while they performed a visual delayed match to sample task. Analysis of peak frequencies in the LFP power spectra reveals multiple discrete frequency bands between 3 and 80 Hz that differ between the two monkeys. The LFP power in each band, as well as the sample entropy, a measure of signal complexity, display distinct spatial gradients across the cortex, some of which correlate with reported spine counts in cortical pyramidal neurons. Cortical areas can be robustly decoded using a small number of spectral and temporal parameters, and significant task-dependent increases and decreases in spectral power occur in all cortical areas. These findings reveal pronounced, widespread, and spatially organized gradients in the spectral and temporal activity of cortical areas. Task-dependent changes in cortical activity are globally distributed, even for a simple cognitive task. NEW & NOTEWORTHY We recorded extracellular electrophysiological signals from roughly the breadth and depth of a cortical hemisphere in nonhuman primates (NHPs) performing a visual memory task. Analyses of the band-limited local field potential (LFP) power displayed widespread, frequency-dependent cortical gradients in spectral power. Using a machine learning classifier, these features allowed robust cortical area decoding. Further task dependence in LFP power were found to be widespread, indicating large-scale gradients of LFP activity, and task-related activity.