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This study presents a large-scale green approach for synthesizing ultralong tellurium nanowires with diameters around 13 nm using a solution-based method. By adjusting key synthesis parameters such as the surfactant concentration, temperature, and reaction duration, we achieved high-quality, ultralong Te NWs. These nanowires exhibit properties suitable for use in semiconductor applications, particularly when employed as channel materials in thin-film transistors, displaying a pronounced gate effect with a high switch of up to 104 and a mobility of 0.9 cm2 V−1s−1. This study underscores the potential of solvent-based methods in synthesizing large-scale ultralong Te NWs as a critical resource for future sustainable nanoelectronic devices.

The Remote Sensing Ecological Index (RSEI) model is widely used for large-scale, rapid Ecological Environment Quality (EEQ) assessment. However, both the RSEI and its improved models have limitations in explaining the EEQ with only two-dimensional (2D) factors, resulting in inaccurate evaluation results. Incorporating more comprehensive, three-dimensional (3D) ecological information poses challenges for maintaining stability in large-scale monitoring, using traditional weighting methods like the Principal Component Analysis (PCA). This study introduces an Improved Remote Sensing Ecological Index (IRSEI) model that integrates 2D (normalized difference vegetation factor, normalized difference built-up and soil factor, heat factor, wetness, difference factor for air quality) and 3D (comprehensive vegetation factor) ecological factors for enhanced EEQ monitoring. The model employs a combined subjective–objective weighting approach, utilizing principal components and hierarchical analysis under minimum entropy theory. A comparative analysis of IRSEI and RSEI in Miyun, a representative study area, reveals a strong correlation and consistent monitoring trends. By incorporating air quality and 3D ecological factors, IRSEI provides a more accurate and detailed EEQ assessment, better aligning with ground truth observations from Google Earth satellite imagery.

Simultaneous optimisation of machines and buffers in a large series-parallel production line is an NP-hard problem. The formulated optimisation model in this study is used to minimise the total investment cost subject to the desired throughput rate and cycle time by optimising the machine types, number of parallel machines, and buffer capacities. To solve this kind of design problem, a decomposition-coordination method is proposed to efficiently and accurately generate allocation solutions for large production lines. The proposed method includes two iterative processes: the decomposition process decouples the original line into several small lines and optimises them separately, while the coordination process ensures that the optimisation problems of the decomposed lines are similar to the corresponding part of the original. The performance of this approach is demonstrated through numerical experiments by comparisons with the simulated annealing algorithm and non-dominated sorting genetic algorithm-II. Finally, the sets of numerical results and a multi-factorial experimental analysis illustrate the influences of target system parameters on the resource configurations. [ABSTRACT FROM AUTHOR]

The traffic checkpoint data collected by video detection and license plate recognition technologies contain abundant traffic information such as vehicle travel time and path. However, due to various factors, there are often many outliers in the travel-time data, which may lead to inaccurate research results. How to extract effective travel-time data from the traffic checkpoint data is one of the key questions for urban traffic evolution. Based on the large-scale checkpoint data from a city in Guangdong Province, China, this study proposes a statistics-based filtering algorithm for travel-time outliers and applies it to urban vehicle travel path and origin-destination (OD) identification. The sampling rate and reading rate of checkpoint data are verified. The sampling rate and reading rate are 99.46% and 93.7%, respectively, and the reliability of the data is high. A statistics-based filtering algorithm for travel-time outliers is proposed and compared with three existing clustering algorithms. The results showed that for a load section of 376.54 m, the maximum travel time is reached during the period 18:00--18:15. The average travel time is 5.55 min, and the maximum travel time is 8.08 min, which is consistent with the actual operation of the road section. The travel time obtained by the three clustering methods does not reflect the actual operation of the road section well. This means that the effective travel time extracted by this method is more practical and can reflect the fluctuation of travel time on urban roads. Then, the sensitivity of the proposed method to the time window and road length is tested. The results showed that the time window setting will affect the outlier-filtering effect, and the road length has less effect on the proposed method. Finally, the proposed algorithm is applied to single travel identification of urban vehicles to extract single travel paths and OD information of vehicles, which can provide accurate data for urban traffic research. [ABSTRACT FROM AUTHOR]

Present study described the facile and efficient synthesis of R‐nosylate (chemical name: (R)‐1‐(5‐(pyridin‐2‐yl)‐1,3,4‐thiadiazol‐2‐yl)‐ethyl,4‐nitrobenzenesulfonate) as new key intermediate of nafithromycin. The R‐nosylate (3 b) was prepared in six steps, starting from readily available methyl‐D‐lactate. To produce a 3 b with a good yield and remarkable chiral high performance liquid chromatography (CHPLC) purity more than 99 %, a large‐scale practical and environmentally friendly process was generated and optimized. The developed method, which was successfully established at the pilot scale and then used as the base for industrial production, considerably decreased the use of harmful reagents. [ABSTRACT FROM AUTHOR]

Based on the convex combination technique, we propose a projection-based hybrid conjugate gradient algorithm for solving nonlinear equations with convex constraints in this paper. The conjugate parameter of the proposed algorithm is a convex combination of the modified Polak-Ribière-Polyak and Dai-Yuan type conjugate parameters, and the search direction has the sufficient descent property without the use of a line search strategy. The proposed hybrid algorithm's global convergence is established under appropriate assumptions. The numerical experiments demonstrate that the proposed algorithm is more efficient and competitive than existing methods under some benchmark test problems. Furthermore, it is also extended to solve the sparse signal and impulse noise image restoration problem that arises in compressive sensing. [ABSTRACT FROM AUTHOR]

The key benefits of qualitative research are rich insights and thick data. However, this may come at the cost of small sample sizes and low generalisability of findings. With traditional focus group sessions (FGDs), this could be addressed by conducting multiple groups. However, this requires significant investment of time and manpower. It was prudent to explore methods to gather thick data quickly, to effectively replace FGDs. This would increase the number of respondents in a single session, without increasing manpower or lengthening fieldwork, and maintaining data quality. This paper details the experience of running a pilot study of a 1.5-hour online discussion with N=103 respondents, to capture in-depth responses at scale. Using pre-programmed questions and artificial intelligence (AI) to provide instant visual analyses of responses and additional probes to respondents live, a full qualitative study was run, with a larger sample and in the same duration required for a typical FGD. The discussion was text-based - respondents could view and give their agreement or disagreement to what others may have said without directly interacting. The data was compared to data collected from a previous study where a total of 35 respondents across 5 FGDs discussed a similar topic, and analysed from an operational aspect of conducting research and gathering insights from both methodologies. While this methodology does not replace traditional FGD, it has proven effective in scaling up qualitative research by gathering large amounts of qualitative data within a short duration, in real-time. It has its limitations, primarily the inability to further nuance responses. Despite this, the pilot appears to be a successful attempt conceptually, as the AI generated valuable instant insights while the study was ongoing, particularly from open-ended (OE) responses. It may add value to specific use cases such as large-scale engagement studies which require both breadth and scalability. [ABSTRACT FROM AUTHOR]

Scaled agile development approaches are now used widely in modern software engineering, allowing businesses to improve teamwork, productivity, and product quality. The incorporation of artificial intelligence (AI) into scaled agile development methods (SADMs) has emerged as a potential strategy in response to the ongoing demand for simplified procedures and the increasing complexity of software projects. This paper explores the intersection of AI-driven assistants within the context of the scaled agile framework (SAFe) for large-scale software development, as it stands out as the most widely adopted framework. Our paper pursues three principal objectives: (1) an evaluation of the challenges and impediments encountered by organizations during the implementation of SADMs, (2) an assessment of the potential advantages stemming from the incorporation of AI in large-scale contexts, and (3) the compilation of aspects of SADMs that AI-driven assistants enhance. Through a comprehensive systematic literature review, we identified and described 18 distinct challenges that organizations confront. In the course of our research, we pinpointed seven benefits and five challenges associated with the implementation of AI in SADMs. These findings were systematically categorized based on their occurrence either within the development phase or the phases encompassing planning and control. Furthermore, we compiled a list of 15 different AI-driven assistants and tools, subjecting them to a more detailed examination, and employing them to address the challenges we uncovered during our research. One of the key takeaways from this paper is the exceptional versatility and effectiveness of AI-driven assistants, demonstrating their capability to tackle a broader spectrum of problems. In conclusion, this paper not only sheds light on the transformative potential of AI, but also provides invaluable insights for organizations aiming to enhance their agility and management capabilities. [ABSTRACT FROM AUTHOR]

Large‐scale production of full‐color carbon dots (CDs) at room temperature under atmospheric pressure will promote their industrial application. Herein, o‐phenylenediamine is used as a single‐precursor carbon source and hydrogen peroxide as an oxidant. Kilogram‐scale CDs are obtained by stirring these reactants at room temperature in air for 30 min. After purification and acid treatment, blue‐, green‐, and red‐fluorescence CDs with photoluminescence peaks at 481, 539, and 625 nm are obtained with quantum yields of 21.76%, 27.12%, and 13.06%, respectively. Based on chemical structural characterizations, a reasonable formation mechanism is deduced, with blue, green, and red fluorescence arising from carbon nuclei stacked in conjugate planes containing fragments of quinoxaline, 2,3‐diaminophenazine (2,3‐DAPN), and protonated 2,3‐DAPN, respectively. High‐quality multi‐color and white light‐emitting diodes are successfully fabricated to demonstrate potential commercial applications in display and illumination. [ABSTRACT FROM AUTHOR]

Background: With the advancement of whole‐genome sequencing (WGS) technology, massively parallel sequencing (MPS) remains the mainstream due to its accuracy, low cost, and high throughput. The development of the analytical pipeline corresponding to MPS has always been of great importance. Increasingly large population genomics studies, as a specific type of big data research, pose new challenges for analysis solutions. Results: Here, we introduce ZBOLT, a comprehensive analysis system that incorporates both software and hardware advancements, making it an appropriate choice for large‐scale population genomic studies that require extensive data processing. In this study, we first evaluate ZBOLT's calling accuracy using the Genome in a Bottle (GIAB) benchmark dataset. Then we apply ZBOLT to a large‐scale population genomics study with 5,616 high sequencing depth samples totaling 1.16Pbp (base pair). As the results show, ZBOLT demonstrates exceptional efficiency and low energy consumption, processing 100Tbp per day and using 1kWh per 100Gbp sequenced sample. Conclusion: This research serves as a valuable reference for analyzing sequencing data from large population cohorts and underscores the significant potential of ZBOLT in large‐scale population genomics studies. [ABSTRACT FROM AUTHOR]

Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems. [ABSTRACT FROM AUTHOR]

Abstract Chronic wasting disease (CWD) can spread among cervids by direct and indirect transmission, the former being more likely in emerging areas. Identifying subpopulations allows the delineation of focal areas to target for intervention. We aimed to assess the population structure of white‐tailed deer (Odocoileus virginianus) in the northeastern United States at a regional scale to inform managers regarding gene flow throughout the region. We genotyped 10 microsatellites in 5701 wild deer samples from Maryland, New York, Ohio, Pennsylvania, and Virginia. We evaluated the distribution of genetic variability through spatial principal component analysis and inferred genetic structure using non‐spatial and spatial Bayesian clustering algorithms (BCAs). We simulated populations representing each inferred wild cluster, wild deer in each state and each physiographic province, total wild population, and a captive population. We conducted genetic assignment tests using these potential sources, calculating the probability of samples being correctly assigned to their origin. Non‐spatial BCA identified two clusters across the region, while spatial BCA suggested a maximum of nine clusters. Assignment tests correctly placed deer into captive or wild origin in most cases (94%), as previously reported, but performance varied when assigning wild deer to more specific origins. Assignments to clusters inferred via non‐spatial BCA performed well, but efficiency was greatly reduced when assigning samples to clusters inferred via spatial BCA. Differences between spatial BCA clusters are not strong enough to make assignment tests a reliable method for inferring the geographic origin of deer using 10 microsatellites. However, the genetic distinction between clusters may indicate natural and anthropogenic barriers of interest for management.

With the increasing adoption of agile methodologies in distributed software development teams, there is a need to adapt these practices for large-scale environments. However, the lack of specific guidance can make this process difficult. To evaluate how large-scale agile distributed teams adapt their practices to meet their specific contexts, this study launches a Systematic Literature Review (SLR). The SLR presents adaptations of agile methodologies in distributed software development teams operating in large-scale environments. With the growing popularity of agile methodologies, there is an increasing need to adapt them to suit the specific needs of distributed teams operating in large-scale contexts. The review identified 96 adapted practices from five agile frameworks (Scrum, Scaled Agile Framework (SAFe), Large Scale Scrum (LeSS), the Spotify model, and Disciplined Agile Delivery (DAD)) used in various case studies between 2007 and 2021. Scrum was the most commonly adapted framework with 32 customized practices, followed by SAFe (25), LeSS (17), the Spotify model (13), and DAD (9). The review provides insights into how these practices have been tailored to meet the needs of distributed teams in large-scale contexts. The findings can guide organizations in adapting agile practices to their specific contexts.

Spatiotemporal fusion (STF) can provide dense satellite image series with high spatial resolution. However, most spatiotemporal fusion approaches are time-consuming, which seriously limits their applicability in large-scale areas. To address this problem, some efforts have been paid for accelerating STF approaches with help of graphics processing units (GPUs), whose effect is dramatic. However, this strategy is hardware dependent, which may not be always satisfied. In this paper, we develop a hardware independent accelerating strategy, named AcSTF. The proposed AcSTF consists of two steps, which are medium resolution STF (MSTF) and local normalization-based fast fusion (LNFM). The MSTF utilizes STF methods to improve the coarse spatial resolution images to a medium spatial resolution, while the LNFM further refines the medium spatial resolution images to provide fine spatial resolution images. To test the AcSTF, the experiments are conducted using five commonly used STF approaches on two public Landsat-MODIS datasets. The experimental results indicate that AcSTF can not only reduce 87%–95% running time of current STF approaches, but also preserve their qualitative and quantitative performance well. After that, we apply the AcSTF to produce an intact 30 m image of the whole Ukraine mainland. Without any hardware which can speed up computing,the time for reconstructing the 30 m image is 5.42 h just using an unremarkable central processing unit (CPU). Compared to the real Landsat image, the reconstructed image achieves remarkable qualitative and quantitative performance, which demonstrates the practicability of the AcSTF.

The key benefits of qualitative research are rich insights and thick data. However, some argue these come at the cost of small sample sizes and low generalisability of findings. With traditional FGDs (FGDs), this could be addressed by conducting multiple groups. However, this requires significant investment of time and manpower. We explored methods to gather thick data quickly, aiming to increase the number of respondents without increasing manpower or lengthening fieldwork while maintaining data quality. In this paper, we detail our experience running a pilot study of a 1.5 hour online discussion with N=100 respondents, to capture in-depth responses at scale. Using pre-programmed questions and artificial intelligence (AI) to provide instant visual analyses of responses and additional probes to respondents live, we ran a full qualitative study with a bigger sample in the same duration required for a typical FGD. The discussion was text-based, with respondents being able to view and give their agreement or disagreement to what others may have said without interaction between them. The data was compared to data collected from a previous study with a similar topic and analysed from an operational aspect of conducting research and gathering insights from both methodologies. While this methodology does not replace traditional FGD, it has proven effective in scaling up qualitative research by gathering large amounts of qualitative data within a short duration, in real-time. The methodology has its limitations, primarily the inability to further nuance responses. Despite this, the pilot study appears to be a successful attempt conceptually, as the AI generated valuable instant insights while the study was ongoing, particularly from open-ended (OE) responses. It may add value to specific use cases such as quick sensing which require both breadth and scalability.

Accurate mapping of paddy rice cropping intensity (PRCI) affects precision agriculture, water use management, and informed decision-making. Many PRCI mapping approaches have been developed and achieved remarkable performance. However, large-scale and high-resolution PRCI mapping in southern China, especially in the Hunan Province, remains challenging. In this region, optical data suffer from serious data loss owing to frequent cloud coverage, whereas radar data are usually affected by mountainous terrain. Multisource data fusion may mitigate data loss; however, many data fusion approaches cannot be used in large-scale applications because of their significant computational complexity. To achieve accurate PRCI mapping in large-scale areas of southern China, we propose a novel large-scale downscaling method for multi-source data fusion. A new framework for PRCI mapping was developed using the Google Earth Engine. To test its performance, we mapped the 10 m PRCI of Hunan Province in 2020. The overall accuracy of the mapping was 90.70 % with a kappa coefficient of 0.81. Furthermore, our estimated sown area was highly correlated with the statistical yearbook (R2 = 0.84, RMSE = 11.47 kha). Our experimental results demonstrated the remarkable performance of the proposed framework for large-scale and high-resolution PRCI mapping in southern China.

There have been many research reports on the reinforcement of small-sized square columns with a cross-section of 200mm–300mm using prestressed carbon fiber-reinforced polymer (CFRP) materials, while there are few studies on piers in bridge and tower columns in cable-stayed bridges with a cross-section of several meters or even tens of meters. The horizontal prestressed steel tendons in the anchorage zone of tower columns in cable-stayed bridge replaced by prestressed CFRP sheets can not only facilitate construction and maintenance, but also have good fatigue resistance. The prestressed CFRP plate is used to reinforce the large-sized tower columns by using a specific device to tension the CFRP plate wrapped around the surface of the members. The tensioning device and test pedestal based on WSGG (wave-shaped-gear-grip) anchor clamping of CFRP plate have been developed in this paper, and the CFRP plate circumferential tensioning tests on the pedestal have been conducted. The test results are as follows: (1) the developed device can achieve circumferential tensioning of single-layer CFRP plate to 0.5ftk of the material, reaching a tensile force of 60kN, and generate effective restraint pressure on a 2-m long composite compression component; (2)The calculation formula for the constraint pressure generated by the circumferential prestressed CFRP sheet on the component has been derived and verified, and the maximum error between the calculated value and the experimental value is within 5%; (3) When iron sheet serves as the interface medium between CFRP plate and compression components, the prestress loss of the CFRP plate tensioned at one end is about 84% and 58%–60% when tensioned at both ends. It can be seen that the effective prestress of the CFRP plate with iron sheet as the interface medium is relatively small. Meanwhile, based on the distribution of compressive stress in the components and the effective pre tension value of CFRP plate, it can be seen that two end tensioning is better than one end tensioning; (4) The tensile stress of CFRP plate along the member is a cubic function when the tension force is 60kN, so it is deduced that the constrained compressive stress generated by CFRP plate on the member is a quadratic function distribution.

Subsurface geothermal energy storage has greater potential than other energy storage strategies in terms of capacity scale and time duration. Carbon dioxide (CO2) is regarded as a potential medium for energy storage due to its superior thermal properties. Moreover, the use of CO2 plumes for geothermal energy storage mitigates the greenhouse effect by storing CO2 in geological bodies. In this work, an integrated framework is proposed for synergistic geothermal energy storage and CO2 sequestration and utilization. Within this framework, CO2 is first injected into geothermal layers for energy accumulation. The resultant high-energy CO2 is then introduced into a target oil reservoir for CO2 utilization and geothermal energy storage. As a result, CO2 is sequestrated in the geological oil reservoir body. The results show that, as high-energy CO2 is injected, the average temperature of the whole target reservoir is greatly increased. With the assistance of geothermal energy, the geological utilization efficiency of CO2 is higher, resulting in a 10.1% increase in oil displacement efficiency. According to a storage-potential assessment of the simulated CO2 site, 110 years after the CO2 injection, the utilization efficiency of the geological body will be as high as 91.2%, and the final injection quantity of the CO2 in the site will be as high as 9.529 × 108 t. After 1000 years sequestration, the supercritical phase dominates in CO2 sequestration, followed by the liquid phase and then the mineralized phase. In addition, CO2 sequestration accounting for dissolution trapping increases significantly due to the presence of residual oil. More importantly, CO2 exhibits excellent performance in storing geothermal energy on a large scale; for example, the total energy stored in the studied geological body can provide the yearly energy supply for over 3.5 × 107 normal households. Application of this integrated approach holds great significance for large-scale geothermal energy storage and the achievement of carbon neutrality.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Abstract Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

This study focuses on the development of a large-dimensional 3D printer and its challenges in general. The major fused deposition modeling (FDM) printers focus on printing small-scale parts due to their challenges in printing large-scale objects using thermoplastic polymer filaments. A novel large-dimensional multi-extrusion FDM printer is developed at the workshop and printed several large-dimensional objects to emphasize its prospects in developing large-scale products. The printer has a print bed with a dimension of 900 mm × 1100 mm × 770 mm with respect to the length–width–height (L–W–H), respectively. There are many challenges to successfully printing large-dimensional objects using FDM technology. The experimental design elaborates on the challenges experienced during printing various large-dimensional objects. In addition, the paper focuses on the qualitative analysis of the optimal process parameters in section 4.5. Based on the experimental results, the key challenges are found to be uneven bed temperature, bending of print bed due to thermal effect, surface unsticks due to lack of adhesive force, surrounding temperature, and irregular filament feed. Experimental results also validate the key design specifications and their impact on enhancing large-scale 3D printing. The developed printer is capable of printing large-scale objects with five different thermoplastic materials using five individual extruders simultaneously. It adds a new dimension of flexible automation in additive manufacturing (AM).

Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse brain for a cardinal cognitive function called working memory, the brain’s ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for interareal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous attractor states, which are self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

Abstract Background With the advancement of whole‐genome sequencing (WGS) technology, massively parallel sequencing (MPS) remains the mainstream due to its accuracy, low cost, and high throughput. The development of the analytical pipeline corresponding to MPS has always been of great importance. Increasingly large population genomics studies, as a specific type of big data research, pose new challenges for analysis solutions. Results Here, we introduce ZBOLT, a comprehensive analysis system that incorporates both software and hardware advancements, making it an appropriate choice for large‐scale population genomic studies that require extensive data processing. In this study, we first evaluate ZBOLT's calling accuracy using the Genome in a Bottle (GIAB) benchmark dataset. Then we apply ZBOLT to a large‐scale population genomics study with 5,616 high sequencing depth samples totaling 1.16Pbp (base pair). As the results show, ZBOLT demonstrates exceptional efficiency and low energy consumption, processing 100Tbp per day and using 1kWh per 100Gbp sequenced sample. Conclusion This research serves as a valuable reference for analyzing sequencing data from large population cohorts and underscores the significant potential of ZBOLT in large‐scale population genomics studies.

Study Region: Europe Study Focus: The semi-distributed Hydrological Predictions for the Environment (HYPE) model for the European domain, E-HYPE4, was calibrated for discharge and sediments within a framework allowing for evaluation of the factors limiting model performance. Calibration was conducted using a multi-phase approach during which an initial multi-objective discharge/sediment calibration was followed by an exhaustive sediment calibration in which combinations of sediment routines were assessed. During each calibration phase, ensembles of parameter sets and model routines were simultaneously evaluated against discharge, evapotranspiration, and sediment observations. In total, 20,000 parameter sets were evaluated during the discharge calibration, and a further 20,000 model setups were assessed during the sediment calibration. New Hydrological Insights for the Region: Model performance was best with a highly regionalized model, and the largest drop in achievable performance occurred when transitioning from an ensemble of candidates to a single model setup. Much of the performance gains from a highly regionalized model could, however, be achieved with a much less regionalized model. Inclusion of sediments as a calibration objective provided more value than that of evapotranspiration in regards to reducing equifinality in the calibration for discharge. Evaluation of the erosion routines indicated that an index-based routine performed equally well as a more complex, process-based routine. Finally, analysis of model performance by subbasin attributes revealed the dominant factors — such as landuse, glaciers, abstractions/regulations, groundwater, and lakes/wetlands — affecting model biases for various regions.

Large-scale short carbon fibre-reinforced silicon carbide (Csf/SiC) ceramic matrix composites (CMCs) have important applications in the field of aerospace engineering. This study proposed the use of material extrusion based additive manufacturing to fabricate large-scale Csf/SiC CMC preforms. In this paper, we determined how the key material extrusion parameters, including solid loading, nozzle diameter and layer height impact the stability of the additively manufactured Csf/SiC CMCs. The solid loading significantly influenced the stability of the Csf/SiC CMCs, and the slurry with 50 vol.% solid loading was better for additive manufacturing. The layer height played a significant role in the void formation in CMCs. It was appropriate for structure retention to set the layer height as 60–75% of the nozzle diameter. The effect of angle from vertical on the stability of out-of-plane structure was also investigated. When the angle was over 40o, the out-of-plane structure additively manufactured without supports tended to collapse. Large-scale Csf/SiC CMC preforms with out-of-plane structures were finally successfully fabricated. This study is believed to provide some fundamental understanding for the fabrication of large-scale fibre-reinforced ceramic matrix composites.

Different types of superhydrophobic materials have been fabricated by constructing surfaces with low surface energy and micro-nanostructures, most of which are coatings. Due to the fragile rough structure of the coating, it is very easy to lose its superhydrophobic properties when exposed to friction, corrosion, high temperature, and other effects of the external environment. This has largely hindered the large-scale application of superhydrophobic materials. Here, a rigid superhydrophobic material with polyurethane foam as the substrate, polytetrafluoroethylene (PTFE) nanoparticles as the matrix phase, and carbon nanotubes as the reinforcing phase have been reported. The fabricated material showed excellent superhydrophobicity (157∘ ± 1∘) and robustness. The contact angle of the superhydrophobic material surface remained above 150∘ and the rolling angle below 10∘ after 1000 cycles (distance is 40 m) of abrasion using 800 # sandpaper at pressure of 5 kPa. [ABSTRACT FROM AUTHOR]

Deep learning approaches offer new ideas for individual identification of communication radiation sources. It is an important issue for researchers to establish a suitable deep learning model for radio frequency fingerprint identification under deep learning based communication framework. To effectively identify IoT objects, and prevent the occurrence of problems such as device cloning, replay attacks and user identity impersonation, given the limitations of existing methods, a novel slice combination convolutional neural network model is designed for individual identification of communication radiation sources. First, radio frequency baseband I/Q signal is collected by a receiver; And then steady-state segment of the I/Q signal is intercepted through variance trajectory detection and divided into many slices; At last, the fingerprint of each radio frequency baseband I/Q signal, which involves physical layer essential properties of the transmitter, can be effectively extracted and learned by the proposed slice combination convolutional neural network model, and the individual recognition of the transmitter can be finally realized. Experimental test on the ADS-B radio frequency fingerprint recognition from 198 civil aviation airplanes in the real-word illustrate that the proposed approach can greatly improve the recognition accuracy compared with existing machine learning based and deep learning based methods, which is very suitable for large-scale real-world individual identification of communication radiation sources. [ABSTRACT FROM AUTHOR]

Large-scale sparse multiobjective optimization problems (SMOPs) widely exist in academic research and engineering applications. The curse of dimensionality and the fact that most decision variables take zero values make optimization very difficult. Sparse features are common to many practical complex problems currently, and using sparse features as a breakthrough point can enable many large-scale complex problems to be solved. We propose an efficient evolutionary algorithm based on deep reinforcement learning to solve large-scale SMOPs. Deep reinforcement learning networks are used for mining sparse variables to reduce the problem dimensionality, which is a challenge for large-scale multiobjective optimization. Then the three-way decision concept is used to optimize decision variables. The emphasis is on optimizing deterministic nonzero variables and continuously mining uncertain decision variables. Experimental results on sparse benchmark problems and real-world application problems show that the proposed algorithm performs well on SMOPs while being highly efficient. [ABSTRACT FROM AUTHOR]

Introduction: This article provides a strategy by which a manufacturing process with a Biosafety Level 2 (BL2) designation can be downgraded to Biosafety Level 1 (BL1). The principles of the downgrading process are based on the robust contamination controls in clinical and commercial manufacturing, which typically are not part of Research and Development processes. These strict requirements along with the application of current Good Manufacturing Practice (cGMP) principles provide a framework by which processes can be suitably managed and controlled to mitigate biohazard risk, specifically for cell lines that may be contaminated with human pathogenic viral agents. Purpose: We demonstrate how a risk assessment guide was used to define the risk profile of a theoretical process with a human cell line intended for clinical/commercial application. Based on the risk assessment, key BL2 elements were identified as suitable for downgrading, including facility containment controls, emergency spill response plans, and storage and shipping requirements. For various reasons, some aspects of the systems were deemed unsuitable for downgrading due to the severity of the control risk and, therefore, remained at BL2. Summary and Conclusions: We have used an established risk assessment guide to show how cGMP compliments and augments biosafety containment. We provide justification for downgrading from BL2 to BL1 for clinical and commercial cell and gene therapy manufacturing with human cell lines. [ABSTRACT FROM AUTHOR]

This study aimed to solve the scheduling problem of large-scale agile software project. It was decomposed into three strong-coupled subproblems; story selection, story allocation and task allocation. Dynamic events such as the addition and deletion of user stories, the change of employee's working hours in each sprint, and other constraints such as team development speed, task duration and skills were introduced. To maximize the total value of user stories completed by the project, a large-scale agile software project scheduling mathematical model was established. According to the characteristics of the problem, the Markov decision process was designed. Ten state features were used to describe the agile scheduling environment at the beginning of each sprint; 12 composite scheduling rules were designed as candidate actions of the agent; and rewards were defined according to the objective function of the scheduling model. A priority experience replay double deep Q network algorithm based on composite scheduling rules was proposed to solve the built model. The double Q network strategy and priority experience replay strategy were introduced to avoid the over-estimation problem of deep Q network and improve the utilization efficiency of trajectory information in the experience replay pool. In order to verify the effectiveness of the proposed algorithm, experiments were carried out in six large-scale agile software project scheduling numerical examples, and the convergence of the proposed algorithm was analyzed. According to the performance measurement of the algorithm, it was compared with the existing representative algorithm DQN, double deep Q network and 12 single composite scheduling rules. The results showed that it had the highest average cumulative reward value in 6 different numerical examples. [ABSTRACT FROM AUTHOR]

ABSTRACTBlock Adjustment (BA) is a critical procedure in the geometric processing of satellite images, responsible for compensating and correcting the geometric positioning errors of the images. The accuracy of the photogrammetric products, including Digital Orthophoto Map (DOM), Digital Elevation Model (DEM), Digital Line Graphic (DLG), and Digital Raster Graphic (DRG), directly depends on the accuracy of BA results. In recent years, the rapid development of related technologies such as Artificial Intelligence (AI), Computer Vision (CV), Unmanned Aerial Vehicles (UAVs) and big data has greatly facilitated and transformed the classical BA in photogrammetry. This paper first reviews the current status of BA and then looks into the future. First, this paper provides a brief review of the key technologies involved in BA, including image matching, the establishment of adjustment model, the determination of the parameters and the detection of gross error. Then, taking the intercross and fusion of current technologies such as AI, cloud computing and big data with photogrammetry into account, this paper explores the future trends of photogrammetry. Finally, four typical cases of large-scale adjustment are introduced, including large-scale BA without Ground Control Points (GCPs) for optical stereo satellite images, large-scale BA with laser altimetry data for optical stereo satellite images, large-scale BA for UAV oblique photogrammetry, and large-scale BA for indoor photogrammetry in caves with a large number of close-range images.

In this paper, we demonstrate the significant impact of the solution flow and electrical field on the homogeneity of large-scale ZnO nanorod electrodeposition from an aqueous solution containing zinc nitrate and ammonium nitrate, primarily based on the X-ray fluorescence results. The homogeneity can be enhanced by adjusting the counter electrode size and solution flow rate. We have successfully produced relatively uniform nanorod arrays on an 8 × 10 cm2 i-ZnO-coated fluorine-doped tin oxide (FTO) substrate using a compact counter electrode and a vertical stirring setup. The as-grown nanorods exhibit similar surface morphologies and dominant, intense, almost uniform near-band-edge emissions in different regions of the sample. Additionally, the surface reflectance is significantly reduced after depositing the ZnO nanorods, achieving a moth-eye effect through subwavelength structuring. This effect of the nanorod array structure indicates that it can improve the utilization efficiency of light reception or emission in various optoelectronic devices and products. The large-scale preparation of ZnO nanorods is more practical to apply and has an extremely broad application value. Based on the research results, it is feasible to prepare large-scale ZnO nanorods suitable for antireflective coatings and commercial applications by optimizing the electrodeposition conditions.

On the shores of the Northeast Pacific Coast, research programs have monitored the rocky intertidal zone for multiple decades across thousands of kilometers, ranking among the longest-term and largest-scale ecological monitoring programs in the world. These programs have produced powerful datasets using simple field methods, and many are now capitalizing on modern field-sampling technology and computing power to collect and analyze biological information at increasing scale and resolution. Considering its depth, breadth, and cutting-edge nature, this research field provides an excellent case study for examining the design and implementation of long-term, large-scale ecological monitoring. I curated literature and interviewed 25 practitioners to describe, in detail, the methods employed in 37 community-level surveys by 18 long-term monitoring programs on the Northeast Pacific Coast, from Baja California, México, to Alaska, United States of America. I then characterized trade-offs between survey design components, identified key strengths and limitations, and provided recommendations for best practices. In doing so, I identified data gaps and research priorities for sustaining and improving this important work. This analysis is timely, especially considering the threat that climate change and other anthropogenic stressors present to the persistence of rocky intertidal communities. More generally, this review provides insight that can benefit long-term monitoring within other ecosystems.

Creating a data management plan serves as both a suitable entry point as well as an accompanying guide to research data management for researchers. While the practice is becoming more common, it is still uncharted territory for most researchers. A comprehensive guidance as well as a facilitating environment are essential to enable researchers to integrate this practice into their everyday work. So far, little research has been directed towards the implications for the work of data stewards in this context, with hardly any recommendations on how they can provide such an enabling guidance and environment. We explored the implications in the Cluster of Excellence "Internet of Production" by conducting a series of interviews with researchers on the topic of research data management practices. Five major requirements emerged for the practicable design and implementation of a data management plan template in large-scale projects. On their basis, a customized template was created, whose project-wide implementation is currently being carried out.

• Characteristics of non-uniform pressure in a twin-screw compressor are revealed. • A new phenomenon of pressure waves inside a twin-screw compressor is discovered. • A fast method to estimate the influence of pressure waves is proposed. • A novel direction for the compressor design is suggested. To achieve higher efficiency of twin-screw compressors for refrigeration, the non-uniform internal pressure distribution in the rotor chamber and the flat section in the p-θ diagram were investigated in this paper. A three-dimensional CFD model of a twin-screw refrigeration compressor for chillers was established to analyze the generating mechanism of the flat section in the p-θ diagram. The p-θ diagrams of the compressor were compared under different rotational speeds and working fluids. Analysis results showed that the flat section in the p-θ diagram is caused by the periodic pressure wave propagation and reflection along the working chamber, and the pressure along the chamber is non-uniform when the sound speed is relatively slow compared with the chamber length in one compression cycle. A dimensionless ratio of chamber length and pressure wave travel distance is proposed to estimate the pressure distribution in the working chamber, based on which the flat section is prone to appear in large-scale compressors with low sound speed refrigerants like R134a. Some improvement directions were put forward to avoid efficiency losses, i.e., appropriately delaying the suction end angle and modifying the discharge port to match the pressure distribution. [ABSTRACT FROM AUTHOR]

In the last two decades, the market for mycorrhizal inoculants has significantly expanded, with Arbuscular mycorrhizal fungus (AMF) dominating the industry. The demand for AM inoculants, especially in field crop production and horticulture, has outpaced the supply. To bridge this gap, it is crucial to understand various mass-cultivation techniques that can produce a large quantity of sterile AM inoculum economically. AMF is the most widespread type of mycorrhiza, forming a symbiotic relationship with 80-90% of terrestrial plant species. The world's most abundant mutualism involves the bi-directional transfer of nutrients, benefiting both the plants and the soil. The AMF colonises the plant host's cortical cells and aids in nutrient transfer, particularly phosphorus, to the plants. In return, the plant provides carbon to the AM fungus. The obligate nature of AMF presents a challenge for mass cultivation, as it requires plant roots to complete its life cycle. Additionally, producing contaminant-free AMF inoculum in bulk has been a bottleneck for applications in agriculture. Over the years, scientists have developed several techniques for large-scale production of AMF, such as conventional put multiplication, aeroponic cultivation, hydroponic cultivation, bioreactors, Root-organ culture (ROC), etc. However, in vitro techniques have proven to be the most viable for sterile propagation in large quantities and in a shorter time and space. [ABSTRACT FROM AUTHOR]

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In this paper, we presented two developments, the first deals with generalizing the modernization of the damped Dai-Liao formula in an optimized manner. The second development is by suggesting a monotone line search formula for our new algorithms. These new algorithms with the new monotone line search yielded good numerical results when compared to the standard Dai-Liao (DL) and minimum description length (MDL) algorithms. Through several theorems, the new algorithms proved to have a faster convergence to reach the optimum point. These comparisons are drawn in the results section for the tools (Iter, Eval-F, Time) which are a comprehensive measure of the efficiency of the new algorithms with the basic algorithms that we used in the paper. [ABSTRACT FROM AUTHOR]