Space Cost Research Articles

Sound Absorption Characteristics of Integrated Membrane-Fabric Materials

Noise pollution has emerged as one of the most important environmental issues, particularly in developing countries. The use of sound absorption material has proven to be a successful approach for minimizing and controlling noise levels. Combining a membrane or panel with two or more porous materials is a practical approach for sound absorption. However, it often makes the absorber thicker, taking up more space and increasing production costs. This research proposes a new integrated sound absorber that combines a rubber membrane with a fabric to absorb sound at different frequencies. The new material eliminates bulky porous materials, saving space and production costs. By varying perforation sizes, percentages, and backed air gap distances, the study investigates the sound absorption characteristics of the integrated membrane-fabric material. The research has determined that the integrated membrane-fabric material exhibits exceptional sound absorption performance across a wide frequency range, which is influenced by several factors, including the diameter and ratio of perforations, and the depth of the air cavity. Furthermore, the investigation revealed that the positioning of the fabric also plays a crucial role in determining the material's absorption performance.

MXene guides microwaves through 3D polymeric structures

With the advances in space technology, weight reduction of components has been a paramount, yet challenging task. Additive manufacturing with high-performance polymers can realize lightweight and complex geometries that can also be manufactured on board. Yet polymers are electromagnetically inefficient for applications requiring electrical conductivity, such as guiding microwave signals. This work presents high-efficiency and lightweight additively-manufactured microwave components enabled by MXene coating. The waveguiding functionality was observed from 8 to 33 GHz, covering low earth orbit (LEO) frequencies, with a power-handling capability of up to 10 dB and a transmission coefficient of 93 %. After a single dip-coating cycle, the polymer waveguide performed only 2 % below an eight times heavier metallic equivalent. Frequency/polarization filtering was enabled by implementing special geometries, and a range of microwave functionalities, including resonance, was demonstrated. The MXene-coated components can replace 3D-printed and bulk metals, greatly decreasing weight and cost in space, and also in various terrestrial applications.

Hierarchical Pruning of Deep Ensembles with Focal Diversity

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study and apply deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This article presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal ensemble diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble team, which can guide ensemble pruning. Second, we design a focal ensemble diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high ensemble execution efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better classification generalizability while being more time and space efficient in ensemble decision making. We have released the source codes on GitHub at https://github.com/git-disl/HQ-Ensemble .

MalOSDF: An Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning

The evolution of malware poses significant challenges to the security of cyberspace. Machine learning-based approaches have demonstrated significant potential in the field of malware detection. However, such methods are partially limited, such as having tremendous feature space, data inequality, and high cost of labeling. In response to these aforementioned bottlenecks, this paper presents an Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning (MalOSDF). Inspired by traditional code slicing technology, this paper proposes a feature engineering method based on opcode slice for malware detection to better capture malware characteristics. To address the challenges of high expert costs and unbalanced sample distribution, this paper proposes the SSEAL (Semi-supervised Ensemble Active Learning) algorithm. Specifically, the semi-supervised learning module reduces data labeling costs, the active learning module enables knowledge mining from informative samples, and the ensemble learning module ensures model reliability. Furthermore, five experiments are conducted using the Kaggle dataset and DataWhale to validate the proposed framework. The experimental results demonstrate that our method effectively represents malware features. Additionally, SSEAL achieves its intended goal by training the model with only 13.4% of available data.

Conflict-Free Parallel Data Access Technology for Matrix Calculation in Memory System of ASIP of 5G/6G Macro Base Stations

Among the physical layer baseband algorithms in macro base stations, the matrix processing has the dom-inant computing cost, large data access overhead and com-plicated addressing mode. The existing data access methods are not the best solution for memory system of ASIP (Ap-plication Specific Instruction Set Processor) in 5G/6G macro stations. We hence proposed a parallel conflict-free data access method for matrix. Moreover, we proposed a non-redundant access method for positive definite matrix that stores only trigonometric part, and the corresponding parallel addressing method with low storage overhead. Our method solves the problem of data conflict and minimizes the memory space of ASIP, and supports ASIP to approach the performance limit of architecture to the maximum ex-tent under the constraint of architecture and data parallel-ism. We implemented the proposed method as a static memory optimizer, which provides a tool for the design and optimization of memory system of ASIP in 5G/6G macro base stations with low overhead. Experimental results show that for 64W64 positive definite matrix inversion algorithm based on Cholesky decomposition, our addressing method can save 32% of the execution time, and the cost of memory space is reduced by half.

Optimization of the catalytic production of methyl stearate by applying response surface Box-Behnken design: an intensified green option for high-cetane biofuel manufacture.

To enhance the efficiency of processes by decreasing the reaction severity and energy consumption, and reducing the equipment size, facilities' space and operation cost, process intensification is an increasingly used option in the chemical industry. Within this framework and in agreement with some of the green chemistry principles (design for energy efficiency and use of renewable feedstocks), this work deals with the implementation of high-shear mixing (HSM) to intensify the homogeneous esterification of stearic acid (SA) with methanol to methyl stearate, a high-cetane number alkyl ester suitable to be added into biofuel streams. The response surface Box-Behnken design (BBD) is applied to quantify the main effects and two-way interactions of four key input reaction factors: methanol : SA ratio (7-16 mol mol-1), catalyst mass (0.25-4.0 wt%), temperature (40-60 °C), time (1-12 min), and to approximate the optimal conditions on the intensified SA esterification. The statistical BBD results indicates that the four linear effects, two of the four possible quadratic effects (catalyst mass and temperature) and only one (catalyst mass-time) of the six existing two-way interactions are statistically relevant at the 95% confidence level. Catalyst mass is the most influencing factor in the reaction, followed by methanol : SA ratio, temperature, and time. The proposed second-order regression model predicts that the intensified esterification requires only 12 min to practically convert all SA (99% ± 6.8%) running the reaction at 12.4 methanol : SA ratio, 4 wt% catalyst mass, 60 °C and 500 rpm, a value experimentally validated (93.2% ± 0.7%). Under these conditions and with the assistance of HSM, the typical reaction length of conventional heterogeneous and homogeneous-phase esterification processes decreases from 5 to 117 and 35 to 90 times, respectively.

Retrofitting Public Space

Public space is critical for urban life to thrive. Yet, a global deficiency exists in available public space, particularly in the cities of the global south, but affecting cities in the global north as well. While much attention has been paid to upgrading existing public spaces, the thorny problem of retrofitting additional public spaces in existing urban fabrics has been under researched. Grounded in a review of the value and costs of public space, it is argued that there is no ‘ideal’ amount of public space that applies universally, and that public space supply should be allowed to change over time as urban and social conditions change. Public space suffers from a directionality problem in which adding public space is much harder than subtracting public space, such as through encroachment or sale of public land. Through a comparative case study approach, this paper responds to this problem by articulating the solution space to retrofit public spaces in existing neighbourhoods through five distinct pathways, namely ‘acupuncture’, ‘superimposition’, ‘marketplace’, ‘reblocking’ and ‘plot subdivision’. It explores the particular characteristics of each approach in varied urban contexts and provides a cross-cutting typology that connects practices across a global north and south context. The five pathways are offered as a practice-oriented typology to empower planners and policymakers to make more informed urban planning and design choices.

External effects of urban automated vehicles on sustainability

The transport sector is responsible for an important share of pollutant emissions, and urban mobility systems are facing major challenges, such as congestion and parking needs. Automated vehicles are expected to mitigate effects of this kind, and may positively affect the sustainability of the mobility sector. We quantify the external effects of human-driven battery electric vehicles in comparison to automated ones, regarding external costs for urban areas in Germany. In our analysis we focus on environmental, social and economic external cost components. We find that vehicle automation can lower external costs by 34%, suggesting savings of 51.6 billion euros per year for Germany alone, with congestion and parking space costs accounting for the largest share of costs and savings. Our results provide an understanding of the magnitude of the external effects of automated vehicles, conducting sensitivity analyses and additionally discussing the impact of travel behavior.

A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams

Designing a space-efficient data structure to answer membership queries while ensuring high accuracy and real-time response is a challenging task in the field of stream processing. Many techniques have been developed to answer these queries in a sliding windows manner. However, assuming the user will conduct the query with the presupposed window size is not always practical. In this paper, we introduce a novel data structure called Learned Cuckoo Filter (LCF). It can provide satisfactory results for the approximate membership query on data streams, regardless of the user-defined query windows. LCF operates by adaptively maintaining cuckoo filters with the assistance of a well-trained oracle that learned the frequency feature of the data within the stream. To further enhance memory utilization, we develop a compact version of LCF (denoted by LCF_C), which selectively removes redundant information to reduce space consumption without compromising query accuracy. Furthermore, we conduct a thorough theoretical analysis of query accuracy and provide detailed guidelines for optimal parameter selection (denoted by LCF_O). Extensive experimental studies on synthetic and real-world datasets demonstrate the superiority of the proposed methods in terms of both space consumption and accuracy. Compared to the state-of-the-art algorithms, LCF_O can reduce up to 61% of space cost at the same error level, and achieve up to 12× improved accuracy with the same space cost.

Paper Invoice Dilemma and Electronic Invoice Solution

This paper will start from the definition and purpose of paper invoice, analyze the shortcomings and problems in the use of paper invoice, and introduce the definition, advantages and purpose of electronic invoice, in order to solve the dilemma faced by the use of paper invoice. As a traditional form of bill, paper invoice plays an important role in commercial transactions, but there are some problems in its use. First of all, paper invoices are prone to damage, loss or tampering, increasing the risk and uncertainty of transactions. Secondly, the preservation and management of paper invoices requires a lot of time, space and labor costs, which brings no small burden to enterprises. In addition, because the paper invoice cannot realize real-time information sharing and tracking, resulting in the information process is not smooth and inefficient. In order to solve these problems of paper invoice, electronic invoice came into being. Electronic invoice is a new form of bill based on electronic data exchange and digital signature technology. It has the following advantages: First, electronic invoice does not need paper carrier, reduces the cost of use and management, and is not easy to be damaged, lost or tampered with, improving the reliability and security of transactions. Secondly, electronic invoices can realize real-time information sharing and tracking, which improves the efficiency and convenience of transactions. In addition, the electronic invoice can also be seamlessly connected with the enterprise's financial management system, to achieve automated accounting processing, reducing manual operations and errors. In short, there are a series of shortcomings and problems in the use of paper invoices, such as easy loss, difficult management, not easy to share information and so on. As a solution, electronic invoice has been more and more widely used and promoted by virtue of its advantages of eliminating paper carrier, improving transaction security and reliability, and realizing real-time information sharing and tracking. In the future, with the continuous development and improvement of technology, electronic invoices will become the mainstream form of business transactions, and promote the process of paper invoices to electronic transformation.

Implementation of VA care coordination program to improve transitional care for veterans post-non-VA hospital discharge: an incremental cost analysis.

The Veterans Affairs (VA) Healthcare System Community Hospital Transitions Program (CHTP) was implemented as a nurse-led intervention to reduce barriers that patients experience when transitioning from community hospitals to VA primary care settings. A previous analysis indicated that veterans who enrolled in CHTP received timely follow-up care and communications that improved care coordination, but did not examine cost implications for the VA. A budget impact analysis used the VA (payer) perspective. CHTP implementation team members and study records identified key resources required to initially implement and run the CHTP. Statistical analysis of program participants and matched controls at two study sites was used to estimate incremental VA primary care costs per veteran. Using combined program implementation, operations, and healthcare cost estimates to guide key model assumptions, overall CHTP costs were estimated for a 5-year time horizon, including a discount rate of 3%, annual inflation of 2.5%, and a sensitivity analysis that considered two options for staffing the program at VA Medical Center (VAMC) sites. Implementation at two VAMCs required 3 months, including central program support and site-level onboarding, with costs of $34,094 (range: $25,355-$51,602), which included direct and indirect resource costs of personnel time, materials, space, and equipment. Subsequent annual costs to run the program at each site depended heavily on the staffing mix and caseload of veterans, with a baseline estimate of $193,802 to $264,868. Patients enrolled in CHTP had post-hospitalization VA primary care costs that were higher than matched controls. Over 5 years, CHTP sites staffed to serve 25-30 veterans per full-time equivalent transition team member per month had an estimated budget impact of $625 per veteran served if the transitional team included a medical social worker to support veterans with more social behavioral needs and less complex medical cases or $815 per veteran if nurses served all cases. Evidence-based care coordination programs that support patients' return to VA primary care after a community hospital stay are feasible to implement and run. Further, flexibility in staffing this type of program is increasingly relevant as the VA and other healthcare systems consider methods to reduce provider burnout, optimize staffing, reduce costs, and address other staffing challenges while improving patient care.

EVALUATION AND CONTROL MANAGEMENT OF THE DEVELOPMENT OF DIGITALIZATION AT THE ENTERPRISE

The article examines the features of the fourth industrial revolution (G4). It is noted that the component of this revolution is Industry 4.0 with many technologies to create a single space for information exchange and virtual visualization of processes and objects. It also provides for the formation and implementation of robotic systems and Internet technologies in the format of "smart" enterprises. It is noted that there is an incorrect understanding of the very essence of the digital economy, which significantly hinders the successful implementation of digital transformation. Digital transformation (digitalization) is presented as a process of transition to such a digital business that uses digital technologies to change business processes in the company and provide new opportunities in the direction of obtaining additional income and prospects for its further development. A comparative analysis of individual approaches to the construction of models to identify the most general and priority directions for assessing the digital maturity of an enterprise is given. A conceptual scheme for managing the digital maturity of an enterprise is proposed. It is noted that the strategy serves as the basis of the entire digital transformation and it determines the focus of transformations and the target level of digital maturity of the enterprise. and also makes it possible to estimate resource, labor and financial costs in time and space. The tools for detailing and implementing the strategy are a business model, a road map and a corresponding portfolio of projects, balanced from the standpoint of the implementation of technological and non-technological innovations aimed at the permanent growth of the digital maturity of the enterprise. Updating the business model is one of the most important components of the growth of the digital maturity of the enterprise, which is carried out in the direction of expanding cooperative interactions, using multilateral platforms and creating a business ecosystem. Recommendations on digital transformation of the enterprise and increasing its digital maturity are given, and the stages of this work are substantiated.

Graph-Indexed kNN Query Optimization on Road Network

The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method’s inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm.

Balanced comparative assessment of thermal performance and energy efficiency for three cooling solutions in data centers

As the demand for data centers rises, efficient cooling systems become increasingly vital to ensure uninterrupted operations and adaptability to IT service changes. The research focuses on quantitatively comparing the thermal performance and energy efficiency of cooling systems capable of handling high-density IT power. It aims to provide objective criteria for selecting suitable cooling solutions, including an assessment of three 150 kW-class IT and facility modules, a novel contribution not extensively explored in previous studies. The study evaluates room-based, row-based, and rack-based cooling options based on the configured ITE power density, ensuring that all three solutions meet ASHRAE's recommended IT operating environment through CFD analysis. The research reveals energy efficiency improvements, with PUEcooling decreasing from 1.33 for room-based cooling to 1.28 for rack-based cooling. These improvements are attributed to reduced fan power by CRAC/H type and decreased primary plant energy consumption by increasing COP through the supply chilled water temperature. The findings offer valuable insights for designing cooling solutions tailored to ITE power density and considering factors such as space requirements, thermal performance, energy efficiency, and cost for new hyperscale data centers. Overall, this research constitutes a significant academic contribution in the field of data center cooling solutions.

Machine learning based data collection protocol for intelligent transport systems: a real-time implementation on Dublin M50, Ireland

The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent/daily commuters and second new/occasional commuters. The proposed protocol’s main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland’s busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols.

Bidirectional piecewise linear representation of time series with application to collective anomaly detection

Directly mining high-dimensional time series presents several challenges, such as time and space costs. This study proposes a new approach for representing time series data and evaluates its effectiveness in detecting collective anomalies. The proposed method, called bidirectional piecewise linear representation (BPLR), represents the original time series using a set of linear fitting functions, which allows for dimensionality reduction while maintaining its dynamic characteristics. Similarity measurement is then performed using the piecewise integration (PI) approach, which achieves good detection performance with low computational overhead. Experimental results on synthetic and real-world data sets confirm the effectiveness and advantages of the proposed approach. The ability of the proposed method to capture more dynamic details of time series leads to consistently superior performance compared to other existing methods.

A Decade of Land Cover Transformations in Akure's Peri-Urban Region (2011-2022) Using Multispectral Landsat Imagery

Aim: To analyze the spatial distribution of land uses in the peri-urban areas of Akure, Nigeria between 2011 and 2022.
 Study Design: Quantitative GIS research.
 Place and Duration of Study: Oke-Odu, Ipinsa and Aule, Akure, Ondo-State, Nigeria, 2022.
 Methodology: ArcGIS was employed in conjunction with Landsat images for 2011, 2016 and 2022 to conduct a supervised classification of land uses in Oke-Odu, Ipinsa and Aule Peri-urban areas of Akure, Nigeria. Data quality was considered such that cloud cover and scene cover of selected imagery downloaded are less than 10%. The classification method used was maximum likelihood, and three distinct classes were targeted: Built up areas, Green Areas and Open Spaces.
 Results: Between 2011 and 2022, Oke-Odu, Akure, Nigeria, experienced significant development, with its built-up area growing from 45 ha (20.15%) to 160 ha (71.45%). However, this came at the cost of green spaces, which decreased from 97 ha (43.43%) to 33 ha (14.78%), indicating a loss of vegetation and natural areas. Similarly, open spaces decreased from 65 ha (29.42%) to 14 ha (6.46%). In Ipinsa, the built-up area expanded from 6 ha (6.50%) to 36 ha (36.40%), while green areas decreased initially but slightly increased later. Open spaces in Ipinsa declined consistently from 17 ha (17.50%) to 3 ha (3.10%). Aule experienced steady development, with built-up areas increasing from 297 ha (56.86%) to 431 ha (82.30%), accompanied by a decline in green spaces and open spaces over time. Overall, built-up areas and open spaces in Oke-Odu, Ipinsa, and Aule have significantly increased over time; however, in both Ipinsa and Aule, there was a slight recovery of green spaces in 2022.
 Conclusion: The study shows that the pattern of land uses in Oke-Odu, Ipinsa, and Aule, Akure demonstrates a consistent trend of urbanization. Built-up areas have expanded substantially over time, while green spaces and open spaces have experienced significant declines, these trends align with global patterns of urbanization and raise concerns about environmental degradation and the need for conservation efforts. Therefore, this study recommends that sustainable development strategies be Implemented to ensure a balance between built-up areas and natural environments.

Thermal simulation analysis of power module of combined converter for metro vehicle

In order to reduce the occupancy space and equipment cost of the on-board converter, a combined traction-auxiliary converter for subway vehicles is proposed in this paper. The power loss of the main traction converter module (MCM) and the auxiliary converter module (ACM) was calculated according to the speed and power variation of the vehicle running between five consecutive stations, and the flow rate of cooling air was calculated through the heat balance theory. The air volume of MCM and ACM and the plate temperature of the radiator are analysed by step method. Then, according to the instantaneous maximum temperature of the radiator obtained by simulation and the thermal parameters of the IGBT module, the junction temperature of the IGBT and diode chips is calculated by means of the junction temperature equivalent circuit. The results show that the plate temperature of the heat sink and junction temperature meet the design requirements. The design and thermal simulation method in this paper provide theoretical reference for the design of railway vehicle converters.

Adaptive error bounded piecewise linear approximation for time-series representation

Error-bounded piecewise linear approximation (l∞-PLA) has been proven effective in addressing challenges in data management and analytics. It works by approximating the original time series with linear segments such that the approximation error on each data point does not exceed a pre-defined threshold. To achieve this goal, most prior works on l∞-PLA use a fixed error threshold during the entire approximation process, assuming a stable time series. However, in many real-world applications, the distribution of values on the time series undergoes substantial changes in different temporal stages. This introduces a need to adaptively tune the error threshold based on the properties of the time series while considering the trade-off between representation error and storage resources. In this work, we propose a general framework for constructing l∞-PLA with adaptive error bounds (AEPLA). It works by dividing the original time series into a set of intervals and assigns adaptive error bounds to these sub-sequences based on their fluctuation levels. Next, these sub-sequences are approximated using a user-defined l∞-PLA method. We implement this framework by employing three different types of l∞-PLA methods and evaluate the performance of our approach on an extensive set of real-world time-series datasets from industrial and scientific domains. Our experiments show that constructing l∞-PLA using the AEPLA framework can provide better a trade-off between representation error and storage resources and achieves lower time and space costs than applying the original l∞-PLA methods.

The Interplanetary School of IR

From the late nineteenth century through the end of World War II, a new theory emerged regarding the future of international relations. Growing out from the budding science-fiction genre popularized by figures such as H. G. Wells, and maturing in the popular interwar space movements of Russia, Western Europe, and the United States, this theory proposed that human spaceflight would have a transformative impact on world society. Rocket engineers, sci-fi authors, and philosophers on both sides of the Atlantic argued that 1) space exploration would provide a vessel through which to channel aggression and violence; 2) the cost and technical difficulty of opening space would force once rival nations to cooperate; and 3) spaceflight would transform human consciousness itself, making of homo sapiens a more magnanimous, egalitarian, and pacific species. This article traces the rise and spread of what I call ‘interplanetary’ theory across time, nations, and professional disciplines, as well as the ways in which that theory departed from prevailing convictions about international relations after World War I.