Space Cost Research Articles

Physician Satisfaction With Lactation Resources Following an Intervention to Improve Lactation Accommodations

Physicians who attempt to continue breastfeeding after returning from childbearing leave identify numerous obstacles at work, which may affect job satisfaction, retention, and the diversity of the physician workforce. To study the association between improved lactation accommodation support and physician satisfaction. This cohort study compared the physician experience before and after a July 2020 intervention to improve physician lactation accommodation support at a large, urban, academic health system. The satisfaction of physicians returning from childbearing leave between July 1, 2018, and June 30, 2020 (preintervention), was compared with that of physicians returning from leave between July 1, 2020, and November 30, 2021 (postintervention). Initial data analysis was performed on February 22, 2022, with additional tests for interaction performed on May 18, 2023. The intervention included creating functional lactation spaces, redesigning communication regarding lactation resources, establishing physician-specific lactation policies, and developing a program to reimburse faculty for time spent expressing breastmilk in the ambulatory setting. The main outcomes were (1) space improvements, use, and costs of the lactation accommodation program and (2) an ad hoc survey of physicians' reported experience with lactation accommodation support before and after the intervention. Survey data were collected using a 5-point Likert scale to assess physician perceptions of institutional support. Responses collected during the preintervention period were compared with those collected during the postintervention period using unpaired t tests. In this study, 70 clinical faculty (mean [SD] age, 34.4 [2.9] years) took childbearing leave in the preintervention period compared with 52 (mean [SD] age, 34.8 [2.7] years) in the postintervention period. Fifty-eight physicians (83%) completed the preintervention survey and 48 completed the postintervention survey. When comparing the pre- and postintervention periods, faculty reported improvements in finding time in their clinical schedule to devote to pumping (mean [SD] response, 2.5 [1.3] vs 3.6 [1.5]; P < .001), initiatives to address the impact of lactation time on productivity (mean [SD] response, 2.0 [1.0] vs 3.0 [1.5]; P = .001), and a culture supportive of lactation (mean [SD] response, 2.8 [1.4] vs 3.4 [1.3]; P = .047). Forty childbearing faculty took advantage of lactation time reimbursement and were reimbursed a total of $242 744.37. Faculty whose return to work overlapped with the entire year of the study received financial support for lactation for a mean (SD) of 8.9 (0.2) months, with an average reimbursement of $9125.78. The findings of this cohort study suggest that a multifaceted intervention to combat common challenges in lactation support in academic medical centers yielded improvements in faculty perceptions of institutional support for pumping breastmilk, addressing the impact of lactation time on productivity, and providing a culture supportive of lactation. These findings support the adoption of interventions to improve physician lactation accommodations.

Micro-Colony Forming Unit Assay for Efficacy Evaluation of Vaccines against Tuberculosis.

Tuberculosis (TB), the leading cause of death worldwide by an infectious agent, killed 1.6 million people in 2022, only being surpassed by COVID-19 during the 2019-2021 pandemic. The disease is caused by the bacterium Mycobacterium tuberculosis (M.tb). The Mycobacterium bovis strain Bacillus Calmette-Guérin (BCG), the only TB vaccine, is the oldest licensed vaccine in the world, still in use. Currently, there are 12 vaccines in clinical trials and dozens of vaccines under pre-clinical development. The method of choice used to assess the efficacy of TB vaccines in pre-clinical studies is the enumeration of bacterial colonies by the colony-forming units (CFU) assay. This time-consuming assay takes 4 to 6 weeks to conclude, requires substantial laboratory and incubator space, has high reagent costs, and is prone to contamination. Here we describe an optimized method for colony enumeration, the micro-CFU (mCFU), that offers a simple and rapid solution to analyze M.tb vaccine efficacy results. The mCFU assay requires tenfold fewer reagents, reduces the incubation period threefold, taking 1 to 2 weeks to conclude, reduces lab space and reagent cost, and minimizes the health and safety risks associated with working with large numbers of M.tb. Moreover, to evaluate the efficacy of a TB vaccine, samples may be obtained from a variety of sources, including tissues from vaccinated animals infected with Mycobacteria. We also describe an optimized method to produce a unicellular, uniform, and high-quality mycobacterial culture for infection studies. Finally, we propose that these methods should be universally adopted for pre-clinical studies of vaccine efficacy determination, ultimately leading to time reduction in the development of vaccines against TB.

Quasi-synchronization of heterogeneous stochastic coupled reaction-diffusion neural networks with mixed time-varying delays via boundary control

In this paper, the quasi-synchronization problem of heterogeneous stochastic coupled neural networks (HSCNNs) is discussed. The effects of the mixed time-varying delay and diffusion phenomenon on the system are considered separately in time and space. Moreover, different from the previous distributed control, boundary control is introduced to realize network synchronization. This not only reduces the space cost of the controller, but also makes it easier to implement. Thus, the mean-square quasi-synchronization of HSCNNs is guaranteed by using matrix inequality and stochastic analysis tools. In addition to focusing on systems with Neumann boundary conditions, we briefly investigate HSCNNs with time-invariant delays and mixed boundary conditions respectively, and provide sufficient conditions to achieve the desired performance. Finally, the correctness of the conclusion is verified by several examples.

CORE-Sketch: On Exact Computation of Median Absolute Deviation with Limited Space

Median absolute deviation (MAD), the median of the absolute deviations from the median, has been found useful in various applications such as outlier detection. Together with median, MAD is more robust to abnormal data than mean and standard deviation (SD). Unfortunately, existing methods return only approximate MAD that may be far from the exact one, and thus mislead the downstream applications. Computing exact MAD is costly, however, especially in space, by storing the entire dataset in memory. In this paper, we propose COnstruction-REfinement Sketch (CORE-Sketch) for computing exact MAD. The idea is to construct some sketch within limited space, and gradually refine the sketch to find the MAD element, i.e., the element with distance to the median exactly equal to MAD. Mergeability and convergence of the method is analyzed, ensuring the correctness of the proposal and enabling parallel computation. Extensive experiments demonstrate that CORE-Sketch achieves significantly less space occupation compared to the aforesaid baseline of No-Sketch, and has time and space costs relatively comparable to the DD-Sketch method for approximate MAD.

Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction

In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks. As a case study, we develop an environment semantics aided <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network architecture</i> for mmWave communication systems, which is composed of a semantic feature extraction network, a feature selection algorithm, a task-oriented encoder, and a decision network.With images taken from street cameras and user’s identification information as the inputs, the environment semantics aided network architecture is trained to predict the optimal beam index and the blockage state for the base station. It is seen that without pilot training or costly beam scans, the environment semantics aided network architecture can realize extremely efficient beam prediction and timely blockage prediction, thus meeting requirements for ultra-reliable and low-latency communications (URLLCs). Simulation results demonstrate that compared with existing works, the proposed environment semantics aided network architecture can reduce system overheads such as storage space and computational cost while achieving satisfactory prediction accuracy and protecting user privacy.

DESIGN FOR X: ENABLING THE REUSE OF SPACE HARDWARE?

AbstractWith a growing number of objects in space, the pressure to be sustainable and more efficient with resources is increasing. Driven by technological advancements, the reuse of space hardware becomes feasible and viable as alternative to spacecraft end-of-life disposal. Reuse of space hardware promises benefits in areas like mitigating space debris risks, cost reductions, and environmental sustainability on Earth and in space. However, challenges related to the space environment, like micro gravity, unknown changes due to radiation, and the energy requirements to perform maneuvers in space must be addressed in order to enable spacecraft reusability. Nonetheless, reuse of space hardware is an important objective related to long-term space exploration with implications on the human expansion into space. This paper investigates the requirements for reusability of spacecraft and if circular economy strategies can support implementing reusability for spacecraft. Based on the finding of expert interviews, it argues for design as a key enabler. It introduces design for X, design for circularity, and design for reusability, and explores how reusability of space hardware implies the need to include the space environment in design decisions.

On Querying Connected Components in Large Temporal Graphs

In this paper, for the first time, we introduce the concepts of window-CCs and window-SCCs on undirected and directed temporal graphs, respectively. We then study the queries of window-CC and window-SCC by developing several efficient index-based query solutions. The space costs of the best indices are linear to the sizes of the temporal graphs. The extensive experimental evaluation on 12 real-world datasets demonstrates the high efficiency and effectiveness of the proposed solutions. In the future, we will develop distributed index construction algorithms, which would be useful for very large temporal graphs containing billions of edges. In the future, we will implement our algorithms by using a distributed computing platform (e.g., Pregel), which would be very useful when the temporal graph is too large to be kept by a single machine.

Efficient Parallel Functional Programming with Effects

Although functional programming languages simplify writing safe parallel programs by helping programmers to avoid data races, they have traditionally delivered poor performance. Recent work improved performance by using a hierarchical memory architecture that allows processors to allocate and reclaim memory independently without any synchronization, solving thus the key performance challenge afflicting functional programs. The approach, however, restricts mutation, or memory effects, so as to ensure "disentanglement", a low-level memory property that guarantees independence between different heaps in the hierarchy. This paper proposes techniques for supporting entanglement and for allowing functional programs to use mutation at will. Our techniques manage entanglement by distinguishing between disentangled and entangled objects and shielding disentangled objects from the cost of entanglement management. We present a semantics that formalizes entanglement as a property at the granularity of memory objects, and define several cost metrics to reason about and bound the time and space cost of entanglement. We present an implementation of the techniques by extending the MPL compiler for Parallel ML. The extended compiler supports all features of the Parallel ML language, including unrestricted effects. Our experiments using a variety of benchmarks show that MPL incurs a small time and space overhead compared to sequential runs, scales well, and is competitive with languages such as C++, Go, Java, OCaml. These results show that our techniques can marry the safety benefits of functional programming with performance.

Minimum taxi fleet algorithm considering human spatiotemporal behaviors.

With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers' attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft-Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81% and 58% and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

Grouping Time Series for Efficient Columnar Storage

Columnar storage is now an industry standard design in most open-source or commercial time series database products, making them HTAP systems. The time column of a time series serves as the key for identifying the other value column, namely single-column storage scheme. When multiple time series share a similar set of timestamps, very likely in a module of multiple sensors, it is natural to group them together, i.e., one time column identifies multiple value columns in a single-group storage scheme. While multiple value columns sharing the same time column reduce the space cost of repeating timestamps, it may introduce extra space cost for recording null values. The reason is that time series may not be exactly aligned on each timestamp, owing to missing values, distinct data collection frequencies, unsynchronized clocks and so on. The columngroups storage scheme is thus to divide columns into multiple groups, within which the value columns share the same time column. Unfortunately, the problem of finding the optimal column groups for the minimum space cost is highly challenging, NP-hard according to our analysis. Thereby, we propose a heuristic algorithm for automatically grouping time series for efficient columnar storage. The column groups storage has been deployed in Apache IoTDB, an open-source time series database. The extensive performance analysis, over real-world data from our industrial partners, demonstrates that the proposed column groups achieve near optimal storage, more concise than the storage of single-column or single-group schemes. Interestingly, both the flushing and querying time costs of column groups are comparable to those of single-column or singlegroup, i.e., without incurring extra time cost.

Modeling of the effectiveness of novel edge seal designs for fast, low‐Cap‐Ex manufacturing

AbstractIn the manufacturing of photovoltaic (PV) modules, the lamination process can take up to 20 min to complete. In this work, new lamination processes are being developed, and have been prototyped, which hope to be able to cut this time down to as little as 30 s. This could provide significant savings in the cost of lamination equipment, floor space, and energy. PV modules are expected to have a lifespan exceeding 20 to 30 years. For moisture‐sensitive PV technologies, the edge seal between the two layers of glass can be the weakest point of its reliability. There is an inherent challenge when evaluating edge seal materials due to their low permeation rates. As part of Colorado State University's Photovoltaic Research and Development 2, work at the National Renewable Energy Laboratory has developed models to evaluate edge seal configurations in glass‐glass PV modules. Here, this new manufacturing process is evaluated for long‐term moisture durability. Different edge seal design options within glass–glass PV modules are explored. Most of these designs are targeting a superstrate on glass configuration, e.g CdTe, but some designs could be used on conventional crystalline Si cells. Using COMSOL finite element simulation software, we investigated the edge seal and interlayer design configurations containing silicone perimeter edge adhesive, desiccated polyisobutylene‐based edge seal, air, and polyolefin while integrating climate conditions equivalent to a hot and humid environment such as Miami, Florida. We found optimized configurations that will allow the module to prevent moisture ingress over 50 years minimizing the amount of time and material used while utilizing polymers that are easily dispensed.

Preconditioned tensor format conjugate gradient squared and biconjugate gradient stabilized methods for solving stein tensor equations

AbstractThis article is concerned with solving the high order Stein tensor equation arising in control theory. The conjugate gradient squared (CGS) method and the biconjugate gradient stabilized (BiCGSTAB) method are attractive methods for solving linear systems. Compared with the large‐scale matrix equation, the equivalent tensor equation needs less storage space and computational costs. Therefore, we present the tensor formats of CGS and BiCGSTAB methods for solving high order Stein tensor equations. Moreover, a nearest Kronecker product preconditioner is given and the preconditioned tensor format methods are studied. Finally, the feasibility and effectiveness of the new methods are verified by some numerical examples.

Design and Development of Archives Blender Tool using ADDIE Framework

Records are organizational assets in the form of documnet that contain information about activities or events, so that the existence of records is important for the organization. Even though records have an important role for the organization, without a disposal process, records will increase and accumulate from time to time. Uncontrolled addition of records volumes will result in various problems such as space provision and costs. For this reason, disposal activities are needed, one of which can be done through destruction. The purpose of this study was to study the design and development of an effective and safe paper-based records pulping machine. This study uses the Research &amp; Development (R&amp;D) method with the ADDIE framework. The results of this study indicate that the development of the tool started with an analysis of the needs for records destruction equipment, designing the tool design, developing the tool at an early stage, implementing the tool, and evaluating the function of the tool.&#x0D;

Pressure-Sensor-Based Sleep Status and Quality Evaluation System

Sleep quality evaluation is a major approach to clinical diagnosis of different types of sleep andmental disturbances.How to render it possible for test subjects to complete sleep status detection and quality assessment in a natural environment is what this paper mainly highlights. Clinically, sleep disturbances are analyzed by the so-called Polysomnography (PSG). However, due to limitations in space and high cost, as well as the fact that test subjects must wear various physiological detection equipment during the test, the collected signal is susceptible to interference due to uncomfortable sensations. As such it usually does not reflect the realistic situation of the subject. For this reason,we propose in this paper a pressure-sensor-based smart mattress to realize sleep status detection and quality evaluation. Regarding sleep posture recognition, a proposed convolutional neural network (CNN) model in conjunction with a pressure distribution image formed by the pressure sensing matrix are applied. With respect to assessing the subjects’ time in bed, a support vectormachine (SVM) is used to determine sitting or lying postures, and further recognize the actual time in bed. As for sleep quality assessment, Fuzzy inference is adopted in this paper base on a set of four predefined sleep parameters. Compared with some of prior arts, the proposed systemdoes not require the test subjects to wear any equipment, and as such the subjects can complete the test in a natural environment. Experimental results show that the accuracy of the proposed SVM classifier for differentiating sitting and lying posture and that of CNN model for the recognition of four different sleep postures can be up to 99.986% and 96.987%, respectively. With the proposed model, precise sleep parameters, including time in bed, number of times of bed-leaving, number of times of body movements all night, standard deviation of time interval between body movements, and sleep posture can be provided. Moreover, the system does not use devices such as microphones or surveillance cameras to collect the data of the test subjects; thus, there is no concern about infringing the privacy of the subjects. It is believed that the system will be of considerable help and serve as an aid to clinical diagnosis of sleep disturbance.

Multiple Nyström Kernel Adaptive Filter Under Minimum Generalized Cauchy Loss Criterion

The multikernel adaptive filters (MKAFs) have been successfully applied to resolve the issue of kernel parameter selection in traditional single kernel adaptive filters. However, owing to the linear growing network structures, conventional MKAFs commonly suffer a lot from large computational and memory burdens. To solve this problem, a multiple Nyström approximation is proposed to curb the computational complexity of MKAFs in this brief. More concretely, the multiple Nyström method is incorporated into the kernel generalized Cauchy conjugate gradient algorithm, generating a novel multiple Nyström kernel generalized Cauchy conjugate gradient algorithm (MNKGCCG). It is noted that the MNKGCCG can achieve the desirable filtering performance with low computational cost in the fixed-dimensional feature space. Experimental results on Mackey-Glass time series and sunspots time series predictions in non-Gaussian noise environments demonstrate the superiorities of the proposed MNKGCCG algorithm in terms of filtering accuracy and robustness.

Evaluation of the Effectiveness of the Separate Anesthesia Induction Rooms on Multidisciplinary Work Flow in Operating Rooms.

Operating suites are multidisciplinary units par excellence, and mostly they are the most expensive units in hospitals. Interdisciplinary workflow and efficiency are therefore crucial, which is influenced by floor plans varying from hospital to hospital. Most operating rooms are equipped with adjacent induction rooms, allowing preparation and anesthesia induction of the next patient, while the previous patient is still in the operating room. Parallelizing the working steps is thought to improve turn-over time, thus increasing throughput, number of cases and finally revenue. However, this assumption has never been challenged. We analyzed workflow during regular working hours in an operating suite equipped with a mixture of operating rooms (OR) with next door induction rooms and operating rooms without induction rooms. This allows a direct comparison of both structural elements for efficiency using utilization data over a 24-months period. Both settings were used for gynecological operations. Key result is that induction rooms do not improve perioperative workflow including turn-over time. Instead, ORs without adjacent induction rooms have a significantly shorter turn-over time and OR occupancy duration per case, although surgical time and staffing were similar. Adjacent induction rooms require extra space, funding, and high maintenance costs, but they do not speed up peri-operative processes. Modern anesthetic techniques allow for fast induction of and emergence from anesthesia. Induction rooms adjacent to the OR are no longer needed if general anesthesia without extended monitoring is used for the majority of cases.

Core Decomposition and Maintenance in Bipartite Graphs

The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining. In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs, only very few indexes are proposed for the same in bipartite graphs. In this work, we present the index called α(β)-core number on vertices, which reflects the maximal cohesive and dense subgraph a vertex can be in, to help enumerate the (α, β)-cores, a commonly used dense structure in bipartite graphs. To address the problem of extremely high time and space cost for enumerating the (α, β)-cores, we first present a linear time and space algorithm for computing the α(β)-core numbers of vertices. We further propose core maintenance algorithms, to update the core numbers of vertices when a graph changes by avoiding recalculations. Experimental results on different real-world and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.

Assessment of Spatial Equity of Public Urban Parks Distribution in Peshawar, Pakistan

To flourish, cities must have enough open green space and parks. Public urban parks distribution, their size, accessibility and equity are the key elements in fostering the city sustainability. Peshawar a provincial capital of Khyber Pakhtunkhwa Province is one of the rapidly expanding urban centers of Pakistan. To cater the needs of growing population large numbers of residential colonies are emerging on the cost of open spaces and green patches. This study is aimed to analyze the distribution and spatial equity of existing parks of Peshawar using field surveys, visual observations, mapping and buffering satellite imageries through computer added design methods. The results revealed that presently 209.89 ha land is covered by parks which is only 1.89% of the total urban area. The study also explored the inequitable distribution of parks in city and emphasizing the development of new accessible parks on equity basis especially in the deprived areas.

Space–Time–Workforce Visualization and Conditional Capacity Synthesis in Uncertainty

Workspace, project duration, and workforce are three critical resources for construction projects. Project managers need to expend time and effort reviewing, comprehending, and coordinating these resources. However, the space–time–workforce interactions and their impact on decision-making in project scheduling are not fully known. Therefore, the objectives of this research were to understand the impact of workforce shifts on space, time, and labor cost performances, develop a three-dimensional (3D) visualization tool to reveal activity-level resource dynamics, and associate the risk aftermath with the occurrence probability to balance subjective risk tolerance and objective system reliability. This research developed a simulation model based on a case project to compare the workflow of five major specialty trades (i.e., bar placer, carpenter, scaffolder, pipefitter, and concreter) in 267 scenarios. A resource-oriented 3D visualization tool was developed to help project managers monitor project schedules. The research established a risk control framework using value-at-risk (VaR) and conditional value-at-risk (CVaR) approaches to associate extreme outcomes with their occurrence probability. Simulation results indicated that pipefitters significantly affected workspace overlap, whereas bar placers and carpenters predominantly impacted project duration. The scholarly contributions are (1) the creation of an intelligent system to generate a project schedule from workforce assignments specified by project managers, (2) development of a tool to visualize the three-way resource dynamics of workforce, time, and space on a 3D model for all possible scenarios, and (3) development of a framework for project managers to balance planning strategies between subjective risk tolerance and objective system reliability. This research provides project managers with a dynamic 3D visualization of space, time, and workforce utilization and interaction in uncertain environments, further facilitating reliable project scheduling decision-making.

Deep Reinforcement Learning for Reactive Content Caching With Predicted Content Popularity in Three-Tier Wireless Networks

With the explosive growth of micro-video applications, the mobile traffic generated by retrieving a few user-generated micro-videos has brought a massive burden to backhaul links and backbone networks. Unlike other types of videos, user-generated micro-videos are typically requested by a large number of users within an extremely short period after their release. Therefore, it is crucial to predict the content popularity and make caching decision for the newly requested content timely. To predict content popularity in different locations, the request probabilities of the content in different locations are translated into rating scores. Then, a recommendation system-based prediction model is designed to predict rating scores of the newly requested content in different locations. To increase caching diversity and realize vertically collaboration in a three-tier wireless network, a deep reinforcement learning-based reactive content caching strategy is proposed to make caching decision for the newly requested content. The main goal is to obtain a higher caching gain at a lower caching space cost. To evaluate the caching performance, a new metric, cache benefit rate, is defined as the download latency reduction brought by each bit of cache. Compared with matrix factorization, the performance of prediction model can be increased by 23.98% and 12.44% in terms of mean absolute error and root mean square error, respectively. Extensive simulations demonstrate that the proposed reactive caching strategy outperforms other caching strategies under different system parameters in terms of the cache hit rate, average download time, and cache benefit rate.