Miracle-dz/UniFAR-FTUs huggingface dataset:
Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia

The correct distribution of service facilities can help keep fixed and overhead costs low while increasing accessibility. When an appropriate location is chosen, public-sector facilities, such as COVID-19 centers, can save lives faster and provide high-quality service to the community at a low cost. The purpose of the research is to highlight the issues related to the location of COVID-19 vaccine centers in the city of Jeddah, Saudi Arabia. In particular, this paper aims to analyze the accessibility of COVID-19 vaccine centers in Jeddah city using maximal coverage location problems with and without constraint on the number and capacity of facilities. A maximal coverage model is first used to analyze the COVID-19 vaccination coverage of Jeddah districts with no restriction on the facility capacity. Then, a maximize capacitated coverage method is utilized to assess the centers’ distribution and demand coverage with capacity constraints. Finally, the minimize facilities model is used to identify the most optimal location required to satisfy all demand points with the least number of facilities. The optimization approaches consider the objective function of minimizing the overall transportation time and travel distance to reduce wastage on the service rate provided to the patients. The optimization model is applied to a real-world case study in the context of the COVID-19 vaccination center in Jeddah. The results of this study provide valuable information that can help decision-makers locate and relocate COVID-19 centers more effectively under different constraints conditions.
Exosomes Derived from Glioma Cells under Hypoxia Promote Angiogenesis through Up-regulated Exosomal Connexin 43

Glioblastoma multiform (GBM) is a highly aggressive primary brain tumor. Exosomes derived from glioma cells under a hypoxic microenvironment play an important role in tumor biology including metastasis, angiogenesis and chemoresistance. However, the underlying mechanisms remain to be elucidated. In this study, we aimed to explore the role of connexin 43 on exosomal uptake and angiogenesis in glioma under hypoxia. U251 cells were exposed to 3% oxygen to achieve hypoxia, and the expression levels of HIF-1α and Cx43, involved in the colony formation and proliferation of cells were assessed. Exosomes were isolated by differential velocity centrifugation from U251 cells under normoxia and hypoxia (Nor-Exos and Hypo-Exos), respectively. Immunofluorescence staining, along with assays for CCK-8, tube formation and wound healing along with a transwell assay were conducted to profile exosomal uptake, proliferation, tube formation, migration and invasion of HUVECs, respectively. Our results revealed that Hypoxia significantly up-regulated the expression of HIF-1α in U251 cells as well as promoting proliferation and colony number. Hypoxia also increased the level of Cx43 in U251 cells and in the exosomes secreted. The uptake of Dio-stained Hypo-Exos by HUVECs was greater than that of Nor-Exos, and inhibition of Cx43 by 37,43gap27 or lenti-Cx43-shRNA efficiently prevented the uptake of Hypo-Exos by recipient endothelial cells. In addition, the proliferation and total loops of HUVECs were remarkably increased at 24 h, 48 h, and 10 h after Hypo-Exos, respectively. Notably, 37,43gap27, a specific Cx-mimetic peptide blocker of Cx37 and Cx43, efficiently alleviated Hypo-Exos-induced proliferation and tube formation by HUVECs. Finally, 37,43gap27 also significantly attenuated Hypo-Exos-induced migration and invasion of HUVECs. These findings demonstrate that exosomal Cx43 contributes to glioma angiogenesis mediated by Hypo-Exos, and suggests that exosomal Cx43 might serve as a potential therapeutic target for glioblastoma.
Predictive Analytics for Early-Stage Construction Costs Estimation

Low accuracy in the estimation of construction costs at early stages of projects has driven the research on alternative costing methods that take advantage of computing advances, however, direct implications in their use for practice is not clear. The purpose of this study was to investigate how predictive analytics could enhance cost estimation of buildings at early stages by performing a systematic literature review on predictive analytics implementations for the early-stage cost estimation of building projects. The outputs of the study are: (1) an extensive database; (2) a list of cost drivers; and (3) a comparison between the various techniques. The findings suggest that predictive analytic techniques are appropriate for practice due to their higher level of accuracy. The discussion has three main implications: (a) predictive analytics for cost estimation have not followed the best practices and standard methodologies; (b) predictive analytics techniques are ready for industry adoption; and (c) the study can be a reference for high-level decision-makers to implement predictive analytics in cost estimation. Knowledge of predictive analytics could assist stakeholders in playing a key role in improving the accuracy of cost forecast in the construction market, thus, enabling pro-active management of the project owner’s budget.
On Robust Millimeter Wave Line-of-Sight MIMO Communications With Few-Bit ADCs

This work focuses on providing robust line-of-sight (LoS) spatial multiplexing at flexible communications distances and directions. Considering oblique LoS uniform linear arrays, we first derive the rank-deficient and orthogonal conditions for the LoS MIMO channel matrices. With this discovery, the topology of high spatial-resolution on one side of the link is shown with a wide full-rank-channel guarantee interval over distance and direction variations. Additionally, to reduce the implementation costs and power consumption, we propose to use low amplitude-resolution quantizers at the side of high spatial-resolution. With numerical evaluations on systems having few-bit analog-to-digital converters (ADCs), the proposed system design is shown to simultaneously achieve a higher spectrum efficiency and higher energy efficiency compared to a conventional single-stream high-amplitude-resolution over a wide signal-to-noise-ratio (SNR) range. Furthermore, we investigate channel equalization under the extreme case of using 1-bit ADCs. After providing a new viewpoint on the generalized approximate message passing (GAMP) algorithm from constrained Bethe free energy minimization, our simulations on bit-error-rates show that the GAMP algorithm can significantly reduce the performance degradation due to coarse quantization and can significantly outperform the Bussgang decomposition based linear minimum-mean-square-error estimator, especially at high SNRs.
Research on Information Fusion of Computer Vision and Radar Signals in UAV Target Identification

As one of the crucial sensing methods, multisensor fusion recognition aids the Internet of Things (IoT) in connecting things through ubiquitous perceptual terminals. The small size, sluggish flying speed, low flight altitude, and low electromagnetic intensity of unmanned aerial vehicles (UAVs) have put enormous strain on air traffic management and airspace security. It is urgent to achieve effective UAV target detection. The radio monitoring method, acoustic detection scheme, computer vision, and radar signal detection are commonly used technologies in this field. The radio monitoring approach has low accuracy, the acoustic detection strategy has a limited detection range, computer vision is limited by weather conditions, and the radar signals at low altitudes are influenced by ground clutter. To address these issues, this paper proposes an information fusion strategy based on two levels of fusion: data-level fusion and decision-level fusion. In this strategy, Computer vision and radar signals complement each other to improve the detection accuracy. For each level, the method of information fusion is introduced in detail. Furthermore, the effectiveness of the method has been demonstrated by a series of comprehensive experiments. The results show that the accuracy of the fusion method is improved, and the proposed method can still work even when the single method loses function.
Category Theory Lecture Notes

Prologue These notes, developed over a period of six years, were written for an eighteen lectures course in category theory. Although heavily based on Mac Lane's Categories for the Working Mathematician, the course was designed to be self-contained, drawing most of the examples from category theory itself. but was attended by a varied audience. Most sections are a reasonable account of the material presented during the lectures, but some, most notably the sections on Lawvere theories, topoi and Kan extensions, are little more than a collection of definitions and facts.
Methods for determining the characteristics of weathered granite reservoirs: application to Binxian Uplift, Dongying Sag, Bohai Bay Basin

Abstract With the discovery of high-productivity oilfields in granite buried hills, it is necessary to systematically investigate the types of granite weathered reservoirs at different depths and their spatial distribution. However, previously subdivided reservoirs have been assumed to exhibit the same vertical zoning in different structural parts, contradicting the fact that the degree of weathering varies with the topography. In addition, comprehensive and quantitative methods for classifying reservoir types are lacking. Taking the Binxian Uplift of the Dongying depression in Bohai Bay Basin as an example, we therefore established a comprehensive identification standard for dividing granite reservoirs using lithology division, logging curve statistics, a dual-medium matrix–fracture model and seismic facies identification. Subsequently, by combining logging and seismic methods, the vertical stacking types and distribution properties of weathered granite reservoirs in various structural positions were analysed. The reservoirs were divided vertically into three zones: regolith, dissolution and fracture. Quantitative logging response standards for the different reservoirs were established using acoustic, density, natural gamma and resistivity logging. In terms of the seismic response, the regolith, dissolution and fracture zone corresponded to high-, medium- and low-amplitude seismic facies, respectively. A dynamic double-layer structure of the reservoir was established, comprising a completely weathered layer and a semi-weathered layer. The reservoir division method proposed in this paper can be used in other areas, and the research results can help promote the exploration of granite buried hill reservoirs.
Algorithmic TCAMs: Implementing Packet Classification Algorithms in Hardware

The adoption of cloud computing and the deployment of new services require more and more computing and networking resources in large data centers. In particular, there is a need for faster, rich featured switches. Today, switching integrated circuits can handle more than 50 Tb/s and are expected to reach 100 Tb/s next year. This trend is likely to continue, approaching petabit capacities in a few years. At the same time, switches need to support more advanced packet classification features. This poses many challenges to the designers of switching integrated circuits, creating a need for innovations to move forward. One of those challenges is to support programmable packet classification with large rulesets at wire speed using limited silicon footprint. Unfortunately, traditional solutions like ternary content addressable memories (TCAMs) have high cost in area and power, and therefore are not a viable option for large on-chip rulesets, so new alternatives are needed. In the last two decades, researchers have extensively studied the problem of packet classification, proposing many algorithms based on standard memories, mostly targeting software implementations. These packet classification algorithms could be a solution to enable large on-chip classification rulesets using standard memories. However, porting the standard packet classification algorithms to a hardware implementation is not a trivial task, due to the different requirements and the different kinds of memory and computing resources available on application-specific integrated circuit (ASIC) switching chips. This article describes a packet classification design tailored for high-speed switching integrated circuits, which is currently used in the NVIDIA® Mellanox® Spectrum® switching ASIC product line, illustrating how to solve the mismatch between the usual software-based classification algorithms and the specific requirements of a hardware implementation.
Reconsidering Access: Using Specific Impact Ranking Metrics to Manage Access in Conventional and Open Higher Education

This paper considers the widening access and participation agenda, its implications for higher education institutions (HEIs) and contends that it must be underpinned by strategic measurement and monitoring. Access is viewed through of the following lenses: (i) supporting participation, and (ii) facilitating equity. Using mixed methods, the paper draws on data from The University of the West Indies (UWI) and provides examples from key plans and initiatives over 20 years to showcase how the UWI has increased access. Concurrently, the need for more nuanced and complex datasets to assess the extent of equity is highlighted with metrics drawn from the Times Higher Education University Impact Ranking. The authors argue that the strategic use and management of data can promote public accountability associated with access and boost institutional reputation. However, universities will have to be innovative and accelerate measures to survive/thrive in the post-pandemic environment by identifying their institutional scope and “system of interest” in widening access.
KneeScale: Efficient Resource Scaling for Serverless Computing at the Edge

Serverless computing is a promising paradigm for delivering services to the Internet of Things (IoT) applications at the edge of the network. Its event-triggered computation, as well as fine-grained and agile resource scaling, is well-suited for a resource-constrained edge computing environment. However, general-purpose auto-scalers that are predominant in the cloud settings perform poorly for serverless computing at the Edge. This is mainly due to the difficulty in quickly determining the optimal resource allocation under resource-budget constraints and dynamic workloads. In this paper, we present an adaptive auto-scaler, KneeScale, that dynamically adjusts the number of replicas for serverless functions to reach a point at which the relative cost to increase resource allocation is no longer worth the corresponding performance benefit. We have designed and implemented KneeScale as lightweight system software that utilizes Kubernetes for resource management. Experimental results with a function-as-a-service (FaaS) benchmark, FunetionBeneh, and an open-source serverless computing platform, OpenFaaS, demonstrate the superior performance and resource efficiency of KneeScale. It outperforms Kubernetes Horizontal Pod AutoScaler (HPA) and OpenFaaS built-in scheduler in terms of cumulative performance under a given resource budget by up to 32 % and 106 % respectively. KneeScale achieves higher cumulative throughput than both competing techniques, lower latencies than OpenFaaS built-in scheduler, and similar latencies compared to HPA for a variety of serverless functions.
Calibration of a multi-residential building energy model – Part II: Calibration using surrogate-based optimization

The present study deals with a calibration of a white-box Building Energy Model (BEM) by using an optimization technique. The studied building is a multi-residential building which consists of 41 apartments having from 1 to 3 bedrooms. The energy consumption data are collected by digital meters installed for each building apartment. As the calibrated energy model will be used for the purpose of energy load prediction as well as for fault detection and diagnosis (FDD) at building and apartment levels, the energy model considers many parameters. Even after a reduction step, i.e., selecting the most influent parameters by a sensitivity analysis (i.e. work presented in a separate paper: Part I – Cluster-Based Sensitivity Analysis), the total number of parameters for the optimization-based calibration is still large. Moreover, the white-box energy model simulation is quite expensive in terms of computational time. Therefore, the meta-modeling technique (e.g., Radial Basis Function network, Kriging model) is employed to reduce the computational cost of the white-box energy model simulation. The evolutionary algorithm, implemented within the Cenaero’s in-house design space exploration and multi-disciplinary optimization platform, called Minamo, is used to figure out the global optimum of the constrained optimization problem.
Exercise Intervention Modulates Synaptic Plasticity by Inhibiting Excessive Microglial Activation via Exosomes

Background Exosomes can activate microglia to modulate neural activity and synaptic plasticity by phagocytosis of neural spines or synapses. Our previous research found that an early 4-week exercise intervention in middle cerebral artery occlusion (MCAO) rats can promote the release of exosomes and protect the brain. This study intended to further explore the intrinsic mechanism of neuroprotection by exosome release after exercise. Methods Rats were randomly divided into four groups: the sham operation (SHAM), middle cerebral artery occlusion (MCAO) with sedentary intervention (SED-MCAO), MCAO with exercise intervention (EX-MCAO), and MCAO with exercise intervention and exosome injection (EX-MCAO-EXO). Modified neurological severity score (mNSS), cerebral infarction volume ratio, microglial activation, dendritic complexity, and expression of synaptophysin (Syn) and postsynaptic density protein 95 (PSD-95) were detected after 28 days of intervention. Results (1) The exercise improved body weight and mNSS score, and the survival state of the rats after exosome infusion was better. (2) Compared with the SED-MCAO group, the EX-MCAO (P = 0.039) and EX-MCAO-EXO groups (P = 0.002) had significantly lower cerebral infarct volume ratios (P < 0.05), among which the EX-MCAO-EXO group had the lowest (P = 0.031). (3) Compared with the SED-MCAO group, the EX-MCAO and EX-MCAO-EXO groups had a significantly decreased number of microglia (P < 0.001) and significantly increased process length/cell (P < 0.01) and end point/cell (P < 0.01) values, with the EX-MCAO-EXO group having the lowest number of microglia (P = 0.036) and most significantly increased end point/cell value (P = 0.027). (4) Compared with the SED-MCAO group, the total number of intersections and branches of the apical and basal dendrites in the EX-MCAO and EX-MCAO-EXO groups was increased significantly (P < 0.05), and the increase was more significant in the EX-MCAO-EXO group (P < 0.05). (5) The expression levels of Syn and PSD-95 in the EX-MCAO (PSyn = 0.043, PPSD−95 = 0.047) and EX-MCAO-EXO groups were significantly higher than those in the SED-MCAO group (P < 0.05), and the expression levels in the EX-MCAO-EXO group were significantly higher than those in the EX-MCAO group (P < 0.05). Conclusion Early exercise intervention after stroke can inhibit the excessive activation of microglia and regulate synaptic plasticity by exosome release.
Sazonalidade anual e a variabilidade horária mensal do índice ultravioleta para a cidade de Humaitá, Amazonas, Brasil

O índice Solar Ultravioleta (IUV) mensura a intensidade da radiação UV (280 a 400 nm) que atinge à superfície terrestre num dado intervalo de tempo. É uma grandeza adimensional que representa os riscos à saúde humana devido à exposição solar. O IUV é pouco divulgado no Brasil, onde, geralmente, apenas as capitais brasileiras dispõem dessas informações. Assim, o objetivo deste trabalho foi descrever a sazonalidade anual e a variabilidade horária mensal do Índice Ultravioleta para a cidade de Humaitá-AM. Os dados do IUV foram coletados entre outubro de 2019 e setembro de 2020, por um sensor de Raio Ultravioleta UVM-30A (200 – 370 nm), conectado a uma plataforma Arduino Uno R3 usada como datalogger para armazenar o IUV. As leituras foram realizadas a cada 10 segundos e as médias armazenadas a cada 10 minutos. Os gráficos boxplot foram plotados utilizando o software livre R-Studio, para avaliar a variabilidade horária do IUV. Os resultados obtidos mostram que na primavera o IUV médio máximo pode variar de 4 a 8, no verão é constante e igual a 4, no outono varia de 4 a 6 e no inverno de 8 a próximo de 11. Portanto, observou-se que os valores mais elevados ocorrem durante o inverno que coincide com o período seco da região.
Assessing the Immunomodulatory Effect of Size on the Uptake and Immunogenicity of Influenza- and Hepatitis B Subunit Vaccines In Vitro

Viral subunit vaccines are a safer and more tolerable alternative to whole inactivated virus vaccines. However, they often come with limited efficacy, necessitating the use of adjuvants. Using free and particle-bound viral antigens, we assessed whether size affects the uptake of those antigens by human monocyte-derived dendritic cells (Mo-DCs) and whether differences in uptake affect their capacity to stimulate cytokine production by T cells. To this end, influenza antigens and hepatitis B surface antigen (HBsAg) were covalently conjugated to polystyrene particles of 500 nm and 3 μm. Cellular uptake of the antigens, either unconjugated or conjugated, and their capacity to stimulate T cells within a population of human peripheral blood mononuclear cells (PBMCs) were measured by flow cytometry. Conjugation of both antigens to particles significantly increased their uptake by Mo-DCs. Moreover, both the 500 nm and 3 μm influenza conjugates induced significantly higher numbers of cytokine-producing CD4+ T cells and induced increased production of the pro-inflammatory cytokines IFNγ and TNFα. In contrast, conjugation of HBsAg to particles did not notably affect the T cell response. In conclusion, conjugation of antigen to 500 nm and 3 μm particles leads to increased antigen uptake by human Mo-DCs, although the capacity of such conjugates to induce T cell stimulation likely depends on the immunological status of the PBMC donor.
Selection of input variables for covering materials through sensitivity analysis based on greenhouse energy demands

We aim to select the input variables of covering materials affecting the cooling and heating energy demands of a greenhouse through a sensitivity analysis. Furthermore, we quantitatively evaluate the effects on these demands for identifying items that must be measured and acquired for modeling. Results indicate that solar transmittance, solar reflectance, infrared emissivity, and conductivity significantly affect the energy demands. An energy prediction model is developed by using a multiple regression analysis, which adequately describe the simulation results. We expect that this model can facilitate the comparison of the approximate performance of covering materials during the initial design stage for ordinary farms having difficulty using simulations.