Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between sustainable urban development and economic growth based on emergy analysis:a case study of Quanzhou City

Cities are centers of surrounding regions and play an important role in regional economic and social developments. Approaches for achieving sustainable urban development has been a hot topic in the study of human-environment relationship. However, the dynamic mechanism of sustainable urban development is relatively neglected in existing urban sustainability studies. In this research, emergy analysis method was used to measure the level of sustainable urban development. On the basis of this analysis, we built a new model of Sustainable Development Kuznets Curve(SDKC), and used Quanzhou City as a case to analyze the dynamic relationship of economic growth and level of sustainable urban development. In addition, an improved model of degree of grey slope incidence was used to preliminarily examine the causes of the SDKC. The results show that the SDKC of Quanzhou City reveals a U-shaped relationship between the level of sustainable urban development and economic growth; the level of sustainable urban development was in negative correlation with economic scale, proportion of secondary industry, proportion of tertiary industry, export dependence, and the influence of government.The generalized pollutant discharge reduction technologies, proportion of primary industry, and foreign investment played a positive role on the level of sustainable urban development. According to the results, we put forward some recommendations on promoting sustain development of the city for decision-makers, including facilitating the transformation of economic growth mode to strengthen the positive effect of modern service industry for sustainable development; adjusting export structure and improving the efficiency of exports; avoiding repetitive public infrastructure development; strengthening technological innovations to promote energy conservation;stringent environmental regulation and barriers to entry, among others. The results of this study may provide some references and foundations for the city government to accurately grasp its sustainable urban development status and dynamic mechanism, and formulate policies that are scientifically sound for sustainable urban development.
As Regiões Metropolitanas no Brasil e o problema institucional de cooperação: a trajetória das Regiões Metropolitanas de Natal e Aracaju

The paper analyzes the evolution of the process of institutionalization of Brazilian metropolitan areas, where the object of empirical cases Natal-RN and Aracaju-SE. Through historical and comparative institutional analysis on the constitution of regulatory arrangements of the metropolitan areas of Natal and Aracaju, seeks to understand how was the process of training, cooperation in the management of metropolitan areas analyzed. Despite their different specific contexts of each metropolitan area, there is a common characteristic in both there are no institutional mechanisms and cooperation so as Federated intrametropolitan that encourage interest from federal agencies for cooperation. Although there is a fragile political motivation in both cases analyzed, they show a low institutionalization, which characterizes these two metropolitan areas as a framework for cooperation incipient making it impossible to advance in terms of metropolitan management.
Some quantitative results on benthic communities of the deep Norwegian Sea

In July-August 1975 a joint French-Swedish expedition (NORBI) with Norwegian participation explored the abyssal benthic fauna of the Norwegian sea about which previous knowledge was fragmentary. Sampling was carried out in depths between 2465 and 3718 m at 11 stations located in all four deep basins, viz the Norwegian (3), Lofoten (2), Spitsbergen (1), and Greenland (5) basins. The gears used were box corer, epibenthic sled, beam trawl, baited trap, and camera sed. [NOT CONTROLLED OCR]
SparseFusion3D: Sparse Sensor Fusion for 3D Object Detection by Radar and Camera in Environmental Perception

In the context of autonomous driving environment perception, multi-modal fusion plays a pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the performance boundary of the perception system. However, directly applying LiDAR-related algorithms to radar and camera fusion leads to significant challenges, such as radar sparsity, absence of height information, and noise, resulting in substantial performance loss. To address these issues, our proposed method, SparseFusion3D, utilizes a dual-branch feature-level fusion network that fully models sensor interactions, effectively mitigating the adverse effects of radar sparsity and noise on modality association. Additionally, to enhance modal correlations and accuracy while alleviating radar point cloud sparsity and measurement ambiguity, we introduce MSPCP, which compensates for point cloud offset. Moreover, we integrate Radar Painter to leverage image information and further enhance MSPCP. SparseFusion3D exhibits competitive performance compared to previous radar-camera fusion models, achieving approximately 1.5x inference speedup with similar performance to dense query methods, while also improving by 20.1% compared to the baseline approach.
Effect of flamboyant flower (Delonix regia) powder on root knot nematodes (Meloidogyne incognita) infestation on tomato plant (Solanum lycopersicum) in Yola, Adamawa State

Plant Parasitic Nematodes (PPN) are regarded as one of the major challenges of sustainable tomato production in the world. Their control which involves the use of synthetic chemicals is being restricted because of their toxicity coupled with the environmental risk associated with their use. To this effect this study was carried out to evaluate the effect of flamboyant flower powder on M. incognita inoculated on tomato plants as an alternative control strategy of PPN. Screen house experiment (potted experiment) was conducted at the landscape garden of Modibbo Adama University Yola to evaluate the efficacy of plant powder for the control of root- knot nematode in tomato plant. The experimental design used was the Completely Randomized Design (CRD) with five treatments replicated three times. D. regia powders were incorporated at different levels into the bucket each containing 4kg of sterilized soil. The plant powder was incorporated at the rate of 40g, 30g, 20g and 10g (T1, T2, and T3) respectively and T4 with no level of powder 0g. D. regia powder and 40g gave the best effect on M. incognita in the potted experiments as higher plant height, number of leaves; fresh shoot weight, galling index and least final nematode of both soil and roots were recorded. Therefore, from these findings, D. regia at 40g exhibited nematicidal effect on M. incognita in tomato plant followed by 30g, 20g, and 10g respectively. The nematicidal characteristics exhibited by this plant material (flamboyant flower) might be due to some phytochemical constituents present in the plant material. The control plant has least plant height, number of leaves, fresh shoot weight, plant fruit and higher fresh root weight as well as galling index and highest final root and soil nematode population.
GPU Graph Processing on CXL-Based Microsecond-Latency External Memory

In GPU graph analytics, the use of external memory such as the host DRAM and solid-state drives is a cost-effective approach to processing large graphs beyond the capacity of the GPU onboard memory. This paper studies the use of Compute Express Link (CXL) memory as alternative external memory for GPU graph processing in order to see if this emerging memory expansion technology enables graph processing that is as fast as using the host DRAM. Through analysis and evaluation using FPGA prototypes, we show that representative GPU graph traversal algorithms involving fine-grained random access can tolerate an external memory latency of up to a few microseconds introduced by the CXL interface as well as by the underlying memory devices. This insight indicates that microsecond-latency flash memory may be used as CXL memory devices to realize even more cost-effective GPU graph processing while still achieving performance close to using the host DRAM.
The French-Italian Concordia Station

Abstract Concordia is a French-Italian permanent station located at Dome C, Antarctica. The station provides accommodation for up to 16 people over winter and more than 70 scientists and technicians during the austral summer. The scientific projects implemented at Concordia are strictly dependent on the characteristics of the site: a) the presence of a 3 300 m thick ice cap that allows access to the planet's climate archives and the reconstruction of glacial-interglacial cycles over more than 800 000 years; b) a particularly stable pure and dry atmosphere ideal for astronomy observations and for research on the chemical composition of the atmosphere; c) a distant location from coastal perturbations favourable to magnetic and seismological observatories to complement a poor world data network in the southern hemisphere; and d) a small totally isolated group of people confined to the station over a long winter, offering an opportunity for a range of medical and psychological studies useful to prepare long duration deep space missions. We will address the main characteristics of this station and its interest for science.
Learning-Based Dynamic Memory Allocation Schemes for Apache Spark Data Processing

Apache Spark is an in-memory analytic framework that has been adopted in the industry and research fields. Two memory managers, Static and Unified, are available in Spark to allocate memory for caching Resilient Distributed Datasets (RDDs) and executing tasks. However, we find that the static memory manager (SMM) lacks flexibility, while the unified memory manager (UMM) puts heavy pressure on the garbage collection of the JVM on which Spark resides. To address these issues, we design a learning-based bidirectional usage-bounded memory allocation scheme to support dynamic memory allocation with the consideration of both memory demands and latency introduced by garbage collection. We first develop an auto-tuning memory manager (ATuMm) that adopts an intuitive feedback-based learning solution. However, ATuMm is a slow learner that can only alter the states of Java Virtual Memory (JVM) Heap in a limited range. That is, ATuMm decides to increase or decrease the boundary between the execution and storage memory pools by a fixed portion of JVM Heap size. To overcome this shortcoming, we further develop a new reinforcement learning-based memory manager (Q-ATuMm) that uses a Q-learning intelligent agent to dynamically learn and tune the partition of JVM Heap. We implement our new memory managers in Spark 2.4. 0 and evaluate them by conducting experiments in a real Spark cluster. Our experimental results show that our memory manager can reduce the total garbage collection time and thus further improve Spark applications’ performance (i.e., reduced latency) compared to the existing Spark memory management solutions. By integrating our machine learning-driven memory manager into Spark, we can further obtain around 1.3x times reduction in the latency.
Energy, Passivity, and Morphological Capacities

The morphology of a system may be exploited to reduce the controller workload or to perform specific tasks, e.g., locomotion processes. However, understanding and characterizing these capacities in terms of physical quantities remains an open problem. In this paper, we pose the question: are energy and dissipation useful physical quantities to characterize morphological capacities? First, to illustrate the relevance of this question, we present some basic concepts of passivity theory that are useful for carrying out an energy-based analysis of the behavior of dynamical systems. Then, we analyze the behavior of a mechanical system, and we show how its morphology affects its energy. Finally, we present a detailed analysis of a passive walker and explain how its locomotion capacities can be interpreted in terms of energy.
Automating Airborne Software Certification Compliance Using Cert DevOps

The development of avionics software follows strict processes to ensure care is taken commensurate with the safety required of the system. This effort is significant and has many aspects that bring safety, but the complex set of artifacts required to prove the process was followed can result in significant cost and schedule. This can put at risk the ability to create new and transformative software for a future that includes advanced aircraft and single pilot operations. We are applying DevOps principles to avionics software development in a transformative way to address these issues.DevOps principles have been used in software development for many years to increase flow through automation, provide faster feedback, and incorporate continuous improvement. This is typically done in the frame of software implementation but is typically not inclusive of domain requirements like DO-178C. Automating the tasks and evidence required by DO-178 can provide a significant speed advantage. In this research project we worked to understand how much could be automated, and how the automation can become a robust infrastructure that reduces the chance of human error in the development process.Our preliminary results indicate > 75% of common defects, especially those liable to become open problem reports (OPRs), can be addressed through robust infrastructure. We have also measured substantial improvement to cycle time and reduction in the use of difficult-to-justify, regression-oriented testing.Our conclusion is that this sort of step change in safety-driven software development is possible. A process we call ‘Cert DevOps’ enables a more robust and agile development cycle necessary for the significant number of new features required for avionics in support of single pilot operation. Implications of this change include the possibility of auditing the automations of the infrastructure rather than the resulting automated output, which could form the basis of future standards work.
Serum Reactive Antibodies against the N-Methyl-D-Aspartate Receptor NR2 Subunit—Could They Act as Potential Biomarkers?

Synaptic dysfunction and disrupted communication between neuronal and glial cells play an essential role in the underlying mechanisms of multiple sclerosis (MS). Earlier studies have revealed the importance of glutamate receptors, particularly the N-methyl-D-aspartate (NMDA) receptor, in excitotoxicity, leading to abnormal synaptic transmission and damage of neurons. Our study aimed to determine whether antibodies to the NR2 subunit of NMDAR are detected in MS patients and evaluate the correlation between antibody presence and clinical outcome. Furthermore, our focus extended to examine a possible link between NR2 reactivity and anti-coagulant antibody levels as pro-inflammatory molecules associated with MS. A cross-sectional study was carried out, including 95 patients with MS and 61 age- and gender-matched healthy controls (HCs). The enzyme-linked immunosorbent assay was used to detect anti-NR2 antibodies in serum samples of participants along with IgG antibodies against factor (F)VIIa, thrombin, prothrombin, FXa, and plasmin. According to our results, significantly elevated levels of anti-NR2 antibodies were detected in MS patients compared to HCs (p < 0.05), and this holds true when we compared the Relapsing-Remitting MS course with HCs (p < 0.05). A monotonically increasing correlation was found between NR2 seropositivity and advanced disability (rs = 0.30; p < 0.01), anti-NR2 antibodies and disease worsening (rs = 0.24; p < 0.05), as well as between antibody activity against NR2 and thrombin (rs = 0.33; p < 0.01). The presence of anti-NR2 antibodies in MS patients was less associated with anti-plasmin IgG antibodies [OR:0.96 (95%CI: 0.92–0.99); p < 0.05]; however, such an association was not demonstrated when analyzing only RRMS patients. In view of our findings, NR2-reactive antibodies may play, paving the way for further research into their potential as biomarkers and therapeutic targets in MS.
Widebeam Coverage Antenna Solution for Low-Complexity mmWave Indoor IoT Network

This article proposes a widebeam coverage antenna solution for a low-complexity millimeter-wave (mmWave) indoor Internet of Things (IoT) network. The proposed switched beamforming antenna (SBA) system eliminates the need for complex feed networks or beamformers by utilizing a single RF switch and multiple radiators. Each radiating element of the antenna is capable of generating multiple main lobe directions, ensuring wide beam coverage and sufficient antenna gain. This design approach effectively addresses the key requirements of distributed antenna system (DAS) configurations, including low complexity, low loss, low power consumption, and cost effectiveness for indoor IoT networks. To validate the effectiveness of the proposed SBA solution, a three-radiator-based SBA was fabricated. The antenna core size was compact, measuring $1.77\,\,\lambda _{0} \times 4.3\,\,\lambda _{0} \times 0.05\,\,\lambda _{0}\,\,(\lambda _{0}$ representing the free-space wavelength at 28 GHz). This compact size is more suitable for indoor low-profile networks compared to previously reported SBAs. Experimental measurements revealed peak gains of 5.62, 7.2, and 5.77 dBi for the three different radiation patterns, respectively. Moreover, each beam pattern exhibited a measured 3-dB beam coverage of 101°, 64°, and 106°. The overall 3-dB beamwidth of the system based on the peak gain of the boresight ranged from −66° to 64°, which is wider than previously reported works.
C3F: Constant Collaboration and Communication Framework for Graph-Representation Dynamic Multi-Robotic Systems

Deep reinforcement learning (DRL) methods have been widely applied in distributed multi-robotic systems and successfully realized autonomous learning in many fields. In these fields, robots need to communicate and collaborate with other robots in real time, and reach agreed cognition for task assignment, which puts high requirements on efficiency and stability. However, robots may often get damaged even crash in complex environments, and have to be dynamically substituted. It seems not robust enough for most existing DRL works to make new robots fast adapt to current team policies, causing performance degradation. In this work, we get inspired by the genetic mechanism of social animals' instincts, and propose a robust multi-robotic collaboration and communication framework, C3F. It introduces graph-based representation to discover more features on the relevance among robots, and takes advantage of meta learning mechanism to conclude the general meta policy. When some robots crash and get replaced by new ones, this meta policy will be reused to guide new robots on how to quickly follow the existing collaboration and communication rules, and fast adapt to their roles in the team. The experiments on both the Webots simulator and the Starcraft II platform indicate that our methods have better performance compared with some SOTA methods, showing strong robustness and remarkable adaptability to the dynamic substitution in multi-robotic systems.
Low Area Implementation of FIR Filter Based on FPGA Using Approximation Method

A new approach is proposed that synthesizes multiplier blocks with minimal hardware requirements than standard block FIR filters, making them appropriate for applications of FIR filters. The proposed method is done firstly by changing the structure of the FIR filter, then secondly by manipulating the coefficients of the FIR filter. This method is called the approximation method. A 16-order effective FIR filter is designed and implemented in this paper, Using the equiripple approach in MATLAB R2012a to create the FIR filter, which was subsequently realized on Spartan-3A xc3s700a-4fg484FPGA and simulated with Xilinx ISE (Integrated Software Environment). The proposed method achieved the lowest area minimization percentage among other techniques, which is as follows: There was a 79.69% decrease in the quantity of 4 input LUTs and a 68.07% decrease in the number of slices that were occupied. At the same time, the reduction in the min period is 79.49%.
National Nutrition Surveys Applying Dietary Records or 24-h Dietary Recalls with Questionnaires: A Scoping Review

Development of an accurate and efficient dietary method is required for national nutrition surveys. Some countries conduct dietary surveys and combine 24-h dietary records or 24-h dietary recalls with dietary questionnaires. This scoping review aimed to summarize studies that used results from national surveys that combined detailed dietary surveys (dietary records or 24-h dietary recall) and dietary questionnaires and identify the purpose of combining the two methods. The PubMed database and manual searches were used for the literature review. We extracted 58 articles from 16 national nutrition surveys from 14 countries. Most studies used 24-h dietary recall for detailed dietary surveys and the food frequency questionnaire (FFQ) or food propensity questionnaire (FPQ) for questionnaire surveys. Among 37 studies from eight countries, the purpose of combining the two dietary survey methods was to estimate energy and nutrient intakes from detailed dietary surveys and habitual food intake from questionnaires. These findings are useful as a reference when introducing new dietary survey methods in future national nutrition surveys.
Nerve Growth Factor and Autoimmune Diseases

NGF plays a crucial immunomodulatory role and increased levels are found in numerous tissues during autoimmune states. NGF directly modulates innate and adaptive immune responses of B and T cells and causes the release of neuropeptides and neurotransmitters controlling the immune system activation in inflamed tissues. Evidence suggests that NGF is involved in the pathogenesis of numerous immune diseases including autoimmune thyroiditis, chronic arthritis, multiple sclerosis, systemic lupus erythematosus, mastocytosis, and chronic granulomatous disease. Furthermore, as NGF levels have been linked to disease severity, it could be considered an optimal early biomarker to identify therapeutic approach efficacy. In conclusion, by gaining insights into how these molecules function and which cells they interact with, future studies can devise targeted therapies to address various neurological, immunological, and other disorders more effectively. This knowledge may pave the way for innovative treatments based on NGF manipulation aimed at improving the quality of life for individuals affected by diseases involving neurotrophins.
Volumetric Bounds on Subsurface Fluid Substitution Using 4D Seismic Time-shifts with an Application at Sleipner

A method for volumetric estimation of subsurface fluid substitution is presented that relies on the analysis of 4D seismic time-shifts. Since time-shifts cannot resolve for fluid saturation and layer thickness simultaneously without additional constraints, mass estimates are derived from the complete set of possible fluid saturations and layer thicknesses. The method considers velocity-saturation relationships that range from uniform saturation to patchy saturation. Based on a generalized velocity-saturation relationship that is parameterized by the degree of patchiness, explicit upper and lower fluid mass bounds are provided. We show that the inherent ambiguity between fluid saturation and layer thickness has a severe impact on the convergence of these mass bounds. That is, roughly linear velocity-saturation relationships with patchy saturation tend to provide significantly better accuracy in a mass interpretation than the strongly non-linear velocity-saturation relationships associated with homogeneous saturation. The method is validated at the Sleipner storage site, where injected fluid masses are known. Moreover, a linear relationship between 4D time-shifts and injected mass is observed, suggesting that the evolving patterns of fluid saturation and fluid mixing in the CO2 plume at Sleipner have remained roughly constant with time.