Search Results (27)

Oily wastewater is extensively generated during the petroleum extraction and refining processes, as crude oil production water and from the effluent systems in petrochemical enterprises. The discharge standards for such wastewater are stringent, with the Oslo–Paris Convention stipulating that the oil content must be below 30 mg/L for permissible discharge. Flotation, a conventional oil–water separation method, relies on the collision and adhesion of rising bubbles with oil droplets in water to form low-density aggregates that float to the surface for separation. The collision and adhesion mechanisms between bubbles and oil droplets are fundamental to this process. However, systematic studies on their interactions remain scarce. This study employs the extended Derjaguin–Landau–Verwey–Overbeek theory to analyze the three mechanical interactions during the collision–adhesion process theoretically and investigates the heterogeneous interaction dynamics experimentally. Furthermore, given the diverse liquid-phase environments of oily wastewater, the effects of salinity, pH, and surfactant concentration are decoupled and individually explored to clarify their underlying mechanisms. Finally, a solution is proposed to enhance the flotation efficiency fundamentally. This work systematically elucidates the influence of liquid-phase environments on the adhesion behavior for the first time through the unification of theoretical and experimental approaches. The findings provide critical insights for advancing flotation theory and guiding the development of novel coagulants. Full article

This paper proposes a generalized maximum access delay time (MADT) estimation method for orthogonal frequency division multiplexing (OFDM) systems operating over multipath fading channels. The proposed approach derives a novel log-likelihood ratio (LLR) formulation by exploiting the correlation characteristics introduced by the cyclic prefix (CP) in received OFDM symbols, thereby enabling the efficient approximation of the maximum likelihood (ML) MADT estimation. A key contribution of this study is represented by the unification and generalization of existing MADT estimation methods by explicitly formulating the bias term associated with the geometric mean. Within this framework, a previously reported scheme is shown to be a special case of the proposed method. The effectiveness of the proposed MADT estimator is evaluated in terms of correct and good detection probabilities, illustrating not only improved detection accuracy but also robustness across varying channel conditions, in comparison with existing methods. Furthermore, the estimator is applied to both noise-canceling channel estimation (NCCE) and time-domain least squares (TDLS) methods, and its practical effectiveness is verified in IEEE 802.11p/OFDM system scenarios relevant to vehicle-to-everything (V2X) communications. Simulation results confirm that when integrated with NCCE and TDLS, the proposed estimator closely approaches the performance bound of ideal MADT estimation. Full article

Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual’s autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring. Full article

The $E_8\otimes E_8$ octonionic theory of unification suggests that our universe is six-dimensional and that the two extra dimensions are time-like. These time-like extra dimensions, in principle, offer an explanation of the quantum nonlocality puzzle, also known as the EPR paradox. Quantum systems access all six dimensions, whereas classical systems such as detectors experience only four dimensions. Therefore, correlated quantum events that are time-like separated in 6D can appear to be space-like separated and, hence, nonlocal, when projected to 4D. Our lack of awareness of the extra time-like dimensions creates the illusion of nonlocality, whereas, in reality, the communication obeys special relativity and is local. Bell inequalities continue to be violated because quantum correlations continue to hold. In principle, this idea can be tested experimentally. We develop our analysis after first constructing the Dirac equation in 6D using quaternions and using the equation to derive spin matrices in 6D and then in 4D. We also show that the Tsirelson bound of the CHSH inequality can in principle be violated in 6D. Full article

As geological exploration technology advances, geoscience relies on digitization and intelligence to address challenges such as data fragmentation, multi-source heterogeneity, and visual analysis. This study develops a big data-driven 3D visual analysis system for regional-scale applications. The system integrates three core technological components: (1) a heterogeneous cloud resource scheduling method employing an optimized CMMN algorithm with unified cloud API standardization to enhance task distribution efficiency; (2) a block model-based dynamic data aggregation approach utilizing semantic unification and attribute mapping for multi-source geological data integration; (3) a GPU-accelerated rendering framework implementing occlusion culling and batch processing to optimize 3D visualization performance. Experimental validation shows the improved CMMN algorithm reduces cloud task completion time by 2.37% while increasing resource utilization by 0.652% compared with conventional methods. The dynamic data model integrates 12 geological data types across eight categories through semantic mapping. Rendering optimizations achieve a 93.7% memory reduction and 60.6% faster visualization compared with baseline approaches. This system provides robust decision support and reliable tools for the digital transformation of geoscience work. Full article

In satellite and airborne electro-optical tracking systems, there are numerous processing devices and complex data flows. To ensure the coordinated operation of the system, the multiple devices within the target system must operate under unified time control for data acquisition, computation, and output. This study introduces a multi-channel data simulator based on a time unification system. The complete simulation system includes the host computer, simulator, target system, and time reference generator. The simulator has programmable input and output interfaces for multi-channel protocols and has storage and real-time working modes. In the storage mode, the simulated data are pre-transmitted to the simulator’s storage and sent to the target system according to the time reference generator. The simulator simultaneously stores the target system results. In the real-time mode, the host computer generates simulated data based on the target system’s results and outputs the data through the simulator in real time. The main contribution of the simulator is that it achieves system-level closed-loop simulation and completes the functional and performance verification of the target system. Through experimental verification, it is found that the simulator can achieve 4.2 Gbps of simulated data transmission and 1.6 Gbps of data reception and storage, with a closed-loop delay of 39.9 µs. Full article

With the existing pressure sensors, it is difficult to achieve the unification of wide pressure response range and high sensitivity. Furthermore, the preparation of pressure sensors with excellent performance for sleep health monitoring has become a research difficulty. In this paper, based on material and microstructure synergistic enhancement mechanism, a hybrid pressure sensor (HPS) integrating triboelectric pressure sensor (TPS) and piezoelectric pressure sensor (PPS) is proposed. For the TPS, a simple, low-cost, and structurally controllable microstructure preparation method is proposed in order to investigate the effect of carbon nano-onions (CNOs) and hierarchical composite microstructures on the electrical properties of CNOs@Ecoflex. The PPS is used to broaden the pressure response range and reduce the pressure detection limit of HPS. It has been experimentally demonstrated that the HPS has a high sensitivity of 2.46 V/10⁴ Pa (50–600 kPa) and a wide response range of up to 1200 kPa. Moreover, the HPS has a low detection limit (10 kPa), a high stability (over 100,000 cycles), and a fast response time. The sleep monitoring system constructed based on HPS shows remarkable performance in breathing state recognition and sleeping posture supervisory control, which will exhibit enormous potential in areas such as sleep health monitoring and potential disease prediction. Full article

An unmanned swarm is usually composed of a group of homogeneous or heterogeneous hardware platforms, software control systems, and interfaces for human–computer interaction that operate collectively to achieve a specific goal by information interaction. They exhibit robustness and fault tolerance when facing complex missions, making it crucial in military, transportation, intelligent traffic, and other fields. However, the coupling between the hardware and software of a heterogeneous unmanned swarm can indeed have significant implications for system flexibility, software development and deployment, and hardware maintenance. Over the years, there has been a significant shift from traditional hardware-focused control systems to a greater emphasis on the core software layer. In this paper, a distributed network architecture is proposed to solve this problem, in which hardware resources are abstracted and represented to accomplish standardization and unification by defining a consistent and uniform set of data formats, and a resource pool of hardware data is constructed to realize the function that the number and scale of platforms is irrelevant, the task module can be plug-and-play at any time, and the software can be configured on demand. The resource scheduling of a single platform is achieved through process and thread communication using shared memory, while the resource scheduling of a cross platform is achieved through a network using request and response and subscription and notification. As a result, it can satisfy the development of functional modules in a software-defined mode and gradually improve the intelligence capability of an unmanned swarm. Based on the above architecture, the overall framework of the autonomous navigation system and the collaborative control system has been successfully established. Finally, a hardware-in-the-loop simulation environment is constructed, and the integration and verification of the proposed distributed architecture is carried out by the cooperative formation experiment, which proves the feasibility of this proposal. Full article

The International Association of Geodesy (IAG) aims to establish the International Height Reference System (IHRS) as one of its primary scientific objectives. Central to the realization of the IHRS is global vertical datum unification, which requires the connection of existing local vertical height reference systems (VHS) robustly and consistently. However, conventional methods are not suitable for estimating the offsets between two distant local height systems. In this paper, we propose a framework for connecting two local VHSs using ultraprecise clock frequency signal links between satellites and ground stations, referred to as the satellite frequency signal transmission (SFST) approach. The SFST approach allows for the direct determination of the geopotential and height differences between two ground datum stations without any location restrictions between the two VHSs. The simulation results show that the VHSs of China and the US can be unified with an accuracy of several centimeters, provided that the stability of atomic clocks used on-board the satellite and at on-ground datum locations reaches $4.8\times {10}^{-17}{\tau }^{-1/2}$ for an averaging time $\tau$ (in seconds). We conclude that the SFST approach shows promise for achieving centimeter-level accuracy in unifying the global vertical height datum and represents a new paradigm for the realization of the IHRS. Full article

China has recently declared its role as a leading developing country in actively practicing carbon neutrality. In fact, its carbon-neutral policy has accelerated from a gradual and macroscopic perspective and has been actively pursued given the changes not only in the overall social system but also in its impact on various stakeholders. This study analyzed the patterns of carbon neutrality (CN) and the actors of policy promotion in China from a long-term perspective. It collected policy discourses related to CN posted on Chinese websites from 2000 to 2022 and conducted text mining and network analysis. The results revealed that the pattern of CN promotion in China followed an exploration–demonstration–industrialization–digitalization model, similar to other policies. Moreover, the policy promotion sector developed in the direction of unification–diversification–specialization. Analysis of policy promotion actors found that enterprises are the key driver of continuous CN. In addition, the public emerged as a critical actor in promoting CN during the 12th–13th Five-Year Plans (2011–2020). Moreover, the central government emerged as a key driving actor of CN during the 14th Five-Year Plan. This was a result of the emphasis on efficiency in the timing and mission process of achieving CN. Furthermore, based on the experience of COVID-19, the rapid transition of Chinese society toward CN emphasizes the need for a central government with strong executive power. Based on these results, this study presents constructive suggestions for carbon-neutral development in China. Full article

This study discusses the assessment of OE (operational efficiency) and RTS (returns to scale) over a time horizon. Many previous DEA (Data Envelopment Analysis) studies have discussed how to measure OE/RTS. However, their works did not consider the measurement over time. The important feature of the proposed approach is that our models are different from standard ones in terms of factor (inputs and outputs) unification. A problem with standard models is that they produce different efficiency measures for input and output orientations. Consequently, they yield different OE and RTS estimates depending upon which production factor is used for measurement. To handle the difficulty, we develop a new DEA formulation whose efficiency measure is determined after combining inputs and outputs, and then we discuss how to measure the types of RTS. The other methodological feature is that the proposed model incorporates a time horizon. As an empirical application, this study considers electricity generation and transmission across Chinese provinces from 2006 to 2019. The first key outcome is that the performance of China’s electricity generation and transmission system tends to improve with an annual growth rate of 0.45% across time. The second outcome is that, during the observed periods, China has more occurrences of decreasing rather than increasing RTS. As an implication, some provinces (e.g., Jiangxi and Hainan) need to increase their generation sizes to enhance their OE measures, while other provinces (e.g., Jiangsu and Zhejiang) should decrease their generation sizes. Finally, this study confirms significant technological heterogeneity across Chinese provinces and groups. Full article

The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems. Full article

Community-based monitoring (CBM) is one of the- most sustainable ways of establishing a national forest monitoring system for successful Reduce Emissions from Deforestation and Forest Degradation (REDD+) implementation. In this research, we present the details of the National Forest Conservation Program (PNCB—Programa Nacional de Conservación de Bosques para la Mitigación del Cambio Climático), Peru, from a satellite-based alert perspective. We examined the community’s involvement in forest monitoring and investigated the usability of 1853 CBM data in conjunction with 445 satellite-based alerts. The results confirm that Peru’s PCNB contributed significantly to the REDD+ scheme, and that the CBM data provided rich information on the process and drivers of forest change. We also identified some of the challenges faced in the existing system, such as delays in satellite-based alert transfer to communities, sustaining community participation, data quality and integration, data flow, and standardization. Furthermore, we found that mobile devices responding to alerts provided better and faster data on land-use, and a better response rate, and facilitated a more targeted approach to monitoring. We recommend expanding training efforts and equipping more communities with mobile devices, to facilitate a more standardized approach to forest monitoring. The automation and unification of the alert data flow and incentivization of the participating communities could further improve forest monitoring and bridge the gap between near-real-time (NRT) satellite-based and CBM systems. Full article

Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and slow detection time. For this reason, a novel method of spherical object detection and parameter estimation based on an improved random sample consensus (RANSAC) algorithm is proposed. The method is based on the RANSAC algorithm. Firstly, the principal curvature of point cloud data is calculated. Combined with the k-d nearest neighbor search algorithm, the principal curvature constraint of random sampling points is implemented to improve the quality of sample points selected by RANSAC and increase the detection speed. Secondly, the RANSAC method is combined with the total least squares method. The total least squares method is used to estimate the inner point set of spherical objects obtained by the RANSAC algorithm. The experimental results demonstrate that the method outperforms the conventional RANSAC algorithm in terms of accuracy and detection speed in estimating sphere parameters. Full article

Due to the past t_P and the future t_F being divided into a pair of opposing times by the now t_N, the generation mechanism of the contradiction is attributed in this paper as the process in which the time increment Δt and Δt’ are transmitted from the past t_P and the future t_F to the present moment t_N, respectively, and then reverse each other. The category and topos of time contradictorily constructed by the mechanism is discussed. It is shown that not only can the laws of the “Unification of Opposites”, “Mutual change of Quality and Quantity” and “Negation of negation” of the contradiction be represented in this form of category, but some of classic constructions appearing in the fields of mathematics, physics, logic, life, nerves, thinking, and intelligence can also be considered as morphosm- or pattern-induced and emerge via this mechanism as well. On the other hands, a series of concepts, models, and algorithms for noetic science, such as the attribute conjunctive monoid category (ACMC), attribute reasoning lattice category (ARLC), attribute coordinate system (ACS), attribute coordinate analysis method based on the learning of ACS for perception, cognition, and decision-making (ACAM), qualitative (conversion degree) mapping from quantity to quality (QM), attribute grid computer based on qualitative mapping, qualitative criteria transformation (AGC), etc., which have been verified through corresponding experiments, have been proposed, so that not only a set of attribute theory methods from perception to cognition and thinking have been constructed, but the synthetized framework of “Quantitative ⨂ Fixed Image ⇒ Qualitative”, called “Framework of Syntenic Three Approaches” (FSTA) can also be induced. It is possible to provide an alternative reference path and technical solution for noetic science and open complex giant systems because FSTA is consistent with the framework of “Quantitative Intelligence ⨂ Fixed Image Intelligence ⇒ Qualitative Intelligence (Meta Synthetic Wisdom)”, as proposed by Hsue-shen Tsien. Full article