Global Point Research Articles

Global point cloud registration network for large transformations

Abstract Three-dimensional registration is an established yet challenging problem that is key in many different applications, such as mapping the environment for autonomous vehicles, or modeling people for avatar creation, among others. Registration refers to the process of mapping multiple data into the same coordinate system by means of matching correspondences and transformation estimation. Novel proposals exploit the benefits of deep learning architectures for this purpose, as they learn the best features for the data, providing better matches and hence results. However, the state of the art is usually focused on cases of relatively small transformations, although in certain applications and in a real and practical environment, large transformations are very common. In this paper, we present ReLaTo (Registration for Large Transformations), an architecture that addresses the cases where large transformations happen while maintaining good performance for local transformations. This proposal uses a novel Softmax pooling layer to find correspondences in a bilateral consensus manner between two point sets, sampling the most confident matches. These matches estimate a coarse and global registration using weighted Singular Value Decomposition (SVD). A target-guided denoising step is applied to both the obtained matches and latent features to estimate the final fine registration considering the local geometry. All these steps are carried out following an end-to-end approach, which has been shown to perform better than 7 state-of-the-art registration methods in two datasets commonly used for this task (ModelNet40 and the Karlsruhe Institute of Technology and Toyota Technological Institute dataset, KITTI), especially in the case of large transformations. Graphic abstract

PV Array Reconfiguration for Global Maximum Power Optimizing under Partial Shading Conditions based on PV Switched System

Background: Due to the partial shading effects on different photovoltaic (PV) modules in a PV array, PV module operating conditions are inconsistent, and PV array output power is significantly reduced. Although the maximum power point (MPP) of a non-uniform irradiance PV array can be observed through global maximum power point tracking (GMPPT), no evaluation of the array's energy potential has been done. Objective: One of the most effective solutions to overcome the negative effects of partial shading in PV systems is the PV array reconfiguration process. To optimize the electrical structure of the PV array as the PV modules are partially shaded in a non-uniform manner, this study proposes a promising technique for dynamic reconfiguring a PV array in order to improve the extracted maximum power from a PV array under partial shading conditions (PSC). Methods: PV modules are rearranged by iteratively sorting them to allow the PV array with nonuniform irradiance to produce as much power as possible. This is conducted by applying a switching matrix to implement a PV-switched system approach. The proposed system with different PV array dimensions (e.g., 3×4, 4×6, and 5×8) is assessed in order to validate the proposed algorithm. A MATLAB/Simulink PV array developed model is used to find the global maximum power point for the different PV array dimensions in both pre-reconfiguration and post-reconfiguration states. Results: A detailed numerical comparison of the extracted power from the proposed system has been provided. Conclusion: The results show that the proposed system has the potential to extract the exact global maximum power for a PV-switched system under PSC, irrespective of array dimensions, according to simulation results.

Predicting the Global Potential Suitable Areas of Sweet Osmanthus (Osmanthus fragrans) Under Current and Future Climate Scenarios.

Osmanthus fragrans is a valuable landscaping tree that is appreciated worldwide. However, the optimal environmental conditions for O. fragrans cultivation have yet to be studied in detail, which hinders the preservation of wild resources of this plant and its commercial exploitation. The maximum entropy model was applied to assess the significance of environment variables influencing O. fragrans distribution. Combining data from 629 global distribution points for O. fragrans, predictions were made on the potential effects of climate change on the geographical distribution of suitable habitats for this species in the present and the future. The results indicated that O. fragrans preferred a warm and humid growing environment. Under the current climatic conditions, the potential habitats for O. fragrans were mostly located in the eastern coastal areas of the continents at medium and low latitudes. The main environmental variables that affected its distribution were the precipitation during the warmest quarter, the temperature seasonality, and the mean temperature of the warmest quarter. The analysis indicated that the continuation of current trends in climate change will result in the further reduction of suitable habitats for O. fragrans growth, and the global centroid will shift to the southeast. These findings provided insight into the impact of climate change on O. fragrans habitats, as well as provide guidance for the conservation of wild resources of this species and the breeding of more climate change-resistant varieties for the future.

Impact of a hub-and-spoke approach to hospital antimicrobial stewardship programmes on antibiotic use in Zambia.

Antimicrobial stewardship programmes (ASPs) aim to optimize antibiotic use and prevent antimicrobial resistance. This study assessed the impact of ASPs, initiated using a hub-and-spoke approach, on antibiotic use in Zambian public hospitals. A pre-post study was conducted in 10 ASP-naive hospitals across Zambia using the Centers for Disease Control and Prevention (CDC)'s hospital-based ASP core elements (i.e. leadership, accountability, pharmacy expertise, stewardship actions, tracking progress, reporting and education) checklist and the global point prevalence survey methodology. The intervention involved technical staff from a national 'hub' hospital with an established ASP providing on-site orientation and mentorship to multidisciplinary teams of healthcare workers at 10 'spoke' hospitals to build capacity in antimicrobial stewardship. ASP core elements and inpatient antibiotic use prevalence (AUP) were assessed before and 12 months after ASP implementation. Data were statistically analysed. The adoption of ASP core elements improved significantly (P = 0.001, 95% CI: -17.8 to -5.42). AUP decreased from 50.1% (±5.8, n = 1477) to 44.3% (±4.6, n = 1400) after 12 months, though the reduction was not statistically significant (P = 0.442; 95% CI: -9.8 to 21.6), with 'Watch' list antibiotics remaining the most commonly prescribed across the hospitals. The hub-and-spoke approach successfully catalysed ASPs in public hospitals in Zambia, demonstrating the potential for improving antibiotic use practices over time, provided structural challenges are addressed. This approach and insights can guide stakeholders in Zambia and similar settings in enhancing hospital ASPs.

Deep Plug-and-Play Non-Iterative Cluster for 3D Global Feature Extraction

Efficient and accurate point cloud feature extraction is crucial for critical tasks such as 3D recognition and semantic segmentation. However, existing global feature extraction methods for 3D data often require designing different models for different input types (point clouds, voxels, and maps). This article proposes an efficient plug-and-play non-iterative clustering method (NICM) to establish a unified point cloud global feature extraction paradigm suitable for any input type to solve the above problems. The core idea of the NICM is to construct the connection between a single point and other global points based only on the cosine similarity between center points to achieve global feature extraction, which has linear complexity characteristics and can be combined with any existing feature extraction model. Additionally, to better integrate the features extracted by NICM and the original model, this article designs an adaptive feature fusion module is designed based on the gate unit, which retains similar features and effectively fuses dissimilar features based on their importance to downstream tasks. We have applied our method to downstream tasks such as point cloud recognition, part segmentation and scene segmentation. Sufficient experiments have proven that our method can provide comprehensive and robust features for the original model, and effectively improve the performance of downstream tasks.

Enhanced MPPT Based on Zebra Optimization Algorithm for Control of a Partially Shaded Photovoltaic System

Objective: The objective of this paper is to adapt the Zebra Optimization Algorithm (ZOA) to track the Global Maximum Power Point (GMPP) of a PV system when subjected to partial shading conditions. Theoretical Framework: Photovoltaic (PV) systems are increasingly being used due to their clean nature and low maintenance cost. However, solar cells exhibit a nonlinear relationship between current and voltage, which necessitates the use of Maximum Power Point Tracking (MPPT) algorithms to ensure the system operates at its full potential. Bypass diodes are integrated into PV modules to protect solar cells from damage during partial shading, but this leads to a distorted multi-peak Power-Voltage (P-V) curve. This distortion presents a challenge for algorithms to distinguish between Global Maximum Power Point (GMPP) and Local Maximum Power Points (LMPPs). Method: The Zebra Optimization Algorithm (ZOA) is applied to track the GMPP of a PV system under partial shading conditions. Simulations were conducted using the MATLAB/Simulink platform to evaluate the algorithm’s performance. The focus was on ensuring fast and accurate tracking of the GMPPT under rapid changes in solar irradiance levels. Results and Discussion: Simulation results demonstrated that the ZOA-based MPPT algorithm effectively tracked the GMPPT of the PV system, even under rapid variations in solar irradiance. The algorithm proved its accuracy and speed in distinguishing the GMPP from LMPPs, addressing the challenges posed by the distorted P-V curve. Research Implications: The successful application of the ZOA for tracking the GMPP in partial shading conditions offers a significant improvement for PV systems. This can lead to enhanced efficiency in energy production and more reliable performance in real-world scenarios, where shading and other environmental factors frequently affect solar panels. Originality/Value: This study introduces the adaptation of the Zebra Optimization Algorithm (ZOA) in MPPT for PV systems, addressing the issue of distinguishing between GMPP and LMPPs under partial shading. The approach demonstrates both innovation and practical value in improving the efficiency of PV systems in challenging environmental conditions.

Existence and density theorems of Henig global proper efficient points

In this work, we provide some novel results that establish both the existence of Henig global proper efficient points and their density in the efficient set for vector optimization problems in arbitrary normed spaces. Our results do not require the assumption of convexity, and in certain cases, can be applied to unbounded sets. However, it is important to note that a weak compactness condition on the set (or on a section of it) and a separation property between the order cone and its conical neighbourhoods remains necessary. The weak compactness condition ensures that certain convergence properties hold. The separation property enables the interpolation of a family of Bishop-Phelps cones between the order cone and each of its conic neighbourhoods. This interpolation, combined with the proper handling of two distinct types of conic neighbourhoods, plays a crucial role in the proofs of our results, which include as a particular case other results that have already been established under more restrictive conditions.

Global burden of multiple sclerosis and its attributable risk factors, 1990–2019

BackgroundMultiple sclerosis (MS) is a progressively debilitating disorder that has seen a notable rise in prevalence in recent years. This study examines the burden of MS from 1990 to 2019, providing a detailed analysis by age, sex, and sociodemographic index (SDI) across 204 countries and territories.MethodsData on the prevalence, death and disability-adjusted life years (DALYs) attributable to MS were obtained from the publically available Global Burden of Disease 2019 project. The estimates are reported as numbers, percentages, and age-standardized rates per 100,000, accompanied by 95% uncertainty intervals.ResultsIn 2019, MS accounted for 1.8 million prevalent cases, 22.4 thousand deaths and 1.2 million DALYs worldwide. There were significant declines in the global age-standardized prevalence, mortality and DALY rates of MS over the period 1990–2019. In 2019, females exhibited a higher global point prevalence and a greater total number of prevalent MS cases than males across all age groups. At the regional level, a non-linear relationship was observed between the age-standardized DALY rate of MS and SDI.ConclusionAlthough the global age-standardized DALY rate of MS decreased between 1990 and 2019, MS continues to account for a considerable number of DALYs and prevalent cases. Integrating MS and its associated risk factors into healthcare planning is vital, especially in areas with high levels of socioeconomic development.

Deep predictive data representation model control for photovoltaic maximum power point tracking under partial shading conditions

As deep learning progresses in recent years, deep neural networks (DNNs) have penetrated applications in various industries. The global maximum power point (GMPP) tracking issue exists in photovoltaic (PV) systems. This study proposes a deep predictive data representation model control (PDRC)-based PV GMPP tracking method. The PDRC approach establishes a data representation control model centered on deep prediction by extracting the data features such as the current, voltage, and duty cycle of PV systems controllers on a time series basis. The proposed PDRC method can receive signals of voltage and current, then output duty cycle control signals. Thereby PDRC method can control PV output power to track GMPP efficiently. Finally, the proposed PDRC approach is evaluated with the perturbation observation method (P&O), the improved particle swarm algorithm (PSO-P&O), and the modified cuckoo algorithm (CS-P&O) to compare the PV GMPP tracking performance. In the simulation experiments, the PDRC approach increases the average tracking efficiency over the comparison algorithms by at least 7.729 % and decreases the average tracking time by at least 0.0155 s under partial shading conditions (PSCs). In the physical simulation validation, the proposed PDRC approach possesses minimal power perturbation and increases the average tracking efficiency over the comparison algorithms by at least 0.407 %.

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

Localization in MR-based Indoor Navigation System using point cloud registration

Abstract. This paper addresses the pervasive challenge of localization within indoor navigation systems. Many existing systems grapple with this issue, relying on marker-based methods, Bluetooth beacon localization, and image processing. However, these solutions often necessitate the placement of predetermined markers within the environment, presenting limitations such as marker deterioration over time, spatial constraints, and associated costs. In response, we propose a novel point cloud registration-based approach. This method involves the creation of a comprehensive global point cloud representing the entire indoor environment, effectively serving as a three-dimensional virtual map. Concurrently, a mixed-reality device is utilized to generate a local point cloud specific to the device’s current location within the building. By aligning and registering the local cloud with the global counterpart, the device’s precise location within the virtual map can be determined. This approach enables seamless navigation within indoor environments without the reliance on physical markers, offering a versatile and cost-effective solution to the challenge of localization in indoor navigation systems. Finally, an error analysis was also done to test localization accuracy for different illumination conditions, demonstrating the effectiveness of this method.

Factors in the Decline of U.S. Agricultural Imports in the MENA Region

The Middle East and North Africa (MENA) region stands on a global pivot point, impacting the world through its diverse political and economic dynamics. While often recognized for its complex challenges, the region's food security remains surprisingly under-considered, even though it carries significant global repercussions. This research focuses on a concerning trend: the decline of U.S. agricultural exports to MENA, potentially jeopardizing regional stability and impacting global food systems. While EU competitiveness is acknowledged, this research delves deeper than mere identification of competitors such as the EU, dissecting the interplay of their advantages – superior pricing and yields – against the U.S. approach from a policy-perspective. The paper’s ultimate goal is to highlight how the US has suffered from out-competition from the EU, not simply MENA countries’ diversification, leading to the latter’s collapse as a source of staple crop importations, in order to prevent such drastic changes in the future as the same trend in other industries could provide much more devastating for the US. This analysis can impact US policy and inspire a shift towards more sensible and outcome-oriented spending policies.

Standardised Tool for the Assessment of Bruxism: Translation, Cultural Adaptation and Pilot Testing in Italy.

Recently, the Standardised Tool for the Assessment of Bruxism (STAB) has been developed for use in clinical and research settings. The aim of the present study is to describe the process of forward and back translation and pilot testing of the STAB into Italian. The English version of the STAB was adopted as a template for translation into other languages, according to a step-by-step procedure led by the expert STAB bruxism panel and mother tongue experts in the field. In detail, the translation team was made up of 12 subjects: three study coordinators, two forward translators, two back-translators and five expert panellists. Following the translation process, a pilot test in patients, dentists and dental students was performed using the 'probing' method (i.e., subjects were questioned by the examiners about the perceived content and interpretation of the items) with the aim to assess the comprehensibility of the questions and response options, and the feasibility of the tool. This paper describes the translation process of the STAB and provides the outcomes of the pilot testing phase and the face validity assessment. The preliminary results suggest that, from a global point of view, the STAB reflects the characteristics required in clinical and research settings. Thanks to the translation process, the Italian version of the STAB can be assessed on-field and introduced in the clinical and research field to get deeper into the study of bruxism epidemiology in Italy.

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance.

Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA's exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA's exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.

The Turning Point in Time from the Serbian Perspective: How to Turn the Digital Tide

Abstract To compare and contrast two points in time the article highlights the social impact of the WWW that was still hidden in 1989 and the social impact of the disappearance of the reading of printed books that is still hidden in 2024. These global points of interest for turning the tide are supplemented by some particulars from a Serbian perspective and a question: should world governments buy time for people to read printed books?

Robust and correspondence-free point cloud registration: an extended approach with multiple hypotheses evaluation

AbstractPoint cloud registration is a fundamental problem in robotics, critical for tasks such as localization and mapping. Most approaches to this problem use feature-based techniques. However, these approaches have issues when dealing with unstructured environments where meaningful features are difficult to extract. Recently, an innovative global point cloud registration algorithm, PHASER, which does not rely on geometric features or point correspondences, has been introduced. It leverages Fourier transforms to identify the optimal rigid transform that maximizes cross-correlation between source and target point clouds. PHASER can also incorporate additional data channels, like LiDAR intensity, to enhance registration results. Because it does not rely on local features and because of its ability to exploit additional data, PHASER is particularly useful when dealing with very noisy point clouds or with many outliers. For this reason, we propose an extension to PHASER that considers multiple plausible rototranslation hypotheses. Our extended approach outperforms the original PHASER algorithm, especially in challenging scenarios where point clouds are widely separated. We validate its effectiveness on the DARPA SubT, and the Newer College datasets, showcasing its potential for improving registration accuracy in complex environments.

TriHuman: A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis

Creating controllable, photorealistic, and geometrically detailed digital doubles of real humans solely from video data is a key challenge in Computer Graphics and Vision, especially when real-time performance is required. Recent methods attach a neural radiance field (NeRF) to an articulated structure, e.g., a body model or a skeleton, to map points into a pose canonical space while conditioning the NeRF on the skeletal pose. These approaches typically parameterize the neural field with a multi-layer perceptron (MLP) leading to a slow runtime. To address this drawback, we propose TriHuman a novel human-tailored, deformable, and efficient tri-plane representation, which achieves real-time performance, state-of-the-art pose-controllable geometry synthesis as well as photorealistic rendering quality. At the core, we non-rigidly warp global ray samples into our undeformed tri-plane texture space, which effectively addresses the problem of global points being mapped to the same tri-plane locations. We then show how such a tri-plane feature representation can be conditioned on the skeletal motion to account for dynamic appearance and geometry changes. Our results demonstrate a clear step toward higher quality in terms of geometry and appearance modeling of humans as well as runtime performance.

Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation.

AlphaFold model has reshaped biological research. However, vast unstructured data in the entire AlphaFold field requires further analysis to fully understand the current research landscape and guide future exploration. Thus, this scientometric analysis aimed to identify critical research clusters, track emerging trends, and highlight underexplored areas in this field by utilizing machine-learning-driven informatics methods. Quantitative statistical analysis reveals that the AlphaFold field is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) and global collaboration (International Co-authorship = 33.33%). Unsupervised clustering algorithm, time series tracking, and global impact assessment point out that Cluster 3 (Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology) has the greatest influence (Average Citation = 48.36 ± 184.98). Additionally, regression curve and hotspot burst analysis highlight "structure prediction" (s = 12.40, R2 = 0.9480, p = 0.0051), "artificial intelligence" (s = 5.00, R2 = 0.8096, p = 0.0375), "drug discovery" (s = 1.90, R2 = 0.7987, p = 0.0409), and "molecular dynamics" (s = 2.40, R2 = 0.8000, p = 0.0405) as core hotspots driving the research frontier. More importantly, the Walktrap algorithm further reveals that "structure prediction, artificial intelligence, molecular dynamics" (Relevance Percentage[RP] = 100%, Development Percentage[DP] = 25.0%), "sars-cov-2, covid-19, vaccine design" (RP = 97.8%, DP = 37.5%), and "homology modeling, virtual screening, membrane protein" (RP = 89.9%, DP = 26.1%) are closely intertwined with the AlphaFold model but remain underexplored, which implies a broad exploration space. In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas.

Global Point Prevalence Survey of Squamous cell carcinoma in Ten Teaching Hospitals in Iraq

Global Point Prevalence Survey of Squamous cell carcinoma in Ten Teaching Hospitals in Iraq