Continuous Object Research Articles

Spatiotemporal Focus and Lightweight Memory Network for Continuous Object Detection With Event Camera

Spatiotemporal Focus and Lightweight Memory Network for Continuous Object Detection With Event Camera

Continuity, limits, and quantity in Al-Fārābī’s paraphrase of Aristotle’s Categories

ABSTRACT Despite its position in an introductory work to logic, the account of continuity presented by Abū Naṣr Al-Fārābī in his paraphrase of Aristotle’s Categories is apparently even less accessible to the beginner than Aristotle’s original. This is in part because Al-Fārābī integrated elements of the accounts of continuity in Aristotle’s Physics and Metaphysics into an account mainly derived from Aristotle’s Categories. While Al-Fārābī’s account chiefly follows Aristotle’s Categories 6 in describing a continuous object that can be divided into parts with a shared boundary, it borrows the terminology of limits from Physics 5 and the notion of quantity as divisible into its parts from Metaphysics 5.13. In doing so, Al-Fārābī defines continuity using a notion of a limit without determining whether or not it was part of the continuous quantity. This definition could also accommodate atomists, since it would work even if the limit were an atom or several atoms that were part of the continuous object. The broadness of this definition is probably intended to allow students of logic who may have atomist tendencies to accept the account of quantity in the categories tradition, even though they may not be ready to reject atomism until after studying physics.

A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information

Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed. SMLP-CR constructs a multilayer perceptron with a presingle-connection layer (SMLP) based on multisource conditional information. The method employs multi-scale mean filtering and local neighborhood sampling to gain spatial information while also taking into account multi-spectral and multi-temporal information as well as pixel similarity. Meanwhile, the feature importance from the SMLP provides a selection order for conditional information—homologous images are prioritized over images from the same season as the restoration image, and images with close temporal distances rank last. The results of comparative experiments indicate that SMLP-CR shows apparent advantages in terms of visual naturalness, texture continuity, and quantitative metrics. Moreover, compared with popular deep-learning methods, SMLP-CR samples locally around cloud pixels instead of requiring a large cloud-free training area, so the samples show stronger correlations with the missing data, which demonstrates universality and superiority.

PERFORMANCE-BASED REWARD SYSTEM AND ORGANIZATIONAL AGILITY IN THE BANKING INDUSTRY IN NIGERIA

As organizations strive to maintain a competitive edge, the need for a more agile workforce has become increasingly apparent. Performance-based reward systems have emerged as a powerful tool to facilitate this transformation, by aligning employee motivation with the organization’s strategic goals. However, the overemphasis on individual performance metrics may inadvertently promote unhealthy competition and undermine collaboration. Hence this study examines the effect of performance-based reward system on organizational agility in the banking industry. Specifically, the study seeks to determine how competence-based reward system will affect organizational agility; to examine the impact of reward and incentives on organizational agility. The study has adopted a survey research design to obtain data from a population of 221 staff members of the Guarantee Trust Bank (GTB), which represent the broader banking industry in Nigeria. A sample of 142 was drawn through Taro Yamane and sampled randomly. The study used primary data and questionnaires. Multiple regression was used to test the hypotheses. The study found that organizational agility was affected by the competence-based reward system; and incentive and reward proxy by continuous improvement incentives, teamwork reward, and balancing reward objectives determines the organizational agility of the GTB and the broader banking industry in the country. In conclusion, the performance-based reward system has a significant effect on organizational agility in the banking industry in Nigeria. Thus, the study recommends that banks and other financial organizations should adopt the following aspects of a competence- based reward system such as continuous learning, cross-functional collaboration, and skill development as a strategy for achieving a high level of organizational agility in the GTB through its employees.

An Algorithm for Automated Measurement of Kinetic Tremor Magnitude Using Digital Spiral Drawings

Introduction: Essential tremor is a common movement disorder. Numerous validated clinical rating scales exist to quantify essential tremor severity by employing rater-dependent visual observation but have limitations, including the need for trained human raters and the lack of precision and sensitivity compared to technology-based objective measures. Other continuous objective methods to quantify tremor amplitude have been developed, but frequently provide unitless measures (e.g., tremor power), limiting real-world interpretability. We propose a novel algorithm to measure kinetic tremor amplitude using digital spiral drawings, applying the V3 framework (sensor verification, analytical validation, and clinical validation) to establish reliability and clinical utility. Methods: Archimedes spiral drawings were recorded on a digitizing tablet from participants (n = 7) enrolled in a randomized placebo control double-blinded crossover pilot trial evaluating the efficacy of oral cannabinoids in reducing essential tremor. We developed an algorithm to calculate the mean and maximum tremor amplitude derived from the spiral tracings. We compared the digitally measured tremor amplitudes to manual measurement to evaluate sensor reliability, determined the test-retest reliability of the digital output across two short-interval repeated measures, and compared the digital measure to kinetic tremor severity graded using The Essential Tremor Rating Assessment Scale (TETRAS) score for spiral drawings. Results: This algorithm for automated assessment of kinetic tremor amplitude from digital spiral tracings demonstrated a high correlation with manual spot measures of tremor amplitude, excellent test-retest reliability, and a high correlation with human ratings of the TETRAS score for spiral drawing severity when the tremor severity was rated “slight tremor” or worse. Discussion: This digital measure provides a simple and clinically relevant evaluation of kinetic tremor amplitude that shows promise as a potential future endpoint for use in clinical trials of essential tremor.

OBJECT RECOGNITION AND LOCALIZING FOR VISUALLY IMPAIRED PEOPLE

It is estimated that there are already tens of millions of visually impaired persons in the world, accounting for a significant fraction of the population. Their integration into society is a primary and continuous objective. Assuring a healthcare system has required a great deal of work. Many guiding system solutions have been developed to assist people with vision impairments in leading normal lives. These systems are often designed with specific goals in mind. Nonetheless, these fixes can greatly increase the security and mobility of these people. Several frame rates are used to record the visual image. The processing of every acquired image comes next. The processing result will alert the user based on the object that was discovered and its position. Key words: computer vision, image processing, guidance system, and object recognition.

Single-center pilot study of remote therapeutic monitoring in patients with operative spinal pathologies

ObjectivesSpine pathology affects a significant portion of the population, leading to neck and back pain, impacting quality of life, and potentially requiring surgical intervention. Current pre- and postoperative monitoring methods rely on patient reported outcome (PRO) measures and lack continuous objective data on patients' recoveries. Remote therapeutic monitoring (RTM) using wearable devices offers a promising solution to bridge this gap, providing real-time physical function data. This study aims to assess the feasibility and correlation between changes in physical function and daily activity levels using RTM for individuals with operative spinal pathologies. MethodsA single-center pilot study involving 21 participants with operative spinal pathologies was conducted at an academic hospital. Participants were provided Bluetooth-enabled Fitbit Inspire 2 activity trackers and asked to wear them daily for 100 days. The Healthcare Recovery Solutions (HRS) mobile application facilitated remote administration of the PROMIS - Physical Function Short Form 6b PROs questionnaire at days 1, 30, and 90. Linear regression, Students' paired T tests, and one-way ANOVA were used to analyze collected data. ResultsAverage compliance with RTM was found to be 82.4% compared to only 48% for PROMs. Changes in daily steps were moderately positively correlated with changes in PROs at both 30 and 90 days. Participant satisfaction with RTM was high, and responses indicated greater satisfaction with RTM compared to PROMs. ConclusionsRTM offers continuous and objective data collection, presenting a potential solution to the limitations of intermittent clinical assessments and self-reported outcomes. The study demonstrated a moderate correlation between changes in activity levels and changes in PROs, suggesting that RTM data could serve as a surrogate for PROs. Participants' high compliance and satisfaction with RTM underscore its feasibility and potential clinical utility. This study lays the groundwork for larger future investigations into the clinical benefits and broader application of RTM in spine care.

Quantifying Aristotelian essences: On some fourteenth-century applications of limit decision problems to the perfection of species

ABSTRACT This paper explores a specific problem within an important philosophical genre of the fourteenth century: the debates over the perfection of species. It investigates how the problem of defining limits for continuous magnitudes – a problem typical of Aristotelian physics – was integrated into these debates at the levels of genera, species, and individuals as these entities began to be conceptualized in quantitative terms. After explaining the emergence of this problem within fourteenth-century metaphysics, the paper examines the contributions of three philosophers – Hugolinus of Orvieto, John of Ripa, and Paul of Venice – who offered varying solutions to the challenge of defining limits between species. It demonstrates that two primary solutions arose, inspired by continuous and discrete mathematical objects. It is shown that whereas Hugolinus of Orvieto advocates for a continuist model, John of Ripa proposes a discrete one. The last part of the paper examines Paul of Venice's hybrid approach, which combines elements from both models, facilitating a more comprehensive treatment of species particularly difficult to analyse, namely geometric figures. The conclusions of this comparative study underscore how profoundly the metaphysical reflections of the Middle Ages contributed to the analysis of the structure of the continuum and the extension of the notion of quantity to various objects.

Continuous Object State Recognition for Cooking Robots Using Pre-Trained Vision-Language Models and Black-Box Optimization

Continuous Object State Recognition for Cooking Robots Using Pre-Trained Vision-Language Models and Black-Box Optimization

A comprehensive framework for strategic knowledge management in civil service organisations of Mauritius

The acquisition and utilisation of knowledge possessed by employees within an organisation, particularly in the context of the Civil Service, can be a crucial asset in achieving the organization's continuous business goals and objectives. This study investigates the methods and implementation of Knowledge Management (KM) in the public service Organisations of Mauritius. The objective is to identify the attributes that contribute to the effectiveness of knowledge management (KM) and address significant obstacles in the KM process. The objective of this study is to increase awareness of Knowledge Management (KM) as a feasible option for enhancing the efficiency of civil service organisations. The study results imply that knowledge management in civil service organisations is still in its nascent phase, indicating a significant need for advancement in the whole knowledge management process. This study examines the present condition of knowledge management in the civil service organisations of Mauritius. The study primarily aimed to collect empirical data on the subjective impressions, attitudes, and perspectives of the respondents or interviewers regarding the investigated issues. The study use interpretive methodology rather than positive approaches. Nevertheless, this research solely concentrates on the 25 governmental ministries of the Republic of Mauritius. The proposed framework is anticipated to be easily implemented in para-statal entities and government-owned firms, necessitating only minimal adjustments. The objective of this project is to build an empirical basis for the creation of a knowledge management system tailored to the needs of Civil Service Organisations. This study focuses on analysing the mechanisms of Knowledge Management (KM) and their impact on productivity and efficiency in Mauritian Civil Service Organisations.

A MaxSAT approach for solving a new Dynamic Discretization Discovery model for train rescheduling problems

Train scheduling is a critical activity in rail traffic management, both off-line (timetabling) and on-line (dispatching). Time-Indexed formulations for scheduling problems are stronger than other classical formulations, like Big-M. Unfortunately, their size grows usually very large with the size of the scheduling instance, making even the linear relaxation hard to solve. Moreover, the approximation introduced by time discretization can lead to solutions which cannot be realized in practice. Dynamic Discretization Discovery (DDD), recently introduced by Boland et al. (2017) for the continuous-time service network design problem, is a technique to keep at bay the growth of Time-Indexed formulations and their response times and, at the same time, ensures the necessary modelling precision. By exploiting the DDD paradigm, we develop a novel approach to train dispatching and, more in general, to job-shop scheduling. The algorithm implemented represents the first application of a Maximum SATisfiability problem approach to the field. In our comparisons on real-life instances of train dispatching, our restricted Time-Indexed formulation solves faster on piece-wise constant objective functions, while the Big-M approach maintains the lead on linear continuous objectives.

Fractal networks: Topology, dimension, and complexity.

Over the past two decades, the study of self-similarity and fractality in discrete structures, particularly complex networks, has gained momentum. This surge of interest is fueled by the theoretical developments within the theory of complex networks and the practical demands of real-world applications. Nonetheless, translating the principles of fractal geometry from the domain of general topology, dealing with continuous or infinite objects, to finite structures in a mathematically rigorous way poses a formidable challenge. In this paper, we overview such a theory that allows to identify and analyze fractal networks through the innate methodologies of graph theory and combinatorics. It establishes the direct graph-theoretical analogs of topological (Lebesgue) and fractal (Hausdorff) dimensions in a way that naturally links them to combinatorial parameters that have been studied within the realm of graph theory for decades. This allows to demonstrate that the self-similarity in networks is defined by the patterns of intersection among densely connected network communities. Moreover, the theory bridges discrete and continuous definitions by demonstrating how the combinatorial characterization of Lebesgue dimension via graph representation by its subsets (subgraphs/communities) extends to general topological spaces. Using this framework, we rigorously define fractal networks and connect their properties with established combinatorial concepts, such as graph colorings and descriptive complexity. The theoretical framework surveyed here sets a foundation for applications to real-life networks and future studies of fractal characteristics of complex networks using combinatorial methods and algorithms.

DeepBranchTracer: A Generally-Applicable Approach to Curvilinear Structure Reconstruction Using Multi-Feature Learning

Curvilinear structures, which include line-like continuous objects, are fundamental geometrical elements in image-based applications. Reconstructing these structures from images constitutes a pivotal research area in computer vision. However, the complex topology and ambiguous image evidence render this process a challenging task. In this paper, we introduce DeepBranchTracer, a novel method that learns both external image features and internal geometric characteristics to reconstruct curvilinear structures. Firstly, we formulate the curvilinear structures extraction as a geometric attribute estimation problem. Then, a curvilinear structure feature learning network is designed to extract essential branch attributes, including the image features of centerline and boundary, and the geometric features of direction and radius. Finally, utilizing a multi-feature fusion tracing strategy, our model iteratively traces the entire branch by integrating the extracted image and geometric features. We extensively evaluated our model on both 2D and 3D datasets, demonstrating its superior performance over existing segmentation and reconstruction methods in terms of accuracy and continuity.

Multi-agent Continuous Control with Generative Flow Networks

Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving as a powerful alternative to reinforcement learning for exploratory control tasks. However, the individual-flow matching constraint in GFlowNets limits their applications for multi-agent systems, especially continuous joint-control problems. In this paper, we propose a novel Multi-Agent generative Continuous Flow Networks (MACFN) method to enable multiple agents to perform cooperative exploration for various compositional continuous objects. Technically, MACFN trains decentralized individual-flow-based policies in a centralized global-flow-based matching fashion. During centralized training, MACFN introduces a continuous flow decomposition network to deduce the flow contributions of each agent in the presence of only global rewards. Then agents can deliver actions solely based on their assigned local flow in a decentralized way, forming a joint policy distribution proportional to the rewards. To guarantee the expressiveness of continuous flow decomposition, we theoretically derive a consistency condition on the decomposition network. Experimental results demonstrate that the proposed method yields results superior to the state-of-the-art counterparts and better exploration capability. Our code is available at https://github.com/isluoshuang/MACFN.

Simple statistical tests selection based parallel computating method ensures the guaranteed global extremum identification

The article proposes a parallel computing oriented method for solving the global minimum finding problems, in which continuous objective functions satisfy the Hölder condition, and the control parameters domain limited by continuous functions is characterized by a positive Lebesgue measure. A typical example of such a task is the discrepancy minimizing problem between the left and right parts of some large system of equilibrium equations (this is a usual situation when describing real process using Markov chain). The method is based on simple statistical tests, thanks to which, at each iteration, growing sets of potential global minima and sets of decrements necessary for estimating the values of the Hölder constants are formed. The article theoretically substantiates and empirically proves the guaranteed convergence of the authors’ method to the real global minimum, which occurs at an exponential rate. For the continuous iterations number, analytical upper estimates of the spacing between the potential global minima and real global minima are formalized, as well as an estimate of the probability of overcoming this spacing is formalized. The decrements sequence approximation, estimation of a priori unknown Hölder constants, estimation of the average number of iterations of the method and probabilistic characteristics of the final solution are analytically justified. In addition to the theoretical proof, the adequacy of the authors’ method has been confirmed empirically. It turned out that both the quality characteristics of the initial results calculated by the authors’ method and the time to obtain them are practically independent of the size of the search area. This expected result is a significant advantage of the authors’ method over analogues.

On the properties of one auxiliary function for calculating the global extremum

In this paper, we investigate the properties of one Auxiliary Function (AF) for calculating the global optimum of a function of many variables. The considered AF is constructed by transforming the objective function using the Lebesgue integral and is a function of one variable. Despite the fact that in previous works this AF was used to find the global minimum of smooth functions on convex closed sets using the algorithm of dividing a segment in half, its properties have not been studied. Here, this AF is used to calculate the global extremum of continuous functions defined on bounded closed sets of a multidimensional Euclidean space. The basic properties of the AF for any continuous objective function are established, such as non-negativity, positive uniformity, uniform continuity, differentiability and strict convexity, moreover, derivatives up to a certain order are found. The optimality criterion is formulated. The main criterion of optimality is that the value of a free variable, at which the AF and its derivatives are equal to zero up to some order, coincides with the global minimum of the objective function. It follows from this optimality criterion that to calculate the global minimum of the objective function, it is sufficient to find the zero of the AF or its derivative up to some order.

PRELIMINARY STUDIES ON SOME MORPHO-STRUCTURAL AND BIOCHEMICAL CHARACTERISATION OF SOME GENOTYPES OF Vitis vinifera L. CULTIVATED IN NORTHEAST ROMANIA

The monitoring of new grapevine varieties with superior agrobiological and technological characteristics, in relation to the evolution of the climatic factors, represents an important and continuous objective of the worldwide viticultural research and breeding programs. Observations and determinations of the current study were performed on 11 new table grapes varieties created in Romania, growing in the Ampelographic collection of the University of Life Sciences Iasi, north-eastern area of Romania. The grapevine varieties were evaluated under the morpho-structural aspect, regarding the leaf area, the average weight of the grapes, the number and weight of the berries, rachis weight, the number and weight of the seeds, the weight of the skin and pulp, calculating the main technological indices. From a biochemical point of view, the content of photosynthetic pigments in leaves, the concentration of soluble dry solids and titratable acidity of the grapes were determined in the climatic condition of the Copou - Iasi vineyard. Therefore, the monitoring of genetic resources provides useful data for grape producers and researchers regarding the integration of new table grape varieties into the viticultural ecosystems, evaluating their yield and quality in correlation with the evolution and influence of the climatic factors.

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Business continuity plan in the management and operations of hospitals: First experience to certify the PDTA processes with the requirements defined by ISO 22301:2019 in emergency medical services.

A business continuity plan (BCP) facilitates the performance of primary functions during emergencies or other situations that can disrupt normal operations. If risk management is done analytically, a business impact analysis (BIA), according to ISO 22301 certification, makes it possible to define the best strategy for supporting the company's assets and image, optimizing the operational efficiency of service recovery and redesigning spaces for health. Since 2015, our healthcare company has embarked on a certification process for all sectors and activities through the implementation and development of diagnostic and therapeutic paths for operational diagnos-tic-therapeutic-assistance pathways (PDTAs). PDTA processes are all certified by the ISO 9001:2015 management system hospital. Our hospital is the first healthcare company to have obtained ISO 22301:2019 certification concerning PDTA processes, offering patients the highest standards of quality and safety of care in emergency medical services. The formal BCP process includes several steps prior to the creation of a BCP: create a BCP team, conduct a BIA, determine the continuity plan by using the results of the analyses, and conduct training and exercises to educate staff and improve the BCP. From the BIA analysis, the team identified the time-employee PDTAs in company paths under emergency and urgency: acute ST-elevation myocardial infarction (STEMI), TRAUMA, and STROKE, providing for a planning path that took advantage of the duration of approximately 12 months. This path included the creation of structural procedures, the redefinition and updating of the PDTA in the light of the BCP, the preparation of exercises aimed at guaranteeing the business continuity objectives, and, finally, the awareness of our stakeholders regarding its correct application. With a business continuity management (BCM) system, companies take preventative measures to ensure they can start operations again quickly in an emergency. An exhaustive BIA in a hospital company reveals the effects when processes fail, how critical each process is for the company, and the amount of time required to get up and running again, thus providing the organization with important information for risk management. The measures for handling risks derived from this analysis are incorporated into a BCM system where the emergency plans are defined, too, so that business operations continue even in the event of an emergency.

Continuous Objects Tracking Based on Geometric Centroid of Feasible Region in USV-Assisted Underwater Acoustic Sensor Networks

Continuous Objects Tracking Based on Geometric Centroid of Feasible Region in USV-Assisted Underwater Acoustic Sensor Networks