Common Solution Research Articles

Unsupervised Feature Selection via Controllable Adaptive Graph Learning and Discriminative Feature Learning.

Unsupervised feature selection is challenging in machine learning, pattern recognition, and data mining. The crucial difficulty is to learn a moderate subspace that preserves the intrinsic structure and to find uncorrelated or independent features simultaneously. The most common solution is first to project the original data into a lower dimensional space and then force them to preserve the similar intrinsic structure under linear uncorrelation constraint. However, there are three shortcomings. First, the final graph generated by the iterative learning process differs significantly from the initial graph in which the original intrinsic structure is embedded. Second, it requires prior knowledge about a moderate dimension of subspace. Third, it is inefficient when dealing with high-dimensional datasets. The first shortcoming, which is longstanding and undiscovered, makes the previous methods fail to achieve their expected results. The last two ones increase the difficulty of applying in different fields. Therefore, two unsupervised feature selection methods are proposed based on controllable adaptive graph learning and uncorrelated/independent feature learning (CAG-U and CAG-I) to address the abovementioned issues. In the proposed methods, the final graph that preserves intrinsic structure can be adaptively learned while the difference between the two graphs can be well controlled. Besides, relatively uncorrelated/independent features can be selected using a discrete projection matrix. The experimental results on 12 datasets in different fields show the superiority of CAG-U and CAG-I.

Performance of complex energy geostructures

Energy geostructures are innovative technologies that combine the functions of structural support and geothermal energy harvesting. To date, theoretical, computational, and experimental investigations have explored the performance of distinct types of energy geostructures, i.e. energy piles, walls, slabs, and tunnels. However, there seemingly exists limited knowledge on the performance of complex energy geostructures that integrate different types of geostructures in a unique building solution, despite being common in practice. This work addresses this knowledge gap by providing a computational study of the performance of two real-world installations of complex energy geostructures. The study resorts to 3-D, time-dependent, thermo-mechanical finite element simulations that consider the dynamic building thermal energy needs and the operation of the considered complex energy geostructures over a design lifetime of fifty years. The work reveals that the energy, geotechnical, and structural performance of complex energy geostructures is far from trivial. Especially the structural performance of complex energy geostructures exhibits responses that are counterintuitive and different compared to those that would be predicted by simplified modeling approaches referring to individual types of energy geostructures. Accordingly, this investigation calls for attention in the analysis of complex energy geostructures and judgment in the application of simplified design methods.

Vision-based dynamic monitoring of a post-tensioned concrete bridge under vehicular traffic

Computer vision for the purpose of structural dynamic monitoring of bridges has received much interest in recent years given the possibility of measuring displacement time-histories in absence of stationary points, using hardware that has accessible costs and is easy to operate. The review of the state-of-the-art shows that vision-based monitoring can potentially provide results equivalent to those obtained with the use of well-established contact sensors such as displacement transducers and accelerometers. Applications to real full-scale steel bridges and footbridges showed the possibilities of this technology in real-world conditions as well as difficulties and challenges posed by the surrounding environment. However, despite the many contributions published in the technical literature, very few applications involved post-tensioned concrete bridges, a very common solutions in the Italian road network and in many other countries. Characterized by a stiffer response as compared to steel bridges and footbridges, post-tensioned concrete bridges with their lower displacement magnitude and higher natural frequency might represent a more demanding testbed for vision-based monitoring. Accordingly, this study focused on the performance analysis of a vision-based structural monitoring system when applied to medium-span post-tensioned simply supported concrete bridges. The experimental outcomes show very interesting potentialities together with some critical aspects relevant to the vibrations induced in the tripod supporting the video camera that require attention to ensure the accuracy of the measurements.

Methodological Development to Improve the Accuracy of External Stereo-Photogrammetric Surveys of Buildings

Although unmanned aircraft (UA) based surveys are becoming a more common solution, their accuracy is questionable. Regulation is a major issue in the use of UA, which is currently having a negative impact on the spread of the technology. Many rules need to be applied during the survey and there are hundreds of settings to use during post-processing. Testing and evaluation of several combinations is necessary for an optimized process definition. In the present research, studies have been carried out focusing on different solutions of survey methodologies. The case studies analyzed different survey, flight rules and their combinations. The study presents the advantages and disadvantages of using UA and makes recommendations for survey methodologies through the evaluations carried out. In addition, several future research directions are presented which could lead to the advancement of building surveys.

The influence of the dowel mounting scheme in facade systems on the thermal protection properties of the enclosing structure

The use of hinged facade systems is a common solution that allows, with the right approach, to effectively solve a wide range of tasks: from issues of thermal protection of buildings and structures to the possibility of giving the building a unique architectural and artistic expression. Such facade systems are multilayer structures, which contain thermal engineering heterogeneities that affect the thermal protection properties of the enclosing structure as a whole. The purpose of the study presented in the proposed article is to study the effect of the dowel fastening scheme in facade systems on the thermal protection properties of the enclosing structure. The facade dowels in the considered enclosing structure will be point thermal inhomogeneities. The object of the study is a hinged facade system with a thin plaster layer. As part of the study, various schemes of fastening thermal insulation boards with facade dowels are considered. The efficiency of using facade dowels, the material of the spacer element of which are innovative materials with a low coefficient of thermal conductivity, is shown.

Double inertial extrapolations method for solving split generalized equilibrium, fixed point and variational inequity problems

<abstract><p>This article proposes an iteration algorithm with double inertial extrapolation steps for approximating a common solution of split equilibrium problem, fixed point problem and variational inequity problem in the framework of Hilbert spaces. Unlike several existing methods, our algorithm is designed such that its implementation does not require the knowledge of the norm of the bounded linear operator and the value of the Lipschitz constant. The proposed algorithm does not depend on any line search rule. The method uses a self-adaptive step size which is allowed to increase from iteration to iteration. Furthermore, using some mild assumptions, we establish a strong convergence theorem for the proposed algorithm. Lastly, we present a numerical experiment to show the efficiency and the applicability of our proposed iterative method in comparison with some well-known methods in the literature. Our results unify, extend and generalize so many results in the literature from the setting of the solution set of one problem to the more general setting common solution set of three problems.</p></abstract>

РАЗРАБОТКА ПРИЛОЖЕНИЯ КАК СРЕДСТВО АВТОМАТИЗАЦИИ БИЗНЕС-ПРОЦЕССОВ В СОПРОВОЖДЕНИИ ПРОЕКТА И ОЦЕНКЕ ЗАДАЧ

The article discusses the issue of project support and task assessment. Particular attention is paid to the data collection process, the main purpose of which is to collect information-rich data. An important part of this process is understanding the type and sources of data required. The problems of fragmentation of data sources and their inconsistency have been identified. The need to conduct an assessment of the project and tasks is also described. Taken together, this will allow you to avoid risks in terms of budget, project timing and product quality. Project performance assessment is carried out at all stages of the project, from planning to completion. Its goal is to adjust the trajectory of the project. Based on the assessment results, amendments are made to the documentation, team composition, product characteristics, or a decision is made to close the project. Automation of business processes is proposed. Among the technical means and software systems that reduce routine operations and optimize the use of enterprise production resources, computer systems, sensors, actuators, controllers, software and other devices are usually used. They allow you to collect and process data, monitor and regulate the operation of various devices and processes, and also carry out management using special software. The authors offer one of the most common solutions, namely, application development. The project management application provides a systematic and automated approach to project evaluation, facilitating the accumulation of experience. It allows you to calculate the time and cost of creating software, and connect evaluation templates. Suitable for assessing different areas of development, users can customize assessments by feature or project phase and upload results for business proposals. An authorization code is provided for the proposed application in the Swift language, as a mandatory element of information security. An algorithm for user actions when implementing the most common function – creating an estimate – is also described. The application also provides export of files to Excel, as the most commonly used package.

Effectiveness of mHealth interventions for improving health attendance: an umbrella review protocol.

The objective of this umbrella review is to assess the effectiveness of mobile health (mHealth) interventions for improving health care attendance. Health attendance is a critical part of health service delivery and is considered a behavior influenced by personal characteristics, service availability, nature of the treatment and consultation, and social determinants. Various interventions are available to foster good health attendance behavior, and mHealth interventions are one of the common go-to solutions; however, there is ambiguity regarding their effectiveness. Systematic reviews, with or without meta-analysis, that specifically examine the impact of mHealth interventions on health care attendance will be considered. The review will not be restricted to any particular disease, health problem, stream, or system of care, nor will it be limited by population characteristics (eg, age, gender, socio-economic status) or context (setting can be at any level of care). The JBI methodology for umbrella reviews will be followed, and MEDLINE (Ovid), Scopus, CINAHL, Cochrane Central, and JBI Evidence Synthesis will be searched for sources of evidence. Unpublished and gray literature sources, such as ProQuest Dissertations and Theses, OAIster, and Google Scholar, will also be searched. The review will be limited to English-language articles published from the inception of the databases to the present. Two independent reviewers will perform title, abstract, and full-text screening, while a customized data extraction form will be used for data extraction. The JBI critical appraisal checklist for systematic reviews and research syntheses will be applied. Certainty of evidence for health attendance outcomes will be assessed following GRADE approach. PROSPERO CRD42023405677.

Evaluating the colour stability of esthetic resin composite: A study of Neospectra ST under different staining conditions

Background:There is a burgeoning interest in the colour permanence of aesthetic restorations, although only a handful of studies have been documented thus far.Aim:To investigate the impact of various common staining solutions on nanohybrid composite resin..Materials and methods:Forty disk-shaped specimens (10 X 2 mm) were fabricated with Neo SpectraST composite material. The specimens were divided into four groups with 10 specimens (n=10) in each, based on the staining solutions, Group I: Turmeric, Group II: Coffee, Group III: Coke, and Group IV: Distilled water. Baseline measurements were taken using an ultraviolet spectrophotometer. Each sample group was immersed in their respective staining solutions, and the colour changes were recorded after 1, 7, and 14 days of immersion using the same spectrophotometer. The absorption values were recorded within the 300 to 800 nm range at baseline and after the immersion. The data was collected and subjected to statistical analysis.Results:Neo spectra ST composite showed the maximum staining capacity with the turmeric group compared to otherstaining solutions. One-way ANOVA showed significant differences (p=0.000) among the staining solutions at different time intervals. Repeated measures ANOVA displayed significant differences within the staining solutions (Turmeric: p=0.005; Coffee: p=0.001; and Coke: p=0.001) at different time intervals.Conclusion:The nanohybrid composite resin showed a significant colour change to turmeric compared to other staining solutions.

Conception of a new quality control method based on neural networks

The prediction of failures in a factory is now an important area of industry that helps to reduce time and cost of non-quality from the data generated from the sensors installed on production lines, this data is used to detect anomalies and predict defects before they occur. The purpose of this article is to model an intelligent production line capable of predicting various types of non-conforming products. For that, we will utilize the neural network methodology within the specific context of a production line specialized in juice manufacturing. Firstly, we introduce the production line under study, along with its distinct manufacturing phases. Secondly, we evaluate the performance indicators of this line, enabling us to gain an overview of its efficiency and overall performance. Subsequently, we present common industrial solutions that are frequently implemented to address the issues identified during our analysis. At this stage, we propose a predictive model based on neural network methodology. This model will possess the capability to detect and identify defective products and potential hazards within a production line before they occur. Throughout this study, we compare between three models of neural networks: LSTM model using Stochastic gradient descend (SGD), Feed forward model using ADAM Optimization and Feed forward model using Levenberg–Marquardt back propagation (LMBP), in order to determine the most optimal method in terms of achieved results. Finally, we demonstrate the effectiveness, performance, and accuracy of the results through the testing phase of the neural networks.

Generalized variational inclusion: graph convergence and dynamical system approach

<p>This work focused on the investigation of a generalized variation inclusion problem. The resolvent operator for generalized $ \eta $-co-monotone mapping was structured, the Lipschitz constant was estimated and its relationship with the graph convergence was accomplished. An Ishikawa type iterative algorithm was designed by incorporating the resolvent operator and total asymptotically non-expansive mapping. By employing the novel implication of graph convergence and analyzing the convergence of the considered iterative method, the common solution of the generalized variational inclusion and the set of fixed points of a total asymptotically non-expansive mapping was obtained. Moreover, a generalized resolvent dynamical system was investigated. Some of its attributes were discussed and implemented to examine the considered generalized variation inclusion problem.</p>

A New Inertial-Projection Method for Solving Split Generalized Mixed Equilibrium and Hierarchical Fixed Point Problems

In this paper, we introduce a new iterative algorithm of inertial form for approximating the common solution of Split Generalized Mixed Equilibrium Problem (SGMEP) and Hierarchical Fixed Point Problem (HFPP) in real Hilbert spaces. Motivated by the subgradient extragradient method, we incorporate the inertial technique to accelerate the convergence of the proposed method. Under standard and mild assumption of monotonicity and lower semicontinuity of the SGMEP and HFPP associated mappings, we establish the strong convergence of the iterative algorithm. Some numerical experiments are presented to illustrate the performance and behaviour of our method as well as comparing it with some related methods in the literature.

A Totally Relaxed Self-Adaptive Subgradient Extragradient Scheme for Equilibrium and Fixed Point Problems in a Banach Space

The goal of this paper is to introduce a Totally Relaxed Self adaptive Subgradient Extragradient Method (TRSSEM) together with an Halpern iterative method for approximating a common solution of Fixed Point Problem (FPP) and Equilibrium Problem (EP) in 2-uniformly convex and uniformly smooth Banach space. We prove the strong convergence of the sequence generated by our proposed method. The proposed method does not require the computation of a projection onto a feasible set, it instead requires a projection onto a finite intersection of sub-level sets of convex functions. Our result generalizes, unifies and extends some related results in the literature.

Double inertial steps extragadient-type methods for solving optimal control and image restoration problems

<abstract><p>In order to approximate the common solution of quasi-nonexpansive fixed point and pseudo-monotone variational inequality problems in real Hilbert spaces, this paper presented three new modified sub-gradient extragradient-type methods. Our algorithms incorporated viscosity terms and double inertial extrapolations to ensure strong convergence and to speed up convergence. No line search methods of the Armijo type were required by our algorithms. Instead, they employed a novel self-adaptive step size technique that produced a non-monotonic sequence of step sizes while also correctly incorporating a number of well-known step sizes. The step size was designed to lessen the algorithms' reliance on the initial step size. Numerical tests were performed, and the results showed that our step size is more effective and that it guarantees that our methods require less execution time. We stated and proved the strong convergence of our algorithms under mild conditions imposed on the control parameters. To show the computational advantage of the suggested methods over some well-known methods in the literature, several numerical experiments were provided. To test the applicability and efficiencies of our methods in solving real-world problems, we utilized the proposed methods to solve optimal control and image restoration problems.</p></abstract>

Underwater vehicle navigation using bearing measurements from a mobile beacon

Autonomous underwater vehicles enable efficient underwater operations such as mapping, surveillance, and inspection. However, due to the lack of global navigation satellite systems (GNSS) signals, drift in pose estimates will occur over time. Common solutions to this issue are using either stationary or mobile beacons to provide range and possibly bearing measurements to bound the navigation error over time. However, this requires occupying communication bandwidth to get reliable round-trip range measurements. In this paper, we propose a system where a mobile beacon passively listens to communications from the underwater vehicle to estimate the position of the vehicle. We show that the estimation error is bounded over time through strategic movement of the mobile beacon.

Some convergence results in modular spaces with application to a system of integral equations

<p>The paper aimed to achieve three primary objectives. First, it introduced significant common fixed point results in the context of newly proposed partial modular $ b- $metric spaces, thus contributing to the advancement of this field. Second, it presented unique results using a direct approach that did not depend on the strong continuity of the mapping, thereby offering a valuable perspective. Finally, it applied previously established convergence techniques to determine a common solution for a system of Fredholm integral equations, demonstrating the practical implications of the theoretical findings.</p>

Calibrate the Inter-Observer Segmentation Uncertainty via Diagnosis-First Principle.

Many of the tissues/lesions in the medical images may be ambiguous. Therefore, medical segmentation is typically annotated by a group of clinical experts to mitigate personal bias. A common solution to fuse different annotations is the majority vote, e.g., taking the average of multiple labels. However, such a strategy ignores the difference between the grader expertness. Inspired by the observation that medical image segmentation is usually used to assist the disease diagnosis in clinical practice, we propose the diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis-First segmentation Framework (DiFF) is proposed. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis-First Ground-truth (DF-GT). Then, the Take and Give Model (T&G Model) to segment DF-GT from the raw image is proposed. With the T&G Model, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF can effectively calibrate the segmentation uncertainty, and thus significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.

Technology Optimisation of Hybrid PV Systems for Covering Energy Needs of Emergency Shelters for Ukrainian War Refugees

Hybrid photovoltaic systems have become a common solution for decreasing energy consumption for specific objects and customers at present time. The efficiency of the entire system depends strongly also on the technology of the used battery inverter. In general, DC-coupled inverters are recognized to be more energy efficient. Surprisingly, in some specific cases, AC-coupled systems can offer better results. Ukraine escalates the problem with internal migration, that can be solved just through constructing new communities of emergency shelters. Integrating these units into Ukrainian overloaded and damaged distribution grids must be projected very precisely to limit not just power consumption but also power injection. Particular savings can be reached with the proper application of AC or DCcoupled systems. This article describes that phenomenon presented on specific real cases defined through own consumption profile, battery storage system management, climate conditions, and PV system design. The presented results show that the proper choice of battery inverter technology can save interesting amount of energy produced from the installed PV system. The AC coupled system can offer not just higher flexibility and modularity, but also higher energy efficiency of the hybrid system, lower grid feed-in.

The forms of $ (q, h) $-difference equation and the roots structure of their solutions with degenerate quantum Genocchi polynomials

<p>We construct a new type of Genocchi polynomials using degenerate quantum exponential functions and find various forms of $ (q, h) $-difference equations with these polynomials as solutions. This paper includes properties of the symmetric structures of $ (q, h) $-difference equations and also presents $ (q, h) $-difference equations with other polynomials as coefficients. By understanding the approximate roots structure of degenerate quantum Genocchi polynomials (DQG), which are common solutions to various forms of $ (q, h) $-difference equations, we identify the properties of the solutions.</p>

Biomechanical Study of the Behavior of the Intervertebral Disc: Intact Lumbar Spine Versus Arthrodesis

Introduction: Arthrodesis is the most common solution for the treatment of degenerative pathology of the lumbar spine but it can expose to adjacent syndrome in the long term. However, this entity is currently debated, disc degeneration could simply be the consequence of degenerative evolution. The aim of our study was to make numerical simulations on finite element model of the lumbar spine, to analyze the behavior of the adjacent upper and lower intervertebral discs after an arthrodesis. Methods: Von Mises stress distribution on intervertebral discs was analyzed from a validated finite element model of lumbar spine (from L2 to L5), and compared to Von Mises stress distribution on intervertebral disks of the same subject, having an L3-L4 arthrodesis. We fixed the inferior surface of L5 vertebral body and applied increasing loads in axial compression and flexion on the upper surface of L2 vertebral body for the two models, with and without arthrodesis. Results: Von Mises stress in the adjacent discs increase, especially for the upper level, causing an accelerated degeneration of the disc responsible for adjacent syndrome. Our results showed also that L4-L5 disc support the maximum of pressure of the lumbar spine. Conclusion: Intervertebral discs adjacent to an arthrodesis are exposed to an increasing pressure and accelerated degeneration, proving the risk of adjacent syndrome.