Eloina Lugo-del-Real, Jorge A. Soto-Cajiga, Antonio Ramirez-Martinez
et al.
Pipeline integrity is crucial for ensuring the safe and efficient transportation of hydrocarbons. One of the essential methods for maintaining pipeline integrity is periodic inspection using Pipeline Inspection Gauges (PIGs). These PIGs traverse extensive pipeline networks, collecting critical data related to inertial navigation and inspection technologies, such as geometric, ultrasonic, or magnetic flux inspection. Following an inspection, data is downloaded for post-processing to identify and accurately locate pipeline anomalies. Accurate positioning of indications is crucial for effective repair or maintenance of the identified pipeline section. Thus, ongoing efforts aim to improve the precision of indication positioning. This study introduces an innovative method and model for deriving pipeline trajectory characteristics to enhance positioning accuracy. The method is based on distance sampling of odometers, improving the PIG displacement measurement by implementing multiple odometries. Using the method described in this work can compensate for odometer slip, since the distance measurement error was reduced from 15.67% to 1.38%. The model simulates (three and four) odometer trajectories in curvature and calculates the curvature along the pipeline based on odometer data. The curvature model is evaluated with real data obtained from a test circuit, demonstrating that the proposed method and model technique can yield trajectory characteristics such as curvature detection; we can differentiate linear sections from bend sections in the test circuit. However, the curvature measurement error remains considerable due to odometer slippage. Therefore, future work proposes using additional odometers to improve measurement accuracy.
Anjan Mukherjee, Ajoy Kanti Das, Nandini Gupta
et al.
<p>This study introduces a novel framework, Generalized Interval-Valued Neutrosophic Rough Soft Sets (GIVNRS sets), designed to improve handling uncertainty, imprecision, and vagueness in complex decision-making scenarios. By integrating soft, rough, and generalized interval-valued neutrosophic set theories, the framework offers a robust methodology for addressing indeterminacy and incomplete data. The theoretical foundation of GIVNRS sets is built upon fundamental operations, including intersection, union, complement, and novel aggregation union operators tailored for multi-criteria decision-making (MCDM) applications. The practical applicability of the framework is demonstrated through a water quality assessment, where it successfully classifies river segments based on key water quality parameters such as pH, Dissolved Oxygen (DO), and Biochemical Oxygen Demand (BOD). The case study results show that the pollution scores for the river segments were computed, classifying the segments such as “Good,” “Moderate,” and “Poor,” with corresponding pollution levels. These findings highlight the framework’s ability to manage incomplete and inconsistent data, providing a reliable and comprehensive water quality evaluation. Compared to traditional models, the GIVNRS set approach offers enhanced flexibility, stability, and adaptability. This study not only contributes to the theoretical development of neutrosophic, soft, and rough set theories but also establishes GIVNRS sets as a powerful tool for water quality decision-making. Future research will explore further advancements in the application and computational efficiency of this framework.</p>
Compendium Manager is a command-line tool written in Python to automate the provisioning, launch, and evaluation of bioinformatics pipelines. Although workflow management tools such as Snakemake and Nextflow enable users to automate the processing of samples within a single sequencing project, integrating many datasets in bulk requires launching and monitoring hundreds or thousands of pipelines. We present the Compendium Manager, a lightweight command-line tool to enable launching and monitoring analysis pipelines at scale. The tool can gauge progress through a list of projects, load results into a shared database, and record detailed processing metrics for later evaluation and reproducibility.
Nowadays, environmental protection has become a global consensus. At the same time, with the rapid development of science and technology, urbanisation has become a phenomenon that has become the norm. Therefore, the urban greening management system is an essential component in protecting the urban environment. The system utilises a transparent management process known as" monitoring - early warning - response - optimisation," which enhances the tracking of greening resources, streamlines maintenance scheduling, and encourages employee involvement in planning. Designed with a microservice architecture, the system can improve the utilisation of greening resources by 30%, increase citizen satisfaction by 20%, and support carbon neutrality objectives, ultimately making urban governance more intelligent and focused on the community. The Happy City Greening Management System effectively manages gardeners, trees, flowers, and green spaces. It comprises modules for gardener management, purchase and supplier management, tree and flower management, and maintenance planning. Its automation feature allows for real-time updates of greening data, thereby enhancing decision-making. The system is built using Java for the backend and MySQL for data storage, complemented by a user-friendly frontend designed with the Vue framework. Additionally, it leverages features from the Spring Boot framework to enhance maintainability and scalability.
Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets.
Abstract This study examines the thermal and hydrodynamic characteristics of Therminol VP-1 oil flow through perforated conical hollow-type turbulence promoters installed in a solar Scheffler dish collector receiver tube, utilizing computational fluid dynamics (CFD) analysis. The research examines these configurations using the RNG k-ε turbulence model with conventional wall functions. Simulations are conducted at Reynolds numbers ranging from 3000 to 15,000, with relative perforated conical hollow-type turbulence promoters ratios (Per ID /Per OD ) varying from 2.11 to 2.33, relative turbulence promoter pitch (P TP /D tube ) spanning from 2.25 to 3.08, and a relative turbulence promoter diameter (DB inlet /DB outlet ) is constant at 2.0 to evaluate heat transfer and friction factor characteristics. An experimental analysis has been conducted on a solar Scheffler dish collector receiver using a plain tube with Therminol VP-1 as the heat transfer fluid to validate the CFD results for the current study. Moreover, the CFD results have been verified through a comparison with a conventional surface solar Scheffler dish collector receiver tube utilizing Therminol VP-1 as the heat transfer fluid. This comparison encompassed theoretical relationships and empirical data pertaining to the Nusselt number and friction factor. The CFD results for the plain surface solar receiver tube demonstrated important alignment with experimental data and theoretical predictions based on the standard Dittus and Blasius equations, exhibiting reasonable deviation throughout the analyzed range. Overall, the CFD results demonstrate that Therminol VP-1, combined with perforated conical hollow-type turbulence promoters, improves thermal efficiency, providing an effective approach for enhancing Scheffler dish receiver tubes while reducing excess pressure losses. According to thermal and hydraulic performance data, hollow-type conical turbulence promoters enhanced heat transfer, with the best performance achieved at Per ID /Per OD of 2.25 and a (P TP /D tube ) of 2.83.
Ardaneswari Dyah Pitaloka Citraresmi, Sri Gunani Partiwi, Ratna Sari Dewi
The creative industry has experienced rapid expansion in emerging economies, substantially contributing to employment and economic growth. However, despite this expansion, understanding how multiple workforce-related factors jointly influence creative performance remains limited. This study’s main contribution is to offer an integrated perspective on how workforce resilience, sustainability, and digital readiness collectively shape the creative output of Micro, Small, and Medium Enterprises (MSMEs). We used a mixed-methods design to collect data through surveys and in-depth interviews with owners and employees to capture insights on adaptability, well-being, and digital competencies. Results derived from Partial Least Squares Structural Equation Modeling (PLS-SEM) reveal that resilient and sustainable workforces positively affect creative performance, with digital readiness as a crucial mediator. This study highlights the importance of digital adoption strategies and workforce preparedness in an evolving industry landscape. Importance-Performance Map Analysis further identifies psychosocial risk management, employee well-being, and workplace safety as high-priority yet underdeveloped areas requiring immediate attention. By clearly articulating how an integrated approach to resilience, sustainability, and digital readiness advances theoretical and practical discourse, this work provides actionable insights for policymakers and MSMEs practitioners seeking to enhance innovation and maintain competitiveness in the face of ongoing digital disruption.
Mojtaba Abaie Shoushtary, Jordi Tubella Murgadas, Antonio Gonzalez
In GPUs, the control flow management mechanism determines which threads in a warp are active at any point in time. This mechanism monitors the control flow of scalar threads within a warp to optimize thread scheduling and plays a critical role in the utilization of execution resources. The control flow management mechanism can be controlled or assisted by software through instructions. However, GPU vendors do not disclose details about their compiler, ISA, or hardware implementations. This lack of transparency makes it challenging for researchers to understand how the control flow management mechanism functions, is implemented, or is assisted by software, which is crucial when it significantly affects their research. It is also problematic for performance modeling of GPUs, as one can only rely on traces from real hardware for control flow and cannot model or modify the functionality of the mechanism altering it. This paper addresses this issue by defining a plausible semantic for control flow instructions in the Turing native ISA based on insights gleaned from experimental data using various benchmarks. Based on these definitions, we propose a low-cost mechanism for efficient control flow management named Hanoi. Hanoi ensures correctness and generates a control flow that is very close to real hardware. Our evaluation shows that the discrepancy between the control flow trace of real hardware and our mechanism is only 1.03% on average. Furthermore, when comparing the Instructions Per Cycle (IPC) of GPUs employing Hanoi with the native control flow management of actual hardware, the average difference is just 0.19%.
Mofijul Hoq Masum, Mohammad Faridul Alam, Md. Shariful Alam
Corporate governance is one of the key factors in corporate performance for the economy. In particular, for a transition economy, which is on the way of developing economies from the least developing economy, the relevant attributes of corporate governance are a vital issue. This study explores the most important board and ownership attributes that affect corporate performance in a transitional economy. A static panel fixed effects model is used to identify the most significant board and ownership attributes that affect corporate performance. It is found that board independence, board size, inclusion of women on the board, foreign shareholding and institutional shareholding significantly influence corporate performance, whereas executive shareholding has an adverse impact on corporate performance in the context of a transition economy. There is a paradoxical finding representing that although the foreign shareholdings significantly influenced the corporate performance in the transitional economy the inclusion of foreign members on the board has no significant impact on corporate performance. In addition, the government shareholding has no significant role in earning profit. These diversified findings implied that not all corporate governance attributes have the same effect on corporate performance. Based on the outcomes of this study, the regulatory body of the transitional economy can design its corporate governance policy.
A resource leak occurs when a program fails to free some finite resource after it is no longer needed. Such leaks are a significant cause of real-world crashes and performance problems. Recent work proposed an approach to prevent resource leaks based on checking resource management specifications. A resource management specification expresses how the program allocates resources, passes them around, and releases them; it also tracks the ownership relationship between objects and resources, and aliasing relationships between objects. While this specify-and-verify approach has several advantages compared to prior techniques, the need to manually write annotations presents a significant barrier to its practical adoption. This paper presents a novel technique to automatically infer a resource management specification for a program, broadening the applicability of specify-and-check verification for resource leaks. Inference in this domain is challenging because resource management specifications differ significantly in nature from the types that most inference techniques target. Further, for practical effectiveness, we desire a technique that can infer the resource management specification intended by the developer, even in cases when the code does not fully adhere to that specification. We address these challenges through a set of inference rules carefully designed to capture real-world coding patterns, yielding an effective fixed-point-based inference algorithm. We have implemented our inference algorithm in two different systems, targeting programs written in Java and C#. In an experimental evaluation, our technique inferred 85.5% of the annotations that programmers had written manually for the benchmarks. Further, the verifier issued nearly the same rate of false alarms with the manually-written and automatically-inferred annotations.
Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling $37.5$ times faster than the optimization while outperforming state-of-the-art forecasting techniques.
A diverse set of Internet of Things (IoT) devices are becoming an integrated part of daily lives, and playing an increasingly vital role in various industry, enterprise and agricultural settings. The current IoT ecosystem relies on several IoT management platforms to manage and operate a large number of IoT devices, their data, and their connectivity. Considering their key role, these platforms must be properly secured against cyber attacks. In this work, we first explore the core operations/features of leading platforms to design a framework to perform a systematic security evaluation of these platforms. Subsequently, we use our framework to analyze a representative set of 52 IoT management platforms, including 42 web-hosted and 10 locally-deployable platforms. We discover a number of high severity unauthorized access vulnerabilities in 9/52 evaluated IoT management platforms, which could be abused to perform attacks such as remote IoT SIM deactivation, IoT SIM overcharging and IoT device data forgery. More seriously, we also uncover instances of broken authentication in 13/52 platforms, including complete account takeover on 8/52 platforms along with remote code execution on 2/52 platforms. In effect, 17/52 platforms were affected by vulnerabilities that could lead to platform-wide attacks. Overall, vulnerabilities were uncovered in 33 platforms, out of which 28 platforms responded to our responsible disclosure. We were also assigned 11 CVEs and awarded bounty for our findings.
Hendra Mayatopani, Nurdiana Handayani, Ri Sabti Septarini
et al.
Wild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicinal properties, especially the leaves. The purpose of this research is to classify the image of weed leaves with medicinal properties based on color and texture characteristics with an artificial neural network using a Self-Organizing Map (SOM). To improve information in feature extraction, RGB and HSV color features are used as well as texture features with Gray Level Co-occurrence Matrix (GLCM). Furthermore, the results of feature extraction will be identified as groups or classes with the Self-Organizing Map (SOM) algorithm which divides the input pattern into several groups so that the network output is in the form of a group that is most similar to the input provided. The test produces a precision value of 91.11%, a recall value of 88.17% and an accuracy value of 89.44%. The results of the accuracy of the SOM model for image classification on medicinal weed leaves are in the good category.
The article discusses some results of the reform of control and supervision activities taking place in the Russian Federation from 2016 to the present. The pros and cons of the reform are illustrated by the example of environmental control, including control over waste management. Purpose: to analyze the results of the reform of control and supervision activities on the example of state environmental control (supervision) over industrial waste management. Methods: data retrieval and collection; data processing: description, generalization, classification, search for patterns; analysis of data processing results. Results: the current legal regulation of relations in the field of environmental control and supervision requires further improvement in order to achieve the goals set for reforming the entire system of control activities in the country, and to the system of state environmental administration. The situation when the achievement of some indicators occurs at the expense of reducing others in the public administration sector seems unacceptable, or rather short-sighted, especially when the achievement of economic indicators is prioritized over environmental ones.
Law in general. Comparative and uniform law. Jurisprudence
Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid-ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically.
Santiago Balseiro, Christian Kroer, Rachitesh Kumar
Single-leg revenue management is a foundational problem of revenue management that has been particularly impactful in the airline and hotel industry: Given $n$ units of a resource, e.g. flight seats, and a stream of sequentially-arriving customers segmented by fares, what is the optimal online policy for allocating the resource. Previous work focused on designing algorithms when forecasts are available, which are not robust to inaccuracies in the forecast, or online algorithms with worst-case performance guarantees, which can be too conservative in practice. In this work, we look at the single-leg revenue management problem through the lens of the algorithms-with-advice framework, which attempts to harness the increasing prediction accuracy of machine learning methods by optimally incorporating advice about the future into online algorithms. In particular, we characterize the Pareto frontier that captures the tradeoff between consistency (performance when advice is accurate) and competitiveness (performance when advice is inaccurate) for every advice. Moreover, we provide an online algorithm that always achieves performance on this Pareto frontier. We also study the class of protection level policies, which is the most widely-deployed technique for single-leg revenue management: we provide an algorithm to incorporate advice into protection levels that optimally trades off consistency and competitiveness. Moreover, we empirically evaluate the performance of these algorithms on synthetic data. We find that our algorithm for protection level policies performs remarkably well on most instances, even if it is not guaranteed to be on the Pareto frontier in theory. Our results extend to other unit-cost online allocations problems such as the display advertising and the multiple secretary problem together with more general variable-cost problems such as the online knapsack problem.
Failure management plays a significant role in optical networks. It ensures secure operation, mitigates potential risks, and executes proactive protection. Machine learning (ML) is considered to be an extremely powerful technique for performing comprehensive data analysis and complex network management and is widely utilized for failure management in optical networks to revolutionize the conventional manual methods. In this study, the background of failure management is introduced, where typical failure tasks, physical objects, ML algorithms, data source, and extracted information are illustrated in detail. An overview of the applications of ML in failure management is provided in terms of alarm analysis, failure prediction, failure detection, failure localization, and failure identification. Finally, the future directions on ML for failure management are discussed from the perspective of data, model, task, and emerging techniques.