La unidad minera objeto de estudio se ubica en el Perú y labora con empresas contratistas para la ejecución de sus proyectos en las actividades conexas. En ella, el empleador debe promover una cultura de prevención de riesgos laborales para la ejecución de sus actividades. En el presente artículo de investigación, se menciona la metodología ágil fundamentada en la identificación de sus procesos y el estado de desempeño en cada lineamiento del sistema de gestión de seguridad y salud ocupacional (SGSSO), los cuales se usaron como base para el diseño y la implementación de herramientas que permitan gestionar la medición y control del SGSSO. Los resultados obtenidos evidenciaron que la medición y el control oportuno del SGSSO permitieron incrementar el nivel de cumplimiento del 62% al 89% para prevenir los riesgos laborales, con lo que se logró culminar el proyecto de manera segura.
Jozef Jaroslav Fekiač, Lucia Kakošová, Michal Krbata
et al.
Additive manufacturing of polymer composites, also known as 3D printing, is one of the progressive technologies in material engineering. It enables the production of parts with complex geometries while optimizing material efficiency. Polylactide (PLA) is a widely used material in additive manufacturing due to its biodegradability and suitable mechanical properties. However, its brittleness and limited thermal stability require further modifications, such as modifying the filler structure or adding reinforcing materials. This paper focuses on analyzing the influence of different filler geometries and densities on the mechanical properties of PLA parts manufactured by the fused filament deposition (FFF) method. Three basic filler structures—cubic, gyroid and rectilinear—were investigated at different density levels from 20%, 40%, 60% and 80%. Experimental tests were performed according to ASTM D638 to determine the strength characteristics of the material. In addition to mechanical tests, dynamic mechanical analysis (DMA) and thermogravimetric analysis (TG) were performed to better understand the influence of the filling geometry on the thermal stability and viscoelastic behavior of the material. Experimental tests according to ASTM D638 showed that higher filling density improves mechanical properties. At 80% filling, the tensile strength reached 21.06 MPa (cubic), 20.53 MPa (gyroid) and 20.84 MPa (linear). The elastic modulus was highest with cubic filling (1414.19 MPa). The yield strength reached 15.59 MPa (cubic), 15.52 MPa (gyroid) and 14.30 MPa (linear).
Abstract Background The diagnostic yield of conventional gastrointestinal (GI) endoscopy for early cancers is low because it is mainly based on morphological changes of tumors. Molecular functional changes in tumors precede morphological changes. Cherenkov luminescence endoscopy (CLE) system can perform molecular imaging of GI cancers, achieving early diagnosis of cancers. However, previous CLE systems had only been able to detect Cherenkov luminescence (CL) from about one μCi nuclide at a minimum (in vivo), but the nuclide probe absorbed by the tumor of a patient was often much less than one μCi at a routinely administered dose. This study aims to construct a clinically usable high-sensitivity CLE for molecular imaging of GI cancers. Results The minimum resolvable radioactivity of the CLE reached 0.020 μCi within 300 s (in vivo), with a sensitivity at the nanocurie for the first time. The detection sensitivity of the CLE increased by up to nearly twenty-two times over the previous system. In tumor-bearing nude mice, CLE could effectively identify all tumors with 100% concordance with both histopathology and PET/CT, and the CL signals of tumors were much stronger than those of the surrounding normal tissues (P < 0.05). The quality of CLE imaging at 60 s was comparable to that at 300 s (signal-to-background ratio, 2.70 ± 0.48 versus 2.98 ± 0.69, P = 0.56). Conclusions We constructed a high-sensitivity CLE that could detect radionuclides at the nanocurie radioactivity. The CLE could detect cancers accurately through rapid molecular imaging and had the potential for early diagnosis of GI cancers in clinical practice.
Medical physics. Medical radiology. Nuclear medicine
web3 wallets are key to managing user identity on blockchain. The main purpose of a web3 wallet application is to manage the private key for the user and provide an interface to interact with the blockchain. The key management scheme ( KMS ) used by the wallet to store and recover the private key can be either custodial, where the keys are permissioned and in custody of the wallet provider or noncustodial where the keys are in custody of the user. The existing non-custodial key management schemes tend to offset the burden of storing and recovering the key entirely on the user by asking them to remember seed-phrases. This creates onboarding hassles for the user and introduces the risk that the user may lose their assets if they forget or lose their seedphrase/private key. In this paper, we propose a novel method of backing up user keys using a non-custodial key management technique that allows users to save and recover a backup of their private key using any independent sign-in method such as google-oAuth or other 3P oAuth.
This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.
Autonomous control of multi-stage industrial processes requires both local specialization and global coordination. Reinforcement learning (RL) offers a promising approach, but its industrial adoption remains limited due to challenges such as reward design, modularity, and action space management. Many academic benchmarks differ markedly from industrial control problems, limiting their transferability to real-world applications. This study introduces an enhanced industry-inspired benchmark environment that combines tasks from two existing benchmarks, SortingEnv and ContainerGym, into a sequential recycling scenario with sorting and pressing operations. We evaluate two control strategies: a modular architecture with specialized agents and a monolithic agent governing the full system, while also analyzing the impact of action masking. Our experiments show that without action masking, agents struggle to learn effective policies, with the modular architecture performing better. When action masking is applied, both architectures improve substantially, and the performance gap narrows considerably. These results highlight the decisive role of action space constraints and suggest that the advantages of specialization diminish as action complexity is reduced. The proposed benchmark thus provides a valuable testbed for exploring practical and robust multi-agent RL solutions in industrial automation, while contributing to the ongoing debate on centralization versus specialization.
The efficiency of pension schemes in Kenya invites elevated interest owing to the increasing pension contribution amounts and the expectation that benefits paid out of these schemes would protect members from old age poverty. The study investigates the intervening effect of risk management on the relationship between corporate governance and the efficiency of pension schemes in Kenya. The study employs panel data consisting of 896 observations from 128 schemes in a sample period from 2015 to 2021. The study finds that risk management significantly mediates the relationship between employee representatives on the board of trustees, as a component of corporate governance, and the efficiency of pension schemes. Consequently, the mediation effect of risk management indicates that when employee representatives are involved in governance, the presence of strong risk management practices ensures that their contributions lead to improved efficiency. Risk management, therefore, serves as a critical safeguard that enables governance structures to function more effectively and contribute to the overall performance of the scheme.
I Gusti Lanang Trisna Sumantara, Made Windu Antara Kesiman, I Made Gede Sunarya
Periapical radiographs are commonly used by dentists to diagnose dental problems and overall dental health conditions. The varying abilities of dentists to diagnose may be limited by their visual acuity and individual skills. To address this issue, there is a need for an application capable of computationally recognizing and classifying periapical radiographs. The commonly used computational method for image processing, specifically image recognition, is the Convolutional Neural Network (CNN) method. This study aims to create an application that can classify periapical radiographs and analyze the capabilities of the Convolutional Neural Network (CNN) method in this classification process. In general, periapical classification is divided into five types: Primary Endo with Secondary Perio, Primary Endodontic Lesion, Primary Perio with Secondary Endo, Primary Periodontal Lesion, and True Combined Lesions. The periapical radiograph classification process was tested using four CNN models: ResNet50v2, EfficientNetB1, MobileNet, and Shalow CNN. The evaluation of the CNN method utilized a confusion matrix-based technique to generate accuracy, precision, recall, F1-score and Weighted Average F1-score values. Based on the evaluation results, the highest accuracy value was achieved by EfficientNetB1 with 82%, followed by ResNet50v2 with 76%, MobileNet with 75%, and Shallow CNN with 71%.
Let \(G\) be a plane graph. Two edges of \(G\) are facially adjacent if they are consecutive on the boundary walk of a face of \(G\). A facial edge coloring of \(G\) is an edge coloring such that any two facially adjacent edges receive different colors. A facial graceful \(k\)-coloring of \(G\) is a proper vertex coloring \(c:V(G)\rightarrow\{1,2,\dots,k\}\) such that the induced edge coloring \(c^{\prime}:E(G)\rightarrow\{1,2,\dots,k-1\}\) defined by \(c^{\prime(uv)}=|c(u)-c(v)|\) is a facial edge coloring. The minimum integer \(k\) for which \(G\) has a facial graceful \(k\)-coloring is denoted by \(\chi_{fg}(G)\). In this paper we prove that \(\chi_{fg}(G)\leq 14\) for every plane graph \(G\) and \(\chi_{fg}(H)\leq 9\) for every outerplane graph \(H\). Moreover, we give exact bounds for cacti and trees.
Michiel Van Kenhove, Maximilian Seidler, Friedrich Vandenberghe
et al.
The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications.
Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann
et al.
The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.
This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.
Swati A. Patil, G. Pradeepini, Thirupathi Rao Komati
Abstract Music Genre Classification (MGC) is a crucial undertaking that categorizes Music Genre (MG) based on auditory information. MGC is commonly employed in the retrieval of music information. The three main stages of the proposed system are data readiness, feature mining, and categorization. To categorize MG, a new neural network was deployed. The proposed system uses features from spectrographs derived from short clips of songs as inputs to a projected scheme building to categorize songs into an appropriate MG. Extensive experiment on the GTZAN dataset, Indian Music Genre(IMG) dataset, Hindustan Music Rhythm (HMR) and Tabala Dataset show that the proposed strategy is more effective than existing methods. Indian rhythms were used to test the proposed system design. The proposed system design was compared with other existing algorithms based on time and space complexity.
Computer engineering. Computer hardware, Information technology
Muhammad Sajid Haroon, Seong Ho Chae, Sang-Woon Jeon
Recently, aerial jammers (AJs) have been actively considered because of their swiftness and adaptability to maximize the impact of jammers. The previous studies mainly focused on the optimization of a single AJ (transmit power, location, etc.) consisting of intended users and eavesdroppers in single-cell environment. In this paper, macroscopic impact of multiple randomly deployed AJs to multiple radio access technologies (multi-RATs) is analyzed, which have been actively studied for future cellular systems. In particular, multi-radio heterogeneous networks consisting of WiFi-enabled small base stations (SBSs) and 5G-enabled macro/micro base stations (MBSs) are considered. An integrated framework is developed and evaluated for multi-RATs in the presence of both co-channel interference and AJ interference based on tools from stochastic geometry. The main challenge is to analyze the optimal control of portion of AJs to interfere two different RATs to maximize the outage probability. Both analytical and simulation results are presented to demonstrate the impacts of various network parameters to the overall outage probability.
Kittens in their first four weeks are in their most critical period because they do not yet have the ability to thermoregulate their bodies, and it is still difficult for them to adapt to environmental temperatures. Due to this condition, veterinary clinics and cat-lover communities need facilities that can maintain a kitten’s body temperature within the normal range. One way to help in the care of these kittens is to use a special incubator for animals. Incubators are useful in situations where animals cannot control their body temperature conditions. The expected method to monitor the work system of the incubator is internet-based monitoring, as part of IoT (internet of things). Monitoring is very important for animal health workers and cat lovers in monitoring the temperature and humidity in the incubator using the internet, which allows monitoring to be carried out anytime and anywhere from a smartphone through the Blynk application. The purpose of this research is to create an IoT-based kitten incubator monitoring system through the blynk application so that the owner or nurse of the kitten can monitor in real time via a smartphone so that time efficiency can be improved, by using NodeMCU ESP8266 microcontroller with fuzzy logic method. The incubator can work automatically to regulate the temperature through lighting and air settings in it, with the applied temperature ranging from 26 degrees to 30 degrees Celsius.
Identity and access management (I&AM) is the umbrella term for managing users and their permissions. It is required for users to access different services. These services can either be provided from their home organization, like a company or university, or from external service providers, e.g., cooperation partners. I&AM provides the management of identifiers with the attributes, credentials, roles, and permissions the user has. Today, the requirements have evolved from simply accessing individual web services in the internet or at a company to the majority of all IT services from different service providers with various accounts. Several identity management models have been created with different approaches within. In order to adjust to heterogeneous environments, use cases, and the evolution of identity management, this paper extends known requirements for identity management. Existing models and approaches for identity management are mapped to the derived requirements. Based on the mapping, advantages, disadvantages, and gaps are identified. Current approaches suffer, as an example, from trustworthiness and liability issues. Interoperability issues are even more inherent as the approaches partly develop apart, forming an heterogeneous environment. The results from this analysis emphasize the need for one holistic identity management framework.
This paper presents a comprehensive classification of identity management approaches. The classification makes use of three axes: topology, type of user, and type of environment. The analysis of existing approaches using the resulting identity management cube (IMC) highlights the trade-off between user control and trust in attributes. A comparative analysis of IMC and established models identifies missing links between the approaches. The IMC is extended by a morphology of identity management, describing characteristics of cooperation. The morphology is then mapped to the life cycle of users and identity management in a further step. These classifications are practically underlined with current approaches. Both methods combined provide a comprehensive characterization of identity management approaches. The methods help to choose suited approaches and implement needed tools.
Alejandro Ramírez-Arroyo, Melisa López, Ignacio Rodríguez
et al.
Industrial environments constitute a challenge in terms of radio propagation due to the presence of machinery and the mobility of the different agents, especially at mmWave bands. This paper presents an experimental evaluation of a FR2 5G network deployed in an operational factory scenario at 26 GHz. The experimental characterization, performed with autonomous mobile robots that self-navigate the industrial lab, leads to the analysis of the received power along the factory and the evaluation of reference path gain models. The proposed assessment deeply analyzes the physical layer of the communication network under operational conditions. Thus, two different network configurations are assessed by measuring the power received in the entire factory, providing a comparison between deployments. Additionally, beam management procedures, such as beam recovery, beam sweeping or beam switching, are analyzed since they are crucial in environments where mobile agents are involved. They aim for a zero interruption approach based on reliable communications. The results analysis shows that beam recovery procedures can perform a beam switching to an alternative serving beam with power losses of less than 1.6 dB on average. Beam sweeping analysis demonstrates the prevalence of the direct component in Line-of-Sight conditions despite the strong scattering component and large-scale fading in the environment.