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Article
Engineering
Bioengineering

Carolina A. Vares,

Sofia P. Agostinho,

Ana L.N. Fred,

Nuno T. Faria,

Carlos A. V. Rodrigues

Abstract:

Fermentations are complex and often unpredictable processes. However, fermentation-based bioprocesses generate large volumes of data that are currently underexplored. These data can be used to develop data-driven models, such as machine learning (ML), to improve process predictability. Among various fermentation products, biosurfactants have emerged as promising candidates for several industrial applications. Nevertheless, biosurfactant large-scale production is not yet cost-effective. This study aims to develop forecasting methods for the concentration of mannosylerythritol lipids (MELs), a type of biosurfactant, produced in Moesziomyces spp. cultivation. Three ML models, Neural Networks (NN), Support Vector Machines (SVM), and Random Forests (RF), were used. NN provided predictions with a mean squared error (MSE) of 0.69 for day 4 and 1.63 for day 7, and a mean absolute error (MAE) of 0.58 g/L and 1.1 g/L, respectively. These results indicate that the model’s predictions are sufficiently accurate for practical use, with the MAE showing only minor deviations from the actual concentrations. Both results are promising, as they demonstrate the possibility of obtaining reliable predictions of MELs production for days 4 and 7 of fermentation. This, in turn, could help reduce process-related costs, enhancing its economic viability.

Review
Engineering
Bioengineering

Saba Salahuddin,

Khan Bahadar Khan,

Abdul Qayyum,

Iftikhar Ahmed,

Kashif Saleem,

Sadia Saeed

Abstract:

The possibility of medical image segmentation within the domain of a federated learning, Federated Learning (FL) may transform the situation and help solve the critical challenges that exist in common centralized machine learning models. While effective, traditional models are limited by issues like the need of huge surveys, high costs in data assignment, high privacy concerns over sensible wellbeing data. Since improvements in the medical imaging field continue, the adoption of FL is a strategic response to such limitations and can be introduced as a collaborative privacy preserving framework for model training. This was a systematic exploration of the literature from 2017 to 2024 where the Google Scholar literature has been explored for studies indexed with the keywords 'federated learning,' 'medical image segmentation,' and 'privacy preservation.' Specifically, this review did not consider studies that did not directly discuss FL concepts. Twenty-one publications were carefully selected from out of thousands of publications because they are relevant and contribute to the area of treatment. Specifically, seven studies directly approached the extent of medical image segmentation using FL and address the technological and the practical challenges. The remaining fourteen studies were foundational in that they further elaborated on the architectural and procedural elements of FL frameworks that are essential for collaborative and secure medical image analysis. A review of the selected studies is presented in detail in the review in terms of the effectiveness of FL in improving medical image segmentation while protecting patient privacy. It makes a powerful evaluation of the strengths and weakness of present FL model, the versatility of data sets, the diversity of the imaging modalities addressed, and scalability of these models across various clinical conditions. Such synthesis of this literature underscores the fact that FL can revolutionize medical diagnostics with opportunity to produce more robust, scalable, and privacy friendly models.

Article
Engineering
Telecommunications

Georgios Giannakopoulos,

Khushbu Mehboob Shaikh,

Maria Antonnette Perez

Abstract: The increasing use of mobile phones worldwide has raised concerns about potential health risks associated with exposure to high-frequency radiation emitted by these devices. This study explores the short-term and potential long-term health effects of mobile phone usage, particularly focusing on risks such as cancer, nervous system disorders, and electromagnetic hypersensitivity. Key research conducted by organizations such as the World Health Organization (WHO) and the Mobile Telecommunications and Health Research (MTHR) program is reviewed, highlighting inconclusive evidence linking mobile phone radiation to health problems. Children and young users are identified as particularly vulnerable to potential risks, given their developing physiology and higher susceptibility to radiation absorption. Experimental studies investigating biological mechanisms, cognitive function, and hypersensitivity have largely found no conclusive evidence of harm, though long-term effects remain under-researched due to the relatively recent widespread adoption of mobile phones. The research article emphasizes the need for adherence to rigorous research methodologies, including randomized, double-blind experiments, and standardized statistical analyses to ensure reliable conclusions. Recommendations for reducing exposure, such as limiting mobile phone usage and adopting hands-free solutions, are provided as precautionary measures. While no definitive causal link has been established between mobile phone use and adverse health outcomes, ongoing research and cautious usage remain essential to safeguarding public health.
Article
Engineering
Electrical and Electronic Engineering

Georgios Giannakopoulos,

Khushbu Mehboob Shaikh

Abstract:

Phased array antennas provide the ability to electronically steer a beam, eliminating the need for mechanical adjustments [1]. While traditionally used in military applications, there is growing interest in their adoption across various fields [1,2]. Conformal antennas, a type of phased array, are designed for installation on curved or non-flat surfaces, enabling focused radio wave radiation [1,2]. These antennas can be integrated into various applications, including aerospace, wearable technology, vehicles, and modern mobile devices [2], while also reducing traditional antenna height to support the integration and coexistence of multiple radio technologies within a compact area [1,2]. Planar arrays, composed of elements with phase shifters in a matrix, are compact and cost-effective due to mass production via printed circuit technology [1–3]. These antennas, when mounted on rigid surfaces, exhibit robustness, provide beam deflection in two planes, and offer high gain with rapid beam-switching capabilities [1,3]. However, planar antennas can experience interference between feed lines and elements, often supporting narrow bandwidths and exhibiting relatively low radiation efficiency [1,3]. Conformal antennas, which are easily mounted on curved surfaces, are particularly suited for wearable applications, spacesuits, and aerospace designs [1,2,4]. By minimizing connection length, they bring electronics closer to the antenna elements, reducing signal loss while enhancing transmission power and receiver sensitivity, especially at higher frequencies [4]. Research into 3Dprinted conformal antennas has emerged as a significant field of study [1,5]. This paper presents the mathematical analysis of both planar and conformal antennas, covering key parameters such as gain, bandwidth, radiation efficiency, and mutual coupling for planar arrays, as well as the width and length calculations for rectangular microstrip patch antennas used in conformal designs [2,6–8]. Furthermore, the role of additive manufacturing in antenna development is highlighted, emphasizing its ability to produce antennas with complex geometries thereby revolutionizing conformal antenna design [1,9].

Article
Engineering
Mechanical Engineering

Darioush Jamshidi,

Daniyal Poureyvaz Borazjani,

Seyed Ehsan Hosseini,

Sajad Davari

Abstract: Optimizing the design of the exhaust manifold plays a crucial role in enhancing engine efficiency, reducing fuel consumption, and minimizing pollutant emissions in internal combustion engines. This study aims to improve the exhaust manifold geometry of the M3 engine, manufactured by PIDOCO (Pioneer Intelligence Design Origin Co.). The primary design objectives include reducing pressure drop and enhancing flow uniformity across the surface of the monolithic catalyst. To achieve these goals, a modified exhaust manifold geometry for the M3 engine was designed, and Computational Fluid Dynamics (CFD) simulations were conducted to evaluate the proposed designs. The geometries were created using CATIA software, while ANSYS Fluent was employed for CFD analysis. The results indicate that optimizing the exhaust manifold geometry reduces pressure drop by 45%, improves the flow uniformity index by 36%, and leads to a minimum 4.5% reduction in vehicle gaseous emissions.
Article
Engineering
Industrial and Manufacturing Engineering

Abdussalam A. Alajami,

Rafael Pous

Abstract: This paper introduces the design and evaluation of an RFID-based inventory robot that uses vision and a 3-degree-of-freedom (DOF) manipulator for dynamic antenna positioning. The robotic system is designed to enhance RFID tag detection performance and efficiency in inventory management by autonomously detecting objects, orienting an RFID antenna towards them, and executing a circular scanning motion that ensures complete coverage of the object’s surface. This paper also present a comparative analysis of three scanning strategies: (1) a conventional fixed antenna approach, where the antenna remains stationary on one side of the robot; (2) a predefined path strategy, where the manipulator moves the antenna across preset spatial points to maximize coverage; and (3) an intelligent detection and dynamic positioning method. In the latter, a pre-trained YOLO model identifies probable products and utilizes forward and inverse kinematics to precisely position the manipulator’s end effector (The antenna) to perform a tailored circular motion around the object, ensuring comprehensive RFID tag scanning. Experimental results, illustrated through comparative graphs, highlight the superior performance of the vision-assisted dynamic positioning approach. This method significantly outperforms the fixed and predefined path strategies in terms of the total number of RFID tags read over time, particularly in scenarios with varied object heights and spatial distributions. The work in this paper marks a step forward in the development of autonomous inventory robots and autonomous warehouse systems, offering enhanced capabilities for real-time inventory tracking and management.
Article
Engineering
Mechanical Engineering

Askar Rzaliyev,

Valeriya Goloborodko,

Serik Bekbosynov,

Olzhas Seipataliyev,

Dauren Kosherbay

Abstract: This study examines the impact of various tillage practices on the agrophysical and hydrophysical properties of sierozem and light chestnut soils in Southeastern Kazakhstan. The objective is to develop an optimized tillage strategy to enhance soil fertility and moisture conservation. The research evaluates both conventional and innovative tillage methods, analyzing their effects on soil erosion, structure, and water retention. Field trials demonstrated that replacing traditional moldboard plowing with chisel and flat-cutting tillage, along with combined implements for leveling, crushing, and rolling, significantly reduces erosion and improves moisture retention. The results suggest that adopting alternative tillage technologies can enhance soil conservation and crop productivity while reducing operational costs.
Article
Engineering
Automotive Engineering

Yiyuan Fang,

Wei-hsiang Yang,

Yushi Kamiya

Abstract: This study aims to describe the advantages of electrifying regular-route buses. The results of a survey on the speed profile of buses operating on actual routes are presented here. Firstly, we focus on the acceleration / deceleration at the starting / stopping stops specifically for regular-route buses and obtain the following information: I. Starting acceleration from a bus stop is particularly strong in the second half of the acceleration process, being suitable for motor-driven vehicles. II. The features of the stopping deceleration at a bus stop are “high intensity” and “low dispersion,” with the latter enabling the refinement of regenerative settings and significantly lowering electricity economy during electrification. And we compare the speed profile of a fuel cell bus with those of a diesel bus and obtain the following information: III. Motor-driven vehicles have “high acceleration performance” and “no gear shifting” which facilitate the high-intensity stop–start acceleration operation unique to regular-route buses. By calculating and analyzing the jerk amount, we could quantitatively demonstrate the comfortable driving experience while riding on this type of bus where there is no shock due to gear shifting. IV. While the “high acceleration performance” of motor-driven vehicles produces “individual differences in the speed change patterns,” this does not translate to “individual differences in electricity consumption” owing to the characteristics of this type of vehicle. With engine-driven vehicles, measures, such as “slow acceleration” and ”shift up early” are strongly encouraged to realize eco-driving, and any driving style that deviates from these measures is avoided. However, with motor-driven vehicles, the driver does not need to be too concerned about the speed change patterns during acceleration. This characteristic also suggests a benefit in terms of the electrification of buses.
Review
Engineering
Civil Engineering

Małgorzata Jastrzębska

Abstract: Geotechnical engineering projects carried out within the framework of the low-emission econ-omy and the circular economy are the subject of many publications. Some of these studies pre-sent the use of various waste materials, as soil additives, for improving geomechanical behav-ior/properties. Many of these materials are eagerly used in geoengineering applications, pri-marily to strengthen weak subsoil, or as a base layer in road construction. Information on indi-vidual applications and types of these materials is scattered. For this reason, this article briefly discusses most of the major waste materials used for achieving weak soil improvement in ge-oengineering applications, and highlights pertinent bibliographic sources where relevant details can be found. The presented list includes waste from mines, thermal processes, end-of-life car tires, chemical processes (artificial/synthetic fibers), and from construction, renovation and demolition works of existing buildings and road infrastructure. In addition to the positive im-pact of using waste instead of natural and raw materials, the paper encourages the reader to ponder whether the waste used really meets the criteria for ecological solutions.
Article
Engineering
Safety, Risk, Reliability and Quality

Yifan Zhao,

Shuicheng Tian,

Junrui Mao,

Guangtong Shao

Abstract: In high-risk mining environments, the emotional state of individuals can play a critical role in shaping decision-making during emergencies. To investigate this phenomenon, we adopted an event-related potential (ERP) approach within a within-subject experimental framework. Participants were first exposed to a series of emotionally evocative cues designed to elicit distinct affective states, immediately followed by decision-making tasks that simulate coal mine emergency scenarios. Behavioral indices—such as reaction speed and task engagement—were recorded concurrently with ERP signals, with a specific focus on components including N1, P2, N2, P300, and LPP. Our analysis revealed that both the valence and intensity of emotional cues substantially modulated the decision-making process, as reflected by significant variations in response times and ERP measures. These findings offer fresh insights into the neural mechanisms through which emotional factors influence critical decisions in crisis situations, highlighting implications for the development of enhanced safety strategies in coal mining operations.
Article
Engineering
Electrical and Electronic Engineering

Oana Vasilica Grosu,

Laurențiu Dan Milici,

Mihaela Paval

Abstract: Shape memory alloys are the key to sustainable technology and future industries. One of the most highlighted alloys around these times is Nitinol. It has special properties to work in extreme conditions and it can be specially projected for specific tasks. The Nickel-Titanium alloy is tested by NASA to build a new type of wheel to be used for future rovers which will be send to Mars. Other recently known devices to be built using this material are stents, root canal files, arches, bone implants used in medicine, aerospace gas turbines engines, thermal bimorph od dynamic actuators used in engineering, innovative SC brace or seismic retrofit applications in seismology and the list can continue. The purpose of this paper is to introduce the ungular actuator that has as a main component a Nitinol spring. In the theoretical part we identified the newest solutions in the sphere of Nitinol devices and presented them though the perspective of bibliometric maps obtained with the software VOSviewer. We focused on articles which analyze and improve the NiTi alloy in order to create a wider applicability range for it, studies over the already existing devices, testing models, novel applications and devices which utilize it and papers identifying new applications for actual NiTi devices/ equipment. Conclusions on the subject are that the industry is more and more interested in the shape memory materials and are open to investing in the research of the Nitinol. The presented device was physically developed and tested at the University Ștefan cel Mare of Suceava. Some results on the testing of the device are presented and discussed in this paper.
Article
Engineering
Industrial and Manufacturing Engineering

Guoqing Du,

Mingyi Yang,

Zhigang Xu,

Junyi Wang,

Cheng Xie,

Yuan Lu,

Pengfei Yin

Abstract: Aiming at the strong spatio-temporal coupling relationship between data in the actual industrial production process, which leads to the problem of insufficient reliability and poor timeliness of traditional process anomaly monitoring methods, a time series anomaly monitoring model based on the graph similarity network with multi-scale features is proposed, which can react to the anomalies in the process in a timely and effective manner to guarantee the production safety.First, a graph-building method for spatio-temporally coupled time-series data using multidimensional time-varying feature map embedding is designed to capture the data’s dependence on time, while the topology of the graph is utilized to learn the spatial coupling of the data; second, a graph similarity-based anomaly monitoring strategy is innovatively proposed to measure the anomalies of the process using the difference degree index between the standard normal process data and the monitoring data. Finally, the proposed method is validated using the standard normal operating condition data of the Tennessee-Eastman (TE) process as well as the standard fault data. The experimental results show that the proposed model can identify anomalies more quickly and accurately than other typical methods, which significantly improves the reliability and timeliness of industrial process anomaly monitoring.
Article
Engineering
Aerospace Engineering

Segundo Urraza Atue,

Paul Bruce

Abstract: Developing spacecraft for efficient aerocapture missions demands managing extreme aerothermal environments, precise controls, and atmospheric uncertainties. Successful designs must integrate vehicle airframe considerations with trajectory planning, adhering to launcher dimension constraints and ensuring robustness against atmospheric and insertion uncertainties. To advance robust multi-objective optimization in this field, the new framework is presented, designed to rapidly analyze and optimize non-thrusting, fixed angle-of-attack aerocapture-capable spacecraft and their trajectories. The framework employs a 3-degree-of-freedom atmospheric flight dynamics model incorporating planet-specific characteristics. Aerothermal effects are approximated using established Sutton-Graves, Tauber-Sutton, and Stephan-Boltzmann relations. The framework computes the resulting post-atmospheric pass orbit using an orbital element determination algorithm to estimate fuel requirements for orbital corrective maneuvers. A novel algorithm that consolidates multiple objective functions into a unified cost function is presented and demonstrated to achieve superior optima with computational efficiency compared to traditional multi-objective optimization approaches. Numerical examples demonstrate the methodology’s effectiveness and computational cost at optimizing Terrestrial and Martian Aerocapture maneuvers for minimum fuel, heat loads, peak heat transfers, and an overall optimal trajectory, including volumetric considerations.
Technical Note
Engineering
Civil Engineering

Antonio Aguero,

Ivan Baláž,

Yvona Kolekova

Abstract: Designing reinforced concrete sections of various shapes, including those with variable geometry, holes, and arbitrary distribution of reinforcing steel bars, is a common task in civil engineering involving reinforced concrete structures. This design process requires integrating non-linear stress fields over complex shapes due to the non-linear behavior of concrete in compression. In this paper, we propose a novel algorithm based on the simplex method to calculate the ultimate strength of reinforced concrete and steel concrete composite sections under biaxial bending and axial force. The proposed algorithm is validated by comparing its analytical results with methods suggested by other authors and with experimental results available in the literature.
Article
Engineering
Other

Julien Colomb,

Moritz Maxeiner,

Robert Mies

Abstract:

Based on the interviews of fifteen contributors in research hardware projects, we shed lights onto the motivations of research hardware engineers to use a hardware publication platform, and derived some high-level features that such a platform should have. Our analysis suggests that the main objectives of the authors are to find their readership and grow an inclusive community of contributors, producers and users around their project. Inclusivity requires the recognition of different types of contributions and a system free of financial or language barriers for authors and readers. They also appear interested in getting feedback on their work, in order to make the hardware better and learn during that process. The creation of a research output that is recognized by the academic system is also important both for their career and for developing their community. In addition, they express wishes for a publication system integrated in their hardware documentation workflow , as well as a system which would be pleasant or even fun to use. Importantly, a research hardware publication ecosystem should link archived and living versions of the hardware project and consider the project as a whole, providing documentation on both the hardware product and the development process.

Article
Engineering
Energy and Fuel Technology

Matthew Niichel,

Riley Madden,

Hannah Pike,

Nafees Bin Kabir,

True Miller,

Brian Jowers,

Stylianos Chatzidakis

Abstract:

First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring fundamental properties of neutron diffusion and transport. However, these assemblies could hold potential for modern applications and nuclear research. The Purdue University subcritical pile previously lacked a substantial testing volume, limiting its utility to simple neutron activation experiments for the purpose of undergraduate education. Following the design and addition of a mechanical and electrical testbed, this paper aims to provide an overview of the testbed design and characterize its neutron and gamma flux of the rearranged Purdue subcritical pile, justifying its use as a modern scientific instrument. The newly installed 1.5*10^5 cubic-centimeter volume testbed enables a systematic investigation of neutron and gamma effects on materials and the generation of a comprehensive dataset with the potential for machine learning applications. The neutron flux throughout the pile is calculated using gold-197 and indium-115 foil activation alongside cadmium-covered foils for two-group neutron energy classification. The neutron flux measurements are then used to benchmark a detailed geometrically and materialistic accurate Monte-Carlo model using OpenMC. The experimental measurements reveal the testbed has a neutron environment with a total neutron flux approaching 8.5*10^3 n/cm^2*s and a thermal flux of 5.8*10^3 n/cm^2*s, following the expected diffusion theory behavior. This work establishes the modified Purdue subcritical pile can provide significant neutron and gamma fluxes and a uniquely large volume to enable radiation testing of integral electronic components and as a versatile research instrument with the potential to support microelectronics testing, limited isotope production, and non-destructive imaging while generating valuable training datasets for machine learning algorithms in nuclear applications.

[M1]Reference citation is not allowed. Please revise.

Technical Note
Engineering
Civil Engineering

Antonio Aguero,

Ivan Baláž,

Yvona kolekova

Abstract: The present paper deals with warping torsion, bending and axial forces. Presenting a method to compute the shear center of the effective section, the warping constant and warping coordinates needed to compute the stresses due to the bimoment. The stresses are compared between stresses in the gross and effective section when a Bimoment only is applied for the sake of simplicity but any combination with axial and biaxial bending moment is allowed. This paper fills the gap of the current codes to deal with bimoment in Class 4 sections.
Article
Engineering
Architecture, Building and Construction

Tzu-Wen Kuo,

Ching-Yuan Lin

Abstract:

This study aimed to shorten firefighter search times during indoor fires, allowing more people to be rescued, by enhancing disaster prevention capabilities using building technologies. In indoor fires, fatalities are often caused by the failure of firefighters to rescue individuals in a timely manner. The question of how to effectively increase the probability of survival while waiting for rescue behind closed doors warrants in-depth research and analysis. Therefore, to ensure that people live in safe environments, there is an urgent need to develop a building door panel material with an emergency call function to prevent such incidents from occurring. Utilizing the PRISMA method, we conducted a comprehensive review of the existing literature to identify the key issues and limitations associated with the current search-and-rescue techniques. Subsequently, the identified primary factors were analyzed using the TRIZ method to determine the key factors that influence the success of rescuing trapped individuals, and a notification system was designed to address this issue. Based on the premise that it is advisable to wait for rescue during a fire, we utilized a smartphone to scan a QR code and transmit the exact location information to the fire department. Through extensive participation and feedback from firefighters, we developed a rescue notification door panel and obtained a patent for it. This system can significantly reduce the time required for search-and-rescue operations in fire incidents. The experimental results show a reduction of one-third in search times.

Article
Engineering
Electrical and Electronic Engineering

Jin Jiang,

Hongmin Chen,

Fenghe Yang,

Chunlai Li,

Jin He,

Xiumei Wang,

Jishi Cui

Abstract: This study explores the mechanisms responsible for the bandwidth reduction observed in Ge-on-Si photodetectors under high incident light power. We investigate the impact of the carrier-screening effect on the bandwidth through simulations, and we mitigate this effect by increasing the applied bias voltage. The increase in the concentration of photogenerated carriers leads to a reduction in the carrier saturation drift velocity, which reduces the bandwidth of the Ge-on-Si photodetector; this phenomenon is studied for the first time. The bandwidth is determined primarily by the carrier saturation drift velocity when the incident light power is below 2.5 mW, and the decrease in bandwidth that is calculated based on the decrease in carrier saturation drift velocity is consistent with the experimental results. However, when the incident light power exceeds 3 mW, both the carrier-screening effect and the reduction in the carrier saturation drift velocity contribute to the bandwidth reduction. This study provides a good theoretical guicdance for the design of high-power Ge-on-Si photodetectors.
Article
Engineering
Mining and Mineral Processing

Daniel Goldstein,

Chris Aldrich,

Quanxi Shao,

Louisa O'Connor

Abstract: Due to limited exploration drilling and analogue mapping, bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis. The Measure-While-Drilling (MWD) system uses sensors to collect drilling data from mining blast hole drill rigs. Historically, MWD studies have focused on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including Stratigraphic Unit, Rock/Soil Strength, Rock Type, Geological Strength Index, and Weathering properties. Feature selection algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Random Forests (RFs), Linear Discriminant Analysis, and Naive Bayes. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI in real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis.

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