2-Phenylethanol (2-PE) can be produced by microorganisms, especially Saccharomyces cerevisiae. Gas Chromatography-Mass Spectrometer (GC-MS) has been used to analyze volatile producing microorganisms. The Gas Syringe-Static Headspace (GS-SHS), Thin Film-Solid Phase Micro Extraction (TF-SPME), Headspace Sorptive Extraction (HSSE), Dynamic Headspace (DHS), and Direct Thermal Desorption (DTD) were used to introduce Saccharomyces cerevisiae LV-007 cultured medium to GC-MS for 2-PE qualitative and quantitative analyses. The numbers of detected volatiles from introducing techniques of GS-SHS, TF-SPME, HSSE, DHS, and DTD were 8, 26, 15, 27, and 16, respectively by qualitative analysis. After 2-PE and viable cell quantifications from LV-007 of various introducing techniques, DTD showed the highest 2-PE ratio of 237.3710-6 μg/ml•CFU. The DTD technique also used short time of 30 min to analyze sample.
This paper proposes a hierarchical mass breakdown for transport aircraft with truss braced wings. The contribution is not another standalone mass formula, but an integration framework that shows how empirical, semi-empirical, and physics-based mass-estimation methods can be combined without gaps or double counting when an aircraft contains components that are not represented well by conventional handbook mass statements. The need is strongest in novel configurations such as truss braced wing configurations, where the wing box, strut, jury members, and offset often require deeper structural sizing than conventional cantilever wing groups in preliminary design level. The proposed hierarchy maps aircraft mass from takeoff gross weight down to detailed items and, at the same time, visualizes the fidelity level at which each mass method operates. This makes it possible to blend existing aircraft methods with newly developed methods and still roll all results back to operating empty weight and takeoff gross weight in a traceable, accurate way during conceptual and preliminary design phases.
The exponential growth of healthcare data – driven by electronic health records (EHRs), wearable sensors, and continuous remote monitoring systems – has created both immense opportunities and complex challenges for modern clinical decision-making. Effectively harnessing this heterogeneous, high-volume, and high-velocity data requires intelligent systems that can not only deliver accurate predictions but also provide interpretable insights to support clinician trust and patient safety. In this paper, a modular hybrid computational intelligence framework designed to advance personalized, real-time healthcare analytics, is proposed. Our approach synergistically integrates deep learning for high-dimensional feature extraction, fuzzy inference systems for transparent reasoning under uncertainty, and genetic algorithms for adaptive optimization. This tri-layered architecture enables the system to learn from multimodal data sources, including physiological signals (e.g., ECG, glucose levels), structured clinical records, and unstructured patient-reported outcomes, to predict critical health risks such as cardiac arrhythmias, myocardial infarction, and diabetes-related complications. Through experimentation on publicly available real-world datasets, the proposed framework demonstrates superior predictive accuracy, enhanced interpretability, and computational efficiency compared to conventional machine learning and deep learning baselines. Importantly, the inclusion of fuzzy logic modules allows clinicians to trace back the reasoning paths of the system, addressing the growing demand for Explainable AI (XAI) in regulated healthcare environments. This research bridges the longstanding gap between model performance and transparency by offering a scalable and modular solution that is adaptable to diverse clinical contexts. By supporting proactive risk stratification and timely interventions, the framework has the potential to transform reactive care models into intelligent, preventative, and patient-centric healthcare delivery systems.
In the IOT environment, network edges have data, but a centralized server may need to obtain the union from edges for efficient data access. PSU is a cryptographic primitive that allows protocol parties to compute the union of their private datasets without revealing any extra information. Traditional PSU protocols presuppose that all parties must input their private datasets. This assumption doesn’t hold in some scenarios where an inputless Third party needs to get the union. For instance, a regulatory organization may need to get the union of patient data from hospitals for statistical analysis, without inputting any dataset. We propose a novel TP-PSU, specifically designed for a setting with three parties and an inputless Third party. Our protocol enables the Third party to compute the union, while preventing the leakage of any other extra information. This includes protecting the origin and duplication of each data item across edge nodes, thus maintaining privacy in the IoT environment.
In tropical regions, approximately 60% of building energy is consumed by cooling systems, with heat gain through the building envelope being a major contributor. The Overall Thermal Transfer Value (OTTV) is a metric used to quantify average heat gain in air-conditioned buildings. However, the standard OTTV calculation does not account for shading from adjacent buildings—an increasingly common feature in high-density urban areas. This paper presents an empirical study on the thermal performance of building envelopes considering adjacent shading in tropical climates. Dynamic simulations of annual heat gain were conducted for buildings with and without adjacent shading for comparative analysis. The results highlight the significant impact of adjacent structures on heat gain performance and offer supplementary data to improve the current OTTV calculation method, especially for multi-block developments.
Reheating processes, which constitute a critical stage in steel industry production, hold significant importance in terms of energy efficiency due to their high energy consumption. In reheating furnaces, by-product gases, natural gas, or fuel oil are typically utilized. The walking beam reheating furnace examined in this study heats billets to the required temperatures— depending on product size and type—through 32 burners positioned in different furnace zones, providing stepwise heating. However, the imbalance in heat transfer within the furnace and the non-uniform distribution of heating rates lead to deviations from the target heating curve, thereby intensifying the scaling problem that causes production losses. The primary aim of this study is to reduce unnecessary natural gas consumption and minimize the carbon footprint by improving the control of the air–fuel mixture inside the furnace through burner optimization and automation systems. As a result of the implemented measures, considering the annual production capacity, a total of 1,950,000 Sm³ of natural gas savings was achieved, corresponding to approximately 3,705–3,900 tons of CO2 equivalent emission reduction. These findings demonstrate a successful large-scale industrial application aimed at enhancing energy efficiency in reheating furnaces while mitigating environmental impacts.
In today’s world, increasing environmental concerns have intensified the focus on renewable energy sources and effective energy management practices. In industrial processes, particularly in areas with high energy consumption, waste heat recovery has become one of the primary research topics. Reheating furnaces used in the steel industry reach extremely high temperatures during rolling operations, with approximately 50–70% of this energy being released directly into the atmosphere through flue gases. In this context, flue gases stand out as one of the most significant sources of waste heat. The aim of this study is to recover the waste energy contained in the flue gases emitted to the atmosphere from a slab reheating furnace used in the hot rolling process of the iron and steel industry by means of a recuperator. Through the newly implemented recuperator replacement, 31,274.93 kW of energy recovery was achieved and 6.32 tons of carbon emissions were reduced. Consequently, the amount of energy released into the atmosphere was decreased, and a significant reduction in natural gas consumption of the furnace was obtained.
To realize sustainable Cultural Heritage buildings and preserve cultural heritage, an appropriate concept of cultural heritage restoration and material appropriate to their function are needed. One of which is brick material for heritage buildings. Current bricks are different from heritage/old bricks, visually evident through their shape and colour. Heritage bricks must be studied to find their characteristics and composition so they can be used as cultural heritage restoration materials in the world. The composition and characteristics of heritage bricks have been studied and the results show that the physical and mechanical properties of heritage bricks vary. Therefore, research is needed on the development of renewable/repair heritage bricks as materials in the restoration of cultural heritage buildings, by identifying the characteristics and composition of renewable bricks. The methods applied include experiments and testing in the field and laboratory. The characteristics of renewable bricks in terms of physical strength have been designed with a size of 5.5x14x25.5 without shrinkage because they come from selected clay, have flat surfaces, sharp corners, and colours similar to heritage bricks. Tests were also conducted on the mechanical properties, such as water absorption, apparent density, and compressive strength, of all heritage bricks, and the best values for use as renewable materials were obtained.
The work discusses the current challenges of university curriculum shaping for pre-service specialists’ training and issues of synchronization of courses learning. Elaborating the approach to the building the synchronized curriculum for pre-service specialists’ training, we applied the theoretical fundamentals of curriculum design and disciplines structuring, artificial intelligence models of knowledge representation (frames and semantic networks) as well as the theoretical basics of Petri nets which have proper properties and facilities for different processes synchronization. On purpose of solving of the said problems, it is proposed the approach to university curriculum simulation based on Petri nets apparatus. The procedure of the learning elements grouping in frames is elaborated. In particular, the precedence relations of various kinds for the learning elements are determined. Based on these relations, there was built Petri net model of the process of learning elements’ mastering. Applying the operations of Petri nets modifications and learning elements’ precedence relations of different kinds, it was created a model of curriculum subject mastering as a whole. Obtained Petri nets models allow to simulate the process of the curriculum disciplines learning by trainees. Synchronization of the learning elements mastering is determined by the rules of Petri net modifications and running. In addition, there were elaborated and presented core stages of design of the Petri nets-based software component that allows to simulate the holistic and coordinated curriculum building. Resting on the theoretical framework, made by the holistic educational paradigm and authors’ approach to the modelling of holistic and coordinated curriculum for the specialists’ training based on Petri nets apparatus, the software module for the automatization of the said coordinated curriculum modelling and the features of its design are characterized. The software module’s structure components are distinguished and the set of requirements in terms of their design are formulated according to the logic of the components work. The groups of the software classes are specified and described. The benefits and practical applications of the depicted approach to the shaping of synchronized curriculum for potential specialists’ training are covered and analysed.
Recently since there exists more companies using web, web attacks to hijacking or manipulate the privacy information have increased. Among web vulnerability OWASP has introduced, SQL injection, XSS, File Inclusion have constantly occurred through more than a decade. It concludes that web servers have trouble with blocking old-fashioned web vulnerabilities. This paper is going to skim through web attack defending methods and compares existing web attack detection machine learning models and new ensemble model DPL with ANOVA, chi-square analysis, correspondence analysis to find out relativity between model and web attack. As result of correspondence analysis, brand new model DPL excels existing models but even DPL model have low relativity on XSS. It is expected that post research must introduce more XSS relevant model.
Acoustic Emission (AE) testing is a non-destructive method widely used for structural damage identification. This study explores AE's application in monitoring corrosion activity in carbon steel material commonly used in pipelines. This research initially focused on examining the correlation between current density and the corrosion rate of carbon steel. ASTM A106 Gr B pipe specimens were submerged in 3.5 wt% sodium chloride solution supplied with varying current density between 0.0125A and 0.25A. Weight loss measurements were conducted for each test, revealing a direct correlation between current density and the corrosion rate. Subsequently, an experiment using AE monitoring device was conducted under identical conditions to establish a relationship between current density and AE count rate, which represents acoustic activity during the corrosion process. Conversely, the results indicated that as the current density increased, the AE count rate decreased. To validate these findings, a potentiodynamic polarization test was conducted, substituting current density variation with temperature as the main variable while maintaining the same concentration of sodium chloride. This test suggests that acoustic emissions are affected by factors beyond corrosion progression, including current densities and temperatures. The study concluded that the AE count rate is inversely proportional to the corrosion rate. This discrepancy is attributed to the formation of a passivation layer on the surface during corrosion, which reduced corrosion activity hence acoustic emission.
Intelligent Body Sensor Networks (IBSNs) based on IEEE 802.15.6, have emerged as a key technology to provide wearable wireless health care systems to establish real-time health monitoring algorithms for long-term recording of the vital signals and diagnose many life-threatening diseases. IBSNs allow continuous monitoring of patients at anytime and anywhere in the hospital and help in prevention medicine and early detection of pathologies to improve the quality of life. The IBSN system is also used in different types of wireless healthcare systems, including monitoring patients with chronic disease, patients in hospitals, elderly patients at home, or even real-time monitoring of patients in any kind of environment. In particular, it can be used for establishing a wearable and wireless health care system. The current version of IEEE 802.15.6 is based on conventional sampling approaches which suffers from many samples and power consumption. In addition, it is not enough to support high data rates applications for advanced wearable and wireless healthcare systems. The main purpose of this paper is to present an advanced version of IEEE 802.15.6 based on Compressed Sensing (CS) and focuses the most important features for wearable and wireless healthcare systems to support low data rate, low power consumption wireless communication in, on or around the body. The advanced version of IEEE 802.15 standard based on a CS focus on low complexity, low cost and ultra-low-power consumption infrastructures using a random sensing matrix to establish a wireless communication standard optimized for ultra-low-power in-body/ on-body wireless nodes to serve a variety of wearable and wireless medical applications. The proposed algorithm out-performs existing algorithm by achieving a good level of Quality of Service (QoS) and Signal-to Noise Ratio (S/N) at the receiver of each wireless node. This ability allows reducing 12 % of Average Packet Delay (APD) at the receiver of each wireless node. The proposed architecture is increased 10 % of throughput and 8 % of S/N at the receiver of each wireless node, which provide a good background for establishing high qualified Wireless Body Area Networks (WBANs).
Eye diseases are a significant health concern that adversely impacts human life. Cataracts, diabetic retinopathy, and glaucoma are some of the diseases that cause irreversible and serious health problems. Eye health is greatly influenced by age, genetics, and environmental factors. Proper diagnosis of eye ailments is crucial, as it ensures accurate and effective treatment. The proximity of disease detection to error for accurate and personalized treatment intensifies the clinician's responsibility further. Developing technology and deep learning make it feasible to determine if an individual has an eye disease, and to identify the specific disease. The objective of this research is to design resolutions for detecting significant health issues such as eye diseases with the aid of deep learning models. DenseNet, EfficientNet, Xception, VGG, and ResNet architectures, which are prominent Convolutional Neural Network models, are utilized to address the issue at hand. Technical term abbreviations are explained where first used. The dataset employed for detecting diseases in retinal fundus images consists of a total of 4217 images, comprising 1038 cataracts, 1098 diabetic-retinopathy, 1007 glaucoma, and 1074 healthy individuals. The performance of the tested models was assessed using evaluation metrics such as accuracy, recall, precision, F1-score, and Matthews's correlation coefficient metrics through 10-fold cross-validation. Upon analysis of the classification performances, the EfficientNet model obtained the best results for these evaluation metrics at 87.84%, 92.84%, 94.41%, 93.53%, and 83.87%, respectively. Thus, EfficientNet architecture delivered the best classification performance in this context.
Balancing the assembly lines plays an important role in increasing the productivity of the manufacturing systems. Depending on the objectives, there are various types of this problem. In this study, a Type-1 assembly line balancing problem is considered. It is known that the distribution of workload and equal distribution of idle time in station balancing is important for worker motivation and ergonomics. Once the number of stations is minimized, which is the primary objective in solving a Type-1 problem, the optimum solution found should be analyzed in terms of the smoothness index. Otherwise, the idle time can be distributed unevenly to the stations. Hence, one of the classical problems in assembly line balancing literature, Sawyer problem, is considered in this study. Firstly, the problem is solved via integer programming with Gurobi in Python. In the second stage, the line was smoothed via two techniques including the classical one and the proposed min-max approach. Different cycle times are tested and a comparison is provided for the two techniques.
Recently, many researchers pay attractive attention to organic memory technology. Polyimide film was used as the resistive switching layer of nonvolatile photomemory in this study. In order to improve the performance of the device, different wavelengths of Ultraviolet (UV) light were used to operate the device, and its impact on the polyimide film was discussed. In this paper, three different UV light bands, UVA, UVB, and UVC, are used to switch devices between low resistance state (LRS) and high resistance state (HRS). It can be seen from the results that all three kinds of energies of UV light can transform the polyimide film from HRS to LRS successfully. According to the data, after 1s of UVB irradiation, the LRS current of polyimide-based photomemory device is the largest, and the data retention ability is also the best, the second is to irradiate UVA, and the last is to irradiate UVC. The results of the optical properties and electrical properties are coincidental. Therefore, different UV energies can affect this photomemory device.
Asbestos is considered one of the most harmful occupational carcinogens causing more than 100,000 deaths per year and exposure to airborne asbestos fibers is held responsible for half of the deaths from occupational cancer (caused by mesothelioma, asbestos-related cancer and asbestosis). Because of this, this substance is now banned in 52 Countries, including Italy, which prohibited the extraction, and marketing of asbestos in 1992. Many areas have therefore been identified in this European Country as highly contaminated by asbestos. Among them also the area of Monferrato, which includes the Municipality of Casale Monferrato and 46 neighboring municipalities. The Eternit asbestos cement plant, located in the municipality of Casale, caused the contamination. The aim of this paper is to describe the complex remediation activities conducted in this wide area, located in northern Italy, highlighting the main phases and the most important issues during the remediation execution; moreover the purpose is to recall the appropriate prevention and protection measures to adopt to prevent new asbestos exposures and to emphasize their importance. This study can serve as an important reference for the academic participants involved in this field at European or international level.
This study presents a comprehensive investigation into the design and structural analysis of lightweight chassis for electric vehicles. A chassis needs to be able to withstand twist, shock, vibration, and other stresses caused by acceleration, braking, road condition, and shock initiated by other parts of a vehicle. It should carry a maximum load under all operating conditions. Two materials, 304L steel, and Ti-6AL-4V alloy were evaluated, with parameters such as total deformation, equivalent stress, and equivalent elastic strain under consideration. The outcome of the Finite Element Analysis revealed that Ti-6AL-4V can withstand higher stresses than 304L Steel. By investigating the static behaviors of the chassis under static loading due to weight and overload conditions, Ti-6AL-4V was chosen as a suitable replacement for a 304L Steel chassis.
This paper presents the impact of the surface topology of the scanned 3D object on parametric fitting. Whether it is a simple NURBS (Non-uniform rational B-spline) or a more complex hierarchical spline version, it is important to apply the fitting procedure. Here we describe the differences between fitting a surface with a given topology as a result of a 3D scanning system and a matrix topology of the surface, where the original surface is replaced by the result of a preset number of sections of the original geometry. We use the matrix and the free-form distribution. The former is more stable with respect to the distribution of the control point, the latter is numerically more suitable. In the future, we plan to adopt the free-form distribution to utilize the advantages of both distributions.
In this study, the focus was on utilizing the polyimide (PI) thin film as a resistive conversion layer for organic polyimide-based resistive random-access memory (ReRAM) applications. We utilized the 3D molecular editor WebMO to manipulate and analyze the structures of Aromatic-PI and Quinoid-PI molecules. The molecular chain length, molecular weight, and HOMO/LUMO molecular orbitals were investigated to gain insights into the fundamental properties of PI molecular structures. The analysis revealed that the Quinoid-PI molecule exhibited a shorter molecular chain compared to Aromatic-PI, attributed to the inability of the cyclic structure in Quinoid-PI to maintain a planar conjugated structure, resulting in an uneven overall structure and reduced molecular chain length (Norcorss, 1988). The research results demonstrate that the optical energy gap of Aromatic-PI remains constant regardless of the number of molecular bonds, with a value of approximately 3.2661 eV. In contrast, for Quinoid-PI, the optical energy gap undergoes a significant decrease as the number of molecular bonds ranges from 1 to 5. As the bond count increases to 20~23, the change in the optical energy gap gradually diminishes, ultimately resulting in an optical energy gap of 0.6932 eV. This observation indicates that longer molecular chains composed of Aromatic-PI act as insulators, while Quinoid-PI transforms into a low-energy gap conductor.
This exploration investigates the potential of enhancing energy efficiency in residential structures in Amman, Jordan, by introducing an innovative material termed "Phase Transition Substance with Fungal Integration (PTSF)." Through computational simulations using Autodesk-Revit, the analysis evaluates the thermal efficiency of a standard residential building, contrasting traditional construction materials with PTSF. The study centers on three crucial elements: outer walls (W.01), roof (R.01), and ground level slab (S.01), all initially not meeting prescribed thermal conductivity regulations. Integration of PTSF notably improves the thermal efficacy of the building's shell, aligning with Jordanian standards for thermal insulation. Furthermore, a systematic examination explores the time delay of materials within the residential envelope, revealing an average lag of approximately 8 hours. This lag implies that, with the incorporation of the PTSF layer, the envelope requires 8 hours to transmit external temperatures (e.g., 30°C) to attain the interior's peak temperature, underscoring the material's potential in curbing energy consumption.
