A miniaturized medical antenna with dual-band operation in the Industrial, Scientific, and Medical (ISM) bands is presented with a low specific absorption rate (SAR) for recommended safety limits. The proposed antenna with dimensions (27.6 mm × 28.2 mm × 0.32 mm) is considered suitable for medical applications. A fractal copper patch, Rogers 3010 biocompatible material, and full ground plane copper are used to get a miniature Antenna design. The research aims to design a compact, biocompatible, and low-SAR medical antenna for implantable and wearable applications. The dual-band antenna uses a fractal copper patch, simulating performance using CST Studio Suite, and optimizing SAR and gain for safety. Different configurations are analyzed for efficiency and medical applications. Low SAR levels were achieved for implantable and wearable designs equal to (0.487, and 0.141 W/kg) and (0.488, and 0.0392 W/kg) for dual bands 0.9 and 2.4 GHz respectively, and Negative gain for safety considerations. The design has been developed into three different forms: implantable, wearable, and capsule design for higher efficiency and more medical applications. The CST studio suite's simulations assessed the antenna's performance using design standards rules and efforts.
Integrated storage solar water heaters (ICS-SWH) are among the simplest and lowest-cost technologies for collecting and storing heat in a single vessel. In the present work, the impact of adding fins within the corrugated storage tank, between the absorber's lower surface and the tank bottom, was experimentally investigated. To increase the heat transfer between the water and the ICS-SWH absorber, an insulated reflective cover was installed on the top of the ICS-SWH to reflect sunlight towards the absorber during the day. At night, it was used as a cover to insulate the glass. The thermal performance of the ICS-SWH was studied under load and without load. The results showed that the highest recorded temperature of the stored water was 67 °C. In contrast, the system's thermal efficiency was 78.5% and 89%, respectively. This result is higher than previous works. A comparison between the present tank (12 cm depth) and the previous work (14 cm) regarding stored water temperature was conducted. The comparison showed that a decrease in the depth of the storage tank by 2 cm led to a 25% increase in the temperature difference.
The presence of residual pharmaceuticals in the aquatic system is a significant concern for ecological risk, requiring the advancement of sustainable remediation technology. This study investigates the adsorptive performance of local agricultural wastes—sawdust (SD) and olive stones (OS)—as ecofriendly biosorbents and substitutes for commercial activated carbon (AC) for the paracetamol (PC) removal from simulated aqueous solutions. The adsorbents were characterized using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). This revealed a highly porous morphology and various active functional groups. Batch adsorption experiments were evaluated to determine the influence of pH, adsorbent dosage, contact time, and temperature. The results demonstrated that the process of adsorption is highly pH-dependent, with maximum removal achieved at an optimal pH of 7. At equilibrium, the removal efficiencies reached up to 92.17% for SD and 89.25% for OS, compared to 94% for AC, using optimal dosages of 1.5 g, 1.25 g, and 1.25 g, respectively. Equilibrium data were well-fitting by the Langmuir model (R2 > 0.99), suggesting a monolayer adsorption mechanism with maximum capacities (qm) of 4.748, 4.531, and 5.621 mg/g for AC, SD, and OS, respectively. A kinetic study showed that the process followed a pseudo-second- order model (R2 > 0.99), indicating that chemisorption is the rate-limiting step. Thermodynamic analysis revealed positive enthalpy ΔH∘ values, confirming the endothermic nature of the process, while the spontaneity increased with temperature. Overall, this study highlights the potential of utilizing undervalued agricultural biomass as a cost-effective and efficient biosorbent for pharmaceutical wastewater treatment.
When studying dynamic systems, the Hamilton function and Lyapunov stability theory are integral to understanding and maintaining them. In the present paper, an eight-dimensional nonlinear system will be considered, and its potential for use in safety applications will be evaluated. In Lyapunov-based robust control theory, a desirable candidate Lyapunov function is revisited, enabling it to assess the stability of the current system even under external attacks. Hamiltonian equations were used to describe the system's energy dynamics, enabling the generation of secure encryption keys for cryptographic applications. The simulation results validate this model’s correctness and security, and it can be widely used in engineering and scientific applications.
Fly ash-reinforced polymer composites, known for their advantageous properties and environmental sustainability, have gained popularity. Heavy fuel oil fly ash, particularly yellow oil fly ash (YOFA), could serve as an enforcement component due to its mechanical properties. This study aims to introduce YOFA, a novel type of heavy fuel oil fly ash, as a potential reinforcement in high-density polyethylene (HDPE). The HDPE/YOFA composites were prepared using the injection molding technique after the extrusion blending process. The effect of YOFA content and particle size on mechanical and physical properties was investigated. The results revealed that the hardness slightly declined when the YOFA content and particle size were increased compared with the net HDPE. In contrast, the experimental density showed a positive correlation with the YOFA content and a negative correlation with the YOFA particle size. Additionally, the void content, water absorption, and thickness swelling increased gradually with YOFA content and particle size. A dry sliding wear test was conducted using the pin-on-disc method, and the following factors were chosen as independent variables: YOFA content, particle size of YOFA, applied load, and sliding time during the wear test. This method significantly affects two responses: wear loss and wear rate. Under steady-state conditions, wear resistance decreases with YOFA particle size and normal applied load. It also decreases with YOFA content up to 2 wt.%, followed by a gradual improvement up to 8 wt.%. On the other hand, with increasing sliding time, wear loss considerably increases while the wear rate significantly improves. The wear test conditions have been optimized using a full factorial design and Taguchi approach. A total of 320 experimental runs, each with one replication, have been conducted. The ANOVA analysis confirmed the robustness of the designed experiment, revealing that the factors contributing significantly, in order of importance, were sliding time, applied load, particle size of YOFA, and YOFA content. To minimize wear loss, the ideal settings were 8 wt.% YOFA content, the smallest particle size, an applied load of 5 N, and a sliding time of 5 minutes. Conversely, for optimizing the wear rate, the parameters were identical except for ......
This article considers matrix multiplication in the problem of finding the transitive closure of a binary relation with the transitivity property, as well as in the construction of the reachability and counter-reachability matrices in general graphs. An analysis of approaches to practical implementation for finding the transitive closure of a binary relation is presented: the Floyd-Warshall algorithm and raising the adjacency matrix to a power until it stabilises. The problem of processing large (thousands to millions of elements) graph diagrams of parallel algorithms on a processor (CPU), and the primary methods for optimising matrix calculations at both the software (algorithmic) and hardware levels, are considered. The main types of digital devices based on the parallel-pipeline data-processing principle are identified, and their advantages and disadvantages are outlined. A specialised computing device for fast multiplication of square binary matrices of size n × n is considered, whose distinctive feature is pipelining the data read operation from a specialised multiport memory. A mathematical model and a method for organising the parallel-pipeline memory of a specialised square binary matrix multiplication device are presented. An estimate of the matrix-processing time and hardware complexity for the developed and prototype devices is presented. Computational experiments showed that, despite a slightly higher hardware complexity (up to 8.8×) than the prototype device, the proposed device multiplies square binary matrices of size n ≤ 512 up to 52.4× faster. This represents a significant advantage when implemented in a semi-custom design using field-programmable gate arrays or a custom design based on application-specific integrated circuits. In this paper, we present a novel systolic device whose core innovation is a pipelined multiport memory architecture. By ensuring a continuous, high-bandwidth data flow to the processing elements, our contribution enables the systolic array to operate at its theoretical peak performance.
This study investigates the feasibility and performance of integrating shallow geothermal energy systems into the structural foundations of high-rise buildings with developed underground parts. A comprehensive experimental program was conducted, including field measurements of soil thermal properties and laboratory testing of heat exchange elements. Thermal response tests using a 100-meter geothermal probe demonstrated effective ground heat extraction, with the maximum temperature drop in the active zone reaching 6.8°C and an 83% temperature recovery within 3 months. Energy piles with diameters of 0.8 and 1.2 meters exhibited heat outputs of 4.6 and 7.2 kW, respectively, confirming that larger surface areas enhance thermal capacity. The ground source heat pump system operated with an average coefficient of performance of 4.21 during heating and 3.82 during cooling, achieving up to 98% of the projected thermal load. Numerical simulations confirmed the experimental findings, indicating an annual heating energy yield of approximately 2450 MWh. The results validate the integration of geothermal systems into foundation structures as an efficient and reliable approach to reducing energy consumption and enhancing sustainability in high-density urban development.
Particle size distribution is a critical determinant in the thermochemical conversion of lignocellulosic biomass. This research evaluates the impact of comminution on the calorific value and thermal kinetics of wheat straw, corn stalks, and hardwood chips (0.25–2.0 mm fractions). Experimental analysis demonstrates an inverse correlation between particle size and Higher Heating Value (HHV), with fine hardwood fractions achieving up to 20.03 MJ/kg—a relative increase of ~5.6%. Thermogravimetric analysis (TGA) identified a significant enhancement in thermal reactivity, evidenced by a 25–30°C shift in exothermic peaks toward lower temperatures. Additionally, the improved bulk density of finer fractions contributes to higher volumetric energy density, crucial for efficient storage and logistics. The study concludes that incorporating granulometric data into predictive models is essential for accuracy. Practically, these optimized properties facilitate more stable ignition and lower start-up loads in industrial grate and fluidized-bed boilers.
Paraffin wax used as Phase Change Material (PCM) in Thermal Energy Storage (TES) is one possible solution to store excess energy as heat and release it when power generation is insufficient. However, the PCM had a disadvantage: low thermal conductivity. So, the researchers developed some methods for increasing it since PCM is critical for a wide range of technologies. The present study describes experimentally the investigation of the discharging process of paraffin wax dispersed with different concentrations (0.5% and 1% ) of gamma-alumina oxide (39.2 nm), titanium oxide (47.2 nm), and alpha-aluminum oxide (186 nm ), in TES system for different heat transfer fluid (HTF) velocities, i.e., 1 m/s, 3 m/s, and 5 m/s. The influence of nanoparticles on the PCM thermo-physical properties. The effect of discharging cycles on the stability of nanoparticles was also investigated. The findings indicated that the improvement in thermal distributions of paraffin mixed with 0.5% Tio2 was better than paraffin mixed with 0.5% gamma and 0.5% alpha at 1 m/s HTF velocity, while at 1% concentration, the gamma alumina was more effective than others in modifying the paraffine wax thermal behavior since the time-saving of it was 19.54% compared to pure paraffine. Repeating the thermal cycle negatively affected the PCM thermal behavior of the paraffine wax test with 0.5% alpha and 0.5% gamma at a velocity of 3 m/s. However, a relative enhancement of nearly 7.69% in solidification time for 0.5% Tio2 was found. At the same velocity (3 m/s), the reduction in solidification time of gamma alumina and Tio2 was 12.3% and 7.69%, respectively, with a mass fraction of 1%. The largest improvement of thermal conductivity of nano-PCM compared to pure wax was 56.80% at 1% Tio2. Furthermore, the improvement in heat transfer rate at 0.5% Tio2 and 1% gamma was 15.67% and 68.64%, respectively. The “negative” results of repeated thermal cycles indicated that the stability of nano-PCM remains a big challenge and requires a multidisciplinary approach to determine the behavior of nanomaterials in a dispersing medium.
This paper investigates theoretically the performance of square solid reinforced concrete (RC) columns circularized with concrete segments and strengthened with Carbon Fiber Reinforced Polymer (CFRP) under bi-axial load eccentricities. The layer-by-layer method is presented to calculate the axial load and bi-axial bending moment. The theoretical results were first verified with experimental results of uni-axially loaded circularized and CFRP confined square solid RC columns. Also, they were verified with experimental results of bi-axially loaded square solid RC columns that exist in the literature. It was proved that the adopted theoretical models and layer-by-layer method were in good agreement with the experimental results. After validating the reliability of the theoretical model, this study theoretically examined the performance of CFRP confined circularized square solid RC columns under bi-axial load eccentricities subjected to the effect of the number of layers of CFRP and the effect of unconfined concrete strength. It was found that circularization increased the performance of CFRP confined square solid RC columns under bi-axial eccentricity for the axial load and bi-axial moments. However, the performance was less than that of the corresponding columns subjected to uni-axial load eccentricity. Also, the performance of CFRP confinement increased with the number of CFRP layers and the unconfined concrete strength. Furthermore, the CFRP confinement performance was less significant with higher bi-axial eccentricity.
Condition-Based Maintenance (CBM) is basically the backbone of keeping industrial IoT (IIoT) setups running smoothly and steadily. Less downtime, more reliability—what’s not to love? Yeah, we’ve got all these slick machine learning (ML) and deep learning (DL) models for predicting Remaining Useful Life (RUL), but honestly, actually getting this stuff working out in the real world? That’s a whole other headache. There’s the mess of scaling, getting different systems to talk to each other, and wrangling real-time data—just to name a few. In this paper, we roll out a hands-on IIoT framework for CBM that ties together data collection, edge/fog processing, some solid ML, and cloud magic. We put it to the test with a case study using the NASA C-MAPSS dataset, where an LSTM model does some seriously impressive RUL predictions. Bottom line: this framework nails real-time monitoring and predictive maintenance in IIoT setups. It should not be taken as a theoretical assumption; rather, it is practical in fact.
In recent years, diabetes mellitus has been increasing rapidly, and due to that, around 380 million people around the globe have been affected. This disease may cause many people to become blind and other health issues. Diabetic Macular Edema (DME) and Diabetic Retinopathy (DR) are medical conditions in humans caused by prolonged high blood sugar levels and have a direct impact on human eyesight, which can subsequently lead to blindness. In the early stages, DR usually progresses without any remarkable symptoms, making early detection difficult. If left untreated for a prolonged period, it can result in permanent vision loss. To facilitate proper diagnosis and timely treatment, computer-based systems today often rely on clinical images. In fact, a vital indicator of DR is the presence of microaneurysms (MA), which are critical for identifying the onset of the disease. In line with the emergence of the Internet of Things (IoT), a wide range of electronic devices can be usefully interconnected and are very capable of collecting, transmitting, and responding to data in real time. In the field of human healthcare, such IoT-powered systems possess sufficient capabilities to support remote diagnosis, particularly through the use of medical sensors in telemedicine scenarios. Nonetheless, such a shift can lead to critical privacy issues for a patient. The protection of critical health-related information becomes particularly critical. Hence, the major challenge here is implementing remote systems to support remote diagnosis while ensuring strict confidentiality to protect the patient's privacy. In the present research work, an IoT-based deep learning approach achieving 98.86% accuracy for Diabetic Macular Edema (DME) and 86.04% for Diabetic Retinopathy is proposed.
This study presents a comprehensive experimental and analytical investigation of energy flows in autonomous power supply systems that integrate renewable energy sources and advanced battery storage technologies. The research evaluated the performance and degradation behaviour of lead-acid AGM and lithium iron phosphate (LFP) batteries under varying discharge currents, discharge depths, and temperature conditions. The experiments showed that Lifepo₄ batteries exhibited superior stability, retaining up to 84% of their nominal capacity at 0 °C and maintaining higher efficiency than AGM batteries, which dropped to 65% under the same conditions. Dynamic load simulations revealed significant increases in internal resistance and temperature, particularly in AGM batteries during high-current discharge cycles. The developed mathematical model captured the combined effects of current, temperature, and discharge depth, predicting capacity degradation with an accuracy of ±3.5%. The integration of renewable generation and battery storage enabled daily energy generation of 8.2- 13.8 kWh, with an average conversion efficiency of 85%. These results highlight the advantages of Lifepo₄ batteries for autonomous systems that require reliable performance across diverse operating conditions. We propose and experimentally validate a lightweight correction term that jointly accounts for discharge current, temperature, and depth of discharge within a single degradation model, yielding a prediction error of ±3.5 % across chemistries and operating regimes. We report a rigorously controlled dataset from 12 batteries (6 AGM, 6 LiFePO₄) tested under dynamic duty cycles and sub-zero conditions using high-precision instrumentation. We present an integrated PV–wind–battery testbed with a measured daily energy balance of 8.2–13.8 kWh and a conversion efficiency of up to 91%, providing actionable guidance for sizing and dispatch in autonomous off-grid systems.
This study investigates the macroscopic kinetics of hydrogen and ammonia oxidation under high-pressure conditions to compare their ignition characteristics, activation energies, and sensitivity to mixture composition. Experiments were conducted in constant-volume and flow reactors over a pressure range of 3–10. MPa and a temperature range of 550–850 K. Hydrogen exhibited significantly shorter ignition delays, reaching as low as 0.14 seconds at 800 K and 10 MPa, compared with 0.35 seconds for ammonia under the same conditions. The activation energy for hydrogen oxidation averaged 171,000 J/mol, whereas that for ammonia was approximately 209,000 J/mol, indicating a higher ignition threshold. The peak pressure during ignition for hydrogen mixtures exceeded 11.5 MPa, whereas that for ammonia mixtures peaked at 8.9 MPa. Hydrogen also exhibited higher concentrations of reactive radicals (H and OH), which explains its more intense chain reaction. Empirical global reaction equations were developed for both fuels, with deviations of up to 10% relative to experimental values. These findings provide a reliable basis for the kinetic modeling of combustion systems operating at high pressures with hydrogen, ammonia, or their mixtures.
This study presents the results of an experimental investigation into integrating moisture recovery and secondary heat exchange systems into the steam reforming cycle of methanol for marine gas turbine power plants. A pilot installation based on a 3.9 MW Siemens SGT-100 unit was used to simulate realistic maritime operating conditions. The experiments demonstrated that optimizing the water-to-methanol molar ratio, along with effective condensation and heat exchange strategies, significantly improved fuel efficiency and reduced greenhouse gas emissions. At a molar ratio of 4.0 and reforming temperature of 660 K, the hydrogen content in the syngas reached 64.1%, with water recovery at 82% and thermal recovery up to 780 kW. These enhancements increased overall thermal efficiency to 44.4% and reduced specific fuel consumption by 15%. Emission measurements showed a 37.3% decrease in CO₂ compared to direct methanol combustion. The system also maintained combustion stability and temperature control under transient conditions, confirming the viability of the proposed approach for maritime applications. Unlike prior marine reforming studies that addressed moisture management and heat recovery in isolation, this work experimentally demonstrates, using a 3.9-MW-class gas turbine rig, a combined moisture-recovery and secondary heat-integration loop that delivers up to 82% water recovery and 780 kW of thermal recirculation with stable transients.
Amid the energy crisis and environmental concerns, this study evaluates the characteristics of soya oil-based biofuels. Soybeans are considered a promising renewable resource for biodiesel production. The work aims to characterize the spectral properties, fatty acid composition, and physicochemical parameters of biofuels obtained by transesterification of soybean oil. The study analyzed samples of the original soybean oil and soybean oil methyl ester (SOME) using spectroscopy, gas chromatography, and physicochemical methods. The analysis confirmed the success of the transesterification reaction and revealed changes in molecular structure. Key properties of the resulting biodiesel were a density of 880 kg m⁻³ and a kinematic viscosity of 4.5 mm² s⁻¹ (both at 20 °C), an acid number of 0.2 mg KOH g⁻¹, and a higher heating value of 39.0 MJ kg⁻¹. These values align with EN 14214 requirements for biodiesel. Gas chromatography showed that the main components were linoleic, oleic, palmitic, linolenic, and stearic acids. Measurements of physicochemical parameters revealed that the density and viscosity of SOME met the biodiesel standards. The calculated calorific value of SOME is slightly lower than that of diesel fuel. The results confirm the possibility of using SOME in diesel engines, but further research is needed to optimize the production process.
Manufacturing companies can achieve their goals of reducing production costs and improving productivity by using an efficient production schedule. In this paper, a formal verification procedure, supported by relevant arguments, has been used to verify the accuracy of flow-shop scheduling behaviour and to reduce total production time. We focus on the algorithm of sequence generation (Johnson’s algorithm), as well as model a three-machine scheduling procedure as a finite-state machine (FSM). The correctness requirements are expressed in Computational Tree Logic (CTL) and Linear-Time Logic (LTL) and are proved by the NuSMV model checker. The associated technique enables automated scheduling of property proofs during the design phase. In our verification, a counterexample demonstrates that Johnson’s rule can yield a suboptimal makespan in a three-machine setting, thereby revealing a limitation of its generalisation. The current findings indicate that using a model checker in CTL/LTL can be an effective method for establishing the logical validity of flow-shop scheduling and failure scenarios, thereby informing future heuristic development.
This study utilized activated carbon derived from Eichhornia crassipes (EC), a cheap plant that grows widely along riverbanks. The plant has fibrous roots and a taproot. It has thin, round, erect, wavy, smooth, and shiny leaves. The flowers are beautiful violet with six petals, and the fruits contain about 300 seeds each. The seeds can remain active for about 20 years. It negative impacts contain environment because it consumes large amounts of water, as a biosorbent in batch adsorption experiments for the removal of paracetamol from synthetic aqueous solutions. It was tested as an adsorbent for heavy metals, dyes, and other pollutants. The biosorbent was characterized using XRD, FTIR, BET, and SEM techniques. Several factors affecting the adsorption were considered in the study, including the adsorbent dosage (AC/NOH) (0.2-2.5 g/L), pH value (3-10), initial paracetamol concentration (10-80 mg/L), and contact duration (15-240 min). The highest removal efficiency of 75% for paracetamol was observed at a contact time of 180 min, pH value of 3, adsorbent dosage of 2 g, and initial paracetamol concentration of 10 mg/L at 25 °C, and the maximum adsorption capacity of 17.42 mg/g was observed with an R² value of 0.9851 on the Langmuir curve, while the pseudo-second order kinetic model provided a better fit with R² = 0.9987. The study utilizes Eichhornia Crassipes as a low-cost and environmentally friendly bio sorbent, which has not been explored in previous research on removing pharmaceutical contaminants. This result suggests its potential application in removing other pharmaceutical contaminants from aqueous solutions.
In the context of fossil fuel depletion and growing environmental concerns, the study of alternative energy sources, especially biofuels, is particularly significant. Given the global challenges, this work examines the effects of a blended biofuel produced by adding linseed oil to diesel on the operational and environmental performance of a diesel engine. During the experiments, the physical and chemical properties of the initial components and their mixtures were determined, followed by engine testing on the engine stand in the external speed characteristic mode and in a 13-stage test cycle. Evaluation of the data obtained showed that the addition of linseed oil reduced exhaust gas smoke opacity and nitrogen oxide emissions, while causing a slight increase in fuel consumption and in carbon monoxide and unburned hydrocarbon emissions. To balance these effects, optimisation of the blended biofuel composition using the convolution method determined the optimal component ratio, achieving the best balance between fuel efficiency and reduced harmful emissions. The results of the study demonstrate that the use of linseed oil as a component of blended biofuel for diesel engines has the potential to mitigate the environmental impacts of transport.
The rising burden of chronic diseases, particularly diabetes, necessitates diagnostic frameworks that can navigate the inherent ambiguity of clinical data. Conventional predictive models often struggle with the stochastic uncertainty stemming from subjective patient narratives and laboratory noise, as well as the 'black-box' lack of interpretability. To transcend these limitations, this research introduces a novel Fuzzy Weighted Bayes Association Rule Mining (FWBARM) framework. This approach integrates fuzzy logic to handle data vagueness with a weighted mechanism that implicitly learns feature importance, thereby generating robust, transparent, and clinically interpretable decision rules. The proposed system, evaluated as the 'Reliable Diabetes Prediction Model,' demonstrated superior diagnostic efficacy, achieving 96.8% accuracy, 98.6% precision, and 97.5% recall. By reconciling high predictive performance with rule transparency, this work offers a scalable solution for personalised medicine and reliable Clinical Decision Support Systems.
