Journal of Techniques

The rapid evolution of technology and the digital age has led to an increase in the spread of fake news, severely undermining the accuracy of information. This study aims to improve fake news detection methods in distinct domains through in-depth dataset analysis using a Convolutional Neural Network (CNN), the research-trained models using an optimized CNN model on publicly available datasets. The findings show that machine learning models trained on domain-specific datasets can accurately identify the nuances of fake news unique to those domains. Compared to models trained on broader datasets, the results demonstrate that models trained on domain-specific data achieved higher accuracy, precision, recall, and F1-score, increasing from 68% to 99% across all metrics when compared with a baseline CNN model. However, while domain-specific models perform exceptionally well in their respective contexts, models trained on a diverse range of datasets exhibit greater generalizability across domains. These findings suggest that dynamic and robust fake news detection systems should integrate both heterogeneous datasets and domain-specific features to enhance effectiveness.

In this study, an artificial neural network (ANN) using backpropagation was utilized to categorize bone images into either
healthy or osteoporotic categories based on various statistical operations. An input matrix was constructed containing the
six statistical features of 125 samples, representing X-ray images of knee joints, with 25 healthy and 100 osteoporotic
samples. Of these, 70% were used for training, 15% for validation, and 15% for network testing. The classification
efficiency of the neural network for the 125 samples was 97%. The research included analysis of arithmetic mean, standard
deviation, variance, energy, homogeneity, and entropy values for the healthy bone samples. The backpropagation neural
network (BNN) was trained with six inputs (representing the six statistical features), 80 hidden layers, and five outputs
(two for healthy and three for osteoporotic conditions). A comparison of K-Nearest Neighbors (KNN), Logistic
Regression, and BNN techniques applied to 2,350 images revealed that BNNs achieved the highest accuracy. This network
has the potential to assist healthcare providers in both detecting the early stages of osteoporosis and developing appropriate
treatment plans

Measuring air quality in some regions under non-ideal circumstances is still a challenge. In many third-world countries,
acquiring expensive air quality testing equipment is beyond capacity. Monitoring non-healthy environments in such
regions is vital, so we implemented a low-cost IoT indoor air quality tester. The system comprises attached field instrument
sensors and a WiFi-to-cloud monitoring unit. The sensing unit includes Arduino UNO attached to MQ-7, CCS811, and
MQ-137 sensors to measure carbon monoxide (CO), carbon dioxide (CO2), and the total volatile organic compounds
(TVOCs), and NH3, respectively. The sensors also include the DHT11 to measure temperature (T) and relative humidity
(RH). To collect data from distributed field sensing devices and monitor it on the ThingSpeak website, an NRF24l01+
wireless model is connected to each data logger and the central data collector ESP32. The proposed low-cost system was
operated in one of the higher education buildings of the Middle Technical University, measuring the concentrations of the
most common air quality factors (CO, CO2, NH3, TVOC, RH, and T).

he performance development of photovoltaic systems has become crucial with the increasing demand for renewable
energy sources. Cooling systems play a crucial role in maintaining solar cell temperatures within optimal ranges, thereby
improving their overall efficiency. This study presents an experimental investigation into the effectiveness of fan-based
cooling systems aimed at enhancing the efficiency of solar cells. In this experiment, a cooling system was tested under
limited environmental conditions to assess its impact on solar cell performance. It was concluded from the results that
reducing the temperature using the air-cooling system did not significantly impact the efficiency of the solar cell when the
temperature was below 50 degrees Celsius; however, the proposed cooling system was effective in reducing the
temperature of the solar cell by 18 °C relative to the reference solar cell.

In this paper, the effect of stitching the core to the skins of composite sandwich panels made of a foam core covered by
two composite skins is studied. The main aim of the research is to investigate the effect of sewing the core of the sandwich
panel using polyester yarn on the mechanical properties of the sandwich panel. Therefore, by connecting two layers of 5
mm polystyrene foam, a core with a thickness of 10 mm is constructed, and fiberglass fabric is used for the manufacturing
of the skins. Four 150^mm×32^mm×12^mm samples of sandwich panels with different dimensions are sewn and tested
under the three-point bending test. The arrangements of the sewing are named N, M, W, and V according to the form of
binding. Based on the results, sewing of the core increases the shear stiffness of the core, but it can cause damage to the
core and reduce the strength of the sandwich panel. Among different sewing arrangements, N sewing showed the highest
strength and maximum core stiffness.

This paper aims to enhance the performance of thermoelectric generators (TEGs) in converting waste heat into electrical
energy by employing nanofluids. The research addresses the limitations of traditional working fluids like water, which
have low thermal conductivity, and explores the use of nanofluids in a thermosiphon system integrated with a TEG. The
CFD tool used in this work is ANSYS Fluent R23 for solving the problem, and the turbulent flow is modeled using the
SST k-ω model. The second-order upwind discretization scheme is used for solving the energy, continuity, and momentum
equations. The study starts with analyzing heat sink geometry, focusing on fin height and spacing. Simulations demonstrate
that reducing fin spacing from 5.50 mm to 2.50 mm, despite 70% reduction in the fins' number, optimizes performance by
increasing the wetted surface area, thereby lowering heat sink temperatures. Similarly, increasing fin height to an optimal
range of 5–25 mm improves heat dissipation, even though it reduces the local convection heat transfer coefficient,
highlighting the importance of surface area in passive cooling systems. The study also explores the cooling potential of
Al₂O₃ and CuO water nanofluids at a 4% concentration. Al₂O₃-water reduces the heat sink temperature by 17%, while
CuO-water achieves a 13% reduction. Further increases in nanoparticle concentration enhance the cooling performance,
with an additional temperature drop of approximately 8°C observed. Under extreme conditions with a heat flux of 75,000
W/m², the optimized heat sink design maintained a cold-side temperature of 39 °C when cooled by the Nano fluid. The
simulations visually confirm the system’s effectiveness, demonstrating how geometric optimization and advanced
materials work together to enhance TEG performance. This investigation offers valuable insights for improving energy
conversion technologies, particularly in waste heat recovery

This study investigates the thermal and mechanical behavior of friction stir spot welding (FSSW) for joining dissimilar
thermoplastics, Polyvinyl Chloride (PVC) and Polyethylene (PE). Given the inherent differences in polarity, crystallinity,
melting temperatures, and surface energies, bonding between PVC and PE presents significant challenges. The
experimental work involved applying FSSW under varying process parameters (rotational speed and dwell time), followed
by tensile shear testing and infrared-based thermal analysis. The results revealed that the rotational speed had a dominant
effect on peak interfacial temperature, reaching up to 137°C, which exceeds the crystalline melting range of both materials
(PVC: 75–105°C; PE: 130–135°C), facilitating localized melting, which promotes effective molecular inter diffusion at
the weld interface. Taguchi-based DOE analysis confirmed that optimal parameters (1700 RPM and 2 min dwell) produced
the highest joint strength and temperature. Tensile shear strength results also indicated that appropriate heat input and tool
interaction facilitated robust bonding without external adhesives or surface treatments. This research offers valuable
insights into optimizing polymer-to-polymer welding conditions, paving the way for scalable, environmentally friendly
joining methods suitable for packaging, automotive, and biomedical applications.

Plastic products are utilized as packaging materials with numerous features including durability, strength, and low density
in comparison to paper and glass. However, the accumulation of plastic would be a considerable pollution issue negatively
affecting the ecosystem. This is because of its high resistance to degradation. Several researchers endeavored to manage
the accumulation of plastic by devising processes such as incineration, recycling, or reuse. However, the disposal of highly
toxic compounds, including hydrocyanic and hydrochloric acids, during incineration is a main associated issue. Realizing
these points, manufacturing bioplastics instead of using plastic oil appears to be a competent option. Specifically,
polyhydroxyalkanoate (PHA) is a well-known type of bioplastic that can be manufactured via fermentation using bacteria.
In this review, the main basics of bioplastics are presented outlining the aspects of biodegradation as a sustainable industrial
process compared to plastic. Specifically, this review intends to shape the synthesis of PHA from waste and wastewater
sources. This includes the review of PHA structures, properties, industrial methods, and an intensive review of the feastfamine
mechanism. Lastly, a critical evaluation of this industry, along with challenges and suggestions for improvements,
is addressed.

The widespread adoption of 5G technology has exacerbated electromagnetic pollution due to the extensive deployment of
high-frequency, energy-intensive electronic devices. This has, in turn, heightened the demand for the development of highperformance
electromagnetic shielding materials. MXenes, a novel class of two-dimensional materials, exhibit exceptional
electrical conductivity (>10,000 S/m) and tunable surface chemistry, making them ideal candidates for shielding
applications. This article systematically reviews MXene synthesis methods, analyzes their advantages and limitations, and
elucidates their electromagnetic shielding mechanisms dominated by interfacial polarization and conductive network
formation. We critically evaluate global advancements in MXene-based architectures, including porous foams and hybrid
composites with graphene/CNTs. Finally, we identify key challenges in balancing electrical conductivity with dispersion
stability during scalable manufacturing and propose strategies to improve environmental durability through surface
passivation and atomic-layer-deposited encapsulation.

The manufacturing of aerospace, automotive, and defense systems depends on aluminum and magnesium alloys because
they provide low density, a high strength-to-weight ratio, and easy machining capabilities, which result in better fuel
efficiency, increased payload capacity, and enhanced maneuverability in aerospace and ground-based military platforms.
Despite these advantages, both alloys suffer from poor corrosion and wear resistance, which restricts their use in harsh
environments and poses risks to the performance and reliability of military systems. Surface modification technologies
provide immediate solutions to these problems through superhydrophobic coatings emerging as a promising approach.
The coatings deliver water repellency while simultaneously improving self-cleaning capabilities, antifouling properties,
drag reduction, anti-icing features, corrosion protection, and wear resistance. Recent studies have made substantial
advancements in designing and manufacturing superhydrophobic surfaces. The current methods for preparing
superhydrophobic surfaces require complicated procedures, expensive equipment, and strict processing conditions, which
create challenges for industrial-scale implementation. This review presents two representative studies for aluminum and
two for magnesium alloys, with emphasis on preparation methods, surface morphology, and performance outcomes. The
aim is to clarify the strengths and limitations of these coatings and support their development in practical applications that
require durability, corrosion resistance, and multifunctionality. It compares their performance across different substrates
while considering their effectiveness for corrosion resistance, anti-bacterial, anti-icing, and friction resistance with selfcleaning
performance.