International Journal of Engineering
Volume:37 Issue: 12, Dec 2024

This paper presents a novel design and analysis of a Low-k Source side Asymmetrical Spacer Halo doped Nanowire TFET. The utilization of high-k hafnium oxide spacer materials in TFET enhance electrostatic control and minimize short-channel effects in nanoscale devices. However, the performance of dynamic circuits suffers with higher fringe capacitance brought on by high-k spacers. Our method focuses on reducing gate capacitance by optimistic utilization of high-k spacer material. The proposed device is constructed in SILVACO TCAD software and results states that the use of Low-k material as silicon dioxide at the Source-side spacer in halo-doped nanowire TFET design results in significantly reduced gate-capacitance and intrinsic-delay. For this proposed TFET device, the circuit performance of advanced nanowire structure can improve drain current characteristics and analog characteristice.  The proposed device exhibits better performance as compared to other spacer engineering devices. As a consequence, the suggested device appears as a strong suitable device for low power digital applications.

The paper reports the production of liquid fuels from waste plastics' thermal and catalytic pyrolysis, including polypropylene (PP), high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polystyrene (PS). For this purpose, three different types of zeolites (4A, ZSM-5, and 13X) and Cu/4A, Cu/ZSM-5, and Cu/13X were used in catalytic pyrolysis. The acidity and textural properties of the catalysts are the main parameters in the decomposition of polymers. The order of acidity of the catalysts was as follows: Cu/13X> Cu/4A> Cu/ZSM-5. The main product of thermal pyrolysis was liquid, mostly linear heavy hydrocarbons, whereas catalytic pyrolysis by copper/zeolite catalysts produced liquid products containing more branched hydrocarbons at lower temperatures. The liquid products carried out analysis through the use of FTIR and GC/MS techniques. The results indicated the presence of paraffin, olefins, and aromatic hydrocarbons in the liquid products. It was also found that light liquid hydrocarbons and gaseous products were produced over Cu/13X (higher acidity, large pore size, and high surface area). For Cu/4A, Cu/13X, and Cu/ZSM-5 catalysts, the main liquid products of catalytic pyrolysis were in the range of diesel, gasoline, and kerosene, respectively.

Concrete filled steel tube (CFST) beams have been limitedly explored, hindering the construction of CFST frame structures. This study numerically explored the distributions of stress and strain in CFST beams under bending. A finite element model of a tested square CFST beam was developed in ABAQUS software. The CFST beam model was verified with the experimental and numerical results performed by other researchers. The verified model was then used to investigate the distributions of stress and strain in concrete and steel tubes of CFST beams. The finite element results showed that only a small portion of concrete on the top of the section participated in resisting the compression force, whereas a large portion of concrete on the bottom was cracked and did not participate in resisting the tension force. In contrast, the strain distribution in steel showed that the steel resisted both compression and tension forces on the section. Therefore, steel tubes govern the mechanical properties and behaviour of CFST beams. It can be noted that the infill concrete indirectly affected the mechanical properties and behaviour of CFST beams by providing conditions for steel tubes to work effectively. The highest stress at midspan showed that failure can occur at the midspan of the beam, although the region between the two loads has a constant bending moment and zero shear force. The outcomes provide some technical information on stress and strain distributions for structural engineers when designing CFST beams.

In the realm of computer vision applied to cricket analysis, classifying batting shots poses a formidable challenge, demanding nuanced comprehension and categorization. The classification of cricket shots is crucial as it empowers the players to strategically assess, adapt, and execute their game plans effectively, shaping the outcome of matches. This article introduces the Cricket Batting Shots Image dataset (CBSId), a new benchmark dataset comprising 2160 meticulously annotated cricket shot images across seven distinct categories. The core objective of this research is to develop a robust system capable of effectively classifying cricket batting shots from images. To address this, we present a fine-tuned Vision Transformer-based model specifically adapted for cricket shot classification, termed Cricket Batting Shot Vision Transformer (Shot-ViT). Our proposed methodology demonstrates exceptional performance, achieving 92.58% validation accuracy on the CBSId. Shot-ViT notably outperforms established models such as VGG19, ResNet50, I-AlexNet, and ViT_B32 in cricket shot classification accuracy, showcasing the remarkable capabilities of Vision transformers in surpassing existing deep learning architectures for complex visual tasks. Vision transformers have the capacity to capture global context and long-range dependencies within images through self-attention mechanisms, enabling effective feature extraction and representation, which traditional models may struggle to achieve. The accurate classification of cricket batting shots holds profound implications for cricket coaching, player development, and match analysis. It has the potential to revolutionize training methodologies, providing players and coaches with precise insights into batting techniques and strategies and thereby contributing to the overall advancement of the sport.

In industrial settings, monitoring both health and environmental variables is crucial for labor safety.  Ensuring complete safety protocols is vital in addressing the issue of dynamic work environment monitoring. Worker safety is critical in minimizing accidents, saving lives, and adhering to medical standards. By implementing security measures and offering a healthy work environment for their employees, employers can lower hazards. While substantial advancements have been made in smart wearables geared toward worker safety, a gap remains in offering holistic solutions for fostering healthier workplaces. Over the past years, there have been significant strides in devising devices aimed at supporting this cause. The aim of combining wearables, smart sensors, cloud technologies, and apps is to improve methods of handling workers and their environments, thereby elevating their overall quality of life. However, many existing devices are either cumbersome or too expensive for widespread use. This article provides an exhaustive comparative analysis of current wearable and handheld devices tailored for industrial worker safety. Subsequently emphasizes the distinctive features of these devices and undertakes a meticulous evaluation based on criteria such as power efficiency, weight, affordability, and user experience. The findings of this study emphasize the importance of incorporating smart wearable technology into existing safety standards to promote a safer and more efficient workplace. The work's novelty lies in its comprehensive analysis of smart wearable sensor devices specifically designed for worker safety monitoring, providing insights beyond previous attempts in the literature by addressing the practical challenges and implications associated with employing such technology in real-world environments.

This paper presents a comparative analysis of the mechanical and thermal properties of two agro-waste natural fibers, pigeon pea (PP) and santa maria feverfew (SMF) based lime concretes. The specimens were tested using destructive and nondestructive testing methods. The mechanical properties were measured using a compression testing machine (C.T.M.). A non-destructive test was performed using an ultrasonic pulse velocity meter. A self-fabricated box was used to evaluate the thermal conductivities of the concrete blocks under controlled conditions with agro-waste. The results showed that the concrete blocks made with 3.45% SMF by weight has compressive strength 33.3% higher than that made with 3.45% PP fiber. The ultrasonic wave velocity was the highest in the control blocks made of plain lime concrete and the lowest in the PP fiber-based lime concrete blocks. Porosity and thermal insulation properties have been reported to be excellent in PP fiber-based concrete blocks compared with SMF fiber-based concrete blocks. Such natural fiber-based concretes, which are light weight and have better thermal resistance, can be used for the construction of non-load bearing partition walls, insulation of walls and slabs, and preparation of lean concrete.

Dunaliela salina has advantages over other microalgae species, including rapid growth, high salt concentration, simple growth requirements, and fast production. However, the harvesting process of D. Salina requires a particular harvesting method due to its tiny size. This research aims to develop an effective D. Salina harvesting method using spiral electrocoagulation (SEC). Optimization of operating parameters including initial D. Salina concentration, voltage, reactor slope, and electrocoagulation time is carried out using response surface methodology (RSM) to maximize the D. Salina harvesting process analysis of wastewater quality produced shortly after the harvesting process. The results showed that the optimum operation for D. Salina harvest until a harvesting efficiency of 85.77% was achieved required 25 V; 4.17 min as a time of electrocoagulation; 68,39 degrees as the angle of the reactor; and 25% initial concentration of D salina. The variable voltage, time, and initial concentration of D. Salina significantly affect harvesting efficiency, while the reactor angle has an insignificant impact. Based on the Central Composite Design (CCD) design, the minimum COD concentration is 3.36 mg/L when SEC operations use a voltage of 25V, time of electrocoagulation 5 min, angle of reactor 75-degree, and concentration of D. Salina 70% of the initial concentration. The concentration of nutrients (nitrate, phosphate, ammonia) produced after the harvesting process varies depending on variations in voltage, time, angle of reactor, and initial concentration of D. salina.

Twisted and coiled polymer actuators (TCPAs), typically made from fishing lines or sewing threads, became increasingly popular across various applications due to their unique capacity to contract or twist when heated, mimicking the behavior of artificial muscles. Their performance can be highly variable due to some manufacturing factors like fishing lines diameter. The study investigates TCPAs' mechanical behavior, considering changes in the diameters of fishing lines, operating temperatures, and applied tensile forces. For this purpose, this study addresses these challenges by experimenting with TCPAs of different diameters (0.5, 0.7, and 0.8 mm), using a hot water circulation system to control actuator temperature, enabling rapid and consistent actuation. Testing TCPAs under various thermal conditions reveals that both displacement and tensile strokes increase with temperature and decrease with tensile force. Also, the TCPA with a 0.7 mm diameter which has the smallest coil spring index and the smallest coil bias angle achieved the best performance, with a maximum displacement of 17 mm and a tensile stroke of 7.65% at 80°C and 1.422 N. These findings provide a clear pathway for creating reliable, high-performing TCPAs, making them suitable for applications requiring precise and consistent actuation.

This paper investigates the effect of the number of input exploration control layers on the prediction ability of the ensuing mineral potential models. For this purpose, several weighted control layers were first produced through different exploration methods. Then, the layers were combined in two individual procedures by using two and three input control layers. The prediction rates were evaluated and compared with the location of known mineral occurrences. In addition, this paper reviews improved multi-class data-driven index overlay for Mineral potential mapping (MPM) and identifying the promising areas, and then, uses the method to identify the exploration targets for lead, zinc, and copper skarn mineralization in the Mahneshan area in Zanjan province, west of Iran. The exploration control layers, including the geological, fault density, and geochemical maps, were produced and integrated for this purpose. A Critical limitation of this method, which is weighting each class of geochemical and fault density maps without expert judgment, was resolved in this paper. After producing the weighted evidential maps of each layer, in order to evaluate the relative importance of different exploration methods, weights were attributed to the layers to evaluate their relative importance in terms of their ability to predict undiscovered Pb-Zn-Cu skarn mineralization. Finally, the three layers were combined using the multi-class index overlay method in which the data were categorized into different classes to determine exploration targets. The areas with high mineralization potential produced in the final mineral potential model properly predict the existing mineral occurrences in the region studied. Also, new areas were identified that could be explored in more detail.

The dynamic and complex nature of the construction industry leads to increased project uncertainty, exposing construction projects to various risks and hazards. Poor risk management can hinder project objectives. Therefore, implementing effective risk management strategies can enhance project quality, safety, and ensure on-time, under-budget completion. This is achievable when the construction industry adopts cutting-edge methods and tools. Building information modeling (BIM) has been widely used to facilitate project risk management due to rapid technological advancements. Given the significance of risk management in construction projects, this study has proposed a novel BIM-based expert system for addressing project risk responses. Data were collected through a questionnaire, and hidden patterns were discovered using SPSS Modeler software (Clementine) through association rule mining. The Apriori algorithm extracted fifty-three top rules from the dataset based on rule evaluation indexes. Subsequently, an expert system was developed using the extracted rules to address project risks. Finally, the expert system was evaluated by five unbiased experts through a questionnaire. This study can serve as a foundation for addressing project risks using BIM and data mining. Subsequent research can apply this method to other construction projects and compare the results with the present study.

A surface-mounted permanent magnet synchronous motor (SPMSM) is an electric machine applied widely in the fields of electric vehicles (EVs) and electrical drives due to good characteristics such as  high power density, lower mass, high efficiency and lower torque of inertia. For the SPMSM, there are two types of  SPMSM, i.e., the inner rotor SPMSM and outer rotor SPMSM. In order to analyze, compute and compare advantages and disadvantages of these two motor types,  this research proposes an analytical model in detail to calculate and design the electromagnetic parameters and thermal characteristics of the SPMSM with both inner and outer rotor configurations. Subsequently, a finite element method is developed to validate output parameters obtained from the analytical model. Simulation results also indicate the performance of both types of motors. However, the inner rotor SPMSM has advantages in terms of high speed and low temperature, while the outer rotor SPMSM has higher torque and stability but it operates at a higher temperature.

Photosynthetic microorganisms such as Chlorella vulgaris can be used for carbon dioxide (CO2) biofixation to reduce greenhouse gas emissions and combat global warming. In this study, the potential of using wastewater from a tuna processing factory as a cultivation medium for C. vulgaris was investigated with the aim of reducing cultivation costs and water consumption while treating the wastewater. Different CO2 concentrations (5%, 10% and 15% in N2) and wastewater dilutions (1:4, 2:4, 3:4 and 4:4) were tested at ambient temperature, controlled pH and cyclic illumination (12h light-12h dark) at 6000 lux, using both batch and semi-continuous cultures. In the batch system, 100% CO2 biofixation was achieved at an effluent dilution of 1:4 and 5% CO2, with biomass concentration doubling after one week. High removal rates of COD (94%), total phosphorus (99%), nitrate (98%) and ammonium (99%) were observed. The semi-continuous photobioreactor showed a CO2 stabilization of more than 50% (at 5% CO2) and a 40% biomass increase, with over 50% nitrate removal in a 1:4 dilution effluent. These promising results demonstrate the potential of the system for simultaneous CO2 biofixation and wastewater treatment and underline the effectiveness of this circular economy approach to reduce greenhouse gas emissions and treat industrial wastewater.

Community question-answering (CQA) systems are helpful for knowledge sharing. However, they can become difficult to manage as the number of questions and answers increases. Effective tag recommendation facilitates the discovery of relevant material, yet prevailing methods typically depend on language-specific resources or necessitate sophisticated Natural Language Processing (NLP) tools, rendering them unsuitable for less-resourced languages. This paper introduces a novel profile-based tag recommendation strategy that transcends language and structural barriers.  The approach leverages raw text data without the need for complex text mining tools. By constructing distinct profiles for each tag from key terms in associated questions, the method enables a nuanced content association. An adaptation of the Term Frequency-Inverse Document Frequency (TF-IDF) metric is proposed to calculate similarity and recommend tags aligned with these profiles.  The efficacy of this approach is validated across datasets in both English and Persian, showcasing comparable to or superior recall rates against baseline models and contemporary advanced systems.  This methodology is straightforward to implement, offering a valuable tool for enhancing content accessibility in CQA platforms, particularly for low-resource languages.

Magnetic Resonance (MR) images have many applications in medical science and play an essential role in the diagnosis and treatment of diseases. However, unavoidable artifacts and noise reduce the resolution of these images. In this paper,  we propose a hybrid noise reduction framework using the wavelet transform, the exponential function thresholding, and the Wiener filter. In particular, we first employ the Genetic algorithm to optimize the exponential function coefficient. Furthermore, we adopt the Winner filter to increase the robustness of the proposed scheme against different types of noise, such as Gaussion and Rician noise. Some common performance measures, such as Mean Square Error (MSE) and Peak Signal-to-Noise-Ratio (PSNR), have been used to evaluate the performance of the proposed method compared to existing counterparts. The results show that the performance of the proposed hybrid method is better than the existing methods, such as universal thresholding and plain exponential function thresholding. For example, for human brain images with Gaussian noise, the obtained PSNR using the proposed method is 53.3947, while the PSNR value is 51.7532 using the universal threshold. Moreover, the results indicate that by using the Winner filter, we can effectively control the robustness against noise and image blurring.