Some spreadable cheese packaging uses flexible plastic without additional features and easily damaged boxes. This packaging causes several problems, such as difficulty in re-storage, loss of product information due to damaged box packaging, wasteful use of packaging materials, and making the product less hygienic. The purpose of this study is to develop packaging that focuses on consumer emotions. The packaging was developed using the Kansei Engineering method, supported by Principal Component Analysis (PCA) as a determinant of the design concept, and Quantification Theory Type 1 (QTT1) to identify design elements. This study produced 46 packaging samples and 40 Kansei words. The resulting design concept has the highest principal component value, namely safety-practical, with an R-Square value of 0.7855. The design elements for the safe concept to being developed consist of a unique box shape (X1), PP plastic material (X2), medium size (X3), modern design style (X4), twist cap (X5), printing technique using stickers (X6), and upward lid orientation (X7). The results of this study successfully realized safe packaging through the selection of rigid plastic (PP) material with a twist cap. These results can also indicate consumer needs for functional packaging that solves product problems.
This study employs the fuzzy analytic hierarchy process (FAHP) to identify the critical factors and their degree of relevance to the vehicle emission process. Its innovation lies in the potential to blend ambiguity and uncertainty with the established AHP. FAHP transforms information into a defuzzification state through signal-to-noise ratios, normalization, and pairwise comparison. The principal parameters considered are revenue, sold packing units, CAGR, packing materials, consumption, and CO2 emissions (A, B, C, D, E, and F, respectively). From the normalized defuzzified weight result, consumption (Parameter E) is the best (normalized weight, 0.8685917), while CO2 emissions (Parameter F) was the worst (normalized weight, 0.050454358). Considering the weights and ranks of the data, run order 4 ranked first with values of 0.2414, 0.0903, 0.1864, 0.1448, 0.2867, 0.0412, and 0.99078 for parameters A, B, C, D, E, and F, respectively. This work is useful for logistics managers who wish to control vehicle emissions. Manufacturers could reduce vehicle emissions by improving the combustion process designs through the data for the ranking of these prioritized parameters.
This paper presents the design and evaluation of a formation control strategy for three quadcopter UAVs, based on a PID controller in a leader-follower structure, under the influence of external disturbances. Each UAV employs a six-degree-of-freedom dynamic model and utilises a cascade PID control architecture, in which the inner control loop stabilises the attitude. In contrast, the outer control loop regulates position and maintains the formation. The PID parameters are tuned using the Ziegler-Nichols method to ensure simple implementation and low computational cost. The performance of the control system is evaluated through simulations in the MATLAB environment for two typical formation shapes (linear and triangular), both in noise-free conditions and under external disturbances. Simulation results show that in noise-free conditions, the system achieves a rise time of about 2-3 seconds, a settling time of 10-15 seconds for the linear formation, and 20-25 seconds for the triangular formation, with overshoot less than 15% on the position axes and a steady-state error of approximately 0.05 meters. When subjected to external disturbances, the UAVs still maintain the desired formation with a steady-state error of less than 0.1-0.5 meters, demonstrating an acceptable level of disturbance resistance. These results prove that, despite its simple structure, the proposed Leader-Follower PID control strategy can still ensure stable formation and effective trajectory tracking for small-scale UAV systems, while also being suitable for practical applications that require low cost and high deployability.
The widespread presence of bots on social media platforms, such as X (formerly Twitter), poses a significant threat to the integrity of online information by facilitating the dissemination of misinformation and manipulating public discourse. This study proposes a robust deep learning-based framework, DeepBot, to detect bot participation in trending hashtags and discussions on X. The approach uses a dataset sourced from Kaggle, comprising user profile metadata, including follower count, tweet frequency, account verification status, and engagement metrics. The data were subjected to comprehensive preprocessing, including noise removal, part-of-speech (POS) tagging, and word embedding using the pre-trained GloVe model. RoBERTa is employed for feature extraction to capture deep contextual semantics, followed by classification through a deep neural network (DNN) to effectively distinguish between human users and bots. The proposed model is evaluated against established baselines using standard performance metrics. Experimental results demonstrate that DeepBot achieves superior performance with an accuracy of 92.82%, precision of 91.24%, and recall of 91.78%, confirming its effectiveness in enhancing the reliability of bot detection in social media trend analysis.
The sustainability of global palm oil production faces significant threats from a range of diseases and infections affecting palm oil crops. This study presents a comprehensive bibliometric analysis to map the scientific landscape and evolution of research related to palm oil diseases over the past decade. Using data from the Scopus database, 366 relevant documents were analyzed using R and VOSviewer to uncover trends in publication volume, citation impact, prolific authors, country contributions, and keyword co-occurrence networks. The findings reveal a strong research concentration on G. boninense, highlighting its critical role as a persistent pathogen in oil palm plantations. Emerging themes, such as bioactive compound research, comparative biology, and plant-microbiome interactions, reflect the growing interdisciplinary interest in sustainable disease management. This study identified key research gaps and proposed future directions, including molecular breeding, biocontrol strategies, and valorization of bioactive compounds from oil palm by-products. The results offer strategic insights to guide future scientific inquiry and inform sustainable solutions for global palm oil production.
Wheelbarrows are essential multipurpose and dual-function materials handling equipment for many industries and homes. They find exclusive use in the movement of finished goods and raw materials where no other form of transportation works. However, the traditional wheelbarrows require enormous human effort to accomplish the task of conveying a reasonable amount of load from one place to another. During the process, shock loads from road irregularities are transmitted directly to the Barrow pusher. Therefore, this study investigated a retrofitted helical coil, compression spring, wheelbarrow design for effective load transportation. A wheelbarrow with a trapezoidal bucket capacity of 50 l was fabricated and mounted on a hollow pipe galvanized steel chassis frame. The bucket was supported on the front end of the frame using two helical coil compression springs to cushion the shock loads transmitted to the Barrow pusher. For a 150-kg payload, three standard bags of cement equivalent, and a deadweight of 22kg, the spring retrofit design wheelbarrow effectively reduces the galloping shocks through bumps and other path surface irregularities better than conventional non-spring wheelbarrows. The new design can therefore provide succor to the regular wheelbarrow pushers who tend to develop unintended health issues, such as muscular build-up and cramps, as they use a wheelbarrow to move materials over a distance.
Local scour is a serious concern for hydraulic engineers. To maintain the reservoir capacity, flushing accumulated sediment is necessary, resulting in a turbulent and scouring jet from the flushing gate. Predicting the maximum scour depth from these water jets is crucial for civil engineers. Despite many proposed equations, one have shown consistent applicability due to the complex nature of the process. Ensuring accurate prediction between hydraulic parameters and the geometry of the scour hole in prototype experiments remains a key issue. To address this issue, this paper examines the use of artificial neural network (ANN) analysis as a computing device for predicting the maximum local scour depth due to horizontal water jets. The neural network is developed using the data collected from previous experiments and an ongoing study. This paper selected four dimensionless parameters as the key variables: the densimetric Froude number (Frd), the relative roughness (d50/hv), the submergence (ht/hv), and the dimensionless apron length or the length of bed protection (La/hv). This paper describes the development of a feed-forward neural network trained by back-propagation for modeling. This study indicates that the ANN is an effective tool for accurately predicting the scour depth.
The effects of airflow dynamics, heat transfer, and mechanical properties on the HDPE blown film extrusion process were examined using a single-lip air ring with a fixed compressed-air valve opening angle of 10 degrees. Reynolds numbers ranging from 9175 to 25911 were analyzed to understand their impact on cooling efficiency, bubble morphology, and film properties. Numerical simulations employing the Standard k-omega turbulence model in ANSYS FLUENT v2023, with mesh refinement achieving y+ approximate to 1, captured detailed flow and heat transfer behavior. Results showed that higher Reynolds numbers significantly enhanced the heat transfer coefficient, with values increasing from 1096 W/m2 & centerdot;K at Reynold number of 9175 to 1438 W/m2 & centerdot;K at Reynolds number of 25911, reducing the axial cooling distance by up to 30%. This rapid cooling improved the cooling rate but led to a reduced lay-flat width (from 29.10 cm at a Reynolds number of 9175 to 27.50 cm at a Reynolds number of 25911) and thicker films. The tensile stress decreased from 25.25 MPa at a Reynolds number of 9175 to 20.84 MPa at a Reynolds number of 25911, reflecting the impact of turbulence on the polymer chain alignment. These findings emphasize the trade-offs between enhanced cooling efficiency and material properties, offering critical insights for optimizing blown film extrusion processes for improved quality and operational performance.
In this study, a cascade PID control structure is proposed and implemented for a 6-degree-of-freedom (6-DOF) unmanned aerial vehicle (UAV) to enhance stability and trajectory tracking capabilities under both noise and non-noise conditions. The controller was designed based on the Tyreus-Luyben tuning method and was evaluated using quantitative metrics, including rise time, settling time, overshoot, and steady-state error. Simulation results on MATLAB/Simulink show that the controller achieves high performance in angular channels (phi, theta, psi) and altitude (z) with a short rise time (< 2s), slight overshoot (< 1%), and nearly eliminated steady-state error. However, the horizontal position channels (x, y) have a longer settling time (similar to 110s) and are sensitive to white noise. Quantitative comparisons with other control methods show that the cascade PID outperforms the standard PID in terms of accuracy and stability, achieving a performance comparable to LQR under noise-free conditions, but is less robust in the presence of noise than advanced methods like SMC and MPC. These results confirm the feasibility of cascade PID in UAV applications and indicate potential future improvements by integrating nonlinear, adaptive, or intelligent control strategies.
This study focuses on designing and implementing a cost-effective and energy-efficient WiFi packet sniffer system using the ESP32. The ESP32-CAM module, which combines WiFi, Bluetooth, and microSD support, is used to capture IEEE 802.11 frames in real-time via promiscuous mode. Packets are stored in packet capture format, which is compatible with tools such as Wireshark and Scapy. Developed using the official ESP-IDF, it offers low-level control and high performance. Two user interfaces were implemented: a UART-based text menu and a web-based HTTPS menu hosted on the ESP32 itself. Functional and performance evaluations were conducted with a focus on capturing broadcast and management frames without payload decryption. The system achieves 99.99% packet capture accuracy at 500,000 packets with a total hardware cost of $25 and power consumption of 2.1 W during active capture, representing a 95% cost reduction and 80% power savings compared to commercial alternatives. The download and file-listing speeds through the web-based menu were also evaluated, revealing that the system performs optimally with fewer files but experiences interruptions during large-scale operations. The proposed system provides a lightweight, standalone, and highly portable alternative to conventional packet sniffing tools despite the limitations in encryption handling and resource constraints. This makes it suitable for educational, diagnostic, and preliminary network analysis, particularly in low-power scenarios.
North Jakarta is predominantly composed of soft soil that is still undergoing consolidation. According to research by Raharjo et al. (2022), excess pore pressure is still present in the soils of Jakarta. The degree of consolidation and excess pore pressure values indicate that consolidation is not yet complete (Raharjo et al., 2022). In this study, the researchers developed two methods to determine excess pore pressure using field testing. The Stress History and Normalized Soil Engineering Properties (SHANSEP) model equation from Ladd and Foot (1974), as well as Finite Element Method (FEM) modeling using the PLAXIS program, were utilized to estimate the excess pore pressure in the soil. The results from the SHANSEP-based analysis were then compared with those from the FEM analysis using PLAXIS. The comparison showed that the excess pore pressure estimated using PLAXIS closely matched the values obtained from CPTu tests based on the SHANSEP model. Interpretation of parameters from the Cone Penetration Test with pore pressure measurement (CPTu) indicates that the soil at the project site is still consolidating and undergoing settlement. Similarly, the settlement results from the PLAXIS analysis support this conclusion.
This paper presents a novel direct control strategy using a polynomial fuzzy neural network-based adaptive fractional order proportional integral derivative (PFNN-AFOPID) controller for nonlinear and time-varying systems. The proposed approach integrates the enhanced flexibility of fractional order calculus PID with the superior nonlinear approximation capabilities of polynomial fuzzy models, enabling dynamic adjustment of all control parameters without requiring precise mathematical modeling of system dynamics. By extending traditional PID control with fractional-order operations, the controller achieves improved frequency response and robustness against disturbances. Experimental validation on a DC motor position control system demonstrates significant performance improvements. Compared to traditional PID, the proposed PFNN-AFOPID achieved a performance improvement of 53.69% in RMSE, 78.56% in ISE, 69.92% in IAE, and 83.98% in ITAE. When compared to the existing fuzzy neural network-based adaptive PID (FNN-APID), our approach delivered improvements of 21.06% in RMSE, 28.79% in ISE, 5.69% in IAE, and 32.86% in ITAE. These results confirm the superior capability of the proposed approach in handling system nonlinearities while maintaining precise control under varying operational conditions, without requiring prior system dynamics knowledge or extensive offline training.
This study evaluates the Hargreaves-Samani model for estimating solar radiation in five locations in Sarawak: Bintulu, Kapit, Sri Aman, Kuching, and Miri (Subis), using minimum and maximum temperatures as key inputs. Analysis reveals stable minimum temperatures (19.7 degrees C to 28.8 degrees C), supporting consistent solar radiation patterns favorable for Photovoltaic (PV) performance. Miri (Subis) recorded the highest daily maximum temperature of 44.8 degrees C, while Kuching had the lowest at 24.7 degrees C, highlighting significant regional climatic variability. Analysis also indicates strong solar potential in Bintulu, Kapit, Sri Aman, Kuching, and Miri, with radiation peaking at 7.5 kWh/m(2) , though seasonal variations impact PV power generation. Initial solar radiation, RI estimates range from 2.49 to 7.81 kWh/m(2) annually, demonstrating substantial seasonal and regional differences. Re-optimized adjustment coefficients (k) indicate that Bintulu (0.190) and Kuching (0.186) exceed coastal standards (0.16), while Kapit (0.168), Sri Aman (0.175), and Miri (0.160) are below inland standards (0.19). These adjustments yield optimized solar radiation, RO values ranging from 2.15 to 7.351 kWh/m(2) , confirming significant PV potential. Cloud cover varies from 20.3% to 100%, peaking during monsoon seasons and decreasing in drier months. The Hargreaves-Samani model demonstrated the highest accuracy in Sri Aman (RMSE: 29.63%) and Kapit (RMSE: 32.76%), with lower accuracy in Bintulu (RMSE: 59.34%) when comparing measured and optimized solar radiation. These findings provide valuable insights for enhancing solar energy utilization and optimizing PV systems in Sarawak's tropical climate.
This study explores the aerodynamic benefits of bio-inspired design modifications for autonomous drones using advanced Computational Fluid Dynamics (CFD) simulations. Four bio-inspired configurations-leading-edge serrations, winglets, riblet surfaces, and curved wings-were assessed and compared against a baseline drone model to evaluate their impact on aerodynamic performance. The results indicated that all bio-inspired designs significantly enhanced lift, reduced drag, and improved overall aerodynamic efficiency. The leading-edge serration configuration achieved the highest performance gains, with a 33.6% increase in maximum lift coefficient (CL) and a 29.5% improvement in lift-to-drag ratio (CL/CD), primarily due to delayed flow separation and reduced turbulence. Winglets minimized wingtip vortices, leading to an 18.3% reduction in drag coefficient (CD) and improved lift efficiency. Riblet surfaces moderately decreased drag by streamlining boundary layer flow, while the curved wing design enhanced stability and manoeuvrability at high angles of incidence. These findings demonstrate the potential of bio-inspired designs to optimize drone performance, extending their operational range and adaptability across varying flight conditions. The study provides valuable insights for development of next-generation UAVs, offering a pathway to improved energy efficiency, flight stability, and versatility in diverse operational environments.
Hand gestures are a natural means of conveying information and thus, there is an increasing interest in utilizing gestures for communication with computers. This study focuses on systematically reviewing different machine learning algorithms while assessing their working mechanisms and accuracy. Articles were analyzed for comparing the performance of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). In accordance with input data, intricacy of gestures, processing resources, and real-time demands, the study shows that each technique has distinct advantages and disadvantages. RNN showed the best accuracy of 99.28% in recognizing dynamic gestures, indicating that it can be employed in applications that need high accuracy. CNN also performed well in recognizing static gestures and provide an accuracy of 93.61% accuracy. In order to improve human-machine interaction through efficient hand gesture detection, this systematic and comprehensive analysis offers some insight into the trade-offs between choice of algorithm and performance.
In this research, the influence of audio-sonic waves on distilled water used as the working fluid in a shell-and-tube heat exchanger was examined. The audio-sonic wave frequencies and flow rates of the cold and hot liquids were varied as follows: 4.85, 6.78, and 13.43 kHz for the audio-sonic waves; 0.3, 0.4, and 0.5 l/min for the cold liquid; and 0.76, 1.0, and 1.5 l/min for the hot liquid. An increase in the audio-sonic wave frequency enhanced both the overall heat transfer coefficient (U) and the enhancement factor (EF). The optimum values of U and EF, measured at 300 W/m3degrees C and 1.05, respectively, were achieved at the highest frequency of 13.43 kHz. Additionally, the flow rates of the hot and cold liquids in the heat exchanger significantly influenced the optimal values of U and EF.
Barnacle shells are a source of the calcium oxide (CaO) catalyst, which is nontoxic, highly alkaline, and biodegradable. This study aims to determine the characteristics and the effect of the CaO catalyst concentration obtained from the calcination of barnacle shell particles at a temperature of 900 degrees C for 2 and 4 h on the transesterification reaction of biodiesel from coconut oil. Transesterification was conducted at 65 degrees C for 2 h with a methanol/coconut oil molar ratio of 6:1. The concentration of the CaO catalyst was varied (i.e., 1wt%, 3wt%, 5wt%, 7wt%, and 9wt%) for coconut oil. The results of the characterization of the catalyst through the X-ray diffraction test showed that the best calcination time was 4 h. The characteristic peaks of CaO appear at diffraction angles (2 theta) of 31.4 degrees, 36.0 degrees, 54.3 degrees, and 64.7 degrees. The optimum catalyst concentration was 5wt%, where the highest yield (92.17%) was obtained with the following biodiesel characteristics: density at 40 degrees C of 863 kg/m3, kinematic viscosity at 40 degrees C of 3.03 cSt, water content of 0.01%, and acid number of 0.26 mg KOH/g. The results of the gas chromatography-mass spectrometry analysis based on the optimum catalyst concentration showed that biodiesel was composed of methyl ester compounds, which were dominated by methyl laurate (54.52%) and methyl myristate (19.37%).
Since their inception, mobile robots have enormously changed the landscape of robotics engineering in recent years. Imperatively, the impact of mobile robots has positively transformed many sectors of human endeavors, i.e., complemented and substituted humans in areas where human interactions were difficult, hazardous, and impossible to thrive and operate. In this regard, the contributions of mobile robots to scientific, social, and economic growth, development, and advancement cannot be overlooked, especially through its decades of transition from Industry 3.0 to 4.0 over the years. To achieve maximum benefits from the use of mobile robots across all important facets, their advancements and technologies need to be continuously improved to address all relevant issues with regard to associated challenges in navigation, control, remote sensing, and tele-operability. This paper presents a comprehensive review of selected key areas of mobile robot technology where major advancements have been made and are currently ongoing to solve numerous problems effectively with less human effort. In addition, highlights of the challenges faced by mobile robots and autonomous vehicle control have been extensively discussed and recommendations have been given to enhance the efficient and safe use of mobile robots in the event of a change in task complexity in all essentials of human life.
Image processing is an advanced technology that significantly supports production, identification, and quality control for fruits. This paper uses image processing techniques to develop a mango classification system based on size and ripeness. The system integrates hardware, including an Arduino microcontroller, camera, sensors, actuators, and a user-friendly computer interface for monitoring and control. The classification algorithm extracts key features of the mangoes, such as their color and shape, to categorize them into predefined quality classes. Experimental results demonstrate that the system achieves an accuracy exceeding 90% for both ripeness and size classification, with a productivity level of 300 kg/hour, surpassing the initial target of 250 kg/hour. Furthermore, the system operates reliably under varying lighting conditions, ensuring flexibility and continuous productivity. These advancements highlight the system's potential to enhance efficiency and quality in fruit processing industries.
Indonesia produced 1,508 tons of nonliving crabs in the January-March 2020 period. The number of shells generated can account for 40%-60% of the overall weight. Therefore, using crab shells made into chitosan is expected to increase the economic value and reduce waste from crab shells. Chitosan can be modified with clay previously activated with an acid solution to form a chitosan-clay composite to improve the adsorption capacity. This study aims to analyze the differences in characteristics between chitosan and clay before and after the immobilization process. A comparison of chitosan and clay (1:2) is conducted on the basis of variations in the contact time for 5, 30, 55, and 80 min and in the concentration of metal solution as much as 10, 30, and 50 ppm. The results of the Fourier transform infrared spectroscopy (FTIR) study found stretching vibrations of-NH originating from the chitosan structure, as well as several functional groups from clay, such as silanol (Si-OH) and siloxane (Si-O-Si). The results of the X-ray diffraction (XRD) test show that the clay used was bentonite clay. Meanwhile, the scanning electron microscopy (SEM) and surface area analyzer (SAA) test results show that the chitosan-clay adsorbent has a rougher and hollower surface with a surface area of 23,666 m2/g. The best adsorption percentage of 90.59% occurred at a concentration of 10 ppm with a contact time of 30 min.
