The valorisation of winemaking by-products is an important approach for the development of functional foods. This study investigated the effect of grape skin powder (GSP, 0–10%, m/m) incorporation on the quality of a laboratory-scale dairy-based frozen dessert prototype. Five formulations (0, 2.5, 5.0, 7.5 and 10% GSP) were evaluated in terms of physicochemical properties, oxidative stability, colour parameters, microbiological quality and sensory acceptability. Increasing GSP levels led to a progressive decrease in pH (6.72– 5.62) and a slight increase in fat content (26.25–27.66%), while dry matter remained above 54%. Lipid oxidation during frozen storage was reduced in GSP-fortified samples, with peroxide values decreasing from 1.70 meq O₂/kg fat in the control to 1.36 meq O₂/kg fat at 10% GSP after 28 days. Pronounced colour changes were observed (ΔE* = 37–55), associated with grape skin anthocyanins. Functional enrichment was confirmed by increased total polyphenol content (up to 139 mg GAE/100 g) and antioxidant activity (up to 33.6%). Moderate GSP addition (2.5–5.0%) was associated with lower microbial counts, while higher levels introduced additional indigenous microflora. Sensory evaluation indicated good overall acceptability, with the 5% GSP formulation providing the most balanced profile. Overall, grape skin powder shows strong potential as a functional ingredient for dairy-based frozen desserts.

Excessive sodium consumption is a global public health concern, negatively impacting life expectancy and overall quality of life. Bread, one of the world’s staple foods, contributes significantly to total sodium consumption, making it imperative to reformulate traditional recipes to reduce salt content while maintaining product acceptability. An effective strategy involves gradually reducing the amount of salt, allowing consumers to adapt to a less intense taste. Another approach involves the use of potassium chloride, which can partially replace sodium in the composition of bakery products, but requires adjustments to eliminate the slightly bitter taste. In this process, yeast plays an essential role, as natural fermentation contributes to the development of a more complex flavor profile, amplifying the perception of salty taste and reducing the need for added salt. Another key factor in reducing sodium is the use of flavor enhancers, such as yeast extracts, glutamates and hydrolyzed protein nucleotides, which enhance the perception of saltiness without increasing sodium content. These ingredients can be combined with salt substitutes, thus helping to maintain an optimal taste balance. Implementing these solutions requires an integrated technological approach, which includes product reformulation, nutritional education and public policies aimed at supporting the reduction of sodium in the diet.

This paper deals with the features of quantum computing for a system containing a relatively large number of qubits n, which is characterized by a half-integer effective spin S in the Schwinger representation of paired bosons. It was shown that for n = 70, the number of boson states corresponding to lowest 2S + 1 excited energy levels of each of the two harmonic oscillators implementing the two-boson representation of the effective spin S is N = 1070 = 1.18 x 1021. Under such conditions, it can be assumed with great precision that for n ≥ 70 all boson states of both harmonic oscillators participate in the implementation of the two-boson representation of the effective spin S. This allows one to apply the quantum field theory methods when performing quantum computations for multi-qubit systems. Another advantage of the Schwinger representation of paired bosons compared to the spinor representation when performing quantum computations for multi-qubit systems is that in the first case the form of the spin projection operators of the effective spin S does not depend on the number of qubits n.

A rigorous theoretical investigation has been made on the propagation of nonlinear self-gravito-acoustic shock structures (SGASSs) in a quantum plasma system consisting of non-inertial degenerate non-relativistic electron species and inertial non-degenerate heavy nucleus species. The nonlinear behaviors for this self-gravitational perturbation (SGP) mode in planar geometry has been examined. The Burgers equation has been derived by employing the standard reductive perturbation technique. To analyze the Burgers equation numerically, the solution of the Burgers equation has been obtained for stationary localized condition. The dissipative force, which is effective on heavy elements, plays vital role for the formation of SGASSs in the plasma system under consideration. The non-relativistic limit has great effect on the basic properties (amplitude, width, etc.) of the SGASSs. The obtained results are applicable in white dwarfs and neutron stars which are the most common examples of astrophysical compact objects. Journal of Engineering Science 16(1), 2025, 31-36

Traffic Simulation has empowered transportation engineers by providing a means of visual interpretation for real-life traffic conditions. PTV VISSIM is a well-known microsimulation software used to analyze and predict traffic operations and behavior by considering factors such as lane configuration, traffic composition, transit stops, etc. A non-laned-based heterogeneous traffic stream characterizes the urban traffic system of Dhaka. This makes it burdensome to calibrate and validate VISSIM models to reflect field-obtained traffic flow. To calibrate VISSIM-developed simulation models, Weidemann 74 and 99 car-following models are widely adopted. These car-following models and other movement parameters, such as lateral movement and lane-changing behavior parameters, are usually adjusted to calibrate the microsimulation model. This study aims to develop a new approach using sampling and machine learning to calibrate the Weidemann 99 car following model parameters in VISSIM microsimulation software for mixed traffic conditions. A portion of Abdul Gani Road, which represents the typical characteristics of the traffic system of Dhaka, was chosen to be the epicenter of this study. Latin Hypercube Sampling has been used to generate the number of combinations required to properly explore the effects of the ten calibration parameters of the Weidemann 99 car following model on the validation accuracy. The validation accuracy has been measured by using the GEH statistic. A total number of 500 simulations were generated, and from these 500 simulations, 37 combinations were obtained to have acceptable GEH values, which is generally considered to be less than 5%. These combinations were further analyzed using a k-means clustering algorithm to generate the centroid line of the acceptable parameter combinations. A sensitivity analysis was conducted using the obtained simulation dataset to determine the impact of changing values of the parameters on traffic flow. The findings of this study will aid future traffic simulation researchers by providing them with a guiding framework in calibrating VISSIM simulation models for mixed traffic conditions similar to Dhaka. Journal of Engineering Science 16(1), 2025, 21-30

Falls are one of the major causes of unintentional injury, deaths in the world and pose a critical threat to occupational safety, particularly in the construction industry. Worldwide, falls account for 684,000 lives per year with construction industry being responsible for 38% of the cases. According to the latest statistics, in Bangladesh alone, 185 construction worker deaths were recorded, of which 70 solely resulted from falling. Therefore, it is imperative to address the issue. This study investigates the root causes of fall hazards at construction sites in Bangladesh. The data collection was carried out through a questionnaire and physical survey of ten construction sites within Dhaka city. 20 items of questions divided into 7 Groups of Factors were developed through an extensive literature review. The data from the questionnaire survey were then analyzed through SPSS 27 statistical software to measure descriptive statistics; reliability and validity were measured by Cronbach's α and Spearman-Brown coefficient. Later on, from the physical survey risk metrics was generated. Finally, this study suggests regular workplace inspections, improved enforcement of OSH regulations, safety awareness campaigns, and development/integration of site-specific training programs, along with technological innovations such as drones and wearable sensors to curb the fall hazards. Synergizing these initiatives with Sustainable Development Goals 3 (Good Health and Well-Being) and 8 (Decent Work and Economic Growth) will build safer workplaces, reducing deaths and enabling the construction industry return on economic sustainability for Bangladesh. Journal of Engineering Science 15(2), 2024, 69-78

This study aimed to fabricate a Fuzzy Logic (FL) model based on dye, salt, and alkali concentrations to predict the color properties of pineapple leaf fiber (PALF) fabric dyed with reactive dyes. The nonlinear and interactive nature of these factors makes it challenging to make an exact model using mathematical or statistical methods. Additionally, artificial neural networks and neuro-fuzzy models require extensive trial data for parameter optimization, which is often difficult to obtain from dyeing industries. In this context, FL is an effective modeling tool, capable of handling nonlinear complex domains with minimal trial data. The test data confirmed the model established in this study, showing a mean absolute error (MAE) for all parameters within the acceptable range (<5%) and coefficients of determination (R²) exceeding 0.96. It is concluded that the FL model is efficient in the prediction of color properties of PALF fabric in nonlinear complex dyeing with high accuracy. Journal of Engineering Science 15(2), 2024, 13-27

This paper explores the integration of inertial sensor-based human activity recognition (HAR) with the optimization of bipedal exoskeletons using artificial intelligence (AI) techniques. The motivation for the study stems from the need to improve the adaptability and energy efficiency of exoskeletons for practical applications. The specific hypothesis is that combining HAR with reinforcement learning (RL) can lead to personalized and efficient control strategies. The aim of the research is to develop a robust HAR system for classifying activities such as normal walking, stair climbing/climbing and sitting/standing, and to optimize exoskeleton control through AI-based simulations. The methodology involves preprocessing sensor data (accelerometer and gyroscope) by segmentation and feature extraction, followed by supervised classification with Support Vector Machines (SVM) and Random Forest, and RL optimization in simulated environments such as Webots. Preliminary results indicate an HAR accuracy of 92% and a 15% reduction in metabolic cost by RL, improving stability and user comfort. This innovative approach contributes to exoskeleton design by reducing manual adjustments, with potential applications in rehabilitation and physical augmentation.

Although mobile devices have widespread popularity, allowing, for example, companies to innovate, streamline operations, and maintain a competitive advantage in a rapidly evolving market, the economic impact of using mobile applications (apps) is less researched. In an attempt to improve the state of affairs in the field, this paper provides a classification (based on business domains) and defines models for calculating the costs of developing and using mobile apps in various situations. To evaluate the economic impact of mobile apps, the opportunity of use is argued and the mathematical expressions for calculating the quantitative indices, Net Present Value, Profitability and Internal Rate of Return, eventually in conjunction with the Equivalent Annual Value method, are determined for 12 specific cases of development and use of mobile apps. Also, multiple qualitative indices for measuring the impact of mobile apps are succinctly characterized and systematized. In confirmation of the significant economic and social impact of mobile apps, three implementations of them in various fields are described.

This study presents a detailed analysis of gait trajectory and orientation using Time of Flight sensors and Inertial Measurement Units during controlled movement tests in a robotic walker. The tests were performed on three distinct path configurations: straight-line, rectangular, and lemniscate paths. The Time-of-Flight sensors measured changes in distance, while the Inertial Measurement Units captured yaw and orientation data. Notably, during sharp turns, the Time-of-Flight sensors detected significant distance fluctuations corresponding to the directional changes captured by the Inertial Measurement Units yaw data. The results highlight the effectiveness of combining Time of Flight and Inertial Measurement Units data to track stable movement, detect sudden turns, and monitor orientation adjustments in complex trajectories. This combined approach can offer critical insights for gait analysis, rehabilitation, and robotics navigation.