Abalone aquaculture is a critical component of the global seafood industry, with growing demand driving the need for optimized breeding techniques. However, challenges such as inconsistent growth rates and prolonged production periods have hindered profitability and operational efficiency within the industry. This study was conducted to address these challenges, specifically focusing on enhancing abalone growth rates. Three months quantitative data were collected from the Abalone farming company on Abalone in response to different environmental factors to study the effects of these factors on the Abalone growth. Each experimental tank was installed with an Automatic Aquatic System integrated with PH, salinity, and temperature sensors for maintaining, monitoring, and controlling the three experimental factors within the set limits. The growth metrics were assessed using the absolute and specific growth rate for a comprehensive comparison across different environmental settings. The study also employed a combination of quantitative methods, like regression analysis and ANOVA, to analyze the impact of these factors. The model results were validated with confirmatory experiments. The results from the study showed that PH and salinity have the highest and lowest influence, respectively, on the Abalone growth with SGR at 2.20%/day (8.5PH, 40ppt salinity, 20℃) and 0.72%/day (8.84PH, 35 ppt salinity, 15℃). Ditto AGR with the highest and lowest value at 4.42g/day and 1.87g/day, respectively, under the same experimental settings. The optimal values of the factors were obtained at PH, salinity, and temperature of 8.18, 31.29 ppt, and 13.62℃, respectively, which yielded 0.88%/day SGR and 2.16g/day AGR. The developed models can therefore be used for an accurate forecast of the AGR and SGR of Abalone under different environmental settings.

Traffic signal control systems play a critical role in managing urban mobility by regulating the flow at intersections. The Florida Department of Transportation (FDOT) installed a new signal timing system at several signalized intersections along multiple corridors in Central Florida. In December 2013, Orange County began implementing this system, which was completed in June 2015. This action was taken to reduce the frequency of red-light running incidents. The primary objective of this study was to assess how signalized intersections and corridors are affected by extended change and clearance intervals. Specifically, it aimed to evaluate FDOT’s signal timing effort and its potential impact on the safety and operational performance of selected intersections. To address this, twenty signalized intersections along three corridors in Central Florida were investigated. Additionally, three signal timing patterns were examined to evaluate the effectiveness and safety of the baseline (Pattern 1), the current FDOT implementation (Pattern 2), and the proposed alternative (Pattern 3). Microsimulation analysis was conducted using SimTraffic, a component of the Synchro 8 software. The study found that extended signal timing in Pattern 2 and the proposed Pattern 3, which incorporate longer change and clearance intervals, significantly increased intersection delay and volume-to-capacity (V/C) ratios. Furthermore, these patterns also led to noticeable increases in overall delay and travel time along the studied corridors.

Traffic congestion is a major issue faced by Batam, a city that continues to grow rapidly as an economic and logistics hub. This study adopts the Design Science Research Methodology (DSRM) to develop an intelligent navigation system based on artificial intelligence (AI) aimed at optimizing urban traffic management in Batam. The system integrates real-time traffic data, machine learning algorithms, and reinforcement learning to predict traffic flow and optimize route selection. Using the DSRM framework, the system was designed, implemented, and evaluated iteratively to ensure its effectiveness in addressing the city's unique traffic challenges. The results of the study indicate that the implementation of the AI-based navigation system successfully reduced the average travel time by 22.8%, distributed traffic loads more evenly, and improved travel efficiency. Furthermore, the system demonstrated a route prediction accuracy of 91.3%, higher than conventional GPS systems. Performance evaluation also showed high responsiveness, with an average latency of only 423 milliseconds. This study concludes that the AI-based navigation system, developed through the DSRM framework, can be an effective solution to address traffic congestion in rapidly developing cities like Batam and can be applied to other cities with similar characteristics.

The objective of this paper is to examine the design effect of the gas flow field on fuel cell performance. A polymer electrolyte membrane (PEM) fuel cell with 10 W power output operating at 3 A and 4.5 V has been simulated. The study investigates seven configurations of fuel cell assemblies featuring a Z-shaped flow field and explores the effects of various flow fields and flow channel designs. Single Z-type serpentine flow fields with a channel width of 1 mm were modeled to create interconnected pathways. CFD COMSOL Multiphysics 6.1 was used to analyze a three-dimensional, steady-state, isothermal fuel cell model with an active area of 9.84 cm². The study focused on pressure loss, reactions and product distributions, and current density within the fuel cell. Results showed that Model E2 achieved the lowest anode pressure drop at 7 Pa, while Model A1 exhibited the highest pressure drop at 180 Pa, indicating Model E2's superior pressure management. Cathode pressure analysis revealed that Models A1 and A2 generated the highest pressures. Polarization curve analysis determined that Model A2 delivered the highest current density but at elevated pressures up to 1200 Pa. Among the tested configurations, Model E2 emerged as the optimal design, offering excellent performance with minimal pressure drop and enhanced current density. It enabled uniform reactant gas dispersion, leading to a consistent and reliable current distribution across the electrode surface. Moreover, the Model E2 design promoted improved lateral species transfer and uniform species distribution within the gas diffusion layer, contributing to its superior performance.

Short-term traffic forecasting is one of the significant subjects in order to create more sophisticated transportation systems that regulate traffic volume and prevent congestion. The number of vehicles in Karbala City is growing quickly, which raises wait times and decreases Level of Service (LOS). It is essential to predict the traffic performance to ensure a correct traffic operation. The aim of this work is to create a short-term traffic forecasting model for intersections within a study area based on an Artificial Neural Network (ANN). The data has been used to create and train a number of ANN models. Two models were selected based on the most effective parameters that cause traffic congestion, longer travel times, and accidents for each type of vehicle, serving as the main input for the models. The results of models to predict the traffic volume and travel time found that the neural network performed in a good way, achieving R2 values of 0.9101,0.9748 and 0.8877, which is a good score. Sensitivity analysis was adopted to evaluate the model's performance when the input values are changed. It was found that the passenger car (PC) was the most effective parameter for both models.

Fiber-plastic composites are increasingly used in the aerospace, automotive, and wind energy industries, often exposed to multi-axial mechanical loads and high climatic stresses. The objective of this study is to investigate the fatigue behavior of these composites as a function of multi-axial mechanical stress by a novel developed degradation model based on continuum-damage-mechanical approaches. The model's simulation performance has been examined and demonstrated it is applicable in engineering practice. CFRC composites exhibit 74.5 MPa of tensile strength, but GF(MLG)/EP glass fiber reinforced composites demonstrate a considerable lack in both stiffness and regular deformation until ultimate failure. The failure of textile-reinforced plastic composites occurred in three stages of degradation. The tensile strength of biaxial NCF glass-reinforced polyester material was increased by 13 percent as well as the fatigue endurance by 20 percent as compared to the woven roving reinforced composites. The damage onset was 25-35% of the beginning stage. The structure then stabilized to 10-15% and then failed. In GF-MLG/EP, a pattern of stiffness change according to a direction was observed, where transverse cracks reduced the stiffness to 75% of its initial value after 10,000 cycles. Fatigue damage is more resistant in biaxial NCF composites than in woven fabric composites.

The study evaluates the accuracy of LiDAR and CCTV technologies for vehicle and pedestrian count collection at a signalized intersection under varied weather conditions. Data collection occurred over a two-hour period during peak morning and evening hours using both technologies. The trajectory identification, entry and exit point determination, and anomaly filtering were utilized to analyze the vehicle counts. The pedestrian counts were carefully analyzed using LiDAR point cloud data and CCTV footage to monitor movements, in areas. Analysis of the data showed differences in vehicle and pedestrian counts depending on the weather conditions. Rainy weather had the variations while sunny conditions also showed differences with snowy weather having the least discrepancies. Interestingly the southbound through and eastbound right movements exhibited the variations in both vehicle and pedestrian counts. Despite challenges like spots and weather impacts, both LiDAR and CCTV technologies hold promise for collecting traffic data. It is vitally important that this study focuses on the limitations of current traffic control systems. The integrity of current systems and improving them is essential for traffic monitoring and enhancing safety measures at signalized intersections.

Biomass, as a separate type of granulated solid fuel, ranks third in terms of the share of generated electricity and in a number of countries is the main type of fuel in the production of thermal energy. Made for home heating systems, but might work in commercial and industrial settings as well. Fuels such as sawdust, wood chips, and wood mill waste, as well as recycled wood from disassembled pallets or furniture, often have a high energy density (16-19 MJ/kg), and their ash level varies depending on the kind of fuel. However, burning these fuels poses environmental challenges such as air pollution and greenhouse gas emissions. This work focuses on the state of production of granular solid fuels, including their types and potential applications. To understand the underlying phenomena and chemistry of combustion, as well as to design and run different combustion devices to enhance the conversion efficiency of these fuels into energy. The main study area centered on a granulation process, whereas fines are agglomerated into larger granules for better handling and combustion characteristics. It evaluates the current technology approaches employed in producing and utilizing these fuels as a granulator for domestic waste. The evidence also points to the importance of understanding the combustion processes desired for optimization, lessening environmental impacts, and the importance of pyrolytic processes in transforming solid particles that determine total combustion efficiency.

This research paper presents an in-depth exploration of container vessel accidents and preventive measures through semi-structured interviews with industry professionals and subject matter experts. Building on a previous study, utilizing the NASAFACS methodology to analyze container vessel accidents, this paper aims to deepen understanding of the underlying challenges and emerging trends in container vessel safety. The interviews focused on key aspects, such as industry insights, causal factors, environmental risks, crew competency, the regulatory landscape, collaboration with authorities, industry partnership, and crisis management. Participants shared valuable perspectives on major challenges affecting container vessels and the wider industry. Interview data were analyzed using MAXQDA Software, allowing a comprehensive thematic analysis. The findings inform recommendations to improve safety, including the development of comprehensive standards for emerging risks. Specific suggestions include the upgrade of firefighting systems for ultra-large container ships, stricter enforcement of cargo declaration and lashing practices, mandatory IMDG training for shippers and freight forwarders, higher manning levels, and structured inspection regimes akin to those in the tanker industry. While the NASAFACS analysis of accident reports identified preconditions as primary contributory factors, the interview findings highlight systemic organizational issues and external influences. This research contributes to the ongoing maritime safety discourse by integrating expert insights with NASAFACS analysis, offering a holistic perspective on container vessel accidents and proactive measures for their prevention.

This study addresses the high costs and emissions associated with diesel freight operations on the busy Dammam–Riyadh corridor by developing a hybrid, data-driven optimization framework that combines regression modeling, the Taguchi method, and a Genetic Algorithm (GA). First, a multiple linear regression model was trained on 30 real freight trips validated via 5-fold cross-validation and reporting R² = 0.87 and RMSE = 3,200 SAR to predict total trip cost from six operational variables. Next, a Taguchi L9 orthogonal array was used to perform a sensitivity analysis under the “smaller-is-better” Signal-to-Noise (S/N) ratio, identifying wagon count and trip duration as the most influential factors, with a minimum predicted cost of 42,388.64 SAR. Finally, we applied a DEAP-based GA (population = 50; generations = 100; blend crossover; Gaussian mutation) to globally optimize all six variables within empirically derived bounds, achieving a predicted cost of 34,054.33 SAR (≈ 44% reduction versus the dataset mean). Key assumptions include linear cost relationships in the regression and fixed stop/truck counts during Taguchi screening; limitations stem from the single-corridor dataset. This combined approach balances rapid factor screening with precise global optimization, offering both strategic insights and actionable recommendations for reducing freight transportation costs while maintaining operational reliability.

In this study, the environmental impact of car noise in the two largest cities of Uzbekistan - Samarkand and Tashkent-was compared in depth. The main objective is to determine how factors such as the level of urbanization of different cities, traffic density, road infrastructure and industrial location affect the level of traffic noise. The study used a modern Assistant SIU 30 v3rt type noise meter at a total of 12 points (8 in Samarkand, 4 in Tashkent) with measurements of car number and noise level at 2-minute intervals of 10-15 minutes per location. During the measurements, the number of cars, maximum and average equivalent noise levels (Leq) were determined. The results showed that noise levels in Tashkent were higher, as well as a very strong correlation (R=0.97) between the number of vehicles and noise. In contrast, in Samarkand, this association is moderately strong (R=0.635), and other environmental and infrastructural factors have also been found to affect noise. The study was also carried out on the basis of international standards, while the results serve as an important basis for ensuring environmental safety, urban planning and the development of anti-noise strategies. The results showed significant differences in noise levels and their relationship to traffic between cities. The analysis confirmed an increase in the permissible noise level in residential areas, public buildings and recreation areas, especially in large cities, taking into account their specific characteristics and factors affecting the noise level. The cited correlation indicators will serve as a statistical basis for the development of noise forecasting and monitoring systems in the future by year. The facts of the article are necessary for the scientific justification of the policy of combating noise in the cities of Uzbekistan.

The digital transformation has fundamentally reshaped brand management, moving from traditional mass communication to data-driven, interactive, and highly personalized strategies. With emerging technologies such as artificial intelligence (AI), augmented reality, and digital ecosystems, brands are now engaging consumers in innovative ways to enhance loyalty and gain a competitive advantage. This study examines how leading brands, such as Nike, Apple, and Coca-Cola, employ digital brand management strategies to enhance brand equity, boost consumer engagement, and maintain market leadership. A multiple-case study approach was employed to analyse this. Data was collected through archival research, social media analytics, and consumer sentiment analysis to assess the impact and effectiveness of these strategies. The study examines key digital branding elements, including direct-to-consumer (DTC) models, experiential marketing, and interactive campaigns. The findings reveal that Nike's DTC strategy fosters direct consumer relationships and strengthens brand equity. Apple's experiential marketing and storytelling foster emotional brand loyalty, while Coca-Cola's personalized and interactive digital campaigns drive consumer engagement and social media virality. These strategies demonstrate the growing importance of AI-driven personalization, omnichannel consistency, and consumer-centric engagement.

The study concludes that brands prioritizing AI-powered personalization and immersive digital experiences achieve stronger consumer engagement and long-term brand growth. Practical implications suggest businesses integrate AI-driven analytics, invest in emerging technologies, and adopt consumer-focused digital strategies. Future research should investigate the long-term effects of AI-driven brand interactions and examine the role of Web3 and the Metaverse in shaping the future of digital brand management.

Green growth practices in avocado farming involve balancing economic productivity, environmental sustainability, and social inclusiveness. These practices could boost resource efficiency, conserve biodiversity, and minimize environmental degradation. While global demand for avocados is increasing, there is little understanding of the factors influencing farmers’ willingness to adopt green growth practices and the factors affecting avocado yields amid market pressures as well as insufficient information and inadequate resources. Therefore, this study investigated the current practices used by farmers and the factors influencing the adoption of green growth practices and avocado yields in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) region, specifically in Rungwe District. A cross-sectional research design and multistage sampling helped select targeted avocado farmers, from whom data was collected via questionnaires and surveys. Results of descriptive statistics showed that 82% of the interviewed farmers were male and 67% of them had primary education. Farmers identified mulching and the use of organic fertilizer as primary green growth practices. Regression analysis performed by SPSS version 27 was the main analytical method. Binary logistic regression indicated that larger avocado farm size, access to information, and perception of larger avocado demand significantly influenced the adoption of green growth practices; meanwhile, gender showed a marginally significant effect. Multiple linear regression further revealed that tree age significantly impacted avocado yields whereas chemical fertilizer decreased yields. The findings emphasized the importance of targeted interventions to improve knowledge dissemination and training of sustainable agricultural practices to enhance productivity in avocado farming.

Neck injuries remain a critical concern in vehicle safety, particularly during dynamic movements and terrain-induced impacts. Traditional test dummies and wearable devices often fail to capture real-time biomechanical neck responses under such conditions. This study introduces a smart mannequin system designed to measure axial forces and cervical moments in realistic vehicle environments. The system integrates S-type load cells and HX711 amplifiers with a Raspberry Pi 4 for real-time processing, enhanced by Kalman filtering for signal clarity. Calibration was conducted using reference weights from 5 N to 40 N in 5 N increments, with each step validated against a force gauge. The mannequin was tested across various terrains, including straight tracks, inclines, sinusoidal roads, and uneven surfaces, representing realistic military and civilian vehicle conditions. Results showed minimal calibration deviation (2–4 N), with peak force measurements reaching 30.63 N and moment readings up to 1.25 Nm. Higher speeds reduced axial loading on stable tracks, while irregular terrain increased neck strain. The system consistently captured neck loading dynamics, offering a safe, repeatable alternative to human-based testing. Its practical application spans ergonomic vehicle design, occupant safety analysis, and fatigue detection in transport environments.

A comprehensive reassessment of the financial trilemma’s applicability to the governance of banking systems in peripheral economies has been conducted through a mixed methods investigation focused on Zimbabwe between 2010 and 2024. Despite the trilemma’s prominence in international financial theory—emphasising the trade-off among financial integration, monetary policy autonomy, and financial stability—its limitations in structurally fragile, postcolonial contexts have remained underexplored. This gap has been addressed by integrating descriptive statistical analysis of 45 archival policy documents with narrative insights derived from 130 semi-structured interviews conducted with risk managers in commercial banking institutions. Analytical triangulation was achieved through the application of Marxist immanent critique, revealing the embedded ideological assumptions underpinning traditional trilemma theory. Findings indicate that when deployed in politically unstable and externally dependent contexts, the trilemma model inadvertently reinforces global financial dependency, entrenched class domination, and extractive policy frameworks. These dynamics have been shown to undermine domestic policy sovereignty and institutional resilience, thereby constraining effective financial governance. Moreover, technocratic framings of the trilemma have been found to obscure its alignment with neoliberal orthodoxies, including financialisation, commodification, elite resource capture, and the enclosure of domestic financial spaces. These processes have facilitated the appropriation of national resources under the guise of liberalisation, revealing the inadequacy of applying conventional trilemma logic in structurally asymmetrical settings. It is therefore proposed that financial governance in such contexts be reconceptualised through heterodox approaches grounded in regional solidarity, decolonisation of international finance, participatory governance mechanisms, and the strategic use of capital controls. The study contributes to the re-theorisation of financial governance in developing economies by challenging the ideological neutrality of mainstream economic models and proposing context-sensitive alternatives better suited to postcolonial realities.