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.

This research focused on assessing groundwater quality in the Alton Kopri, Kirkuk Province, northern Iraq. Twenty-two samples were selected from twenty-two wells randomly distributed in the study area to assess the subsurface water for drinking purposes. The samples were analyzed for parameters (pH, T.D.S, Na⁺, Mg²⁺, K⁺, Ca²⁺, NO₃^-, SO₄^2-, HCO₃^-, and Cl^-) to compute Water Quality Index (WQI). Pearson's correlation and principal components analysis (PCA) were adopted to study the physicochemical parameters sources in groundwater. The dominant cations were ordered as follows: Na > Ca > Mg > K, and the dominant anions were arranged as follows: SO₃ > Cl > HCO₃ > NO₃. The average concentrations of TDS, Ca, Mg, Na, SO₄, and Cl were 1118.45, 173.54, 132.59, 341.36, 873.63, and 414.50, respectively, surpassing the maximum permissible limits set by WHO. The average concentrations of K, NO₃, HCO₃, and pH were 5.90, 35.02, 172, and, 8.05 respectively, and were within acceptable limits. The WQI ranged from 33.3 to 1024. The findings designated that 23% of the samples were categorized as excellent, 27% as good, 18% as poor, 14% as very poor, and 18% as inappropriate for drinking purposes. The Pearson correlation matrix has been created and analyzed to appraise the important factors impacting groundwater quality. The PCA technique was adopted to analyze water quality parameters, resulting in the extraction of three components that together account for 81.574% of the total variance. The extracted components suggest that the predominant contributors to groundwater contamination include geological characteristics, agricultural practices, precipitation, domestic wastewater, and manufacturing activities. This study stands out from others due to various local factors that impact groundwater quality in the Alton Kopri area. Agricultural practices, including fertilizer and pesticide use, lead to chemical seepage into the aquifer, while pastoral activities contribute organic contaminants. Insufficient sewage infrastructure in some areas results in wastewater infiltration. The region’s geology, dominated by limestone and clay, affects groundwater hardness and heavy metal levels. Additionally, the Little Zab River, which recharges groundwater, can transport pollutants during floods. Minor industrial activities may also introduce trace metals and oils. Understanding these influences is key to interpreting water quality variations and informing sustainable management strategies.

Vehicle tires are subjected to sudden and significant loads when driving at high speeds due to unexpected bumps in the road. To reduce the occurrence of these cracks, this research will address the occurrence of these cracks using various techniques. The Solid Works program will be used to design various wheel models and reinforce the areas where cracks occur. The models will then be loaded into the ANSYS program to determine the various deformations and stresses they experience, as well as the degree of improvement of the wheel models whose designs have been developed. The results demonstrated that the deformation models' values were substantially lower than those of the first model, with the third model showing the biggest percentage decrease (59.56%). The results showed that the Von Mises models' values and the maximum shear stress were considerably lower than those of the first model, with the third model showing the biggest percentage decline at (68.12 and 61.2%), respectively. The fact that these improved percentages are reached in the three models (64.74, 93.12, and 88.72%) indicates that the fatigue damage values of the three improved models in the design are significantly lower than the fatigue damage values of the first model. It is clear that the third model, with a safety factor increase of 93.12%, has the highest increase. This suggests that the third model, which has three collars reinforcing it in the region where cracks are developing, is the best.

Most road accidents are caused by drivers engaging in driving practices and being distracted while driving, which contributes to the concern of road safety awareness in society today. Activities like using a phone while driving carelessly and displaying driving habits increase the likelihood of these actions leading to an accident. In this research paper, we introduce a driver behavior detection system based on CNN technology that employs a 22-layer convolutional neural network (CNN) to identify intricate behaviors in real time situations. The proposed method systematically incorporates layers with 3×3 kernels and ReLU activations, along with max pooling layers to classify five main categories: turning movements, using a phone for texting or talking, safe driving practices, and other activities. The system underwent training and testing on a dataset of 10776 RGB images, in 480×640 pixels resolution, depicting driving situations and surroundings. The first test results showed a notable drop in misclassification errors and a notable rise in accuracy rates for classification tasks using a CNN approach could have advantages for enhancing vehicle safety systems by accurately and swiftly detecting driving behaviors to reduce accident risks and enhance road safety overall. The experiment findings were obtained through GPU processing in Matlab. Resulted in a training accuracy of 100% along with a testing accuracy of 100%, achieved within 23.46 seconds. The method suggested for assessing driving habits has been effectively executed.

Multi-modal transportation systems (MMTS) play a critical role in enhancing urban mobility by integrating multiple transport modes to improve efficiency and accessibility. This paper presents a comprehensive review of modelling approaches in MMTS, focusing on optimization techniques, travel behaviour analysis, and network resilience. The study synthesizes a range of methods, including agent-based models, equilibrium approaches, and data-driven simulations, aimed at improving system efficiency, adaptability, and user satisfaction. While significant strengths include real-world data integration and dynamic performance modelling, a thematic analysis reveals recurring limitations across studies, such as model assumptions, data limitations, limited behavioural realism, narrow scope, and high computational complexity. These weaknesses constrain the scalability and applicability of current MMTS models. The review emphasizes the need for frameworks that integrate real-time analytics, support diverse travel behaviours, and incorporate emerging trends like Mobility-as-a-Service (MaaS) and micromobility. It concludes by recommending that future research prioritize cross-regional validation, computational scalability, and dynamic system responsiveness to ensure MMTS can meet evolving urban transport demands. This synthesis serves as a critical reference for researchers, planners, and policymakers aiming to develop resilient and efficient multimodal transit networks.

This work analyzes how inlet tube velocities (2, 4, and 6 m/s) impact the water-ethylene glycol mixture’s flow behavior within the inlet tube in the engine oil heat exchanger cooling system in terms of temperature distribution, oil viscosity, pressure difference, and flow velocity distribution. From simulation findings, oil's viscosity reduced from 0.021 Pa.s at 2 m/s flow velocity to 0.015 Pa.s at 6 m/s flow velocity, suggesting a direct relationship between the thermal and flow rate. Pressure drop rises with the inlet velocity increase, from 2 to 6 m/s, with values of 0.45 and 0.92 Pa. In the tube-end bending investigation, influences on the velocity profile for emulsion were observed. Depending on the velocity gradient in curved tubes at 2 and 1.2 m/s was the maximum velocity at the sharply curved wall, 2.3 m/s, and at the inner wall, 1.7 m/s. The gradient at 4 m/s was 1.6 m/s, whereas at 6 m/s the gradient was 3.0 m/s. Heat transfer coefficient increases with velocity, ranging from 500 W/m²·K at 2 m/s to 950 at 6 m/s. This shows remarkable enhancement in convective heat transfer resulting from increased turbulence. There is also significant fluctuation in the velocity inside the tubes, and while it increases, the velocity towards uniform flow distribution will improve heat transfer within the tubes. This change inside the tubes reduces uneven heat distribution and helps increase the flow rate, especially when temperature differences grow and the main fluid experiences strong heat transfer. Heat transfer rate rises from 15 kW at 2 m/s velocity to 35 kW at 6 m/s velocity, and efficiency increases to 70% due to increasing inlet velocity.

This study was conducted during the 2023-2024 growing season at the Agricultural Technical College, Northern Technical University, Nineveh Governorate, to evaluate the effects of organic and amino acid fertilization on the vegetative growth and yield performance of eggplant (Solanum melongena L.). A randomized complete block design (RCBD) was employed, comprising two fertilizer types (organic and amino acid) and three concentrations (0, 10, and 15 g·L⁻¹), resulting in six treatment combinations, each replicated three times, for a total of 18 experimental units. Statistical analysis was performed using SAS software. Significant improvements were observed in several vegetative and physiological parameters, including plant height, number of branches, stem diameter, and chlorophyll content. Organic fertilization produced the most substantial increases in plant height (43.000 cm), number of branches (5.444 branches·plant⁻¹), stem diameter (16.367 mm·seedling⁻¹), and leaf chlorophyll content (22.723 SPAD), significantly outperforming amino acid fertilization and the control. In contrast, amino acid fertilization resulted in a higher number of fruits per plant (5.888 fruits·plant⁻¹). The interaction between organic fertilization and the 10 g·L⁻¹ concentration yielded the highest plant height (52.667 cm) and number of fruits (6.000 fruits·plant⁻¹). Additionally, the combination of organic fertilization and 15 g·L⁻¹ concentration significantly increased the number of branches (6.666 branches·plant⁻¹) and chlorophyll content (29.417 SPAD). The stem diameter reached its maximum value (20.050 mm·seedling⁻¹) under amino acid fertilization at a concentration of 10 g·L⁻¹. The control treatment consistently produced the lowest values across all evaluated parameters. These findings demonstrate that both organic and amino acid fertilization can significantly enhance specific growth and yield components in eggplant, with organic fertilizers exhibiting superior overall performance in vegetative traits and amino acids promoting reproductive output. The results highlight the potential of integrating amino acid and organic nutrient management strategies to optimize eggplant productivity under field conditions.

This study introduces Chebyshev Metaheuristic Solver Approach (CMSA), a new computational approach, to get approximate solutions with high-accuracy to a vast range of linear and non-linear differential equations (DEs). The main idea is changing the differential problem into a continuous optimization task. First the approximate solution was written as a truncated series of Chebyshev polynomials, where they are chosen due to their numerical stability and optimal approximation properties. The undetermined coefficients of this series turn into the decision variables in an optimization task. The objective function is derived from the residual of the differential equation, integrated with penalty terms to achieve initial or boundary conditions enforcement. Then the Flower Pollination Algorithm (FPA), a nature-inspired metaheuristic algorithm, is used to find the optimal polynomial coefficients via the minimization of this objective function. This hybrid approach symbiotically integrates the spectral method’s exponential convergence properties with the metaheuristic’s powerful global search capabilities. The demonstration of the efficiency and robustness of the approach is done through rigorous computational tests on benchmark problems, involving integro-differential and non-linear boundary value problems. A comparison of the computed results with known exact solutions, validates this optimization-driven spectral technique, showing excellent accordance. The approach is simple to implement and displays outstanding potential for tackling complex DE systems where traditional methods maybe stick.

This study focuses on using bamboo as a substitute for indoor flooring, emphasizing its sustainability, economic benefits, and aesthetic potential. This research systematically reviewed existing literature using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to analyze factors influencing the adoption of bamboo flooring materials as an alternative sustainable material. These factors include cost, durability, sustainability, availability, and aesthetics, positioning bamboo floors as a viable sustainable alternative to other flooring materials. A total of 50 studies were reviewed, with 20 meeting the inclusion criteria, showing a noticeable increase in interest in bamboo flooring in recent years. The findings highlighted bamboo floors' cost-effectiveness, visual appeal, and strong durability when properly processed and treated. The chemical and mechanical properties of bamboo contribute to the durability of bamboo flooring, especially after the manufacturing process. However, some issues persist, like high transportation costs, limited market reach, and low awareness in many regions, particularly outside of tropical areas.

A transition toward organic fertilizers has increasingly been adopted as a key strategy to support sustainable agriculture, particularly in highland farming systems. In Sumber Brantas Village, Batu City, East Java—one of Indonesia's major highland potato-producing regions—potato (Solanum tuberosum) cultivation plays a critical role due to its high market value, adaptability to altitude, and importance as a carbohydrate source. This study investigated the effects of Tithonia diversifolia-derived organic fertilizer and varying plant densities on potato growth and productivity. Four fertilizer application rates (0, 120, 175, and 230 kg N/ha) and three plant densities (35,000, 47,000, and 71,000 plants/ha) were evaluated using a randomized block design arranged in a split-plot layout. Results indicated that the application of Tithonia diversifolia organic fertilizer significantly enhanced plant height, tuber biomass, and nitrogen (N) uptake. The highest fertilizer dose (230 kg N/ha) was associated with a 25% increase in N absorption and a 28% improvement in tuber yield relative to the unfertilized control. However, plant density did not exert a statistically significant effect on measured agronomic parameters. These findings underscore the agronomic value of Tithonia diversifolia as an organic fertilizer capable of improving nutrient use efficiency and tuber productivity under highland cultivation conditions. The results support the integration of this bioresource into sustainable nutrient management strategies for potato production, particularly in regions where agroecological conditions favor organic inputs.