The logistics sector is overburdened trying to keep up with the demand for package shipments due to the impact of the growth of online sales on various platforms that make it easier for people to shop. However, there are still a few manual parts involves measuring and calculating the cargo volume. This research proposes a solution with a three-dimensional package measurement approach based on the Ultrasonics HC-SR04 sensor, Arduino, and DC motor to make volume calculation easy, cheap, and automatic. Volume measurement is equipped with a moving arm mechanism from 3 axes simultaneously. The system's ability was tested using a variety of package shape measurement scenarios. According to the measurement results, it can measure package dimensions and volume with an overall success rate of 82.66%, a flat box-shaped package success rate of 93.35%, a cylindrical shape success rate of 96.65%, and an irregular shape success rate of 33.33%. According to the test findings, it can be concluded that this method is highly effective to contribute to calculating the volume of regular-shaped package objects. This is because over 90% of the package shapes received are regular-shaped. However, measuring irregular shapes requires more enhancement to achieve accurate results.

The aerodynamic and structural performance of aircraft wings constructed from Boron Aluminum Metal Matrix Composites (Boron Al MMC) and conventional aluminum alloys has been comprehensively evaluated through Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) studies. The CFD analysis was conducted using ANSYS CFX to investigate the aerodynamic behavior, while the FSI analysis was performed using ANSYS Structural to assess the interaction between fluid flow and structural response under various loading conditions. The findings have demonstrated that wings composed of Boron Al MMC exhibit superior performance in terms of strength, stiffness, and durability when compared to aluminum alloys. Under similar aerodynamic loads, the Boron Al MMC material maintained higher structural integrity, demonstrating a 2.28% reduction in equivalent stress, a 30.1% decrease in induced shear stress, a 69.12% reduction in induced deformation, and a 66.35% lower strain energy relative to the aluminum alloy. These results suggest that Boron Al MMC offers enhanced structural stability at high speeds, especially at speeds exceeding Mach 1, as well as under diverse flight conditions involving high G-forces. The significant reduction in deformation and stress concentrations indicates that Boron Al MMC provides improved resilience against damage under high aerodynamic loads. This analysis underlines the potential of Boron Al MMC as a promising material for aircraft wing construction, capable of delivering improved aerodynamic performance, extended service life, and heightened safety margins. Such properties make it a viable alternative to traditional materials, particularly in advanced aerospace applications where strength, stiffness, and durability are critical. The integration of Boron Al MMC could lead to significant advancements in the development of more efficient and reliable aircraft wings.

In response to the global momentum toward carbon neutrality, the concept of “zero carbon" parks has gained significant attention in the energy and construction sectors. While existing research primarily focuses on optimizing standalone energy systems, a comprehensive methodological framework for evaluating the planning and management of integrated energy systems (IES) within zero-carbon parks remains underexplored. This study addresses this gap by examining the challenges inherent in the zero-carbon transformation of parks and proposing a multi-dimensional assessment index system tailored to IES. The evaluation framework encompasses five critical dimensions: environment, technology, economy, energy, and sustainability. To accurately determine the relative importance of these dimensions, the Analytic Hierarchy Process (AHP) and the Criteria Importance Through Intercriteria Correlation (CRITIC) method are employed for initial weight assignment, which is subsequently refined through game theory optimization. The fuzzy comprehensive evaluation method is then utilized to rigorously assess the benefits of IES across the planning, construction, and operational phases of zero-carbon parks. The findings highlight that the planning and operational stages are of greater significance than the construction phase. Specifically, the planning stage prioritizes environmental impact and technical advantages, while the operational phase emphasizes the equilibrium between economic benefits and ecological responsibilities. This research provides a scientific basis for the strategic planning and management of IES in zero-carbon parks, offering valuable insights for project managers and decision-makers in prioritizing resources across different project stages to achieve sustainable development. By addressing the current research gap, the study not only advances the understanding of IES in zero-carbon parks but also contributes practical guidance for achieving global carbon reduction goals.

The Junin region, located in the central Andes of Peru, boasts a great diversity of natural resources and commercial flows. This region has reported a high number of positive COVID-19 cases in a short period, which raises interest in understanding the most significant factors influencing the spread of this epidemic. Meteorological variables influencing the spread of COVID-19 in a commercial and Andean-Amazonian region of Peru were analyzed. Secondary data on epidemiology, climate, and social aspects from 124 districts in Junin were used to analyze the evolution and territorial distribution patterns of positive COVID-19 cases from March 10 to November 27, 2020. This was achieved through correlations and multiple regression (α = 0.05) between temperature, absolute humidity, solar radiation, altitude, population density, number of markets, poverty, and elementary occupations with infection rates. All variables showed significant correlations (p < 0.01) except for solar radiation (r = 0.08). The most important factors were temperature (r = 0.39; p = 0.006) and the number of markets (r = 0.61; p < 0.001). The results suggest that one of the most important factors in the spread of COVID-19 in a commercial region is the number of local markets, which are key social interaction spaces and primary hotspots for respiratory pandemic infections.

This research article presents a comprehensive comparative analysis of driving patterns in Kuala Terengganu during various peak hours, shedding light on the dynamic nature of urban traffic flows. The study aims to provide valuable insights into the temporal variations in vehicular behaviour within this Malaysian city, with the ultimate goal of informing transportation planning and policy decisions. To achieve this objective, a diverse dataset of vehicle trajectories, collected through GPS tracking systems, was meticulously analysed. The data encompassed two different peak hours, including morning and evening peaks which is go-to-work time and back-from-work time. Several key parameters such as speed, acceleration, deceleration, and others were meticulously extracted and statistically compared across different timeframes. The findings of this study reveal striking disparities in driving behaviour during distinct peak hours. Evening peak hours, characterized by rush hour congestion, displayed significantly lower average speeds, higher traffic density, and increased instances of abrupt acceleration and deceleration. In contrast, morning peaks exhibited more fluid traffic conditions with higher average speeds and reduced congestion. This research provides a comprehensive understanding of the nuances of driving patterns in Kuala Terengganu, shedding light on the temporal dynamics of urban traffic. Finally, the insights generated by this comparative study could be useful for urban planners, traffic control bodies, and policy-makers to minimize peak-hour traffic by using the existing transportation infrastructure more effectively. Moreover, the methodology followed in this research could be useful as a model study approach for similar research in other urban areas, resulting in normalized and efficient urban transportation.

The sustainability of organic rice farming has become a significant focus in agricultural development, as it addresses the interconnected challenges of economic viability, environmental preservation, and social equity. This study evaluates the sustainability of organic rice farming across five districts in Central Java and Yogyakarta, Indonesia, through a comprehensive assessment of economic, ecological, and social dimensions. A proportional stratified random sampling approach was employed, involving 150 farmer respondents, with 30 farmers selected from each district. Descriptive analysis revealed an average sustainability score of 2.94, indicating a moderate level of sustainability. In addition, the Rapid Appraisal for Sustainability (RAPS) tool yielded an average index score of 68.56, categorising the farming systems as "fairly sustainable." The model was further validated through a normalization test, which demonstrated strong consistency across the three sustainability dimensions, with a Standardized Residual Sum of Square (STRESS) value of 0.14 and an R-Squared (RSQ) value of 0.95, suggesting that the data were robust and the model reliable. Sensitivity analysis identified seven critical factors influencing sustainability: agricultural product prices, financial management, poverty alleviation, crop rotation, the involvement of women and young farmers, and preservation of tradition. The results of validation and stability tests indicated that the sustainability model was both stable and reliable across all three dimensions, with an overall sustainability score of 1. These findings underscore the importance of promoting sustainable agricultural practices in organic rice farming and highlight the need for enhanced government involvement in raising awareness, providing training, and fostering educational initiatives to support the economic, ecological, and social dimensions of sustainability in the region.

The enhancement of ionic conductivity and tensile strength in electrolyte membranes by nanoparticles is a key factor driving increased interest in their use. Increasing conductive and strong membranes has the same meaning for energy storage. Conductive solid electrolyte membranes are made by mixing Potassium Hydroxide (KOH), Polyvinyl Alcohol (PVA), and Glycerol with the addition of nanocrystalline cellulose (NCC) paper. Paper NCC is made using the hydrolysis method. In this study, an increase in conductivity and tensile strength due to differences in NCC composition with variations of 0 g, 1 g, 3 g, and 5 g in the electrolyte membrane was observed. The test results show that the highest conductivity of 0.0512 S.cm^-1 was obtained from 3 g NCC according to the membrane test results. The addition of NCC weighing 5 g resulted in the highest tensile strength, namely 6.91 MPa. Furthermore, the addition of 5 g of NCC resulted in the largest energy production of 0.000188 W/cm². The inclusion of NCC in the PVA-KOH membrane was found to increase the tensile strength and ionic conductivity of the electrolyte membrane. The results show that the incorporation of NCC increases the conductivity and strength of the membrane, thereby showing its potential for use in the future development of aluminum air batteries.

Given mounting concerns surrounding the escalating greenhouse gas emissions (GHG) associated with fossil fuel extraction, production, and utilization by both Russian and global oil and gas corporations, devising novel strategies to mitigate the impacts of climate change is imperative. This study is underpinned by a comprehensive review and analysis of global trends in greenhouse gas emissions, diverse decarbonization methods applicable to the oil and gas industry, and established approaches to assess decarbonization initiatives in this sector. These insights underscore the need to advance conceptual frameworks to refine the analysis of decarbonization efforts undertaken by Russian oil and gas enterprises. This study makes a valuable scientific and methodological contribution toward fostering sustainable low-carbon development within the oil and gas industry. This goal is achieved through the implementation of a comprehensive model that passes the ecological and economic impacts of decarbonization initiatives at Russian oil and gas companies. The model proposes an approach to evaluate the effects of these initiatives on the competitiveness of the oil and gas sector using a Balanced Scorecard (BSC) approach supplemented with a range of ecologic metrics. Additionally, the model introduces an integral indicator to quantify the influence of the Balanced Scorecard on key operations of an oil and gas company during decarbonization efforts.

The cost of maritime intermodal freight transport is competitive against that of road transport on long corridors. The length of the major corridor in Java Island is medium. The land distance of the transport corridor in the island is relatively equal to the maritime distance. The objective of this research is to compare the cost of freight transport using maritime intermodal transport with the one using road transport in Java Island. The commodities, origin, destination, and potential freight flow are decided based on the secondary data analysis and the field surveys. The transport costs are estimated using secondary and survey data. The maritime intermodal transport is competitive on the time and distance related costs, while the road mode transport is competitive on the node charges and the first and last mile costs. There is a relatively close cost difference between the maritime intermodal transport and the road transport on the corridor of which the origin is close to the port. Hence, maritime intermodal transport may compete with road transport in the medium long corridor provided that the land and the maritime distances are relatively equal and the origin and the destination are close to ports.

In Indian cities, pedestrian fatalities and injuries have emerged as significant concerns. However, obtaining consistent and reliable crash information poses a significant challenge, particularly in mid-sized Indian cities. In this framework, this study aims to identify and quantify the critical factors influencing pedestrian perceived safety and satisfaction levels in a mid-sized Indian city with respect to diverse land use patterns. A dataset comprising perceptions of 2112 pedestrians regarding 'safety' and 'satisfaction level' has been collected and analyzed across six major intersections characterized by three distinct land use patterns—religious places, commercial areas, and educational hubs—in the central business district area of Patiala city, Punjab, India. With the help of ordered logit models, it has been concluded that the predominant land use pattern, the presence of a pedestrian signal, carriageway width, presence of a curve section at an intersection, vehicular speed, average value of time-to-collision (TTC) at the junction, pedestrian's gender and educational background, and trip purpose significantly affect pedestrians' perceived safety and satisfaction levels. The model outcomes are further constructively utilized to frame suitable policy interventions and recommend remedial measures to enhance pedestrian safety in Indian cities and comparable cities in other low- and middle-income countries (LMICs).

Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO₂ emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance.

The rapid emergence of blockchain technology has facilitated the rise of Initial Coin Offerings (ICOs), offering an innovative approach to raising capital for startups and entrepreneurial ventures. Unlike conventional financing, where projects rely on internal resources or traditional external investments, ICOs enable firms to secure funding directly from the public through token sales. As this new form of crowdfunding gains momentum, the structure of national economic and financial systems has been identified as a critical factor influencing the success and performance of ICOs. Recent research has increasingly focused on comparing ICO markets with traditional corporate financing to better understand the dynamics at play. In this study, an econometric model was constructed to investigate how variations in a country’s financial and economic structures shape the fundraising outcomes of ICOs. A sample of 100 startups from diverse countries, including the United Kingdom, the United States, Austria, and South Africa, was analysed. The ordinary least squares (OLS) method was employed to estimate the model, defined as: Log(funds) = α + β1(fin) + β2(b) + β3(n) + β4(in) + $\in$. The variables represent key economic and financial indicators hypothesised to affect ICO performance, with rigorous statistical tests conducted using R Studio and Excel. Findings are expected to contribute to the growing body of literature by clarifying the extent to which national financial systems either facilitate or hinder the success of ICO fundraising campaigns. This research also provides valuable insights into the evolving role of financial innovation and regulation in the cryptocurrency ecosystem.

Rainfall is crucial for agricultural practices, and climate change has significantly altered rainfall patterns. Understanding the dynamic nature of rainfall in the context of climate change through Machine Learning (ML) and Deep Learning (DL) algorithms is essential for ensuring food security. ML techniques provide tools for processing large-scale data to extract meaningful insights. This study aims to compare the performance of a ML algorithm, Random Forest (RF), with a DL algorithm, Long Short-Term Memory (LSTM), in predicting rainfall in six state capitals in Southwest Nigeria: Osogbo, Ikeja, Ibadan, Akure, Ado-Ekiti, and Abeokuta. The dataset for this study was sourced from the HelioClim website archive, which offers high-quality solar radiation and meteorological data derived from satellite measurements. This archive is known for its accuracy and reliability, providing extensive and consistent historical datasets for various applications. The monthly rainfall data spanning from January 1, 1980, to December 31, 2022, were used, with 80% allocated for training and 20% for validation. As the data are time series, each model was constructed using a look-back period of five months, meaning the past five months' rainfall data served as input features. The performance of these algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results indicated that the RF algorithm yielded the lowest MSE, RMSE, and MAE across all selected cities in Southwest Nigeria. This study demonstrated the superiority of RF regression over LSTM in predicting rainfall in these regions, providing a valuable tool for agricultural planning and climate adaptation strategies.