In this paper, a solar tracking device that can continuously track the sun by adjusting the direction and angle of the solar panel in real time is designed and fabricated to improve the power generation efficiency of the solar cell panel. The mechanical parts as well as the automatic control part of the passive sun-tracking system are described, and the efficiency enhancement with the sun-tracking solar panel is characterized in comparison with the fixed panel system. The test results show that in the spring season in Qingdao city of eastern China, the sun-tracking system can improve the solar cell power generation efficiency by 28.5%–42.9% when comparing to the direction and elevation angle fixed system in sunny days. Even in partly cloudy days, the PV power output can increased by 37% with using the passive sun-tracking system. Economic analysis results show the cost-benefit period is about 10 years, which indicates that the passive sun tracking device can substantially contribute to the solar energy harvest practices.

The growing of plants hydroponically is a soilless form of growing in modern day agriculture. It helps to make feed available for animals throughout the season since it is not affected by what is faced by field grown crops. The use of animal waste, that is, their faeces, in the growth of forage was compared with commercial hydroponics solutions as a way of looking for a reduction in the cost incurred in the purchase of commercial hydroponics solutions. The study evaluated the use of organic nutrient solutions (ONS) alongside a standard/commercial nutrient solution in growing crops hydroponically on the growth, dry matter yield, water use efficiency, and chemical composition of hydroponic maize fodder. The ONS used were formulated from the dried faeces of cattle, poultry, rabbits, and swine. The prepared organic nutrient solutions with the control were used in growing the maize seeds for 10 days, and growth, yield, and chemical composition were determined. Results show the highest (196 g) dry matter yield for maize hydroponic fodder irrigated with poultry ONS. Similarly, maize irrigated with poultry ONS was significantly (P < 0.05) higher in CP content, while it was not significantly different from maize irrigated with cattle, swine, and commercial solutions. A lower water use efficiency value (0.19 kg DM/m³) was recorded for maize irrigated with cattle ONS. According to the study, irrigating maize with different organic nutrient solutions produced maize fodder with a higher yield and a similar chemical composition as the commercial nutrient solution. 

The Olefin aromatization is an important method for the upgrade of catalytic cracking (FCC) gasoline and production of fuel oil with high octane number. The nano-ZSM-5 zeolite was synthesized via a seed-induced method, a series of modified nano-ZSM-5 zeolite samples with different Ga deposition amount were prepared by Ga liquid deposition method. The XRD, N₂ physical adsorption, SEM, TEM, XPS, H₂-TPR and Py-IR measurements were used to characterize the morphology, textural properties and acidity of the modified ZSM-5 zeolites. The catalytic performance of the Hexene-1 aromatization was evaluated on a fixed-bed microreactor. The effects of Ga modification on the physicochemical and catalytic performance of nano-ZSM-5 zeolites were investigated. The Ga species in the modified nano-ZSM-5 zeolites mainly exist as the form of Ga₂O₃ and GaO⁺, which provide strong Lewis acid sites. The aromatics selectivity over Ga modified nano-ZSM-5 zeolite in the Hexene-1 aromatization was significantly increased, which could be attributed to the improvement of the dehydrogenation activity. The selectivity for aromatics over the Ga4.2/NZ5 catalyst with suitable Ga deposition amount reached 55.4%.

This research article examines the relationship between the level of social welfare expenditure and economic growth rates, based on unbalanced panel data from 38 OECD countries covering the period from 1985 to 2022. Four hypotheses are formulated regarding the impact of social expenditure on economic growth rates. Through multiple iterations of regression model building, employing various combinations of dependent and independent variables, and conducting tests for stationarity and causality, compelling empirical evidence was obtained on the negative influence of social welfare spending on economic growth rates. The study takes into account both government and non-governmental expenditures on social welfare, a novelty in this field. This approach allows for a detailed examination of the effects of different components on economic growth and provides a more comprehensive understanding of the relationships. The findings indicate that countries with high levels of social welfare spending experience a slowdown in economic growth rates. This is associated with increasing demands on social security systems, their growing inclusivity, and the escalating required levels of financing, which are increasingly covered by debt sources. The research highlights the need to strike a balance between social expenditures and economic growth rates and proposes a set of measures to ensure economic growth outpaces the indexing of social expenditures. The abstract underscores the relevance of the study in light of the widespread recognition of the necessity to combat inequality, poverty, and destitution, and calls on OECD countries’ governments to pay increased attention to social policy in order to achieve sustainable and balanced economic growth.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).

Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.