Economical Solution Research Articles

Degradation of Methyl Orange from Aqueous Solution Using Fe-Ni-Co-Based Trimetallic Nanocomposites: Optimization by Response Surface Methodology

Effluent-containing dye molecules is a significant environmental hazard. An economical and energy-saving solution is needed to combat this issue for the purpose of environmental sustainability. In this study, Fe-Ni-Co-based trimetallic nanocomposite was synthesized using the coprecipitation method. Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), and Fourier Transform Infra-Red spectroscopy were conducted to explore the physical morphology, phase structure and functional groups of the synthesized catalyst. Among dyes, methyl orange is considered as a major contaminant in textile effluent. The current study focused on the degradation of methyl orange using a trimetallic Fe-Ni-Co-based nanocomposite. A central composite design in response surface methodology was employed to analyze the independent variables including dye concentration, catalyst dose, temperature, hydrogen peroxide, irradiation time, and pH. Dye degradation has been achieved up to 81% in 20 min at the lowest initial concentration (5 mg/L) in optimized conditions. Based on ANOVA, the predicted values were in great agreement with the actual values, signifying the applicability of response surface methodology in the photocatalytic decolorization of dyeing effluents. The results gained from this research demonstrated that the synthesis method of trimetallic nanocomposite (Iron Triad) is a cost-effective and energy efficient method that can be scaled up to a higher level for industrial application.

Archaeobotany in an era of change and challenge: potential and fragility of macro- and micro-remains

ABSTRACT Apart from helping us understand past communities’ response to climate change and their plant management resilience mechanisms, archaeobotanical information may also serve as a basis to rethink our economic system and implement new solutions to current challenges (e.g. re-adopt forgotten crops or implement circular economy models). Already fragile by nature, the integrity of archaeobotanical heritage is affected by current climate events, such as changes in temperature and precipitation. One consequence is the loss of precious knowledge about past economies and human-environment interactions and its potential to inform us on questions relevant to the present and future. With the Iberian Peninsula as an example, we present a thoughtful insight into the manifold kind of information derived from archaeobotanical assemblages and the harm in losing it. Finally, we call for action to fight against climate change while drawing archaeologists’ attention to the importance of protecting archaeobotanical heritage.

Combined economic emission load dispatch solution using Cuckoo Search Algorithm

The Cuckoo Search Algorithm (CSA) has been selected to find the best optimum solution for Combined Economic and Emission Load Dispatch Network (CEELD) problems. The objective, in the (CEELD) Analysis, that operating a generator schedule is necessary with both minimum fuel costs and emission levels, together, while satisfying the load demand and operational limitations. In this paper, the objective of research is oriented to minimize the fuel cost and emission for generation sets. The numerical results captured from the suggested method are compared with other techniques such as the Gravitational Search Method, strength Pareto evolutionary algorithm and nondominated sorting genetic algorithm-II to illustrate the efficiency of suggested method.

Shanghai cooperation organization economic and transport cooperation at the present stage

The article analyzes the development of foreign trade in the countries of the Shanghai Cooperation Organization (SCO) for the period 2000–2022 in the commodity segment. The predominant and dominant role of China both in the international market and in trade with the SCO states is shown. It is concluded that, along with the growth of mutual trade in the SCO space, the imbalance of trade flows in the future may contribute to the development of negative phenomena, especially in the case of a transition in this trade to settlements in national currencies. The article shows that the SCO countries, especially China and Russia, have intensified and strengthened their presence in the innovation sphere and the global digital segment. The interaction of the SCO countries in innovative and digital directions, according to the author, makes it possible in the future to increase the SCO’s digital leadership in the global economic space and implement digital solutions in other SCO countries, which are less actively developing and using digital tools today. The Shanghai Cooperation Organization has been developing various areas of bilateral and multilateral cooperation between partners for more than 20 years. One of these areas is the transport sector. The importance of this area is growing both in national economies, for example, in China, and in cooperation. The article describes new modern directions of cooperation between the SCO countries in the transport sector. This strengthens the integration processes in the interstate association in the field of economics, transport and improvisation.

Development of Eco‐Friendly Chitosan‐Dextran Polyblend Electrolyte for Enhanced Performance in Primary Magnesium Batteries

The potential of next‐generation batteries lies in solid biodegradable polymer electrolytes. This research delves into a solid blend polymer electrolyte (SBPE) for magnesium conduction, utilizing a chitosan‐dextran blend matrix doped with magnesium perchlorate (Mg(ClO4)2) salt. The electrolyte films are prepared using a conventional solution casting technique. Through techniques like X‐ray diffraction and Fourier transform infrared spectroscopy, the successful incorporation of Mg(ClO4)2 into the blend matrix is confirmed. Notably, the SBPE containing 30 wt% of Mg(ClO4)2 demonstrates the highest ionic conductivity of 6.99 × 10−4 S cm−1 and a prominent ionic transference number of 0.84. Thermogravimetric analysis is carried out to study thermal stability. Differential scanning calorimetry analysis of the electrolyte systems gives insight into their thermal properties. Additionally, it showcases favorable electrochemical stability of 2.66 V. The oxidation and reduction peaks are observed in the cyclic voltammetry curve of the highest conducting sample. Furthermore, the discharge performance of Mg/(CS + DN + Mg(ClO4)2)/cathode cells is explored with varied cathode materials, illustrating the SBPE's potential for magnesium‐ion batteries. This study unveils a sustainable, biodegradable, and economical electrolyte solution for advanced energy storage systems.

Chronological DC transmission expansion planning considering new energy and load uncertainties

Abstract The integration of renewable energy into the grid and the expansion of new loads have significantly increased the uncertainty of the power system, which has brought many new challenges to power system planning. Hence, it is necessary to establish a transmission expansion planning model that adapts to future development to improve the economic benefits of power grid planning. This paper proposes a chronological DC transmission expansion planning method for new energy sources, such as wind and solar energy, considering the time characteristics of source loads. First, the method establishes a chronological DC transmission expansion planning model by simulating long-term grid operations. Among them, the objective function aims to minimize the sum of investment costs and power generation operating costs related costs. Secondly, to simulate unit operation, a loop optimization modeling method is used to optimize the model structure. Finally, simulation analysis is conducted using the improved Garver’s 6 bus system under random planning scenarios as a basis for comparison. By comparing various scenarios, it is concluded that the proposed chronological DC transmission expansion planning can provide a more economical solution than non-chronological planning. This helps ensure that the system operates cost-effectively under uncertain source and load scenarios while enhancing the integration capabilities of wind and solar power generation.

EFFECTIVE RESOURCE MANAGEMENT IN MEDICAL FACILITIES THROUGH ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) technologies are widely used in the medical field. This article analyzes the methods of effective management of resources using SI in medical institutions and their economic solutions.

Privacy-preserving reservation model for public facilities based on public Blockchain

Ensuring fairness in the utilization of government-funded public facilities, such as co-working spaces, sports fields, and meeting rooms, is imperative to accommodate all citizens. However, meeting these requirements poses a significant challenge due to the high costs associated with maintaining digital infrastructure, employee wages, and cybersecurity expenses. Fortunately, Blockchain smart contracts present an economical and secure solution for managing digital infrastructure. They offer a pay-per-transaction schema, immutable transaction records, and role-based data updates. Despite these advantages, public blockchains raise concerns about data privacy since records are publicly readable. To address this issue, this study proposes a privacy-preserving mechanism for public facilities' reservation systems. The approach involves encrypting the reservation table with fully-homomorphic encryption (FHE). By employing FHE with binary masking and polynomial evaluation, the reservation table can be updated without decrypting the data. Consequently, citizens can discreetly book facilities without revealing their identities and eliminating the risk of overlapping schedules. The proposed system allows anyone to verify reservations without disclosing requested data and table contents. Moreover, the system operates autonomously without the need for human administration, ensuring enhanced user privacy.

Circular Economy: Sustainable Agricultural Tourism Development Solution in Phu Giao District, Binh Duong Province

Abstract Agriculture applying circular solutions is an appropriate approach for sustainable agricultural development and delivering high-quality agricultural products to consumers. Additionally, coordinating tourism activities brings benefits in promoting agricultural products and establishing closer connections with consumers. Phu Giao is a district located in the northeastern part of Binh Duong province, known for its favorable land, climate, and water resources for agricultural development. Currently, most ecological agriculture production facilities in Phu Giao district are privately-owned farms with extensive land areas. Identifying potentials, opportunities, and further developing circular agriculture models in conjunction with tourism is a necessary prerequisite for researching sustainable local economic development solutions. Through on-site surveys, in-depth interviews, and questionnaire surveys in the study area, the authors propose solutions for develop circular agriculture tourism model.

Digital Implementation of Chaotic Systems UsingNvidia Jetson AGX Orin and Custom DAC Converter

Digital implementation of chaotic systems has numerous advantages, offering higher flexibility and computational accuracy compared to traditional analog systems. This is particularly important for engineering applications. Digital solutions are also more cost-effective. In this study, the Nvidia Jetson AGX Orin board and a 16-bit converter board are used for the digital realization of chaotic systems. The Nvidia Jetson AGX Orin provides high processing power and energy efficiency, while the 16-bit converter card ensures precise data processing. This setup allows for advanced, flexible, reliable, and economical solutions, enhancing various engineering applications, such as modeling, simulation, and control of complex systems. Digital platforms also facilitate parametric investigations and optimization processes, enabling more in-depth analysis.

Dual Systems of Urban Villages: Autonomy and Informal Governance Mechanisms of Yuan Village in Guangzhou

Implementing demolition and transformation policies in urban villages is essential for effective urban governance. However, these villages possess distinctive governance models and entities that set them apart from general urban and rural communities. This study examines the autonomous “informal governance” process in Guangzhou's Yuancun urban village through a case study approach. It investigates the formation mechanisms of the “dual system”. The study underlines the significance of grassroots innovation and the Chinese Communist Party's role in attaining governance autonomy. Despite the government's aim to integrate urban villages into the urban governance system, persistent challenges arise from these unique models and entities. The Village Company plays a pivotal role in promoting stable governance through participatory practices and economic advantages. Resolving the complex issues in urban villages necessitates integrated economic, political, and social solutions. Empowering the Village Company to handle specific public affairs can facilitate the transition towards the modernization of grassroots governance.

Pelatihan Pemanfaatan Stik Es Krim Sebagai Alternatif Dekorasi Dinding Rumah

This research focuses on the utilization of ice cream sticks as an alternative material for wall decoration. The objective of this study is to enhance the creativity of participants, provide an economical solution for home decoration, increase awareness of the use of recycled materials, and explore new economic opportunities. The training program developed in this research aims to teach participants how to create aesthetically pleasing and unique wall decorations using ice cream sticks, a commonly available and inexpensive material. The training includes theoretical and practical sessions to ensure participants gain both knowledge and hands-on experience. The results show that participants not only developed new skills but also gained insights into potential business opportunities in the craft industry. Additionally, this study contributes to environmental sustainability by promoting the use of recycled materials. The findings suggest that with proper guidance and training, ice cream sticks can be effectively transformed into valuable decorative items, thereby offering both economic and environmental benefits.

Dynamic quantitative phase microscopy: a single-shot approach using geometric phase interferometry

There is a significant gap in cost-effective quantitative phase microscopy (QPM) systems for studying dynamic cellular processes while maintaining accuracy for long-term cellular monitoring. Current QPM systems often rely on complex and expensive voltage-controllable components like Spatial Light Modulators or two-beam interferometry. To address this, we introduce a QPM system optimized for time-varying phase samples using azobenzene liquid crystal as a Zernike filter with a polarization-sensing camera. This system operates without input voltage or moving components, reducing complexity and cost. Optimized for gentle illumination to minimize phototoxicity, it achieves a 1 Hz frame rate for prolonged monitoring. The system demonstrated accuracy with a maximum standard deviation of ±42 nm and low noise fluctuations of ±2.5 nm. Designed for simplicity and single-shot operations, our QPM system is efficient, robust, and precisely calibrated for reliable measurements. Using inexpensive optical components, it offers an economical solution for long-term, noninvasive biological monitoring and research applications.

CRRFNet: An adaptive traffic object detection method based on camera and radar radio frequency fusion

A large number of studies have proved that camera and radar fusion is a useful and economical solution for traffic object detection. However, how to improve the reliability and robustness of fusion methods is still a huge challenge. In this paper, an adaptive traffic object detection method based on a camera and radar radio frequency Network (CRRFNet) is proposed, to solve the problem of robust and reliable traffic object detection in noisy or abnormal scenes. Firstly, two different deep convolution modules are designed for extracting features from the camera and radar; Secondly, the camera and radar features are catenated, and a deconvolution module is built for upsampling; Thirdly, the heatmap module is used to compress redundant channels. Finally, the objects in the Field of View (FoV) are predicted by location-based Non-Maximum Suppression (L-NMS). In addition, a data scrambling technique is proposed to alleviate the problem of overfitting to a single sensor by the fusion method. The existing Washington University Camera Radar (CRUW) dataset is improved and a new dataset named Camera-Radar Nanjing University of Science and Technology Version 1.0 (CRNJUST-v1.0) is collected to verify the proposed method. Experiments show that CRRFNet can detect objects by using the information of radar and camera at the same time, which is far more accurate than a single sensor method. Combined with the proposed data scrambling technology, CRRFNet shows excellent robustness that can effectively detect objects in the case of interference or single sensor failure.

OVERHEAD WATER TANK CONSTRUCTION: AN INNOVATIVE STRATEGY

The construction of an overhead water tank leads to extensive scaffolding and formwork for the full height of the tank. A steel mill in Sadat City, Egypt, required a tank with a total capacity of 275 m3 at a staging height of 36.75 meters above ground level, so it was built using the “Slip Forming and Heavy Lifting Technique”. It comprises twin chambers, each having a capacity of 75 m3 and 200 m3, supported by a circular shaft. A comparison between the methods of "slip-forming and heavy lifting techniques” and “conventional methods of construction” has been presented. “Slip Forming and Heavy Lifting Technique” is the need of the hour to provide an economical solution to the requirement of a time-bound finish to any project.

Design solutions and characterization of a small scale and very high concentration solar furnace using a Fresnel lens

The use of Fresnel lenses for solar energy concentration technology dates back to the 1950 s. These lenses feature a plano-convex optical design with a series of discontinuous convex grooves. Typically made from materials like polymethyl methacrylate, Fresnel lenses are lightweight, resistant to sunlight, thermally stable, and cost-effective.This study presents a novel Fresnel lens-based solar furnace configuration installed at the Eduardo Torroja Institute for Construction Science in Madrid, Spain. The novelty of this work lies in the exceptional performance and operability of the facility. Experimental characterization revealed a record peak irradiance over 7 MW m−2 for an incident target power exceeding 800 W. Comparison with ray tracing simulations shows good agreement with experimental results. This setup enables high temperature experiments up to 2000 °C with rapid execution times. A fixed receiver, a shutter system and a closed-loop heliostat tracking control system allow for flexible operation up to 5000 suns and straightforward maintenance. The concentrator element costs less than 300 USD (2022) m−2, offering an economical solution to solar-powered high concentration and temperature applications. This innovative design overcomes previous operational challenges, providing a robust and economical method for high-temperature material processing and other industrial applications.

A Review of Mechanism and Technology of Hybrid Bonding

Abstract With the development of semiconductor technology, traditional flip-chip bonding has been difficult to meet the high-density, high-reliability requirements of advanced packaging technology. As an advanced three-dimensional stacked packaging technology, Cu-SiO2 hybrid bonding technology can achieve high-density electrical interconnection without bumps, which expands the transmission performance and interconnection density of chips greatly. However, the investigation on Cu-SiO2 bonding is far from mature, and many researchers are studying Cu-SiO2 bonding passionately. There are many technologies that use different bonding mechanisms to achieve Cu-SiO2 bonding, which will affect the bonding strength directly. We review the mechanism and research progress of Cu-Cu bonding, SiO2-SiO2 bonding. What is more, we summarize the comparison of bonding conditions and bonding strength of various methods furtherly. According to the bonding mechanism, we propose some economical solutions for low-temperature Cu-SiO2 hybrid bonding, with the aim of providing certain references for the further development of advanced semiconductor packaging.

Simultaneous seismic data de‐aliasing and denoising with a fast adaptive method based on hybrid wavelet transform

AbstractMissing data and random noise are prevalent issues encountered during the processing of acquired seismic data. Interpolation and denoising represent economical solutions to address these limitations. Recovering regularly missing traces is challenging because of the spatial aliasing, and the extra difficulty is compounded by the presence of noise. Hence, developing an effective approach to realize denoising and anti‐aliasing is important. Projection onto convex sets is an effective method for recovering missing seismic data that is typically used for processing data with a good signal‐to‐noise ratio. The computational attractiveness of the projection onto convex sets reconstruction approach is compromised by its slow convergence rate. In this study, we aimed to efficiently implement simultaneous seismic data de‐aliasing and denoising. We combined a discrete wavelet transform with a seislet transform to construct a hybrid wavelet transform. A new fast adaptive method based on the fast projection onto convex sets method was proposed to recover the missing data and remove random noise. This approach adjusts the projection operator and iterative shrinkage threshold operator. The result is influenced by the threshold value. We enhanced the processing accuracy by adopting an optimal threshold strategy. Synthetic and field data tests indicate the effectiveness of the proposed method.

A multi-sensor fusion framework for localization using LiDAR, IMU and RGB-D camera

Abstract In recent years, the increasing demand for precise and intelligent systems has been driven by advancements in indoor unmanned
navigation and positioning technologies. Nonetheless, the indoor environment poses challenges as objects often intersect or obstruct
each other due to spatial constraints, significantly impeding sensor recognition capabilities and compromising localization accuracy.
This paper presented a multi-sensor fusion framework within a system primarily comprised of LiDAR, complemented by RGB D and IMU sensors, to mitigate these challenges. To mitigate point cloud misalignment arising from occlusions, an improved PL-ICP algorithm have been proposed, resulting in improved accuracy and speed in occlusion scenarios. Meanwhile, this paper enhanced the Otsu algorithm by leveraging RGB-D matching to extract additional feature information, consequently enhancing the system’s convergence when occlusion happens. Ultimately, the Extended Kalman Filter (EKF) algorithm is employed to fuse point cloud and image data. Extensive experimental results demonstrate that the proposed approach not only enhances localization accuracy and stability but also exhibits superior convergence characteristics, offering an economical and efficient solution for robotic navigation.

Rapid assay development for low input targeted proteomics using a versatile linear ion trap.

Advances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4 + and CD8 + T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings.