Uneven Distribution Research Articles

Double-Timescale Multi-Agent Deep Reinforcement Learning for Flexible Payload in VHTS Systems

With the expansion of the very-high-throughput satellite (VHTS) system, the uneven distribution of traffic demands in time and space has become increasingly significant and cannot be ignored. It is a significant challenge to efficiently and dynamically allocate scarce on-board resources to ensure capacity and demand matching. The advancement of flexible payload technology provides the possibility to overcome this challenge. However, computational complexity is increasing due to the unsynchronized resource adjustment and the time-varying demands of the VHTS system. Therefore, we propose a double-timescale bandwidth and power allocation (DT-BPA) scheme to effectively manage the available resources in the flexible payload architecture. We use a multi-agent deep reinforcement learning (MADRL) algorithm aiming to meet the time-varying traffic demands of each beam and improve resource utilization. The simulation results demonstrate that the proposed DT-BPA algorithm enhanced the matching degree of capacity and demand as well as reduced the system’s power consumption. Additionally, it can be trained offline and implemented online, providing a more cost-effective solution for the VHTS system.

Valencian working mothers during lockdown. Discourses on family life in uncertain times

ABSTRACT Lockdowns during the COVID-19 pandemic brought about changes in both the labour market and in family arrangements, when care, work and personal life all took place at home. The main aim of this article is to analyse the impact of the pandemic on family life in Spain from the subjective perspective of women. We develop a topology of narrative configurations on work-life balance through 20 semi-structured interviews with heterosexual women with children or dependants in Valencia, Spain. The most significative idea that emanates from the women’s discourse is a kind of romanticisation of family-domestic space and time generated during the lockdown despite the uneven distribution of caregiving and household responsibilities during this timeframe.

Genome-Wide Identification of APX Gene Family in Citrus maxima and Expression Analysis at Different Postharvest Preservation Times.

Ascorbate peroxidase (APX) is a crucial enzyme involved in cellular antioxidant defense and plays a pivotal role in modulating reactive oxygen species (ROS) levels under various environmental stresses in plants. This study utilized bioinformatics methods to identify and analyze the APX gene family of pomelo, while quantitative real-time PCR (qRT-PCR) was employed to validate and analyze the expression of CmAPXs at different stages of fruit postharvest. This study identified 96 members of the CmAPX family in the entire pomelo genome, with uneven distribution across nine chromosomes and occurrences of gene fragment replication. The subcellular localization includes peroxisome, cytoplasm, chloroplasts, and mitochondria. The CmAPX family exhibits a similar gene structure, predominantly consisting of two exons. An analysis of the upstream promoter regions revealed a significant presence of cis-acting elements associated with light (Box 4, G-Box), hormones (ABRE, TCA-element), and stress-related (MBS, LTR, ARE) responses. Phylogenetic and collinearity analyses revealed that the CmAPX gene family can be classified into three subclasses, with seven collinear gene pairs. Furthermore, CmAPXs are closely related to citrus, pomelo, and lemon, followed by Arabidopsis, and exhibit low homology with rice. Additionally, the transcriptomic heat map and qPCR results revealed that the expression levels of CmAPX57, CmAPX34, CmAPX50, CmAPX4, CmAPX5, and CmAPX81 were positively correlated with granulation degree, indicating the activation of the endogenous stress resistance system in pomelo cells by these genes, thereby conferring resistance to ROS. This finding is consistent with the results of GO enrichment analysis. Furthermore, 38 miRNAs were identified as potential regulators targeting the CmAPX family for post-transcriptional regulation. Thus, this study has preliminarily characterized members of the APX gene family in pomelo and provided valuable insights for further research on their antioxidant function and molecular mechanism.

Investigation of MHD flow balancing in electrically conductive manifolds of liquid metal blankets

In the liquid metal (LM) blanket design for magnetic confinement fusion reactors, manifolds including main and branch ducts are commonly used to direct fluids to various locations of the blanket to cool its components. The primary objective of this structural configuration is to achieve flow balancing within each branch duct. However, magnetohydrodynamic effects will cause uneven flow distribution, consequently, uncontrollable overheating phenomena in certain blanket segments may be caused by this flow distribution. To assess its impact, the influences of external magnetic fields, inlet velocities and coupling wall thicknesses on the flow partitioning in conductive manifolds are investigated in this study. The results indicate that the flow distribution among the branch ducts becomes less uniform as the external magnetic field strength increases, while varying the inlet velocity has minimal impact. In addition, increasing the coupling wall thickness leads to more even flow distribution when the rectangular branch ducts are perpendicular with the magnetic field direction. Based on above physical understanding, three controlling methods for the flow partitioning in conductive manifolds are proposed to achieve flow balancing in each branch duct and then validated through numerical simulations. The results indicate that flow balancing can be achieved by adjusting the coupling wall thickness when the rectangular branch ducts are aligned with the magnetic field direction, however, it is challenging when they are perpendicular. Furthermore, the flow balancing can be realized by a symmetrical design of the manifold with circular branch pipes and it is independent of external magnetic fields and inlet velocities. These findings will enhance the physical understanding of flow distribution patterns in conductive manifolds under strong magnetic fields, which is important for the flow control and design of manifolds in LM blankets.

Challenges of sustainable public procurement in Chinese higher education institutions: a Delphi study

PurposeThe importance of sustainable public procurement is growing in many countries and industries, including higher education institutions (HEIs) in China. However, in China, the practice of sustainable public procurement is currently limited due to various reasons. This study aims to identify the main challenges faced by Chinese HEIs when it comes to sustainable public procurement. The identification of the challenges encountered by HEIs in sustainable public procurement practices not only has practical significance in terms of improving efficiency, sustainability, competitiveness and guiding policymaking but also has theoretical significance in terms of enriching the knowledge body, facilitating the dissemination of sustainability and supporting international comparative research. To identify these challenges, the study gathered data from a small sample of procurement supervisors, experts and commissioners in HEIs using the Delphi method. The study concludes that Chinese HEIs can address the challenges of sustainable public procurement through measures such as fundraising, technology and equipment upgrading, supply chain optimization, regulatory compliance and raising awareness and providing training.Design/methodology/approachTo pinpoint the challenges of sustainable public procurement faced by HEIs in China, this study began by obtaining data from a small sample of procurement supervisors, experts and commissioners in HEIs who are knowledgeable about the issue through the Delphi method. Secondly, the collected challenge factors were also subjected to a literature review to identify the most common challenges in recent papers published around the world. Thus, it was possible to assess the relationship between the challenges mentioned by researchers in China and other countries.FindingsThe main challenges faced by HEIs in China in implementing sustainable public procurement are the focus on economic efficiency, the experience and awareness of procurement staff, policy ambiguity as well as culture, management mechanisms and leadership attitudes in the organizations. The study concludes that Chinese HEIs can effectively overcome the challenges of implementing sustainable public procurement through a variety of measures, including fundraising, technology and equipment upgrading, supply chain optimization, regulatory compliance and awareness raising and training.Research limitations/implicationsSome limitations should be considered in this study. Judging by the sample size, it is clear that the results of the study are limited. Although the number of experts involved in the study is required, caution should be exercised when generalizing the results, as this may not be representative of the entire population. In future studies, the use of a larger sample size could be considered to overcome this problem. Related to this limitation is the uneven geographical distribution of the sample, and this study only considered the situation of HEIs in Jiangsu Province, China, which is not representative of the national region.Practical implicationsThe practical significance can be seen in two aspects. First, accurately identifying challenges can help HEIs optimize the implementation of their sustainable purchasing policies, leading to more efficient resource allocation and reduced unnecessary resource consumption. Second, by addressing these challenges, HEIs can better fulfill their social role in sustainable development, contributing to the triple bottom line of the economy, environment and society. Implementing sustainable procurement strategies not only enhances the social image of HEIs but also attracts a broader student base and financial support, ultimately improving their competitive position in the education market. A deeper understanding of these challenges provides policymakers with a basis to develop a more operational and targeted policy framework to support sustainable procurement in HEIs.Social implicationsThis study succeeded in answering two research questions that provide practical implications for public procurement practitioners, researchers and policymakers. Although HEIs in China do not practice sustainable public procurement at a high rate, however, with the spread of sustainability and environmental awareness, more and more HEIs are beginning to realize the importance of sustainability and are gradually taking it into account in their procurement.Originality/valueThe findings of the study not only significantly enrich the existing body of knowledge on sustainable public procurement, but also support the selection of challenge variables that must be addressed first to facilitate the application of sustainable procurement in China’s HEIs.

Development of Peptide Paratope Mimics Derived from the Anti-ROR1 Antibody and Long-Acting Peptide-Drug Conjugates for Targeted Cancer Therapy.

Antibody-based targeted therapy in cancer faces a challenge due to uneven antibody distribution in solid tumors, hindering effective drug delivery. We addressed this by developing peptide mimetics with nanomolar-range affinity for Receptor Tyrosine Kinase-Like Orphan Receptor 1 (ROR1) using computational methods. These peptides showed both specific targeting and deep penetration in vitro and in vivo. Additionally, we created peptide-drug conjugates (PDCs) by linking targeting peptides to toxin drugs via various linkers and enhancing their in vivo half-life with fatty side chains for albumin binding. The antitumor candidate II-3 displayed exceptional affinity (KD = 1.72 × 10-9 M), internalization efficiency, anticancer potency (IC50 = 0.015 ± 0.002 μM), and pharmacokinetics (t1/2 = 2.6 h), showcasing a rational approach for designing PDCs with favorable tissue distribution and strong tumor penetration.

Utilizing Laser-Induced Fluorescence to Evaluate the Efficiency of Carbon Dioxide and Diode Lasers in Dentin Sealing after Tooth Preparation: An in-vitro Study.

Adhesive dentistry has made it feasible to design restorations with high aesthetic qualities and little invasiveness. However, the freshly cut dentin after the tooth preparation needs to be sealed to prevent its contaminations, bacterial leakage, and hypersensitivity. Consequently, the immediate dentin sealing (IDS) method has been advised. This study examines different dentinal tubule sealing methods via CO2 laser, diode laser (980-nm) and a two-step self-etch adhesive system applied directly to the fresh cut dentin preceding the placement of the provisional phase. The sealing efficiency of each laser and bond system was evaluated based on the laser-induced fluorescence (LIF) properties and image analysis by scanning electron microscopy. Moreover, the obtained LIF spectra were evaluated using partial least square progression. A two-step adhesive containing a high concentration of S-PRG fillers produced a thick layer that was not perfectly uniform at all sites due to uneven filler distribution in the bond with totally and partially closed dentinal tubules. However, the peaks of the LIF spectra dropped after applying laser because of its sealing effectiveness. Accordingly, CO2 and diode lasers have strong evidence in dentinal tubule sealing and a definitive treatment modality for dentinal hypersensitivity. Moreover, IDS with an adhesive system is superior in occluding dentinal tubules in a biomimetic manner based on its filler content and bioactive properties.

Utilizing Laser-Induced Fluorescence to Evaluate the Efficiency of Carbon Dioxide and Diode Lasers in Dentin Sealing after Tooth Preparation: An in-vitro Study.

Adhesive dentistry has made it feasible to design restorations with high aesthetic qualities and little invasiveness. However, the freshly cut dentin after the tooth preparation needs to be sealed to prevent its contaminations, bacterial leakage, and hypersensitivity. Consequently, the immediate dentin sealing (IDS) method has been advised. This study examines different dentinal tubule sealing methods via CO2 laser, diode laser (980-nm) and a two-step self-etch adhesive system applied directly to the fresh cut dentin preceding the placement of the provisional phase. The sealing efficiency of each laser and bond system was evaluated based on the laser-induced fluorescence (LIF) properties and image analysis by scanning electron microscopy. Moreover, the obtained LIF spectra were evaluated using partial least square progression. A two-step adhesive containing a high concentration of S-PRG fillers produced a thick layer that was not perfectly uniform at all sites due to uneven filler distribution in the bond with totally and partially closed dentinal tubules. However, the peaks of the LIF spectra dropped after applying laser because of its sealing effectiveness. Accordingly, CO2 and diode lasers have strong evidence in dentinal tubule sealing and a definitive treatment modality for dentinal hypersensitivity. Moreover, IDS with an adhesive system is superior in occluding dentinal tubules in a biomimetic manner based on its filler content and bioactive properties.

Does green finance matter for agricultural carbon abatement? Fresh insight from China.

Green finance constitutes a fundamental financial institutional arrangement aimed at achieving the dual-carbon objective during China's agricultural transition toward low-carbon, which aligns with the pragmatic necessity for reducing agricultural carbon emissions. For this purpose, this study investigates the impact of green finance on agricultural carbon reduction and its underlying mechanism by analyzing panel data from 30 regions using a two-way fixed effects model. Results indicate that green finance significantly promotes agricultural carbon reduction. The promotion effect of green finance on agricultural carbon reduction demonstrates an uneven spatial distribution, declining from eastern to western to central regions. Notably, non-Yangtze River Economic Belt provinces exhibit more pronounced effects compared to the Yangtze River Economic Belt region. Industrial structure upgrading and green innovation serve as crucial pathways for green finance to augment agricultural carbon reduction. Our finding reveals policymakers should implement differentiated green finance implementation strategies to accelerate the restructuring of the agricultural industry and encourage the introduction and use of innovative low-carbon technologies.

Towards reliable healthcare Imaging: conditional contrastive generative adversarial network for handling class imbalancing in MR Images

Background Medical imaging datasets frequently encounter a data imbalance issue, where the majority of pixels correspond to healthy regions, and the minority belong to affected regions. This uneven distribution of pixels exacerbates the challenges associated with computer-aided diagnosis. The networks trained with imbalanced data tends to exhibit bias toward majority classes, often demonstrate high precision but low sensitivity. Method We have designed a new network based on adversarial learning namely conditional contrastive generative adversarial network (CCGAN) to tackle the problem of class imbalancing in a highly imbalancing MRI dataset. The proposed model has three new components: (1) class-specific attention, (2) region rebalancing module (RRM) and supervised contrastive-based learning network (SCoLN). The class-specific attention focuses on more discriminative areas of the input representation, capturing more relevant features. The RRM promotes a more balanced distribution of features across various regions of the input representation, ensuring a more equitable segmentation process. The generator of the CCGAN learns pixel-level segmentation by receiving feedback from the SCoLN based on the true negative and true positive maps. This process ensures that final semantic segmentation not only addresses imbalanced data issues but also enhances classification accuracy. Results The proposed model has shown state-of-art-performance on five highly imbalance medical image segmentation datasets. Therefore, the suggested model holds significant potential for application in medical diagnosis, in cases characterized by highly imbalanced data distributions. The CCGAN achieved the highest scores in terms of dice similarity coefficient (DSC) on various datasets: 0.965 ± 0.012 for BUS2017, 0.896 ± 0.091 for DDTI, 0.786 ± 0.046 for LiTS MICCAI 2017, 0.712 ± 1.5 for the ATLAS dataset, and 0.877 ± 1.2 for the BRATS 2015 dataset. DeepLab-V3 follows closely, securing the second-best position with DSC scores of 0.948 ± 0.010 for BUS2017, 0.895 ± 0.014 for DDTI, 0.763 ± 0.044 for LiTS MICCAI 2017, 0.696 ± 1.1 for the ATLAS dataset, and 0.846 ± 1.4 for the BRATS 2015 dataset.

The Evolution of Forest Landscape Connectivity and Ecological Network Construction: A Case Study of Zhejiang’s Ecological Corridors

As main components of terrestrial ecosystems, forests play irreplaceable roles in maintaining ecological balance and protecting the basic environment for human survival and development. In this study, the dynamic changes in the forest landscape connectivity in Zhejiang province in 2000, 2010, and 2020 were detected by identifying ecological sources and evaluating connectivity indexes based on morphological spatial analysis (MSPA) and a minimum cumulative resistance (MCR) model. The results are as follows: (1) The forest area of Zhejiang increased by 64.88% from 2000 to 2020, indicating that the overall habitat quality has improved and that ecological risks have decreased, which are attributed to Zhejiang’s adherence to comprehensive environmental management. (2) Regions with low connectivity are distributed mainly in the north, where human activities are intensive. The overall pattern of high connectivity in the middle of the region and low connectivity elsewhere demonstrates the uneven distribution of forest landscape connectivity across the province. (3) The extracted ecological corridors have a mesh-like structure that is dense in the middle and slack in the north. Important corridors have disappeared over time, indicating potential issues in maintaining connectivity for species migration. (4) These results can provide optimization strategies for ecological infrastructure planning in Zhejiang province and offer a theoretical reference for the optimization of the ecological network system.

Can China's energy policies achieve the "dual carbon" goal? A multi-dimensional analysis based on policy text tools

AbstractThe continuous increase in global temperatures and frequency of extreme weather events underscore the urgency of achieving "dual carbon" goals. Systematically examining the textual characteristics of energy policies under the "dual carbon" framework, synthesizing the implementation pathways of "dual carbon" initiatives contribute to enhancing comprehension, execution, and optimisation of these policies. This study selecting 409 energy policies under the "dual carbon" goals issued by the central government, the State Council, and ministries from 1992 to 2022 as samples, using policy econometrics and content analysis methods, external characteristics of energy policies including publication time, publication type, and publication subject were analysed. Multidimensional content analysis was conducted for policy tools, objectives, and participants. The implementation path of the "dual carbon" goals was summarised. The study found that China's energy policy under "dual carbon" target has undergone four development stages before and after the release of the energy policy, and energy policy system has been continuously supplemented and improved. Various energy policy tools, diverse policy participants, and policy objectives are of concern and focus. From the "tool-subject-target" perspective, six paths for the implementation of "double carbon" targets in China's energy field are summarised. The energy policy under the "dual carbon" goal has problems such as lack of laws and regulations, over-use of environmental policy tools, uneven distribution in the internal segmentation of policy tools, insufficient involvement of market players and the public, and insufficient attention to the reform of energy systems and mechanisms and the goal of establishing energy-saving and low-carbon concepts.

Development of Peptide-Based Probes for Molecular Imaging of the Postsynaptic Density in the Brain.

The postsynaptic density (PSD) comprises numerous scaffolding proteins, receptors, and signaling molecules that coordinate synaptic transmission in the brain. Postsynaptic density protein 95 (PSD-95) is a master scaffold protein within the PSD and one of its most abundant proteins and therefore constitutes a very attractive biomarker of PSD function and its pathological changes. Here, we exploit a high-affinity inhibitor of PSD-95, AVLX-144, as a template for developing probes for molecular imaging of the PSD. AVLX-144-based probes were labeled with the radioisotopes fluorine-18 and tritium, as well as a fluorescent tag. Tracer binding showed saturable, displaceable, and uneven distribution in rat brain slices, proving effective in quantitative autoradiography and cell imaging studies. Notably, we observed diminished tracer binding in human post-mortem Parkinson's disease (PD) brain slices, suggesting postsynaptic impairment in PD. We thus offer a suite of translational probes for visualizing and understanding PSD-related pathologies.

Autocleandeepfood: auto-cleaning and data balancing transfer learning for regional gastronomy food computing

AbstractFood computing has emerged as a promising research field, employing artificial intelligence, deep learning, and data science methodologies to enhance various stages of food production pipelines. To this end, the food computing community has compiled a variety of data sets and developed various deep-learning architectures to perform automatic classification. However, automated food classification presents a significant challenge, particularly when it comes to local and regional cuisines, which are often underrepresented in available public-domain data sets. Nevertheless, obtaining high-quality, well-labeled, and well-balanced real-world labeled images is challenging since manual data curation requires significant human effort and is time-consuming. In contrast, the web has a potentially unlimited source of food data but tapping into this resource has a good chance of corrupted and wrongly labeled images. In addition, the uneven distribution among food categories may lead to data imbalance problems. All these issues make it challenging to create clean data sets for food from web data. To address this issue, we present AutoCleanDeepFood, a novel end-to-end food computing framework for regional gastronomy that contains the following components: (i) a fully automated pre-processing pipeline for custom data sets creation related to specific regional gastronomy, (ii) a transfer learning-based training paradigm to filter out noisy labels through loss ranking, incorporating a Russian Roulette probabilistic approach to mitigate data imbalance problems, and (iii) a method for deploying the resulting model on smartphones for real-time inferences. We assess the performance of our framework on a real-world noisy public domain data set, ETH Food-101, and two novel web-collected datasets, MENA-150 and Pizza-Styles. We demonstrate the filtering capabilities of our proposed method through embedding visualization of the feature space using the t-SNE dimension reduction scheme. Our filtering scheme is efficient and effectively improves accuracy in all cases, boosting performance by 0.96, 0.71, and 1.29% on MENA-150, ETH Food-101, and Pizza-Styles, respectively.

Autocleandeepfood: auto-cleaning and data balancing transfer learning for regional gastronomy food computing

AbstractFood computing has emerged as a promising research field, employing artificial intelligence, deep learning, and data science methodologies to enhance various stages of food production pipelines. To this end, the food computing community has compiled a variety of data sets and developed various deep-learning architectures to perform automatic classification. However, automated food classification presents a significant challenge, particularly when it comes to local and regional cuisines, which are often underrepresented in available public-domain data sets. Nevertheless, obtaining high-quality, well-labeled, and well-balanced real-world labeled images is challenging since manual data curation requires significant human effort and is time-consuming. In contrast, the web has a potentially unlimited source of food data but tapping into this resource has a good chance of corrupted and wrongly labeled images. In addition, the uneven distribution among food categories may lead to data imbalance problems. All these issues make it challenging to create clean data sets for food from web data. To address this issue, we present AutoCleanDeepFood, a novel end-to-end food computing framework for regional gastronomy that contains the following components: (i) a fully automated pre-processing pipeline for custom data sets creation related to specific regional gastronomy, (ii) a transfer learning-based training paradigm to filter out noisy labels through loss ranking, incorporating a Russian Roulette probabilistic approach to mitigate data imbalance problems, and (iii) a method for deploying the resulting model on smartphones for real-time inferences. We assess the performance of our framework on a real-world noisy public domain data set, ETH Food-101, and two novel web-collected datasets, MENA-150 and Pizza-Styles. We demonstrate the filtering capabilities of our proposed method through embedding visualization of the feature space using the t-SNE dimension reduction scheme. Our filtering scheme is efficient and effectively improves accuracy in all cases, boosting performance by 0.96, 0.71, and 1.29% on MENA-150, ETH Food-101, and Pizza-Styles, respectively.

Evaluating the properties that affect the quality of the charcoal product, determining the limits of toxic emissions during combustion, and studying their impact on human health.

This research focuses on examining the potential impact of charcoal briquettes and lumps on human health due to the emissions they release, and verifying their quality standards. Quality assessment was conducted using a device capable of measuring toxic gases to identify contaminants from various sources such as biomass, synthetic resins, coal, metals, and mineral matter. Toxicity assessments were carried out on five types of briquettes and two varieties of lump charcoal. All charcoal samples were subjected to elemental analysis (SEM/EDAX), including the examination of Ca, Al, Cr, V, Cu, Fe, S, Sr, Si, Ba, Pb, P, Mn, Rb, K, Ti, and Zn. The results showed that burning lump charcoal had toxicity indexes ranging from 2.5 to 5, primarily due to NOx emissions. Briquettes, on the other hand, exhibited higher toxicity indices between 3.5 and 6.0, with CO2 being the main contributor to toxicity. The average 24-h CO content of all charcoal samples exceeded the World Health Organization's 24-h Air Quality Guideline of 6.34ppm, with a measurement of 37ppm. The data indicates that most of the products tested did not meet the prevailing quality standard (EN 1860-2:2005 (E) in Appliances, solid fuels and firelighters for barbecuing-Part 2: Barbecue charcoal and barbecue charcoal briquettes-Requirements and test method, 2005), which specifies a maximum of 1% contaminants, with some products containing as much as 21% impurities. The SEM analysis revealed irregularly shaped grains with an uneven distribution of particles, and the average particle size distribution is quite broad at 5μm. Malaysia Charcoal had the highest calorific value at 32.80MJ/Kg, with the value being influenced by the fixed carbon content-higher carbon content resulting in a higher calorific value.

An Improved Underwater Visual SLAM through Image Enhancement and Sonar Fusion

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

Recovery of high-purity hydromagnesite from seawater through carbonation using Ca(OH)2

The economic challenges arising from resource depletion and uneven distribution of Mg in land reserves have spurred interest in technologies for Mg extraction from seawater. Thus, this study introduces a novel method to recover high-purity magnesium carbonate, specifically hydromagnesite, from seawater through carbonation using Ca(OH)₂ as an alkali source. A significant achievement of this method is the production of high-purity hydromagnesite despite the presence of large amounts of Ca, realized only through carbonation without the need for expensive additives such as NaOH. Experimental results reveal that the Ca(OH)₂ content predominantly influences the Mg dissolution efficiency during carbonation and the overall Mg recovery rate. Maximizing the efficiency of this technology relies on using an appropriate amount of Ca(OH)₂. Notably, the appropriate amount of OH− supply enhances CO₂ dissolution, facilitating Mg dissolution. Conversely, an excess of Ca(OH)₂ impedes Mg dissolution. The overall Mg recovery rate reaches a peak of 67 %, enabling the recovery of 3.4 kg of hydromagnesite from one ton of seawater. The findings highlight the effectiveness of the proposed technology as a promising and cost-effective method for extracting high-purity Mg from seawater, utilizing Ca(OH)2, previously considered an impurity, as a cost-effective alkali source.

Unequal Inaccessibility in Health Services Reason for Rural Migration: An Application of Lee's Push Pull Theory

Rural-to-urban migration has been a growing phenomenon in recent years in Nepal. Federal Democratic Republic Nepal is combating the unequal distribution of resources, power and opportunities induced by a unitary and centralized government in the past. The aim of restructuring the state was to eliminate the uneven distribution of services, power and resources distribution in terms of region. Now, Nepal is implementing three levels of governments- federal, provincial, and local government to eliminate existing inequalities and to make powerful local areas. In this sense, rural-urban migration is not a growing phenomenon but is now a reverse situation. Local governments are developing in the local areas, the local people are migrating to cities. So, the objective of this paper is to apply the push-pull theory of migration and interlink Lee's theory and the distribution of health services in rural areas including Diprung Chuichumma Rural Municipality, Khotang. It is based on the positivistic philosophy and its approach is deductive approach. The causes of rural-to-urban migration are both push-pull factors. Unequal inaccessibility in health services between rural and urban areas is the main reason for migration.

Study on the application of atomized corona discharge combined with screen electrode in dust collection

This paper presents the design of a novel atomized corona discharge coupled screen electrode dust collector, which integrates electrostatic capture and wet electrostatic dust removal technologies. The upper part of the dust collector features an atomized corona discharge electrode utilizing a threaded-wire hole design with uniform water distribution. This design effectively addresses the issue of electrode fatness that may arise from uneven distribution of the discharge electrode. In contrast, the lower part of the collector is equipped with four side-by-side silk screen electrodes, which serve to expand the dust collection area, facilitate secondary particle capture, and consequently enhance dust removal efficiency. The study delves into a detailed analysis of the impact of various parameters on discharge characteristics and dust removal efficiency, culminating in the identification of optimal parameters.