Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.

Attributes of Specialized Households'Resilience and Its Impact on Rural Industrial Advancement:A Case Study of National Musical Instrument Production Specialized Households in Lankao County,Henan,China

Specialized households serve as the primary units within specialized villages in China,and their capacity to withstand risks and external influences significantly shapes the future trajectory of specialized villages and the overall vitality of the rural economy.In this study,we established a measurement indicator system based on the definition of specialized households’resilience,elucidating the logical connection between specialized households’resilience and rural industrial development in China.The musical instrument industry in Lankao County,Henan Province of China,was employed as a case;survey data,the entropy method,and an obstacle diagnosis model were used to examine how instrument production specialized households responded to the challenges posed by Corona Virus Disease 2019(COVID-19)and the tightening of national environmental protection policies,yielding the following key findings:1)there exists substantial variation in the comprehensive resilience levels among different specialized households;2)the ability to learn and adapt is the most significant contributor to the overall resilience level of specialized households;3)technological proficiency and access to skilled talent emerge as pivotal factors influencing specialized households’resilience;4)the positioning of specialized households within the industrial supply chain and the stability of their income have a direct bearing on their resilience level.The influence of specialized households’resilience on industrial development primarily manifests in the following ways:stronger resilience correlates with increased stability in production and sales,fostering a more proactive approach to future actions.However,heightened exposure to the external macroeconomic environment can lead to a higher rate of export reduction.To enhance the development resilience of entities like specialized households and family farms,and to invigorate rural economic development,escalating investments in rural science and technology and prioritizing the training of technical talent become imperative.

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

The Oral Health Status of Person with Special Needs in Kumasi

Background: The most prevalent non-communicable disease worldwide is oral health-related disease, with dental caries and periodontal conditions being common. Oral health status significantly impacts overall health and quality of life. Barriers to dental care affect children with special needs. This study evaluates the oral health status and treatment requirements of people with special needs. Methodology: This descriptive cross-sectional study in Kumasi, Ghana examined oral health status among people, 121 children with special needs over two months, in June-July 2022. Dental caries, periodontal changes, malocclusion, and trauma were assessed. The data was analyzed using SPSS 20.0. Results: The oral health status among people with special needs in this study was generally moderate, with a range from very good to very poor. The prevalence of dental caries was 37%, with a mean DMFT score of 2.82. Periodontal health showed unsatisfactory indicators such as mobile teeth, poor gingival health, high plaque scores, and the presence of halitosis. Conclusion: This descriptive study aimed to provide baseline data on the oral health status of special needs students in Kumasi, addressing the lack of knowledge in this area. It highlighted the importance of dental care in maintaining good oral health and overall well-being in individuals with special needs. Coordinated efforts by dental professionals are needed to provide dental health education and preventive interventions for these children.

Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks.However,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be guaranteed.In this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different operators.To minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each ECS.We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution.Finally,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain.Simulation results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

Empowering Rice Farmers in Nueva Ecija, Philippines: A Strategic Approach to Boosting Income through Special Purpose Rice Production

Low income is the main problem of millions of farmers who are cultivating 4.81 million hectares in the Philippines. This problem is attributed to challenges specifically low yield, low quality of produce, and significant post-harvest losses, particularly in a resource-scarce setting. This study aimed to help increase the farmers’ income in selected towns of Nueva Ecija through the promotion and use of special purpose rice production technology. Twenty (20) farmer-cooperators in three towns of Nueva Ecija were selected to demonstrate the production technology covering a total area of 4.75 hectares during the dry season, and 3.3 hectares during the wet season, both in 2023. Extension modalities such as training, techno-demo establishment, information material and technical assistance provision, and market linkage were undertaken to enhance their knowledge, attitude, and skills on special rice production and enterprise. The study utilized the descriptive quantitative method wherein the data were gathered through surveys, specifically personal interviews. Results showed that 70% of the farmer-cooperators were male and 60% were living below the poverty threshold. The farmers were 53 years old on average and engaged in farming as their primary source of income. The average yield of special purpose rice particularly the CLS-2 variety was higher (6.46 MT/ha and 6.22 MT/ha during dry and wet seasons, respectively) than that of inbred or non-special rice. The income per hectare from special purpose rice production per hectare was Php 75,420.00, which is higher than that of regular rice at Php 29,389.00, giving a higher benefit of Php 46,031.00. The technology adopters and area planted increased (from 6 to 9 and from 1.4 hectares to 2.75 hectares, respectively) in Licab, Nueva Ecija. The opposite was true in Sto. Domingo and Guimba, Nueva Ecija where farmers were discouraged by the marketing issues that arose.

Ethics of Human Resources Management in the Cameroonian Health System, Medical Nomadism and the Ineffectiveness of the Fight against High Blood Pressure

Context/objectives: The fight against Chronic Non-Communicable Diseases (NCDs) is a long-term undertaking, which requires available, motivated and well-managed human resources (HR). The administrative management of skills on both qualitative and quantitative levels is one of the essential functions of a health system. To better implement policies of fight against High Blood Pressure (HBP) and other chronic diseases, it is important to establish strategies to retain health personnel. This loyalty requires favorable working conditions and consideration of the contribution-reward couple. Good working conditions are likely to reduce the phenomenon of medical nomadism;conversely, poor HR management can contribute to their exodus towards exotic “green pastures”, thus leading to an additional crisis in the Cameroonian health system. The fight against HBP is a complex, multifaceted and multifactorial reality that requires appropriate management model for all types of resources mainly HR. The main objective of this research is to show the impact of poor management of human resources in Cameroon health system on medical nomadism and the ineffectiveness of the fight against High Blood Pressure. Method: A cross-sectional descriptive survey among five hundred (500) health facilities in the center region of Cameroon has been conducted. A stratified probabilistic technique has been used, and the number of health facilities to be surveyed has been determined using the “sample size estimation table” of Depelteau. The physical questionnaires have been printed and then distributed to data collectors. After data collection, the latter were grouped during processing in Excel sheets. The Chi-square test was used for data with a qualitative value and that of Kolmogorov-Sminorf for data with a quantitative value to assess the normality and reliability of data. The Crochach’s Alpha reliability test allowed us to have a summary of the means and variances and then to search for intragroup correlations between variables. Descriptive analysis was possible with the XLSTAT 2016 software. Results: 43.60% of Health Facilities (HF) managers were unqualified. 82.20% of HF managers have staff in a situation of professional insecurity. They are mainly contractual (49.00), decision-making agents (24.40%), casual agents (08.80). The proportion of unstable personnel is average of 22.00% and very unstable, 12.00%.

Mega-Constellations Based TT&C Resource Sharing: Keep Reliable Aeronautical Communication in an Emergency

With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control Center(OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking,and Command(TT&C) architecture named Collaborative TT&C(CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium(NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Parent-offspring relationship recognition based on SSR and mtDNA confirmed resource supplement effect of Fenneropenaeus chinensis release

The resource of Fenneropenaeus chinensis has declined sharply due to excessive fishing intensity,ecological changes and diseases.In order to supplement the fishing yield and restore resources of F.chinensis,the relevant authorities have carried out the activities of stock enhancement and releasing.It can increase biomass and recover resources.However,compared with increasing biomass,there were still few reports on its effect on the recovery of resources.Resource recovery is a process related to whether the released individuals can form a reproductive population.Up to now,there has been a lack of evidence whether the released F.chinensis can complete the entire life history,and form reproduction population.In this study,gravid female shrimp after spawning migration were captured from coastal waters of Haiyang,Qingdao,and Yellow Sea.After identifying parentage relationships using simple sequence repeat(SSR)and mtDNA haplotype,it was finally confirmed that there were eight released individuals in the recapture samples.It was confirmed for the first time that at least part of the released F.chinensis can complete overwintering and reproductive migration,and maintain the migration habits as their wild counterparts.Therefore,we infered that the released shrimp can reproduce under natural conditions,these F.chinensis can form reproductive populations theoretically if without human intervention.These results indicated that enhancenment and release activities have a positive effect on resource recovery.

Spatial and Temporal Distribution Characteristics of Solar Energy Resources in Tibet

The Tibet Plateau is one of the regions with the richest solar energy resources in the world.In the process of achieving carbon neutrality in China,the development and utilization of solar energy resources in the region will play an important role.In this study,the gridded solar resource data with 1km resolution in Tibet were obtained by spatial correction and downscaling of SMARTS model.On this basis,the spatial and temporal distribution characteristics of solar energy resources in the region in the past 30 years(1991–2020)are finely evaluated,and the annual global horizontal radiation resource is calculated.The results show that:1)The average annual global horizontal radiation amount in Tibet is 1816 kWh/m^(2).More than 60%of the area belongs to the“Most abundant”(GHI≥1750 kWh/m^(2))area of China’s solar energy resources category A,and nearly 40%belongs to the“Quite abundant”(1400≤GHI<1750)area of China’s solar energy resource category B.2)In space,the solar energy resources in Tibet increased gradually from north to south and from east to west.Lhasa,Central and Eastern Shigatse,Shannan,and Southwestern Ali are the most abundant cities,with a maximum annual radiation level of 2189 kWh/m2.3)In terms of time,the total horizontal radiation in Tibet was the highest in May and the lowest in December.74%of the total area belongs to the“Very stable”(R_(w)≥0.47)area of solar resource stability category A,and 26%belongs to the“stable”(0.36≤R_(w)<0.47)area of solar resource stability category B.Solar energy resources in the region show the characteristics of both strong and stable.Average solar energy resources in the region have shown a fluctuating downward trend over the past 30 years,with an average decline of about 12.86(kWh/m2)per decade.4)In terms of solar radiation resources reaching the earth’s surface,the theoretical total amount of annual horizontal radiation in Tibet is about 240.07 billion tons of standard coal or 222.91 billion kilowatts on average.

A new quantum key distribution resource allocation and routing optimization scheme

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.

Effect of special types of bread with select herbal components on postprandial glucose levels in diabetic patients

BACKGROUND Nutrition recommendations in patients with type 2 diabetes mellitus(T2DM)are to consume rye or integral bread instead of white bread.A positive effect on glucoregulation has been achieved by enriching food with various biologically active substances of herbal origin,so we formulated an herbal mixture that can be used as a supplement for a special type of bread(STB)to achieve better effects on postprandial glucose and insulin levels in patients with T2DM.AIM To compare organoleptic characteristics and effects of two types of bread on postprandial glucose and insulin levels in T2DM patients.METHODS This trial included 97 patients with T2DM.A parallel group of 16 healthy subjects was also investigated.All participants were given 50 g of rye bread and the same amount of a STB with an herbal mixture on 2 consecutive days.Postprandial blood glucose and insulin levels were compared at the 30^(th),60^(th),90^(th) and 120th min.A questionnaire was used for subjective estimation of the organoleptic and satiety features of the two types of bread.RESULTS Compared to patients who consumed rye bread,significantly lower postprandial blood glucose and insulin concentrations were found in T2DM patients who consumed STB.No relevant differences were found among the healthy subjects.Subjectively estimated organoleptic and satiety characteristics are better for STB than for rye bread.CONCLUSION STB have better effects than rye bread on postprandial glucoregulation in T2DM patients.Subjectively estimated organoleptic and satiety characteristics are better for STB than for rye bread.Therefore,STB can be recommended for nutrition in T2DM patients.

Resource Allocation for Cognitive Network Slicing in PD-SCMA System Based on Two-Way Deep Reinforcement Learning

In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

Enhancing transboundary natural tourism resources governance:unveiling the spatial pattern and its influencing factors

Tourism resources that span provincial boundaries in China play a pivotal role in regional development,yet effective governance poses persistent challenges.This study addresses this issue by constructing a comprehensive database of transboundary natural tourism resources(TNTR)through amalgamation of diverse data sources.Utilizing the Getis-Ord Gi^(*),kernel density estimation,and geographical detectors,we scrutinize the spatial patterns of TNTR,focusing on both named and unnamed entities,while exploring the influencing factors.Our findings reveal 7883 identified TNTR in China,with mountain tourism resources emerging as the predominant type.Among provinces,Hunan boasts the highest count,while Shanghai exhibits the lowest.Southern China demonstrates a pronounced clustering trend in TNTR distribution,with the spatial arrangement of biological landscapes appearing more random compared to geological and water landscapes.Western China,characterized by intricate terrain,exhibits fewer TNTR,concurrently unveiling a significant presence of unnamed natural tourism resources.Crucially,administrative segmentation influences TNTR development,generating disparities in regional goals,developmental stages and intensities,and management approaches.In response to these variations,we advocate for strengthening the naming of the unnamed transboundary tourism resources,constructing a geographic database of TNTR for government and establishing a collaborative management mechanism based on TNTR database.Our research contributes to elucidating the intricate landscape of TNTR,offering insights for tailored governance strategies in the realm of cross-provincial tourism resource management.

Investigation and analysis of vascular plant resources and diversity in Wuyi Mountain,Fujian Province

Wuyi Mountain,located in the north of Fujian Province,China,is renowned for its abundant medicinal plant resources.In July 2014,the 8th(second team)of Shenyang Pharmaceutical University’s Chinese Medicine Resources Scientific Expedition Team conducted field investigation in the area.Through specimen collection and extensive literature review,the team identified and analyzed 223 vascular plant species from 175 genera and 85 families.The most dominant families were Compositae and Rosaceae,and perennial herbs were the predominant species,accounting for 44.39%of the total species identified.Notably,we documented five precious and rare medicinal plants unique to Wuyi Mountain.This study updates the database of plant resources and diversity in the region,providing a valuable reference for future studies.Finally,we put forward some suggestions to enhance the conservation and sustainable utilization of Wuyi Mountain’s plant resources.