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.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Peat properties of a tropical forest reserve adjacent to a fire-break canal

Tropical peat comprises decomposed dead plant material and acts like a sponge to absorb water,making it fully saturated.However,drought periods dry it readily and increases its vulnerability to fire.Peat fires emit greenhouse gases and particles contributing to haze,and prevention by constructing fire-break canals to reduce fire spread into forest reserves is crucial.This paper aims to determine peat physical and chemical properties near a fire-break canal at different fire frequency areas.Peat sampling was conducted at two forest reserves in Malaysia which represent low fire frequency and high fire frequency areas.The results show that peat properties were not affected by the construction of a fire-break canal,however lignin and cellulose content increased significantly from the distance of the canal in both areas.The study concluded that fire frequency did not significantly influence peat properties except for porosity.The higher fibre content in the high frequency area did not influence moisture content nor the ability to regain moisture.Thus,fire frequency might contribute differently to changes in physical and chemical properties,hence management efforts to construct fire-break canals and restoration efforts should protect peatlands from further degradation.These findings will benefit future management and planning for forest reserves.

Many Kinds of Reserved Judgement in No-Data Problem

The reserved judgment can be broadly categorized into three types: Re-Do, Re-Set, and Natural Flowing Case (i.e. step by step in Re-Try). Hori et al. constructed the Bayes-Fuzzy Estimation and demonstrated that system theory can be applied to the possibility of Markov processes, and that decision-making approaches can be applied to sequential Bayes estimation. In this paper, we focus on the Natural Flowing Case within reserved judgment. Here, the possibility of oblique (or principal) factor rotation is considered as a part of the tandem fuzzy system that follows step by step for sequential Bayes estimation. Ultimately, we achieve a significant result whereby the expected utility can be calculated automatically without the need to construct a utility function for reserved judgment. There, this utility in Re-Do can be calculated by the prior utility, and that utility in Re-set does not exist by our research in this paper. Finally, we elucidate the relationship between fuzzy system theory and fuzzy decision theory through an applied example of Bayes-Fuzzy theory. Fuzzy estimation can be applied to only normal making decision, but it is impossible to apply abnormal decision problem. Our Vague, specially Type 2 Vague can be applied to abnormal case, too.

Analysis of vascular plant resources and diversity in Fanjing Mountain,Guizhou Province

Fanjing Mountain Nature Reserve is located at the junction of Jiangkou,Yinjiang and Songtao counties in the Tongren region of Guizhou Province.Because of the topography and humid climate of the central subtropical monsoonal mountains,the plant species in this area are rich and diverse.The vascular plant resources and diversity of Fanjing Mountain Nature Reserve in Guizhou Province were studied through field survey,literature review and specimen identification.The results show that there are 284 species of vascular plants in the region,belonging to 205 genera in 93 families,mainly angiosperms.Liliaceae,Asteraceae,Polygonaceae,Leguminosae,Ranunculaceae and Polygonum have the most species,followed by Sedum,Dioscorea,Actinidia Lindl and Thalictrum.In terms of life types,perennial herbs are the most dominant,accounting for 68.47%of the total number of species,and annual(or biennial)herbs,shrubs and vine types also present.There are 222 species identified as medicinal vascular plants,accounting for 78.2%of the total number of species.The medicinal parts are primarily found in the whole herb category,followed by the root and rhizome category.

Swelling pressure evolution characterization of strong expansive soil considering the influence of reserved expansion deformation

Numerous engineering cases have demonstrated that the expansive soil channel slope remains susceptible to damage with the implementation of a rigid or closed protective structure. It is common for the protective structure to experience bulging failure due to excessive swelling pressure. To investigate the swelling pressure properties of expansive soil, the constant volume test was employed to study the influence of water content and reserved expansion deformation on the characteristics of swelling pressure in strong expansive soils, and also to explore the evolution mechanism of the swelling pressure. The findings demonstrate that the swelling pressure-time curve can be classified into swelling pressure-time softening and swelling pressure-time stability type. The swelling pressuretime curve of the specimen with low water content is the swelling pressure-time softening type, and the softening level will be weakened with increasing reserved expansion deformation. Besides, the maximum swelling pressure Psmax decreases with increasing water content and reserved expansion deformation, especially for expansion ratio η from 24% to 37%. The reserved deformation has little effect on reducing Psmax when it is beyond 7% of the expansion rate. The specimen with low water content has a more homogeneous structure due to the significant expansion-filling effect, and the fracture and reorganization of the aggregates in the specimens with low water content cause the swelling pressure-time softening behavior. In addition, the proposed swelling pressure-time curve prediction model has a good prediction on the test results. If necessary, a deformation space of about 7% expansion rate is recommended to be reserved in the engineering to reduce the swelling pressure except for keeping a stable water content.

Efficacy of intraovarian autologous platelet-rich plasma administration in women with low ovarian reserve: A systematic review and meta-analysis

Objective:To conduct a systematic examination and meta-analysis of the most reliable data from experimental studies evaluating the efficacy of autologous platelet-rich plasma(PRP)on low ovarian reserve.Methods:A comprehensive search was performed utilizing pertinent search terms across electronic databases,including PubMed,Cochrane,and Google Scholar.We included studies that assigned infertile women with low ovarian reserve in experimental studies.Ovarian reserve parameters were measured before and after PRP injection into ovaries.The data of each study was retrieved and subsequently compiled.Results:Of 301 articles collected and reviewed,six studies were finally included in the meta-analysis.Following PRP injection,infertile women showed a non-significant increase in anti-Mullerian hormone(AMH)level(MD=0.10;95%CI-0.04,0.23),a significant increase in antral follicular count(AFC)(MD=1.88;95%CI 0.47,3.29),and a non-significant reduction in follicle-stimulating hormone(FSH)level(MD=-0.22;95%CI-8.32,7.87).Conclusions:Autologous PRP may increase AFC,but not AMH.Although it is found beneficial in enhancing ovarian reserve(AFC),further research with strong evidence is still required.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter(CSIT).A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CsI feedback stage.In fact,the downlink communication generally includes three stages,i.e.,pilot training,CsI feedback,and data transmission.These three stages are mutually related and jointly determine the overall system performance.Unfortunately,there exist few studies on the reduction of csIT acquisition overhead from the global point of view.In this paper,we integrate the Minimum Mean Square Error(MMSE)channel estimation,Random Vector Quantization(RVQ)based limited feedback and Maximal Ratio Combining(MRC)precoding into a unified framework for investigating the resource allocation problem.In particular,we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio(SNR)based on the deterministic equivalence theory.Then the three performance metrics(the spectral efficiency,energy efficiency,and total energy consumption)oriented problems are formulated analytically.With practical system requirements,these three metrics can be collaboratively optimized.Finally,we propose an optimization solver to derive the optimal partition of channel coherence time.Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

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.

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.

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.

The Effect of Uterine Artery Occlusion with Tourniquet on Ovarian Reserve during Open Myomectomy at a University Teaching Hospital in Southern Nigeria

Background: The most common surgical treatment for symptomatic uterine fibroids, particularly in women with fertility concerns, is open myomectomy. Given the high vascularity of the uterus, haemorrhage during the procedure is a serious risk that is often mitigated with a uterine tourniquet. Aim and Objectives: To evaluate the effect of uterine artery occlusion with a tourniquet during open myomectomy on ovarian reserve using serial anti-Mullerian hormone (AMH) measurements. Materials and Methods: This was a prospective longitudinal study with a quasi-experimental design and a convenient sampling technique. The study enrolled 47 women who had abdominal myomectomy between September 1, 2021, and March 31, 2022, at the University of Port Harcourt Teaching Hospital. Blood samples were collected before anaesthesia was administered in theatre, on day two, and three months after open abdominal myomectomy for anti-Mullerian hormone assay. The data was collected using a semi-structured proforma, entered into an Excel spreadsheet, and analyzed using SPSS version 25.0 with a 95% confidence interval. Statistical significance level was set at 0.05. Results: The pre-surgery AMH mean value was 1.67 ± 1.44 ng/ml, while the values after using a uterine tourniquet at myomectomy on the second day and three months later were 1.22 ± 1.24 ng/ml and 1.59 ± 1.43 ng/ml, respectively. There was no statistically significant change in AMH levels, and there was no statistically significant relationship between blood loss and tourniquet time and AMH after open abdominal myomectomy. Conclusion: The use of a uterine tourniquet and blood loss during open myomectomy has no effect on ovarian reserve.

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.

Impacts of Human Activities and Season on Species Diversity and Abundance of Butterflies in Mpanga Kipengere Game Reserve and Surrounding Farmlands, Tanzania

Maintaining natural habitats is crucial for the preservation of insects and other species that indicate environmental changes. However, the Mpanga/Kipengere Game Reserve and its surrounding farmlands are facing disturbance due to human activities, which is putting many wildlife species, particularly larger mammals, at risk. To determine the impact of human activities on butterfly species diversity and abundance in the reserve and its surrounding areas, we conducted a study from November 2021 to October 2023. We collected butterfly data using transect walks and baited traps in two habitat types. Our study yielded 2799 butterfly Individuals ranging in 124 species divided into five families habitat, season, and anthropogenic factors are significant environmental variables influencing species diversity and abundance of butterflies. Therefore, it’s important to protect habitat and dry-season water for the conservation of invertebrates such as butterflies. Our study findings provide essential information for ecological monitoring and future assessment of the Mpanga/Kipengere Game Reserve ecosystem health.

Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency.To tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell.To fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent cells.To tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV position.Then,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation schemes.We further propose an iterative power allocation algorithm based on the sequential convex approximation theory.The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Seasonal Abundance and Diversity of Birds of Prey and Owls in Al Wathba Wetland Reserve in Abu Dhabi, UAE

Inland wetlands in Abu Dhabi Emirate are wintering and stopover sites for migratory birds of prey. We conducted long-term regular monitoring surveys in Al Wathba Wetland Reserve (AWWR) from January 1995 to December 2022. Both diurnal and occasionally nocturnal surveys were undertaken to record the migratory raptors and owls in the Wetland Reserve. During the study, a total of 1282 regular monitoring visits were undertaken and 27 species of diurnal raptors and owls representing five families and three orders were detected. These represent 57% of the total species of birds of prey recorded in the UAE. Overall, 63% of all the species that we observed were Accipitriformes followed by 26% Falconiformes and 11% Strigiformes. We found that changes in mean daily temperature have a positive effect on raptor species diversity and abundance in the Wetland Reserve. The species encounter rate was higher in low temperature as compared to high temperature and overall regression equation was statistically significant F (4, 1126) = 8.49), p = 0.00). However, the numbers of raptors did not vary significantly across the years (p = 0.51). Western Marsh-harrier (Circus aeruginosus) and Greater Spotted Eagle (Clanga clanga) were recorded to be the most abundant species in the wetland reserve followed by Common Kestrel (Falco tinnunculus). However, the encounter rate of globally threatened Greater Spotted Eagle was detected to have significantly decreased since 2016. Moreover, 63% of the species detected were uncommon and rarely recorded such as 1) Saker Falcon 2) Lanner Falcon 3) Long-eared Owl & Merlin, which were the rare records from the wetland reserve. Furthermore, 27 years of regular monitoring in the wetland have yielded diverse diurnal raptors and owl fauna (H) = 0.83, (E) = 1.43 (Shannon Diversity Index). The results demonstrate that long-term monitoring surveys in arid environments are essential to determine the trends in the raptor populations and to document rare and globally important species.

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.