Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

A cognitive radio network(CRN)intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization.In CRNs,the secondary users(SUs)opportunistically access the primary users(PUs)spectrum.Therefore,unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs.Cooperative spectrum sensing(CSS)is rated as the best choice for making reliable sensing decisions.This paper employs machinelearning tools to sense the PU channels reliably in CSS.The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput.The fine-k-nearest neighbor algorithm(FKNN),employed in this paper,estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands.The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules,validating the effectiveness of the proposed scheme.Furthermore,the global decision made at the fusion center(FC)based on the modified sensing samples,results low energy consumption,higher throughput,and improved detection with low error probabilities.

CTAB-assisted fabrication of hierarchical flower-like magnesium oxide adsorbent for enhanced removal performance towards phosphate

In this work, a series of hierarchical flower-like magnesium oxide(MgO) adsorbents were successfully fabricated in a cetyltrimethylammonium bromide(CTAB) assisted solvothermal route using hexamethylenetetramine(HMTA) as a precipitating agent. Effects of CTAB feeding amount on the structure, morphology, pore structure, and corresponding adsorption behavior were investigated. The hierarchical gardenias flower-like MgO demonstrated a surface area of 336.54 m^(2)·g^(-1) at a minimum ratio of the CTAB/Mg^(2+)was 0.02 in the reaction system. The hierarchical MgO phosphate removal capacity was 348.32 mg·g^(-1), which followed the pseudo-second-order and Freundlich isotherm model obtained from the large surface area and appropriate pore size. The value of n also suggests the feasible nature of phosphate adsorption under the examined conditions. Indeed, this CTAB assisted solvothermal method can provide a new understanding to tune the desired properties of a material by merely adjusting the reaction parameters of MgO.

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.

Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.

从恐惧到希望:一个孟加拉男孩的奇迹重生(下)

美国有线电视新闻网（CNN）是其中一家将欧可伊的故事播报出来的有线网络。而在那些收看了节目的观众中，有一位商人身在约八千英里之外的美国俄亥俄州哥伦布市，他名叫亚拉姆·科瓦什。

从恐惧到希望:一个孟加拉男孩的奇迹重生(上)

强迫儿童乞讨——在我们国家，可能你会偶有听闻，但在许多特别贫困的东南亚国家，这种事件已经发展到了猖狂的地步。

Fentanyl and xylazine crisis:Crafting coherent strategies for opioid overdose prevention

The United States is in the throes of a severe opioid overdose epidemic,primarily fueled by the pervasive use of fentanyl and the emerging threat of xylazine,a veterinary sedative often mixed with fentanyl.The high potency and long duration of fentanyl is compounded by the added risks from xylazine,heightening the lethal danger faced by opioid users.Measures such as enhanced surveillance,public awareness campaigns,and the distribution of fentanylxylazine test kits,and naloxone have been undertaken to mitigate this crisis.Fentanyl-related overdose deaths persist despite these efforts,partly due to inconsistent policies across states and resistance towards adopting harm reduction strategies.A multifaceted approach is imperative in effectively combating the opioid overdose epidemic.This approach should include expansion of treatment access,broadening the availability of medications for opioid use disorder,implementation of harm reduction strategies,and enaction of legislative reforms and diminishing stigma associated with opioid use disorder.