Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

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.

On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

Sensing-Assisted Accurate and Fast Beam Management for Cellular-Connected mmWave UAV Network

Beam management,including initial access(IA)and beam tracking,is essential to the millimeter-wave Unmanned Aerial Vehicle(UAV)network.However,the conventional communicationonly and feedback-based schemes suffer a high delay and low accuracy of beam alignment,since they only enable the receiver to passively“hear”the information of the transmitter from the radio domain.This paper presents a novel sensing-assisted beam management approach,the first solution that fully utilizes the information from the visual domain to improve communication performance.We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction.Besides,we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments.Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay,association accuracy,tracking error,and communication performance.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Exploring the bioactive compounds of Feiduqing formula for the prevention and management of COVID-19 through network pharmacology and molecular docking

Background:To explore the effective chemical constituents of Feiduqing formula for prevention and treatment of coronavirus disease 2019(COVID-19).Methods:The compounds and action targets of twelve herbal medicines in Feiduqing formula were collected via Traditional Chinese Medicine Systems Pharmacology Database and Analytic Platform.The genes corresponding to the targets were queried through the UniProt database.The“herbal medicine-ingredient-target”network was established by Cytoscape software.The Gene Ontology function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed by Database for Annotation,Visualization and Integrated Discovery.Molecular docking was used to analyze the binding force of core active compounds of Feiduqing formula with PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and angiotensin converting enzyme II(ACE2).Results:The“herbal medicine-ingredient-target”network included 434 nodes and 1948 edges,including 222 components such as quercetin,kaempferol,luteolin,etc.The key targets are PTGS2,HSP90AA1,PTGS1,ESR1,AR,NOS2,etc.Gene Ontology function enrichment analysis revealed 2530 items,including RNA polymerase II-specific,response to oxidative stress,transcription factor activity,etc.Kyoto Encyclopedia of Genes and Genomes pathway enrichment screened 169 signal pathways,including Human cytomegalovirus infection,Kaposi sarcoma-associated herpesvirus infection,Hepatitis B,Hepatitis C,IL-17,TNF,etc.The results of molecular docking showed that quercetin,luteolin,β-sitosterol,stigmasterol and other core active compounds have a certain degree of affinity with PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and ACE2.Conclusion:The active compounds of Feiduqing formula may have a therapeutic effect on COVID-19 pneumonia through the action on PTGS2,HSP90AA1,SARS-CoV-23CL hydrolase and ACE2,and regulating many signaling pathways.

Patient satisfaction and follow-up adherence to glaucoma case management clinic in China

AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.

Overview of the immunological mechanisms in hepatitis B virus reactivation:Implications for disease progression and management strategies

Hepatitis B virus(HBV)reactivation is a clinically significant challenge in disease management.This review explores the immunological mechanisms underlying HBV reactivation,emphasizing disease progression and management.It delves into host immune responses and reactivation’s delicate balance,spanning innate and adaptive immunity.Viral factors’disruption of this balance,as are interac-tions between viral antigens,immune cells,cytokine networks,and immune checkpoint pathways,are examined.Notably,the roles of T cells,natural killer cells,and antigen-presenting cells are discussed,highlighting their influence on disease progression.HBV reactivation’s impact on disease severity,hepatic flares,liver fibrosis progression,and hepatocellular carcinoma is detailed.Management strategies,including anti-viral and immunomodulatory approaches,are critically analyzed.The role of prophylactic anti-viral therapy during immunosuppressive treatments is explored alongside novel immunotherapeutic interventions to restore immune control and prevent reactivation.In conclusion,this compre-hensive review furnishes a holistic view of the immunological mechanisms that propel HBV reactivation.With a dedicated focus on understanding its implic-ations for disease progression and the prospects of efficient management stra-tegies,this article contributes significantly to the knowledge base.The more profound insights into the intricate interactions between viral elements and the immune system will inform evidence-based approaches,ultimately enhancing disease management and elevating patient outcomes.The dynamic landscape of management strategies is critically scrutinized,spanning anti-viral and immunomodulatory approaches.The role of prophylactic anti-viral therapy in preventing reactivation during immunosuppressive treatments and the potential of innovative immunotherapeutic interventions to restore immune control and proactively deter reactivation.

Heat transfer enhanced inorganic phase change material compositing carbon nanotubes for battery thermal management and thermal runaway propagation mitigation

Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase change material(PCM)with nonflammability has the potential to achieve this dual function.This study proposed an encapsulated inorganic phase change material(EPCM)with a heat transfer enhancement for battery systems,where Na_(2)HPO_(4)·12H_(2)O was used as the core PCM encapsulated by silica and the additive of carbon nanotube(CNT)was applied to enhance the thermal conductivity.The microstructure and thermal properties of the EPCM/CNT were analyzed by a series of characterization tests.Two different incorporating methods of CNT were compared and the proper CNT adding amount was also studied.After preparation,the battery thermal management performance and TR propagation mitigation effects of EPCM/CNT were further investigated on the battery modules.The experimental results of thermal management tests showed that EPCM/CNT not only slowed down the temperature rising of the module but also improved the temperature uniformity during normal operation.The peak battery temperature decreased from 76℃to 61.2℃at 2 C discharge rate and the temperature difference was controlled below 3℃.Moreover,the results of TR propagation tests demonstrated that nonflammable EPCM/CNT with good heat absorption could work as a TR barrier,which exhibited effective mitigation on TR and TR propagation.The trigger time of three cells was successfully delayed by 129,474 and 551 s,respectively and the propagation intervals were greatly extended as well.

Whole-process case management effects on mental state and selfcare ability in patients with liver cancer

BACKGROUND Regarding the incidence of malignant tumors in China,the incidence of liver cancer ranks fourth,second only to lung,gastric,and esophageal cancers.The case fatality rate ranks third after lung and cervical cancer.In a previous study,the whole-process management model was applied to patients with breast cancer,which effectively reduced their negative emotions and improved treatment adherence and nursing satisfaction.METHODS In this single-center,randomized,controlled study,60 randomly selected patients with liver cancer who had been admitted to our hospital from January 2021 to January 2022 were randomly divided into an observation group(n=30),who received whole-process case management on the basis of routine nursing mea-sures,and a control group(n=30),who were given routine nursing measures.We compared differences between the two groups in terms of anxiety,depression,the level of hope,self-care ability,symptom distress,sleep quality,and quality of life.RESULTS Post-intervention,Hamilton anxiety scale,Hamilton depression scale,memory symptom assessment scale,and Pittsburgh sleep quality index scores in both groups were lower than those pre-intervention,and the observation group had lower scores than the control group(P<0.05).Herth hope index,self-care ability assessment scale-revision in Chinese,and quality of life measurement scale for patients with liver cancer scores in both groups were higher than those pre-intervention,with higher scores in the observation group compared with the control group(P<0.05).CONCLUSION Whole-process case management can effectively reduce anxiety and depression in patients with liver cancer,alleviate symptoms and problems,and improve the level of hope,self-care ability,sleep quality,and quality of life,as well as provide feasible nursing alternatives for patients with liver cancer.

AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

Estimation of the Direct Cost of Management of Venous Thromboembolism in Three Reference Hospitals in the City of Yaoundé: A Retrospective Study over a Three-Year Period

Background: Venous thromboembolism (VTE) is a major public health problem due to its increasing frequency, mortality and management cost. This cost may require major financial efforts from patients, especially in developing countries like ours where less than 7% of the population has health insurance. This study aimed to estimate the direct cost of managing VTE in three reference hospitals in Yaoundé. Methods: This was a cross-sectional retrospective study over a three-year period (from January 1st 2018 to December 31 2020) carried out in the Cardiology departments of the Central and General Hospitals, and the Emergency Centre of the city of Yaoundé. All patients managed during the study period for deep vein thrombosis and pulmonary embolism confirmed by venous ultrasound coupled with Doppler and computed tomography pulmonary angiography respectively were included. For each patient, we collected sociodemographic and clinical data as well as data on the cost of consultation, hospital stay, workups and medications. These data were analysed using SPSS version 23.0. Results: A total of 92 patient’s records were analysed. The median age was 60 years [48 - 68] with a sex ratio of 0.53. The median direct cost of management of venous thromboembolism was 766,375 CFAF [536,455 - 1,029,745] or $1415 USD. Management of pulmonary embolism associated with deep vein thrombosis was more costly than isolated pulmonary embolism or deep vein thrombosis. Factors influencing the direct cost of management of venous thromboembolism were: hospital structure (p = 0.015), health insurance (p 0.001), type of pulmonary embolism (p = 0.021), and length of hospital stay (p = 0.001). Conclusion: Management of VTE is a major financial burden for our patients and this burden is influenced by the hospital structure, health insurance, type of pulmonary embolism and length of hospital stay.

Current state and influencing factors in airbag management among emergency department nurses:A multicenter study

BACKGROUND The emergency department(ED)plays a critical role in establishing artificial airways and implementing mechanical ventilation.Managing airbags in the ED presents a prime opportunity to mitigate the risk of ventilator-associated pneumonia.Nonetheless,existing research has largely overlooked the understanding,beliefs,and practical dimensions of airway airbag management among ED nurses,with a predominant focus on intensive care unit nurses.AIM To investigate the current status of ED nurses'knowledge,beliefs,and practical behaviors in airway airbag management and their influencing factors.METHODS A survey was conducted from July 10th to August 10th,2023,using convenience sampling on 520 ED nurses from 15 tertiary hospitals and 5 sary hospitals in Shanghai.Pathway analysis was utilized to analyze the influencing factors.RESULTS The scores for ED nurses'airway airbag management knowledge were 60.26±23.00,belief was 88.65±13.36,and behavior was 75.10±19.84.The main influencing factors of airbag management knowledge included participation in specialized nurse or mechanical ventilation training,department,and work experience in the department.Influencing factors of airbag management belief comprised knowledge,department,and participation in specialized nurse or mechanical ventilation training.Primary influencing factors of airbag management behavior included knowledge,belief,department,participation in specialized nurse or mechanical ventilation training,and professional title.The belief in airbag management among ED nurses acted as a partial mediator between knowledge and behavior,with a total effect value of 0.513,and an indirect effect of 0.085,constituting 16.6%of the total effect.CONCLUSION ED nurses exhibit a positive attitude toward airbag management with relatively standardized practices,yet there remains room for improvement in their knowledge levels.Nursing managers should implement interventions tailored to the characteristics of ED nurses'airbag management knowledge,beliefs,and practices to enhance their airbag management proficiency.

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation management.Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced data.However,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update module.This paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system.Here,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance.Computation of user reputation scores relies on their behavioral history,actively discouraging undesirable conduct.Additionally,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and reputation.Our scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior.This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

Etiology and management of urethral calculi:A systematic review of contemporary series

Objective:To conduct a systematic literature review on urethral calculi in a contemporary cohort describing etiology,investigation,and management patterns.Methods:A systematic search of MEDLINE and Cochrane Central Register of Controlled Trials(CENTRAL)databases was performed.Articles,including case reports and case series on urethral calculi published between January 2000 and December 2019,were included.Full-text manuscripts were reviewed for clinical parameters including symptomatology,etiology,medical history,investigations,treatment,and outcomes.Data were collated and analyzed with univariate methods.Results:Seventy-four publications met inclusion criteria,reporting on 95 cases.Voiding symptoms(41.1%),pain(40.0%),and acute urinary retention(32.6%)were common presenting features.Urethral calculi were most often initially investigated using plain X-ray(63.2%),with almost all radio-opaque(98.3%).Urethral calculi were frequently associated with coexistent bladder or upper urinary tract calculi(16.8%)and underlying urethral pathology(53.7%)including diverticulum(33.7%)or stricture(13.7%).Urethral calculi were most commonly managed with external urethrolithotomy(31.6%),retrograde manipulation(22.1%),and endoscopic in situ lithotripsy(17.9%).Conclusion:This unique systematic review of urethral calculi provided a summary of clinical features and treatment trends with a suggested treatment algorithm.Management in contemporary urological practice should be according to calculus size,shape,anatomical location,and presence of urethral pathology.

Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Management of Acute Malnutrition in Children Aged 0 to 24 Months Boulbinet Health Center

Introduction: Malnutrition is a pathological state resulting from the relative deficiency or excess of one or more essential nutrients, whether manifested clinically or detected only by biochemical, anthropometric or physiological analyses. The overall objective was to assess the quality of management of acute malnutrition in children aged 0 - 24 months at the Boulbinet health center. Methodology: This was a prospective descriptive study lasting six (06) months from May 5 to October 5, 2018. The study included all children aged 0 to 24 months. Results: Acute malnutrition in children aged 0 - 24 months accounted for 2.11% of cases. The sex ratio was 1.41 in favor of males. The mean age of our patients was 5 months 7 days, with extremes of 1 month and 6 months. The majority came from Ra toma (40.24%). Exclusive breastfeeding was most common (54.02%). The main clinical signs were: pallor 49.42%, diarrhea 46.67, oral lesions37.96%. SAM represented 89.66% and MAM 10.34%. Most associated pathologies: anemia 49.42% and oral candidiasis 37.93%. In terms of outcome, we recorded 56.32% cures, 20.69% deaths, 18.39% dropouts and 4.60% cures. Conclusion: Improving the quality of care for malnourished children aged 0 - 24 months requires raising awareness among mothers and the general public of the consequences of malnutrition.

Uncovering the Challenges of Faecal Sludge Management in Benin’s Urban Hubs: The Cases of Abomey-Calavi and Natitingou

The issue of faecal sludge management in developing countries remains a significant challenge due to the indiscriminate discharge of sludge into the environment. This practice generates more environmental problems than it solves. This study aims to assess the management of household faecal sludge in the communes of Abomey-Calavi and Natitingou, Benin, in order to optimise the best storage conditions for subsequent more effective treatment. To this end, a sociological survey was conducted among households in Abomey-Calavi and Natitingou, as well as among manual emptiers. The results of our studies revealed that two types of latrines are used in households. 20.55% of households use traditional latrines, while 59.83% use ordinary latrines. 7.97% of households use both types of latrine. Moreover, the depth of the latrines varies from 2.5 m to 7 m in Abomey-Calavi and from 2.5 m to 8 m in Natitingou. Among households with a latrine, 28.26% empty their pit at least once, while 71.74% have never emptied it. The emptying cost varies between 35,000 FCFA and 90,000 FCFA. The mixing of faecal sludge with solid waste, including plastic materials, makes emptying difficult due to the obstruction of the vacuum pump. The results obtained will serve as a reliable database to facilitate decision-making in the context of faecal sludge management.