Birds’Diversity in Kalfou Forest Reserve and Its Peripheral Zone, Far North, Cameroon

The Far North Region of Cameroon is home to a great diversity of bird species, which unfortunately remains very little explored. This work was initiated to establish an inventory of birds and the factors affecting their diversity and distribution for sustainable management in the Kalfou Forest Reserve (KFR) and its periphery. Two methods were used for sampling, linear strip transects from which direct counts and indirect observations were made and the mist netting to complement the first. In total, 2525 birds were observed, including 149 species, belonging to 20 orders and 55 families. Accipitridae had the greatest number of species (11). The species richness was greater in the KFR (117 species) compared to the periphery (95 species). The specific richness was higher in wooded savannah compared to other habitats. Shannon index was significantly higher in the KFR (3.99) compared to that obtained in the periphery (3.80). The value of the Simpson index was higher on the outskirts of the KFR than on the periphery. The indices of species diversity were greater in the wooded savannah compared to other vegetation types. The seasons had no influence on bird diversity. Among the human activities encountered, the pressure indices were more important for grazing (7.3 contacts/km). Human activities have resulted in a significant decrease in specific richness. Six endangered species were encountered, four belonging to the Accipitridae family. The greater bird diversity in the reserve compared to the periphery shows that protected areas are a long-term solution for biodiversity conservation.

粒子图像测速技术:从实验室到工业应用的进展(特邀)

粒子图像测速(PIV)已成为测量流体流动速度场的重要实验技术。该方法产生瞬时流型的定量表示,通常用于帮助构建复杂流动的现象学模型和数值模拟验证。PIV分析可广泛用于实验流体力学等多个研究领域。两步处理法,即自相关和互相关,已被用于检查时空流演化,可以实现作为空间分辨率函数的涡度测量和精度估计。PIV的发展与测量流场的复杂性和难度逐渐增加紧密相关。用于测量的PIV技术发展迅速,并呈现出新的趋势,主要体现在立体PIV、断层PIV、大型PIV、微型PIV、3D PIV、行人PIV以及使用高时间/空间分辨率设备。PIV在医学研究、能源燃料、燃烧、流场测量、实时过程监测、结构变形、灌溉、声学、地质学、海洋学、水资源、林业、人群监测和采矿矿物加工等各种研究领域有着广泛的应用。本文对实验室PIV发展进行全面概述,通过在工业过程和日常生活中采用PIV进行实时监控,讨论了PIV如何以精确的时间分辨率和特殊分辨率取代现有的分析成像技术。此外,还综合比较了数值模拟和目前可用的PIV分析技术。

Survey on digital twins for Internet of Vehicles:Fundamentals,challenges,and opportunities

As autonomous vehicles and the other supporting infrastructures(e.g.,smart cities and intelligent transportation systems)become more commonplace,the Internet of Vehicles(IoV)is getting increasingly prevalent.There have been attempts to utilize Digital Twins(DTs)to facilitate the design,evaluation,and deployment of IoV-based systems,for example by supporting high-fidelity modeling,real-time monitoring,and advanced predictive capabilities.However,the literature review undertaken in this paper suggests that integrating DTs into IoV-based system design and deployment remains an understudied topic.In addition,this paper explains how DTs can benefit IoV system designers and implementers,as well as describes several challenges and opportunities for future researchers.

Valorization of wheat bran arabinoxylan: A review on nutritional and materials perspectives

Wheat bran, a principal byproduct of flour milling, stands as an abundant source of dietary fiber, yet its economic potential remains under-exploited in current forage applications. Arabinoxylan(AX), constituting the core of dietary fiber, emerges as a versatile compound with multifaceted functionalities. Its nutritional significance,coupled with its role in cereal food processing, has prompted a surge of studies focusing on the valorization of wheat bran AX. Moreover, the hydrolyzed derivative, arabinoxylan oligosaccharides(AXOS), demonstrates prebiotic and antioxidant properties, offering potential avenues to mitigate the risk of chronic diseases. This review summarizes current knowledge on the valorization of wheat bran AX in terms of the processing and nutritional properties of AX. Moreover, multiple novel applications of AX in the materials area, including biodegradable food packaging films, delivery of bioactive substances as nanoparticles, and the manufacture of food emulsifiers, are also highlighted to extend the utilization of AX. This review underscores the immense potential of wheat bran AX, advocating for its exploitation not only as a nutritional asset but also as a primary ingredient in advanced materials. The synthesis of nutritional and materials perspectives accentuates the multifaceted utility of wheat bran AX, thereby paving the way for sustainable valorization pathways. By unraveling the latent potential within AX, this paper advocates for the holistic and sustainable utilization of wheat bran in diverse, value-added applications.

Screening of glucosinolates degrading lactic acid bacteria and their utilization in rapeseed meal fermentation

Rapeseed meal is a promising food ingredient, but its utilization is limited by the presence of some potentially harmful ingredients, such as glucosinolates. Fermentation is a cost-effective method of detoxication but a food-grade starter culture with glucosinolates degradation capacity is required. In this study, 46 strains of lactic acid bacteria from traditional paocai brines were screened for their ability to glucosinolate degradation. The results showed that more than 50% of the strains significantly degraded glucosinolates. Two strains of Lactiplantibacillus(p7 and s7) with high capacity of glucosinolates degradation through producing enzymes were identified. Then,an optimized condition for rapeseed meal fermentation by p7 was established to degrade glucosinolates, which can achieve about 80% degradation. UPLC/Q-TOF-MS analysis showed that the degradation rate of individual glucosinolates was different and the degradation rate of gluconapin and progoitrin in rapeseed meal can reach more than 90%. Meanwhile, fermentation with p7 can improve safety of rapeseed meal by inhibiting the growth of Enterobacteriaceae and improve its nutritional properties by degrading phytic acid. The in vitro digestion experiments showed that the content of glucosinolates in rapeseed meal decreased significantly during gastric digestion. Meanwhile, fermentation with p7 can greatly improve the release of soluble protein and increase the contents of free essential amino acids, such as lysine(increased by 12 folds) and methionine(increased by 10 folds).

荧光介孔二氧化硅负载丙硫菌唑纳米颗粒的制备及性能研究

近年来,丙硫菌唑作为一种新颖的广谱杀菌剂备受关注,但由于其代谢产物对操作人员存在健康风险,导致其在中国的使用和登记受到限制。将丙硫菌唑制备为具有缓慢释放、可提高农药稳定性等特点的缓释制剂可有效改善其带来的健康风险问题。本研究采用纳米荧光介孔二氧化硅制备了丙硫菌唑缓释纳米颗粒,通过扫描电镜(SEM)、透射电镜(TEM)、傅里叶变换红外光谱(FTIR)、X-射线光电子能谱(XPS)、Brunauer-Emmett-Teller(BET)比表面积分析、热重分析(TGA)、荧光光谱以及高效液相色谱仪等手段对其结构与特性进行表征。采用透析袋法探讨了丙硫菌唑在释放介质中的释放行为;利用菌丝生长速率法测定了该载药颗粒对小麦赤霉病原菌的杀菌活性;通过激光共聚焦研究了荧光二氧化硅在小麦赤霉病菌菌丝体以及小麦中的传输情况。结果表明:所制备的载药纳米颗粒外观形貌较为规整,平均粒径为139 nm,其最大载药量可达27.14%;该纳米载药颗粒具有缓释性能,释放曲线符合Ritger-Peppas方程;载药颗粒与丙硫菌唑原药相比具有相同的毒力效果。此外,激光共聚焦结果表明,该荧光二氧化硅纳米颗粒可以在菌丝体以及小麦植株根部吸收和传导。

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

Assessing Secure OpenID-Based EAAA Protocol to Prevent MITM and Phishing Attacks in Web Apps

To secure web applications from Man-In-The-Middle(MITM)and phishing attacks is a challenging task nowadays.For this purpose,authen-tication protocol plays a vital role in web communication which securely transfers data from one party to another.This authentication works via OpenID,Kerberos,password authentication protocols,etc.However,there are still some limitations present in the reported security protocols.In this paper,the presented anticipated strategy secures both Web-based attacks by leveraging encoded emails and a novel password form pattern method.The proposed OpenID-based encrypted Email’s Authentication,Authorization,and Accounting(EAAA)protocol ensure security by relying on the email authenticity and a Special Secret Encrypted Alphanumeric String(SSEAS).This string is deployed on both the relying party and the email server,which is unique and trustworthy.The first authentication,OpenID Uniform Resource Locator(URL)identity,is performed on the identity provider side.A second authentication is carried out by the hidden Email’s server side and receives a third authentication link.This Email’s third SSEAS authentication link manages on the relying party(RP).Compared to existing cryptographic single sign-on protocols,the EAAA protocol ensures that an OpenID URL’s identity is secured from MITM and phishing attacks.This study manages two attacks such as MITM and phishing attacks and gives 339 ms response time which is higher than the already reported methods,such as Single Sign-On(SSO)and OpenID.The experimental sites were examined by 72 information technology(IT)specialists,who found that 88.89%of respondents successfully validated the user authorization provided to them via Email.The proposed EAAA protocol minimizes the higher-level risk of MITM and phishing attacks in an OpenID-based atmosphere.

Text Augmentation-Based Model for Emotion Recognition Using Transformers

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

Optimizing techno-functionality of germinated whole wheat flour steamed bread via glucose oxidase(Gox)and pentosanase(Pn)enzyme innovation

Germination,a powerful biofortification technique,holds immense potential in bolstering the micronutrient profile of essential staple grains,thereby paving the way for optimal nutritional enhancement.The primary goal of this study was to improve the technological functionality of germinated wheat flour by incorporating pentosanase(Pn)and glucose oxidase(Gox)enzymes,with particular emphasis on the evolutionary changes in its components.The inclusion of Gox did not produce any substantial impact on the volumetric characteristics of the steamed bread.The incorporation of Pn and Gox has been seen to enhance the overall excellence of steamed bread by optimizing loaf volume and textural characteristics while also improving the thermal stability of the dough.The existence of two endothermic peaks could be attributed to bound water or alterations in the granules within the starch crystallization region.Adding Pn and Gox reduced and increased the formation and stability time of the dough,respectively.A certain ratio was employed to assess alternations in the crystallinity of starch granules over a limited range.After steaming,a significant decrease in IR1047/1022 was observed,indicating that the elevated temperature partially disrupted the internal starch crystal structure,leading to a gelatinization reaction with water.The ratio of tensile resistance(R)and elongation(E)of dough increased significantly compared to the control.The results obtained from this study indicate that the simultaneous inclusion of enzymes(Pn+Gox)holds significant promise for expanding the technological functionality of germinated wheat flour dough and improving the quality attributes of steamed bread.

Hash Table Assisted Efficient File Level De-Duplication Scheme in SD-IoV Assisted Sensing Devices

The Internet of Things(IoT)and cloud technologies have encouraged massive data storage at central repositories.Software-defined networks(SDN)support the processing of data and restrict the transmission of duplicate values.It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead.Existing State of the art schemes suffer from computational overhead due to deterministic or random tree-based tags generation which further increases as the file size grows.This paper presents an efficient file-level de-duplication scheme(EFDS)where the cost of creating tags is reduced by employing a hash table with key-value pair for each block of the file.Further,an algorithm for hash table-based duplicate block identification and storage(HDBIS)is presented based on fingerprints that maintain a linked list of similar duplicate blocks on the same index.Hash tables normally have a consistent time complexity for lookup,generating,and deleting stored data regardless of the input size.The experiential results show that the proposed EFDS scheme performs better compared to its counterparts.

Advancements in biocatalysis:From computational to metabolic engineering

Through several waves of technological research and un‐matched innovation strategies,bio‐catalysis has been widely used at the industrial level.Because of the value of enzymes,methods for producing value‐added compounds and industrially‐relevant fine chemicals through biological methods have been developed.A broad spectrum of numerous biochemical pathways is catalyzed by enzymes,including enzymes that have not been identified.However,low catalytic efficacy,low stability,inhibition by non‐cognate substrates,and intolerance to the harsh reaction conditions required for some chemical processes are considered as major limitations in applied bio‐catalysis.Thus,the development of green catalysts with multi‐catalytic features along with higher efficacy and induced stability are important for bio‐catalysis.Implementation of computational science with metabolic engineering,synthetic biology,and machine learning routes offers novel alternatives for engineering novel catalysts.Here,we describe the role of synthetic biology and metabolic engineering in catalysis.Machine learning algorithms for catalysis and the choice of an algorithm for predicting protein‐ligand interactions are discussed.The importance of molecular docking in predicting binding and catalytic functions is reviewed.Finally,we describe future challenges and perspectives.

Simulation and Experimental Analysis of Vortex Tube Using Steel Material

Experimental and computational fluid dynamics (CFD) are investigated through the vortex tube system. The benefit of vortex tube is a counter flow type tube, which has further designed and fabricated for investigation. The whole set up is consisting of a simple device that can separate a single stream of compressed air into two streams;one is at high temperature and the other is lower temperature following an inlet gas stream. The advantages of this tube are their compactness, safety, and low equipment cost mainly used in cooling and heating applications. This study addressed three-dimensional flows;the domain is using computational fluid dynamics (CFD) method and experimental approach to optimize the direction of RHVT. Through the CFD analysis, the best cold end diameter (dc), number of nozzles, and the best parameters for obtaining the highest hot gas temperature and lowest cold gas temperature were obtained and verified by experimental procedures.

Therapeutic Effectiveness of Hijama in Sciatica Pain

Hijama is an alternative mode of treatment also known as cupping therapy. It involves removal of subcutaneous stagnant blood through suction cups after making superficial incisions at particular area of the body. This study was undertaken to evaluate the efficacy of hijama in sciatica pain at Aligarh Shifa hospital. 92 patients with the history of sciatica were selected randomly between 18 - 75 years of age and hijama cups were applied generally at C7, T2 and L5/S1 vertebrae, while two cups were also applied bilaterally on L4/L5 vertebrae, four cups were additionally applied on hip joint, back of thigh, knee and calf muscle, all cups were applied thrice at an interval of 15 days between each session. The decrease in sciatic pain was assessed after three sessions of Hijama by numeric pain rating scale there was overall significant reduction in pain with 67 percent patients showing relief in pain up to varying degree. Present study suggests that hijama has been found to be effective in relieving pain and improving quality of life in majority of the patient’s, hence may be used as effective alternative tool to alleviate pain.

Role of flavonoids in plant interactions with the environment and against human pathogens-A review

Flavonoids are widely-distributed polyphenolic secondary metabolites with diverse biological activities in plants and benefit human health as protective dietary agents.They participate in plants' responses to harsh environmental conditions and effectively regulate the cell differentiation and growth.In plants,the majority of their functions are attributed to their strong antioxidative properties.Similarly,dietary flavonoids protect the human body against free radicals which are associated with the development of cancer and atherosclerosis.Plants rich in polyphenols have been used to cure various diseases because of their antibacterial,antiviral,antifungal and anticancer properties.This review summarizes the up-to-date research trends and development on flavonoids and its derivatives,working mechanisms and potential functions and applications particularly in relation to plant protection and human health.Towards the end,notable concluding remarks with a close-up look at the future research directions have also been presented briefly.

MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles

Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly dynamic.Clustering is one of the promising solutions to maintain the route stability in the dynamic network.However,existing algorithms consume a considerable amount of time in the cluster head(CH)selection process.Thus,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of vehicles.The MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean distance.The CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.

Purification and characterization of manganese peroxidases from native and mutant Trametes versicolor IBL-04

Extracellular manganese peroxidases （MnPs） produced by native and mutant strains of Trametes versicolor IBL‐04 （EB‐60, EMS‐90） were purified by ammonium sulphate precipitation and dialysis, followed by ion‐exchange and gel‐permeation chromatography. The purified enzymes elucidated a single band in the 43‐kDa region on sodium dodecyl sulphate‐polyacrylamide gel electrophoresis. The optimum pH and temperature of the purified enzymes were found to be 5.0 and 40 °C, respec‐tively. Mutant strain MnPs exhibited a broader active pH range and higher thermal stability than native MnP. Purified MnPs from selected mutants showed almost identical properties to native MnP in electrophoresis, steady‐state kinetics, and metal ion and endocrine‐disrupting compound （EDC） degradation efficiency. Although the fastest reaction rates occurred with Mn2＋, MnPs displayed the highest affinity for ABTS, methoxyhydroquinone, 4‐aminophenol and reactive dyes. MnP activity was significantly enhanced by Mn2＋and Cu2＋, and inhibited in the presence of Zn2＋, Fe2＋, ethylene‐diaminetetraacetic acid and cysteine to various extents, with Hg2＋ as the most potent inhibitory agent. MnPs from all sources efficiently catalyzed the degradation of the EDCs, nonylphenol and triclosan, removing over 80%after 3 h of treatment, which was further increased up to 90%in the presence of MnP‐mediator system. The properties of T. versicolor MnPs, such as high pH and ther‐mal stability, as well as unique Michaelis‐Menten kinetic parameters and high EDC elimination effi‐ciency, render them promising candidates for industrial exploitation.

Clinical indicators for progression of nonalcoholic steatohepatitis to cirrhosis

Non-alcoholic fatty liver disease(NAFLD),is a disease spectrum characterized by fat accumulation in hepatocytes presenting as hepatic steatosis to advance disease with active hepatic inflammation,known as nonalcoholic steatohepatitis.Chronic steatohepatitis will lead to progressive hepatic fibrosis causing cirrhosis and increased risk for developing hepatocellular carcinoma(HCC).Fatty liver disease prevalence has increased at alarming rates alongside obesity,diabetes and metabolic syndrome to become the second most common cause of cirrhosis after alcohol related liver disease worldwide.Given this rise in prevalence,it is becoming increasingly more important to find non-invasive methods to diagnose disease early and stage hepatic fibrosis.Providing clinicians with the tools to diagnose and treat the full spectrum of NAFLD will help prevent known complications such as cirrhosis and HCC and improve quality of life for the patients suffering from this disease.This article discusses the utility of current noninvasive liver function testing in the clinical progression of fatty liver disease along with the imaging modalities that are available.Additionally,we summarize available treatment options including targeted medical therapy through four different pathways,surgical or endoscopic intervention.

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Engineered Hybrid Materials with Smart Surfaces for Effective Mitigation of Petroleum-Originated Pollutants

The generation and controlled or uncontrolled release of hydrocarbon-contaminated industrial wastewater effluents to water matrices are a major environmental concern.The contaminated water comes to surface in the form of stable emulsions,which sometimes require different techniques to mitigate or separate effectively.Both the crude emulsions and hydrocarbon-contaminated wastewater effluents contain suspended solids,oil/grease,organic matter,toxic elements,salts,and recalcitrant chemicals.Suitable treatment of crude oil emulsions has been one of the most important challenges due to the complex nature and the substantial amount of generated waste.Moreover,the recovery of oil from waste will help meet the increasing demand for oil and its derivatives.In this context,functional nanostructured materials with smart surfaces and switchable wettability properties have gained increasing attention because of their excellent performance in the separation of oil–water emulsions.Recent improvements in the design,composition,morphology,and fine-tuning of polymeric nanostructured materials have resulted in enhanced demulsification functionalities.Herein,we reviewed the environmental impacts of crude oil emulsions and hydrocarbon-contaminated wastewater effluents.Their effective treatments by smart polymeric nanostructured materials with wettability properties have been stated with suitable examples.The fundamental mechanisms underpinning the efficient separation of oil–water emulsions are discussed with suitable examples along with the future perspectives of smart materials.