Current Research Works Research Articles

Formulation and evaluation of anti-acne herbal face wash

Herbal cosmetics are the preparation used to enhance the human appearance. Herbal formulations have significant demand in the global market. It is more acceptable to believe that natural remedies are safer with synthetic subjects than with fewer side effects. The current research works focuses extraction of neem leaves (Azadirachta indica), turmeric (Curcuma longa), aloe vera, glycerin, lemon juice, rose water and xanthan gum they have anti-acne, anti-inflammatory, anti-oxidant properties, with the help of this herbal ingredient developed and evaluates as a herbal anti-acne face wash. The face wash showed a multipurpose effect and all these herbal ingredients showed significant different activities. The ingredient uses in herbal face wash having properties softening of skin, remove acne as well as promote healing. The prepared formulation was evaluated based on number of criteria including consistency, pH test, spread ability, stability test, cleansing test, foam ability and grittiness.

NRMG: News Recommendation With Multiview Graph Convolutional Networks

The emergence of a news recommendation system can effectively improve the news reading experience of users. The most important task of the system is how to learn news and user representations accurately. In the process of learning news representations, most of the current research works do not fully utilize the news features, which makes it difficult to learn more comprehensive news representations. Most research work only learns user representations from a single perspective, which may not be sufficient to learn diverse and dynamic user representations. Therefore, we propose a news recommendation system with a multiview graph convolutional network (NRMG). It contains two parts: news representation and user representation. The knowledge–content collaboration network is adapted to learn news representations from news content and entities, while the multiview graph convolutional network (GCN) is utilized to learn user representations from the user’s click history. The advantage of the NRMG system is that we not only expand the available features by constructing a subclass knowledge graph (KG), but also effectively improve the ability of the news recommendation system to accurately learn news and user representations. Experimental results on the real dataset MIcrosoft News Dataset (MIND) show that the NRMG achieves a 2.25% improvement in area under the receiver operating characteristic (ROC) curve (AUC) value compared with state-of-the-art methods.

Surgical terminological units: structure, meaning, distribution

The article focuses on a crucial human-related issue of nominating surgical interventions. The authors dwell upon the terms of cardiovascular manipulations and lower limb amputation. Medical terminology is of primary significance to both professionals and non-medical subjects due to its vital nature. Given the prevalence of current anthropocentric research works, this study is relevant. The relevance is further supported by a set of approaches to the surgical terminology followed in the study, in particular, semantic, structural, contextual analysis as well as the tools of online and offline text processing. The complexity and sophistication of the surgical discourse requires detailed and multifaceted analysis for their meaning and contextual use clarification. The article aims at revealing the semantic and structural specifics of the surgical terms based on medical written and spoken texts. The research rested on approximately 200 terminological units retrieved from the texts of a surgical Handbook and surgical manipulation video scripts. The work was performed in four stages. The analysis revealed that structurally, nominal word combinations prevail over verbal ones. The most frequent verbal meanings are ‘to open’, ‘to close’, ‘to introduce’, ‘to remove’, ‘to connect’ and ‘to find’. The semantic classification of the terms revealed that the stages of surgery, anatomical structures and instruments are nominated more frequently. The concordance lines generated by the AntConc tool for the key terms provided vivid representation of their contextual functions, features and collocations with body parts, body systems, and implementation methods.

TECHNOLOGY-ENHANCED FORMATIVE ASSESSMENT: UNRAVELING INDONESIAN EFL LEARNERS’ VOICES

The current research works have put great concern on the positive impacts of the implementation of formative assessment in EFL learning and teaching. Simultaneously, the swift technological development also takes effect on supporting the practices of formative assessment. This study aims to explore EFL learners’ voices on implementing technology-enhanced formative assessment in EFL classrooms. It is a case study intended to obtain detailed information from the participants for a particular case. Five EFL learners from different universities were involved in this study by using a telephone interview. The findings of this study reveal that EFL learners have positive views on the use of formative assessment in terms of getting a better understanding and promoting learning independence. Besides, most EFL learners also perceived that the integration of technology-enhanced positively affects them in working with formative assessments assigned by the lecturers. The last, the challenges of technology-enhanced formative assessment by EFL learners comprised the overload of work assignments, the difficulty of time management, the incapability of staring at laptops for a long time, the instability of LMS, and the unclear instructions as well as insufficient feedback by the lecturers. Several implications are provided in this study.Keywords: challenges; EFL learners; formativ

Emerging 6G/B6G Wireless Communication for the Power Infrastructure in Smart Cities: Innovations, Challenges, and Future Perspectives

A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs. A contemporary review of the utilization of emerging 6G wireless communication for the major applications of smart grids, especially in terms of massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations, are presented in this paper. This article starts with the key enablers of the smart city, along with the necessity of the smart grid for the key enablers of it. The fundamentals of the smart city, smart grid, and 6G wireless communication are also introduced in this paper. Moreover, the motivations to integrate 6G wireless communication with the smart grid system are expressed in this article as well. The relevant literature overview, along with the novelty of this paper, is depicted to bridge the gap of the current research works. We describe the novel technologies of 6G wireless communication to effectively perform the considered smart grid applications. Novel technologies of 6G wireless communication have significantly improved the key performance indicators compared to the prior generation of the wireless communication system. A significant part of this article is the contemporary survey of the considered major applications of a smart grid that is served by 6G. In addition, the anticipated challenges and interesting future research pathways are also discussed explicitly in this article. This article serves as a valuable resource for understanding the potential of 6G wireless communication in advancing smart grid applications and addressing emerging challenges.

PPO-TA: Adaptive task allocation via Proximal Policy Optimization for spatio-temporal crowdsourcing

With the pervasiveness of dynamic task allocation in sharing economy applications, the online bipartite graph matching has attracted people’s increasing attention to its research in recent years. Among its application in sharing economy, the crowdsourcing allocate the tasks to workers dynamically. There are still three main problems that need to be addressed from previous studies. (1) These task allocation algorithms usually ignore the long-term utility on crowdsourcing platforms. (2)The current research works show that it has low allocation numbers. (3) Due to the low number of allocations, it becomes difficult to improve total allocation utilities. In this paper, we considered the long-term utility and drawed an idea of dynamic delay bipartite graph matching(DDBM). We proposed a Policy Gradient Based Discrete Threshold Task Allocation algorithm (DTTA) and a Proximal Policy Optimization Based Continuous Threshold Task Allocation algorithm (PPOTA) to solve these problems. The extensive experimental results on two real datasets demonstrate that the proposed algorithms are superior to the existing algorithms in both effectiveness and efficiency.

Analysis of the resistance to crack propagation in SENT test specimens printed in ABS using parallel or crossed filaments between layers

Additive manufacturing techniques continue to develop and cover all industrial fields. However, the performances of aspect and mechanical behavior of the parts obtained by this process remain to be mastered and are still the subject of current research works. Among these performances, the one corresponding to the resistance to the propagation of cracks. In order to improve this very interesting property in various industrial fields, it is desirable to master the understanding of crack propagation in this type of structure obtained by 3D printing. The objective of this paper is to analyze and understand the effect of the adopted raster angle on the crack propagation in SENT specimens obtained by FDM in ABS (Acrylonitrile-Butadiene-Styrene). Two approaches were developed: one is experimental to determine the critical stress intensity factor KIC and the other is numerical to predict the possible paths of crack propagation.

A Review on Phase Field Modeling for Formation of η-Cu6Sn5 Intermetallic

Formation of intermetallic compounds (IMCs) exhibits remarkable microstructural features and provides opportunities for microstructure control of microelectronic interconnects. Excessive formation of brittle IMCs at the Cu/Sn interface such as η-Cu6Sn5 can deteriorate the reliability and in turn lead to solder joint failure in the Pb-free Sn-based solder joints. Phase field method is a versatile tool for prediction of the mesoscopic structure evolution in solders, which does not require tracking interfaces. The relationships between the microstructures, reliability and wettability were widely investigated, and several formation and growth mechanisms were also proposed for η-Cu6Sn5. In this paper, the current research works are reviewed and the prospective of the application of phase field method in the formation of η-Cu6Sn5 are discussed. Combined phase field simulations hold great promise in modeling the formation kinetics of IMCs with complex microstructural and chemical interactions.

A novel YOLOv4‐modified approach for efficient object detection in satellite imagery

AbstractInterpreting high‐resolution satellite imagery could be an expensive and time‐consuming task for human eyes. Computer Vision and Deep Learning techniques can help to solve this major problem by applying detection algorithms, which can ease the task of analysing such images for the benefit of humans. It can help in changing the way we comprehend and anticipate the economic activity around the world. Such techniques help us to observe the urban development in high security areas such as national and international borders. Constant progressions in improving and making satellites deployment, a cost‐effective process to strengthen the networks of satellite orbiting the earth is one of the reasons such tasks can be easily solved with the help of high‐resolution images. Current computer vision research works have achieved significant milestones in accuracy and speed but, there are still room for improvements. In this paper, we addressed some of these methods to bring them to a combined pipeline and proposed a set of improvements to further improve the speed and the accuracy of the detections. We proposed a unified framework, which combines several object detection algorithms and the state‐of‐art architecture of YoloV4 along with the TensorFlow object detection API. This framework can detect small and well as large objects with improved speed and accuracy by using two detectors for different scales. Evaluation ran on these high‐resolution images yield mAP of 85.6% F1‐score of 0.84.

A Deep Learning-Based Fast Route Planning Model for Location-Based Social Networks

Nowadays, location-based social networks (LBSN) are a kind of popular media in cyberspace. The LBSN provide geography-aware social services to users, so as to create special social network activities. For users, where to go and how to arrange following positions are the most intuitive problems. As a result, many researchers began to pay attention to the position recommendation in LBSN, in the past decade. However, existing position recommendation methods in LBSN are mostly oriented with situations involving single positions or not many positions. In fact, many scenarios require suggestion of series positions, which is usually ignored by the current research works. To deal with this problem, this paper proposes a deep learning-based fast route planning model for LBSN. Specifically, dependency inside position sequences for each user is modeled with the use of recurrent deep learning model. Then, such deep learning model is used to output prediction results for series future positions for a user. Hence, series positions can constitute the routes for users. Finally, experiments on a real LBSN dataset show the efficiency of the proposed route planning model.

P21-Activated Kinase: Role in Gastrointestinal Cancer and Beyond.

Simple SummaryGastrointestinal tumors are the most common tumors with a high mortality rate worldwide. Numerous protein kinases have been studied in anticipation of finding viable tumor therapeutic targets, including PAK. PAK is a serine/threonine kinase that plays an important role in the malignant phenotype of tumors. The function of PAK in tumors is highlighted in cell proliferation, survival, motility, tumor cell plasticity and the tumor microenvironment, therefore providing a new possible target for clinical tumor therapy. Based on the current research works of PAK, we summarize and analyze the PAK features and signaling pathways in cells, especially the role of PAK in gastrointestinal tumors, thereby hoping to provide a theoretical basis for both the future studies of PAK and potential tumor therapeutic targets.Gastrointestinal tumors are the most common tumors, and they are leading cause of cancer deaths worldwide, but their mechanisms are still unclear, which need to be clarified to discover therapeutic targets. p21-activating kinase (PAK), a serine/threonine kinase that is downstream of Rho GTPase, plays an important role in cellular signaling networks. According to the structural characteristics and activation mechanisms of them, PAKs are divided into two groups, both of which are involved in the biological processes that are critical to cells, including proliferation, migration, survival, transformation and metabolism. The biological functions of PAKs depend on a large number of interacting proteins and the signaling pathways they participate in. The role of PAKs in tumors is manifested in their abnormality and the consequential changes in the signaling pathways. Once they are overexpressed or overactivated, PAKs lead to tumorigenesis or a malignant phenotype, especially in tumor invasion and metastasis. Recently, the involvement of PAKs in cellular plasticity, stemness and the tumor microenvironment have attracted attention. Here, we summarize the biological characteristics and key signaling pathways of PAKs, and further analyze their mechanisms in gastrointestinal tumors and others, which will reveal new therapeutic targets and a theoretical basis for the clinical treatment of gastrointestinal cancer.

Austenite-Based Fe-Mn-Al-C Lightweight Steels: Research and Prospective

Fe-Mn-Al-C lightweight steels have been investigated intensely in the last a few years. There are basically four types of Fe-Mn-Al-C steels, ferritic, ferrite-based duplex/triplex (ferrite + austenite, ferrite + austenite + martensite), austenite-based duplex (ferrite + austenite), and single-austenitic. Among these steels, austenite-based lightweight steels generally exhibit high strength, good ductility, and outstanding weight reduction effects. Due to the addition of Al and high C content, κ’-carbide and κ-carbide are prone to form in the austenite grain interior and at grain boundaries of lightweight steels, respectively, and play critical roles in controlling the microstructures and mechanical properties of the steels. The microstructural evolution, strengthening mechanisms, and deformation behaviors of these lightweight steels are quite different from those of the mild conventional steels and TRIP/TWIP steels due to their high stacking fault energies. The relationship between the microstructures and mechanical properties has been widely investigated, and several deformation mechanisms have also been proposed for austenite-based lightweight steels. In this paper, the current research works are reviewed and the prospectives of the austenite-based Fe-Mn-Al-C lightweight steels are discussed.

Aminolysis of polyesters for cracking and structure clarifying: A review

AbstractCracking of polyester bottles and recycling the waste, which is of concern in the current research works have their roots in the environmental stress cracking and the physical structural analysis, which has been the subject of a huge number of research reports, that are collected in the present review. Polyesters are polymers of high importance in industry and academia, and amines are versatile chemicals that are used for different purposes. The reaction of polyester with amines has been used to reveal structure, improve the properties of products, convert polyesters waste into benign materials, enhance biocompatibility, and so on. The research works reported on the aminolysis of polyesters (PET, PCL, PLA, PTT, PBT, PHB, PEN, PEA) for structure and morphological determination, etching, and stress cracking are critically reviewed. The chemistry and mechanism of the reactions of aminolysis of an ester or a polyester are well documented, and a vast amount of useful information is available. There is a need for further research to answer the remaining questions on the intermediates. Solutions of amines act as environmental stress‐cracking agents, may induce crystallization, and probably it can dissolve and remove a certain amount of disordered regions as well. In the subject of stress cracking that can help the elucidation of the process of aging, it is not yet clear how cracks patterns and sizes (from nano up to micro size) observed on the surface of polyester products depend on the thermal and mechanical history of the samples, and it is not known how to produce cracked patterns according to a pre‐planned design on the surfaces of polyester articles. To make polyesters more useful and safe the structure must be revealed in order to get benefit from the structure–property relations.

Digital Forensics and Cyber Security Graduate Programme Department of Information Systems & Innovations Ghana Institute of Management & Public Administration Greenhill, Accra, Ghana E-mail: rosemonddartey@gmail.com

As the dependence on digital information and the Internet continues to grow, it changes the way of computer crime. The number of computer crimes increases dramatically in recent years and investigators have been facing the difficulty of admissibility of digital evidence. To solve this problem, we must collect evidence by digital forensics techniques and analyze the digital data or recover the damaged data. One place to collect evidence is from application software. This paper seeks to find out how to collect, store and analyze application information for forensic purposes. This is also to determine gaps in current research works and proffer recommendations on what future works relate to application forensics. Keywords: Digital Forensics, Evidence, Application Information, Analysis, Registered Host BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Rosemond O. Addo-Sampong (2022): Application Information for Forensic Analysis (Considerations for Registered Host / Users / Device Name & Bearer Tokens) Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 167-172 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P27

A heterogeneous couplings and persuasive user/item information model for Next Basket recommendation

Next Basket Recommender Systems (NBRS) are session-based recommenders that leverage a user’s sequential behaviour and context to predict the next items they could buy during the next shop visit. This field is popular now. Current research works rely primarily on item–user relations, ignoring complicated heterogeneous couplings and persuasive item/user information, causing sparsity and cold-start, stifling NBRS performance. This research implements a Heterogeneous Coupling and Persuasive User/Item Information Model (HCPIM) comprising of heterogeneous couplings and a persuasive embedding layer (HCPEL), the consecutive basket layer, and a basket prediction layer. The HCPEL learns heterogeneous couplings (user–user, user–item, item–item) and persuasive user information for every basket to model the composite intra-basket associations across the items inside a basket given a user’s behaviour sequences. The consecutive basket layer learns inter-basket associations from the HCPEL using a Gated Recurrent Unit network. In the basket prediction layer, an improved Particle Swarm Optimization (PSO) algorithm optimizes the network’s weight and bias to iteratively learn heterogeneous couplings and persuasive information inside and across baskets to recommend the next basket. In PSO, we incorporate the exploratory Gravitation Search Algorithm to balance exploration and exploitation. Our architecture includes an Adaptive Response to Particle Adjustment Strategy to increase exploitation and prevent particles from being trapped. Extensive tests are run to evaluate HCPIM on TaFeng and the IJCAI 2015 dataset. The HCPIM improves performance by 27.87, 29.5, and 27.29 percent when density is 50, 100, and 150 on the IJCAI-15 dataset, and 8.53, 10.57, and 4.85 percent on the TaFeng dataset.

DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia.

A respiratory syndrome COVID-19 pandemic has become a serious global concern. Still, a large number of people have been daily infected worldwide. Discovering COVID-19 infection patterns is significant for health providers towards understanding the infection factors. Current COVID-19 research works have not been attempted to discover the infection patterns, yet. In this paper, we employ an Association Rules Apriori (ARA) algorithm to discover the infection patterns from COVID-19 recovered patients’ data. A non-clinical COVID-19 dataset is introduced and analyzed. A sample of recovered patients’ data is manually collected in Saudi Arabia. Our manual computation and experimental results show strong associative rules with high confidence scores among males, weight above 70 kilograms, height above 160 centimeters, and fever patterns. These patterns are the strongest infection patterns discovered from COVID-19 recovered patients’ data.

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the current research works employ linear correlation coefficient methods to select input features in modeling, which may not fully take the nonlinear and coupling relationships between features into account. Therefore, this article considers the input feature selection issue of silicon content prediction model from a new perspective and proposes a multiobjective evolutionary nonlinear ensemble learning model with evolutionary feature selection mechanism (MOENE-EFS), in which extreme learning machine is adopted as the base learner. MOENE-EFS takes the input feature scheme of each base learner as well as their network structure and parameters as decision variables and proposes a modified nondominated sorting differential evolution algorithm to optimize two conflicting objectives, i.e., accuracy and diversity of base learners, simultaneously. Through the optimization, a set of Pareto optimal base learners with high accuracy and strong diversity can be obtained. Moreover, different from the linear ensemble methods commonly used in classical evolutionary ensemble learning, this article proposes a nonlinear ensemble method to combine the obtained base learners based on differential evolution. Experimental results indicate that the two proposed strategies, i.e., evolutionary feature selection and nonlinear ensemble, are very effective in improving the accuracy and stability of the prediction model. MOENE-EFS also outperforms the other prediction models in both benchmark data and practical industrial data. Furthermore, analysis on the input features of all Pareto optimal base learners shows that the evolutionary feature selection is capable of selecting essential features and is consistent with human experience, which indicates it is a promising method to deal with the input feature selection issue in silicon content prediction.

Clinical Practice of Umbilical Cord Blood Stem Cells in Transplantation and Regenerative Medicine - Prodigious Promise for Imminent Times.

The umbilical cord blood is usually disposed of as an unwanted material after parturition; however, today, it is viewed as a regenerative medication so as to create the organ tissues. This cord blood gathered from the umbilical cord is made up of mesenchymal stem cells, hematopoietic stem cells, and multipotent non-hematopoietic stem cells having many therapeutic effects as these stem cells are utilized to treat malignancies, hematological ailments, inborn metabolic problem, and immune deficiencies. Presently, numerous clinical applications for human umbilical cord blood inferred stem cells, as stem cell treatment initiate new research. These cells are showing such a boon to stem cell treatment; it is nevertheless characteristic that the prospect of conservation of umbilical cord blood is gaining impetus. Current research works have demonstrated that about 80 diseases, including cancer, can be treated or relieved utilizing umbilical cord blood stem cells, and every year, many transplants have been effectively done around the world. However, in terms of factors, including patient selection, cell preparation, dosing, and delivery process, the treatment procedure for therapy with minimally manipulated stem cells can be patented. It is also worth thinking about how this patent could affect cord blood banks. Meanwhile, the utilization of cord blood cells is controversial and adult-derived cells may not be as successful, so numerous clinicians have begun working with stem cells that are acquired from umbilical cord blood. This review epitomizes a change in outlook from what has been completed with umbilical cord blood cell research and cord blood banking on the grounds that cord blood cells do not require much in the method of handling for cryopreservation or for transplantation in regenerative medicine.

Effective placement of self‐centering damage‐free connections for seismic‐resilient steel moment resisting frames

AbstractIn recent years, significant advancements have been made in the definition of innovative “minimal‐damage structures,” chasing the need for more resilient societies against extreme seismic events. In this context, moment resisting frames (MRFs) equipped with self‐centering damage‐free (SCDF) devices in column bases and beam‐to‐column joints represent a viable solution to improve structural resilience and damage reduction. However, the extensive use of these devices significantly increases complexity and costs compared to conventional structures, thus limiting their practical application. To overcome this drawback, current research works are focusing on the definition of effective placement for SCDF devices, maximizing their beneficial effect on the seismic response and controlling their impact on the overall structural complexity. Within this context, the present study investigates the influence of the placement of SCDF devices in a steel MRF. An eight‐story MRF is designed, and 50 configurations with different locations of SCDF joints are considered. Numerical models are developed in OpenSees, and non‐linear static push–pull and incremental dynamic analyses (IDAs) are carried out. The influence of the placement of SCDF devices is assessed by considering residual and peak interstory drifts, residual top story drifts, peak story accelerations, and the total dissipated energy as performance parameters. The results of IDAs for a seismic intensity corresponding to the ultimate limit state (ULS) are analyzed and compared, and fragility curves are successively derived for some relevant configurations. The paper provides insights and observations to understand how including a different number of SCDF BCJs at different stories affects the seismic response.

STUDY ON RESEARCH WORKS AND FUTURE RESEARCH DIRECTIONS OF POST-GRADUATE PROGRAM AT PATHOLOGY DEPARTMENT, IAAS, RAMPUR, CHITWAN, NEPAL

The Institute of Agriculture and Animal Science (IAAS) under Tribhuwan University (TU) has been carrying out various disciplinary and system research works through post-graduate students' research and publishing their findings through the M.Sc.Ag and Ph.D. thesis dissertations. Various thesis written by researchers under IAAS were collected and the appraisal of pathological research works on major cereals, vegetable crops, legume crops, oilseed crops and high-value crops along with ideas on management practices was done with the help of a thorough literature review. Data were gathered from IAAS/TU libraries, Rampur Library, Kritipur library, GAASC library and IAAS journals. A total of 68 types of research were conducted at IAAS. It is revealed that major diseases associated with cereals area brown spot of rice, rice blast, yellow rust of wheat, helminthosporium leaf blight of wheat, spot blotch of wheat and leaf blast of finger millet. Similarly, diseases of vegetables include late blight of potato, a white mold of french beans. The major diseases of legumes are wilt of lentil & chickpea, powdery mildew of pea, anthracnose of soybean and those of oilseeds are mainly leaf blights. Large cardamom, greatly known as high- value crop, is found to be suffered from Chirkey Foorkey and leaf blight diseases. Fungal pathogens cause most of the diseases in almost all crops. The management practices at their best results included the use of biological agents along with commonly available fungicides. The study found out a research gap in agricultural pathology as well. It also luminated the future area of research works that is lacking in current research works