Miracle-dz/UniFAR-FTUs huggingface dataset:
ROOM-BASED ENERGY DEMAND CLASSIFICATION OF BIM DATA USING GRAPH SUPERVISED LEARNING

Abstract. Nowadays, cities and buildings are increasingly interconnected with new modern data models like the 3D city model and Building Information Modelling (BIM) for urban management. In the past decades, BIM appears to have been primarily used for visualization. However, BIM has been recently used for a wide range of applications, especially in Building Energy Consumption Estimation (BECE). Despite extensive research, BIM is less used in BECE data-driven approaches due to its complexity in the data model and incompatibility with machine learning algorithms. Therefore, this paper highlights the potential opportunity to apply graph-based learning algorithms (e.g., GraphSAGE) using the enriched semantic, geometry, and room topology information extracted from BIM data. The preliminary results are demonstrated a promising avenue for BECE analysis in both pre-construction step (design) and post-construction step like retrofitting processes.
Mechanical Properties of Compressed Wood with Various Compression Ratios

This paper investigates five groups of compressed wood (CF), four of them made from compressed Japanese cedar with four different compression ratios (CR) of 33%, 50%, 67% and 70% and one without compression (control). The specimens were conditioned in relative humidity (RH) of 60% with moisture content (MC) of 12%. Mechanical properties tested were shear modulus in LR, LT and RT planes by single cube test method, Young’s modulus in the L, R, T directions and poisson’s ratios in all planes. Results showed that in comparison with control specimen, the average improvement on density with CR improvement were 25%, 75%, 175% and 261% corresponding to CRs of 33%, 50%, 67% and 70% respectively. It was also found that Young’s modulus in the L and T directions increased significantly with the increase of CR. Shear modulus of RT plane increased with the rise of CR. Poisson’s ratios tended to decrease with increasing compression ratio of CW.
Review on physicochemical, medicinal and nutraceutical properties of poppy seeds: a potential functional food ingredient

The poppy seed plant (Papaver somniferum L.) is well known for its health benefits. It contains plenty of nutrients including proteins, oil content, dietary fiber, antioxidants, tocopherols and other micronutrients. It is also under exploration considering its in vitro bioactive potential against various health issues such as hypertension, pain and cancer. Poppy is mainly grown for its opium and oil contents; however, poppy seeds are also widely used for cooking purposes. Poppy seeds contain various essential bioactive compounds including alkaloids, flavonoids, phenolic compounds and polyunsaturated fatty acids that can be effectively used as food ingredients in various applications. Poppyseed oil is considered a good quality oil owing to its rich polyunsaturated fatty acid content. Thus, tremendous potential exists in poppy seeds to be used as functional food ingredients and nutraceutical agents in various formulations. Medicinal and functional uses of poppy seed have been discussed, including some aspects of the utilization of the poppy seeds in the food industry. Further research is required to bring out its ingredient potential in food and health supplements. Keywords: Poppy, seed oil, health attributes, industrial uses, medicinal uses
Seasonal and Regional Chemical Variability of the Wild Population of Lantana camara Leaf Essential Oil From Kenya

Studies examining the variability in wild plant metabolic expression propose that environmental factors significantly influence the essential oil (EO) quality and quantity in a plant. Lantana camara is a widely distributed invasive plant species worldwide. However, its immense metabolites can become a source of novel compounds to produce biopesticides in the agricultural industry. Although, the quality aspect has to be considered due to the environmental influence on the metabolites synthesised. Therefore, this research aimed to understand the influence of environmental factors and how it shapes the plant’s metabolite profile in multiple populations of L. camara. Leaf samples were collected from six different geographic regions of Kenya and the corresponding monthly climatic data and soil samples. GC-MS data from leaf EO were analysed with environmental variables (climate and soil data) using unimodally unconstrained and constrained ordination methods for untargeted metabolomics analysis. Partial Least Squares-Discriminant Analysis (PLS-DA) and Random Forests (RF) were used to confirm the variability further. Seasonal and regional variability was observed for secondary metabolites (SMs) in the leaf EO, which correlated to climatic factors and soil attributes. We highlight the season-al-geographic metabolism relationship for L. camara and the combined analytical approach to obtain data that contributes to understanding the influence of environmental factors on the synthesis and accumulation of SMs. This research will have all-embracing implications for maximising phytochemical uniformity.
The Efficiency of Google Classroom during Pandemic for Maritime English Learning in Akademi Maritim Nusantara

For almost 2 years, the educational system around the world has been influenced by the COVID-19 pandemic that leads to the shutting down of educational institutions including universities to control the spreading of the virus. This phenomenon coerces the government and also educational institutions to adapt their learning and teaching process. Along with various policies to handle the situation, the online learning system is suggested by Indonesian government as one of the solutions to facilitate distance learning. One of the methods to conduct distance learning is by using Google Classroom. This is a descriptive qualitative research with the fourth semester Engineering students of Akademi Maritim Nusantara as the subject. The key purpose of this research is to determine the efficiency of Google Classroom in Maritime English subject. The data are analyzed with Technology Acceptance Model (TAM). The efficiency of the platform is measured with the Ease of Access (PEU) and Perceived Usefulness (PU) towards Google Classroom Maritime English learning activity . The result of this study shows that most of the students are satisfied with Google Classroom that is applied in their class during COVID-19 pandemic in terms of the easiness of accessing their assignments, hence their efficiency as a learning tool can be considered appropriate.
Pseudomonas aeruginosa as a priority group representative of bacteria with multiple antibiotic resistance

The aim: the aim of the research is an analytical review of the scientific literature on Рseudomonas aeruginosa as a priority group representative of bacteria with multiple antibiotics resistance. Materials and methods. The research was conducted by the method of scientific literature open source analysis: PubMed, Elsevier, electronic resources of the National Library named after V. I. Vernadsky and others. Results. The problem of antibiotic resistance is rightly called the “apocalypse of the XXI century”. P. aeruginosa bacteria are characterized by a very high natural ability to form resistant forms to antimicrobial drugs due to the formation of specific resistance genes, the ability to resist the entry of antibiotics into the cell or remove antibiotics from the cell, and form biofilms. A characteristic feature of the epidemic and epizootic processes of P. aeruginosa, as an opportunistic ubiquitous microorganism is host-pathogenic interaction - the interaction of the pathogen with the host organism. The peculiarity of this bacterium is opportunism and long-term persistence in the body of the host and in the environment. The global trend towards the spread of antibiotic-resistant gram-negative bacteria, including P. aeruginosa, underscores the need to develop comprehensive response strategies targeting all sectors of health. Conclusions. Bacteria P. aeruginosa is classified in 1th Critical Group of the WHO list of resistant to antibiotics "priority pathogens". Among the main reasons that contribute to the emergence of resistance are irrational antibiotic therapy in both humans and animals, and the use of antibiotics as growth stimulants in animal husbandry. While more R&D is vital, alone, it cannot solve the problem. To address resistance, there must also be better prevention of infections and appropriate use of existing antibiotics in humans and animals, as well as rational use of any new antibiotics that are developed in future. New methods of combating antibiotic resistance and antibacterial substances, alternatives to antibiotics (biofilm-destroying drugs, antimicrobial peptides (AMP), bacteriophages, nanopreparations, etc.) can make a positive contribution to overcoming the multiple drug resistance of gram-negatives
Wireless communication technologies for the Internet of Things

Internet of Things (IoT) is the inter-networking paradigm based on many processes such as identifying, sensing, networking and computation. An IoT technology stack provides seamless connectivity between various physical and virtual objects. The increasing number of IoT applications leads to the issue of transmitting, storing, and processing a large amount of data. Therefore, it is necessary to enable a system capable to handle the growing traffic requirements with the required level of QoS (Quality of Service). IoT devices become more complex due to the various components such as sensors and network interfaces. The IoT environment is often demanding for mobile power source, QoS, mobility, reliability, security, and other requirements. Therefore, new IoT technologies are required to overcome some of these issues. In recent years new wireless communication technologies are being developed to support the development of new IoT applications. This paper provides an overview of some of the most widely used wireless communication technologies used for IoT applications.
Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods

The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.
The first molecular insight into the genus Turanium Baeckmann, 1922 (Coleoptera: Cerambycidae: Callidiini) with a description of a new species from Middle Asia

This paper sheds the first light on the phylogeny of the Central Asian genus Turanium Baeckmann, 1922. By applying an integrative taxonomy approach, we revealed and described a new species from Kyrgyzstan—Turanium losi Karpiński, Plewa & Hilszczański sp. nov. Distinguishing characters from closely related Turanium pilosum (Reitter, 1891) are presented and their ecological associations are discussed. The key characters, including the male terminalia, were examined by means of scanning electron microscopy. High-quality stacked photographs of the habitus of the specimens are presented for both species and their geographical distributions are mapped. While the new species shows stable morphological characters that allow its differentiation from T. pilosum and the COI genetic distance between them is approx. 3%, the different species delimitation methods gave discordant results. Although the new species remained unrecognized for so long, it seems that these cerambycids are common in the region and both can be considered potentially invasive as they are apparently highly polyphagous. It has also been documented that they occur sympatrically in Kyrgyzstan. Both the Bayesian and maximum likelihood analyses of COI sequences confirmed the monophyly of the genus Turanium with strong support (PP 1 and BS 90, respectively). Moreover, the recently revealed polyphyly of the tribe Callidiini was supported by our analyses and, consequently, the discussion on the establishment of a new tribe Ropalopini is raised. This study further corroborates the effectiveness of DNA barcoding as a tool in detecting new species and provides some of the first sequences for Central Asian cerambycids, which remain almost completely unknown in terms of molecular studies.
A new framework for utilizing side information in sparse representation

An underdetermined system of linear equation has infinitely number of answers. To find a specific solution, regularization method is used. For this propose, we define a cost function based on desired features of the solution and that answer with the best matches to these function is selected as the desired solution. In case of sparse solution, zero-norm function is selected as the cost function. In many engineering cases, there is side information which are omitted because of the zero-norm function. Finding a way to conquer zero-norm function limitation, will help to improve estimation of the desired parameter. In this regard, we utilize maximum a posterior (MAP) estimation and modify the prior information such that both sparsity and side information are utilized. As a consequence, a framework to utilize side information into sparse representation algorithms is proposed. We also test our proposed framework in orthogonal frequency division multiplexing (OFDM) sparse channel estimation problem which indicates, by utilizing our proposed system, the performance of the system improves and fewer resources are required for estimating the channel.
Potential Applications of Nanotechnology in Apiculture

The use of materials with tiny size less than 100 nanometers (nm) to perform specific tasks or nanotechnology is currently applied in many agricultural, industrial, and medicinal aspects. The science of nanotechnology is developed rapidly and there are many techniques available to prepare nanoparticles. In agriculture, nanotechnology has been used mainly to develop nanomaterials to control diseases and pests of plants. So far, very few studies have implemented nanotechnology with beekeeping. Bee products prepared as nanoparticles for medicinal purposes have been studied more than any other aspects. In this article, the potential applications of nanotechnology on beekeeping including; tools, feeding, pollination, swarming, instrumental insemination, control of pests and diseases, and bee products are presented and previous studies are reviewed. Moreover, the potential hazards of nanoparticles on honey bees are also discussed. This article aims to present new trends to enhance beekeeping and to encourage bee researchers to perform more studies on nanotechnology.
A COMPARATIVE STUDY ON INTRUSION DETECTION SYSTEMS FOR SECURED COMMUNICATION IN INTERNET OF THINGS

The virtual and physical worlds are bridged using the largest digital mega-trend called the Internet of Things (IoT). Between mankind, new interactions and new business models are emerging due to the incremental growth in the Internet, machines, objects, and people connectivity. Secured communication is a typical challenge that is raised due to IoT high diversity, restricted computational resources, and protocols and standards. Because of the huge attack surface in IoT networks, they are highly vulnerable to various attacks, even with some security measures. So, for detecting attacks, it is necessary to design defense mechanisms. In IoT environments, it is highly crucial to have security defense measures like Intrusion Detection Systems (IDS). Hence, authentication and encryption traditional security countermeasures are not sufficient. At network level, to solve those issues and to protect Internet-connected frameworks, major solutions are provided by IDS. Highly unique challenges are faced by IoT specific characteristics like malware detection, ransomware, processor architecture heterogeneity, and the gap in security design. However, as in literature, various problems are raised in traditional IDS, like the high false alarm rate. In IoT, for intrusion detection, a detailed study of traditional Deep Learning (DL) and Machine Learning (ML) techniques and recent technologies is presented in this review. For presenting every selected work objective and methodology, they are analysed and this review work discusses their results. IoT systems cannot be secured by applying traditional security techniques directly due to their computational constraints and intrinsic resources. In real time, on IoT devices, unknown and known attacks are detected using ML techniques in IDS. An IDS is presented in this review and its working is independent of network structure and IoT protocols. This IDS do not require any prior knowledge of security threats. Therefore, for providing security as a service to IoT networks, an artificially intelligent IDS is developed. This review paper provides a clear discussion of various attack detection techniques, along with their benefits and drawbacks.
Machine Learning as a New Search Engine Interface: An Overview

The essence of a web page is an inherently predisposed issue, one that is built on behaviors, interests, and intelligence. There are relatively a ton of reasons web pages are critical to the new world, as the matter cannot be overemphasized. The meteoric growth of the internet is one of the most potent factors making it hard for search engines to provide actionable results. With classified directories, search engines store web pages. To store these pages, some of the engines rely on the expertise of real people. Most of them are enabled and classified using automated means but the human factor is dominant in their success. From experimental results, we can deduce that the most effective and critical way to automate web pages for search engines is via the integration of machine learning.
Effect of weather factors on the population dynamics of litchi leaf roller Platypeplus aprobola (Meyrick)

The present study provides the observations obtained from the field experiment on the effect of weather factors on the population dynamics of litchi leaf roller Platypeplus aprobola carried out in 2018-19 at the State Horticulture Nursery, Department of Horticulture, Jammu, J&K. The results revealed that peak activity of leaf roller (Platypeplus aprobola) was observed from September I fortnight (50.74%) to October II fortnight (54.97%). The correlation and regression studies showed positive influence with weather factors like average temperature, average relative humidity, sunshine hours and average rainfall and negative influence of average wind velocity with leaf roller incidence.
Quantifying endothelial cell proliferation in the zebrafish embryo

Introduction: Endothelial cell (EC) proliferation is a fundamental determinant of vascular development and homeostasis, and contributes to cardiovascular disease by increasing vascular permeability to blood-borne lipoproteins. Rodents have been traditionally used to analyse EC proliferation mechanisms in vascular health and disease; however, alternative models such as the zebrafish embryo allow researchers to conduct small scale screening studies in a physiologically relevant vasculature whilst reducing the use of mammals in biomedical research. In vitro models of EC proliferation are valuable but do not fully recapitulate the complexity of the in vivo situation. Several groups have used zebrafish embryos for vascular biology research because they offer the advantages of an in vivo model in terms of complexity but are also genetically manipulable and optically transparent. Methods: Here we investigated whether zebrafish embryos can provide a suitable model for the study of EC proliferation. We explored the use of antibody, DNA labelling, and time-lapse imaging approaches. Results: Antibody and DNA labelling approaches were of limited use in zebrafish due to the low rate of EC proliferation combined with the relatively narrow window of time in which they can label proliferating nuclei. By contrast, time-lapse imaging of fluorescent proteins localised to endothelial nuclei was a sensitive method to quantify EC proliferation in zebrafish embryos. Discussion: We conclude that time-lapse imaging is suitable for analysis of endothelial cell proliferation in zebrafish, and that this method is capable of capturing more instances of EC proliferation than immunostaining or cell labelling alternatives. This approach is relevant to anyone studying endothelial cell proliferation for screening genes or small molecules involved in EC proliferation. It offers greater biological relevance than existing in vitro models such as HUVECs culture, whilst reducing the overall number of animals used for this type of research.
Phenology and diurnal behavior of common  shelduck Tadorna tadorna at Sebkhet Bazer (El-Eulma, Nouth-East of Hauts Plateaux, Algeria)

This work aims to study phenology and daytime budget of common shelduck Tadorna tadorna (Anatidae) for two wintering seasons (2017/2018, 2018/2019) at Sebkhet Bazer (El-Eulma) North-East of Algeria from September to March. The obtained results show that common shelduck has the status of a wintering species. It was observed presently in the site during the study period. Common shelduck numbers begin to increase from the first decade of September to reach the maximums between late December and early January, where we had recorded the peaks of abundance 840 for the first season and 930 for the second. During our monitoring, budget time results showed that feeding is the dominant activity (surface water feeding, feeding on banks, and feeding by tipping), which take a ratio of more than 68%, the second activity is swimming with 18.71%, followed by preening which holds 9.14%, sleeping activity with a ratio of 2.38%, flight take the five range with a ratio of 0.82%. Finally, courtship and agonistic behavior recorded the lowest ratio on all budget time – 0.42% and 0.38% respectively. We can conclude that Sebkhet Bazer is a diurnal grazing ground for this Anatidae species.
The Role of User Experiences Towards Responsive Web

-In today’s growing world of technology, User Experiences (UX) known as a vital to the end user especially in the field of Human-Computer Interaction (HCI). The initial requirement for an exemplary UX is to meet the specific needs of customer without hassle. It also noted that, the UX role towards responsive web design is also connected to the Joomla open sources system. Joomla is a great Content Management System (CMS) with a lot of flexibility. However, to establish the website, empathizing UX is the key fundamental architecture in order to create the website skeleton before crafting the tangible HCI. Most public university website in Malaysia are using Joomla as their medium to interact with the world and this include Faculty of Art & Design, Universiti