bluuebunny/crossref_metadata_2025 huggingface dataset:
FCM-Enabled Approach for Investigating Interdependencies of BIM Performance Factors in the Sustainable Built Environment
Authors: Kumar Pavan , Pal Aritra , Chang Yun-Tsui , Hsieh Shang-Hsien
Published: February 2024

In pursuit of a sustainable built environment, BIM plays a crucial role in the project's performance and has egressed as a powerful technology in the construction industry, impacting the outcome and the project delivery workflows. Numerous dynamic and interdependent factors influence BIM performance. However, Existing literature prominently focuses on exploring the influencing factors for BIM performance, ignoring the impact and strength of the interplay of these factors on one another, therefore offering an inadequate picture of optimizing BIM performance. The evolving nature and degree of complexity of construction projects necessitate the identification and comprehensive understanding of the interdependencies between factors contributing to BIM performance in the sustainable built environment. A Fuzzy Cognitive Map (FCM) is a modeling method that represents and analyses the interplay between the factors in a complex system. So, this study proposes an FCM-enabled approach to investigate the interdependencies of factors contributing to BIM performance and conduct what-if scenarios, including predictive analysis. The developed FCM model can help reveal the hidden cause-effect relationships among a complex system of BIM performance factors, enabling stakeholders to develop more informed strategies and proactively plan to optimize BIM Performance
A Robotic Method to Insert Batt Insulation into Light-Frame Wood Wall for Panel Prefabrications
Authors: Han Xiao , Yang Cheng-Hsuan , Chen Yuxiang , Sanchez Alejandra Hernandez
Published: February 2024

Currently, industrial robot arms are trending in prefabricated building construction; however, a notable gap exists in established automated processes and related research specifically for the insertion of batt thermal insulation. The current method for accomplishing this task relies on manual insertion, which is labour-intensive for the workers and poses long-term health and safety concerns. This research presents an ongoing research project aimed at developing a feasible robotic process for the automated insertion of batt thermal insulation into prefabricated light-frame wood wall frames. This research focuses on the utilization of a single 6-degree-of-freedom robot arm for the insertion process, complimented by the design of a custom-built end-effector. The proposed robotic insertion process, named GLITPP, comprises of six major steps: (1) Grasp, (2) Lift, (3) Insert, (4) Tilt, (5) Push, and (6) Press. The GLITPP insertion process, along with the custom-built end-effector effectively mitigates the influence of the insulation’s nonlinear mechanical properties, while also taking collision avoidance into consideration. This ensures a tight-fitting insulation within the frame cavity, without visible gaps and deficiencies. The necessary physical operating parameters for the insertion process, such as angles, offset, and force requirements, are identified to ensure the precision, efficiency, and repeatability of insertion. A prototype of the designed end-effector is used to demonstrate and validate the robotic method, achieved a high success rate of 93.3%. The development of this research will further advance the complete automation of light-frame wood wall panel prefabrication, offering the industry a wider range of options for selecting thermal insulation for their processes
The Impacts of Digital Fabrication on the Construction Industry: A Systematic Review
Authors: Keshtkar Mehdi , Daniel Emmanuel , Gyoh Louis
Published: February 2024

The building industry is a major consumer of natural resources and a large contributor to environmental degradation, leading to a need to rethink current building practices. Digital fabrication (Dfab) technologies, which transform design and engineering data into physical products, are gaining traction in the Architecture, Engineering and Construction (AEC) industry. This study aimed to evaluate the implications of digital fabrication in the construction industry, by identifying the current Dfab applications and the hindrances that are limiting its implementation. The research questions addressed were why Dfab is essential in the construction sector, the current state-of-the-art of Dfab in the construction industry, and how Dfab is improving the construction industry. Through a systematic literature review, the findings proposed that Dfab can revolutionize the construction sector, enabling freeform architecture, reducing construction costs, cutting material waste, and increasing worker safety. Nevertheless, further research is needed to overcome obstacles such as high costs and the lack of digital skills in the construction industry
Efficient Data Curation Using Active Learning for a Video-Based Fire Detection
Authors: Joshi Keyur , Aziz Angelina , Dietrich Philip , König Markus
Published: February 2024

Video-based fire detection is a crucial object detection problem that relies on accurate and reliable data to detect fires. However, collecting and labeling fire-related data can be time-consuming and expensive, making it difficult to obtain sufficient data for training machine learning models. To address this challenge, uncertainty-based active learning techniques can be used to iteratively select the most informative samples for labeling. This can reduce the amount of labeled data needed to achieve high model performance and has the potential to even prune the training data with fewer informative samples. The traditional sampling-based uncertainty estimation methods are computationally expensive. Hence, an efficient prior network-based ensemble distillation State-of-the-Art approach is evaluated on an internal dataset which still requires relatively higher overhead computation making it difficult for production deployment. A biased softmax differencing-based uncertainty approach and a feature-based hard data mining approach are proposed and compared with the distillation approach. The novel approaches are found to have a very low overhead uncertainty estimation time compared to the ensemble distillation approach and traditional sampling techniques. The methods are evaluated in the context of curating the unlabeled pool data and improving the training data. For completeness, the experiments are performed on three different data sizes, and overall, the frame-wise selection strategy is proved to be better than the sequence-wise querying strategy. The Principal Component Analysis (PCA)-based hard data mining outperformed other methods and improved the model performance by 16.33% with AUC2% metric when compared with the random selection of data. The approach even outperformed the main network trained on full data by 7.33%, henceforth improving the training data by using informative 26.39% data. The results indicate that novel data mining provides efficient training and pool data curation
iSafe Welding System: Computer Vision-Based Monitoring System for Safe Welding Work
Authors: Zaidi Syed Farhan Alam , Hussain Rahat , Abbas Muhammad Sibtain , Yang Jaehun , Lee Doyeop , Park Chansik
Published: February 2024

The construction industry faces significant challenges, including a high prevalence of occupational incidents, often involving fires, explosions, and burn-related accidents due to worker non-compliance with safety protocols. Adherence to safety guidelines and proper utilization of safety equipment are critical to preventing such incidents and safeguarding workers in hazardous work environments. Consequently, a monitoring system tailored for construction safety during welding operations becomes imperative to mitigate the risk of fire accidents. This paper conducts a brief analysis of OSHA rules pertaining to welding work and introduces the iSafe Welding system, an advanced real-time safety monitoring and compliance enforcement solution designed specifically for construction site welding operations. Harnessing the real-time object detection algorithm YOLOv7 in conjunction with rule-based scene classification, the system excels in identifying potential safety violations. Rigorous evaluation, encompassing precision, recall, mean Average Precision (mAP), accuracy, and the F1-Score, sheds light on its strengths and areas for improvement. The system showcases robust performance in rule-based scene classification, achieving high accuracy, precision, and recall rates. Notably, the iSafe Welding system demonstrates a formidable potential for enhancing construction site safety and regulatory compliance. Ongoing enhancements, including dataset expansion and model refinement, underscore its commitment to real-world deployment and its strength in ensuring worker safety
Machine Learning-Based Construction Planning and Forecasting Model
Authors: Keser Ahmet Esat , Tokdemir Onur Behzat
Published: February 2024

Construction planning and scheduling are crucial aspects of project management that require a lot of time and resources to manage effectively. Machine learning and artificial intelligence techniques have shown great potential in improving construction planning and scheduling by providing more accurate insights into project progress and forecasting. This paper proposed a machine learning model that utilizes regularly updated site data to generate predictions of quantity variances from the plan and enable a more accurate forecast of future progress based on historical data on concrete activities. Also, the outputs of this model can be used when creating a schedule for a new project. New schedules created with the help of this model will be more consistent and reliable due to its vast data pool and ability to generate realistic forecasts from this data. The model utilizes data from completed and other ongoing projects to generate insights and provide a more accurate and efficient construction planning and scheduling solution. Within the scope of this study, different attributes of concrete pouring activities of different projects and locations were used as input data for a machine learning process, and then, using this model on test data, the forecast concrete quantities were obtained. This model provides a more advanced solution than traditional project management tools by incorporating machine learning techniques while significantly improving construction planning, scheduling accuracy, and efficiency, leading to more successful projects and increased profitability for construction companies
Concept for Enriching NISO-STS Standards with Machine-Readable Requirements and Validation Rules
Authors: Zentgraf Sven , Ali Sherief , König Markus
Published: February 2024

During building project planning, various standards, such as material specifications, value ranges, and construction regulations, must be considered. When analyzing a regulation for its BIM-based use, it must be identified which information can be checked directly or indirectly using a BIM model. The basis for the directly checkable information requirements is the explicit description of object classes, object types, properties, and values. Additionally, complex validation rules can be derived from the standards. These information extractions are mostly performed manually and laboriously on text-based regulatory documents. To provide a better data format, the NISO proposed the Standard Tag Suite (NISO-STS), which is an XML format for publishing and exchanging full-text content and metadata of standards. This paper proposes a concept to enrich standards in NISO-STS format with information requirements and validation rules to provide a machine-interpretable semantic knowledge base for BIM processes. To achieve this, the concept utilizes natural language processing (NLP) methods to extract semantic information from the standards. Furthermore, the paper introduces a workflow to transfer the gathered knowledge into the XML-based standard. This allows the acquired semantic knowledge to be used BIM-based and directly updated in future versions of the standards. To show the applicability of the concept an approach is presented in which the obtained information is stored and used as a queryable knowledge base. The resulting database is used by a querying assistant, in which a user can enter keywords and questions that are translated into SPARQL queries to provide answers for the given input
Transforming Building Industry Knowledge Management: A Study on the Role of Large Language Models in Fire Safety Planning
Authors: Ashkenazi Ori , Isaac Shabtai , Giretti Alberto , Carbonari Alessandro , Durmus Dilan
Published: February 2024

This paper discusses the potential use of AI in general, and large language models (LLMs) in particular, to support knowledge management (KM) in the building industry. The application of conventional methods and tools for KM in the building industry is currently limited due to the large variability of buildings, and the industry’s fragmentation. Instead, relatively labor-intensive methods need to be employed to curate the knowledge gained in previous projects and make it accessible for use in future projects. The recent development of LLMs has the potential to develop new approaches to KM in the building industry. These may include querying a variety of relatively unstructured documents from previous projects and other textual sources of technical expertise, processing these data to create knowledge, identifying patterns, and storing knowledge for future use. A proposed framework is defined for the use of LLMs for KM in construction. We will perform preliminary analyses on how to train models that can generate information and knowledge required to make decisions in the development of specific tasks of fire safety planning
Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval
Authors: Zheng Yuan , Seppänen Olli , Seiß Sebastian , Melzner Jürgen
Published: February 2024

Recently there has been a strong interest in using semantic technologies to improve information management in the construction domain. Ontologies provide a formalized domain knowledge representation that provides a structured information model to facilitate information management issues such as formalization and integration of construction workflow information and data and enables further applications such as information retrieval and reasoning. SPARQL Protocol And RDF Query Language (SPARQL) queries are the main approaches to conduct the information retrieval from the Resource Description Framework (RDF) format data. However, there is a barrier for end users to develop the SPARQL queries, as it requires proficient skills to code them. This challenge hinders the practical application of ontology-based approaches on construction sites. As a generative language model, ChatGPT has already illustrated its capability to process and generate human-like text, including the capability to generate the SPARQL for domain-specific tasks. However, there are no specific tests evaluating and assessing the SPARQL-generating capability of ChatGPT within the construction domain. Therefore, this paper focuses on exploring the usage of ChatGPT with a case of importing the Digital Construction Ontologies (DiCon) and generating SPARQL queries for specific construction workflow information retrieval. We evaluate the generated queries with metrics including syntactical correctness, plausible query structure, and coverage of correct answers
Modelling and Managing Built Heritage Knowledge: An Ontology-Based Approach for Multi-Layered Archaeologies and Historical Production Process Representation
Authors: Cui Cassia De Lian , Fioravanti Antonio , Currà Edoardo , Simeone Davide , Cursi Stefano
Published: February 2024

Classical and industrial archaeologies are a complex cultural field where singularity and uniqueness are expressed through past memory evidence and identity recognition. In order to obtain these values acknowledgement, it is compelling to highlight the material and intangible knowledge by using suitable ICT tools capable of handling complexity and managing large sets of heterogeneous data usually subjected to changes, different interpretations, inconsistencies and sometimes uncertainty. Although the HBIM method has been largely used in the past years, it shows significant limits when dealing with large and heterogeneous information requiring the introduction of advanced methods and tools. In this context, this study presents an approach to the architectural heritage and historical manufacturing activity representation based on integrating the HBIM process with a structured knowledge base, demonstrated through its application to the Sanctuary of Hercules and the former Segrè Papermill case study. The work develops an ontology-based system using existing ontologies for the three domains of interest: architectural artefact, cultural heritage and industrial processes directly connected with the informative model. The intent is to give an overall support system for the complex semantics formalization of these assets to aid the interpretation, intervention and valorization activities
Semantic Web Based Integration Between BIM Cost and Geometric Domains
Authors: Cassandro Jacopo , Mirarchi Claudio , Pavan Alberto , Zamborlini Andrea , ZANCHETTA CARLO
Published: February 2024

In the architecture, engineering, construction, and facilities management (AEC/FM) industry methodologies are needed to ensure the interoperability of data and effective management of information from different sources. Integration of the cost domain and cost estimation within the Building Information Model (BIM) in the AEC/FM sector is still an unresolved problem and one of the most critical tasks due to the lack of a standardised cost domain, especially in the tendering phase.
To ensure interoperability between cost data and geometric data, this research aims to address this gap by analyzing methods of converting cost data into Linked Building Data, thereby defining a cost domain in the Semantic Web, by collecting them into a graph database. This allows for structuring a cost domain, translating an IFC based structure previously developed by the research group, visualizing it using a graph system, and connecting it to the BIM geometric domain. Furthermore, it is possible to extend the cost ontology previously identified in the IFC model and facilitate the queries and analysis of cost data currently fragmented and based on unstructured data.
The results show how Semantic Web technology can be used to improve data interoperability, develop a cost ontology, and join both cost data and BIM models
Ontology-Based Construction Inspection Planning: A Case Study of Thermal Building Insulation
Authors: Seiß Sebastian , Boden Markus , Melzner Jürgen , Zheng Yuan , Thibaut Delval , El Chamaa Rayan
Published: February 2024

Poor construction quality is one of the most significant challenges for the construction industry. However, failures can be avoided or minimized by inspections based on detailed quality inspection plans as a part of quality assurance. Therefore, structured and project-specific planning of inspection plans is required to provide inspectors with the right information.
Nevertheless, inspection planning is mainly manual, dependent on the individual’s experience and high level of effort. As a result, inspection planning is often neglected and limited to providing general checklists that often lack semantically rich descriptions and are unspecific concerning individual project requirements. Furthermore, proper planning of inspections requires multiple information sources, such as building design, schedules, contractual and supplier guidelines, and standards, all of which must be provided or linked via an information model. Current research lacks an adequate formalized knowledge model to provide the knowledge-driven inspection planning process with the necessary domain knowledge to support inspection planning with heterogeneous information defined in isolated systems.
Therefore, this paper extends the Ontology for Construction Quality Assurance (OCQA) with the OCQA-Thermal Insulation (OCQA-TI) to formalize thermal insulation inspection planning knowledge. The OCQA offers a new linked data model that provides explicit knowledge of quality inspection planning. The development of the OCQA-TI follows the Linked Open Terms (LOT) methodology and is implemented using the Web Ontology Language (OWL).
The proposed ontology is evaluated using various approaches, including automatic consistency checking, answering competency questions, and criteria-based evaluation. The results indicate that the OCQA-TI can provide inspectors with relevant inspection planning knowledge and integrate various related information streams, thus providing a more comprehensive and efficient approach to insulation inspection planning. The functionality of OCQA-TI enables the fulfillment of increased sustainability and energy efficiency requirements by providing insulation inspection knowledge
Topological Relationship Modelling for Industrial Facility Digitisation Using Graph Neural Networks
Authors: Jayasinghe Haritha , Brilakis Ioannis
Published: February 2024

There is rising demand for automated digital twin construction based on point cloud scans, especially in the domain of industrial facilities. Yet, current automation approaches focus almost exclusively on geometric modelling. The output of these methods is a disjoint cluster of individual elements, while element relationships are ignored. This research demonstrates the feasibility of adopting Graph Neural Networks (GNN) for automated detection of connectivity relationships between elements in industrial facility scans. We propose a novel method which represents elements and relationships as graph nodes and edges respectively. Element geometry is encoded into graph node features. This allows relationship inference to be modelled as a graph link prediction task. We thereby demonstrate that connectivity relationships can be learned from existing design files, without requiring domain specific, hand-coded rules, or manual annotations. Preliminary results show that our method performs successfully on a synthetic point cloud testset generated from design files with a 0.64 F1 score. We further demonstrate that the method adapts to occluded real-world scans. The method can be further extended with the introduction of more descriptive node features. Additionally, we present tools for relationship annotation and visualisation to aid relationship detection
Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework
Authors: Hou Fangli , Ma Jun , Cheng Jack C. P. , Kwok Helen H.L.
Published: February 2024

Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios
Gen AI and Interior Design Representation: Applying Design Styles Using Fine-Tuned Models
Authors: Jeong Hyun , Kim Youngchae , Yoo Youngjin , Cha SeungHyun , Lee Jin-Kook
Published: February 2024

This paper explores the applicability of Image-generation AI in the field of interior architectural design, with a particular focus on automating interior design representation based on design styles. Interior design representation involves a complex process that integrates visual elements with functionality and user experience. Effectively visualizing this process is essential for facilitating communication among the various stakeholders involved in the design process. However, traditional visualization methods are constrained by expert resources, costs, and time limitations. In contrast, image-generation AI has the potential to automate various design elements, including design styles, components, and spatial arrangements, to enhance representation. In this study, we evaluated the performance of a base model using various design styles and, based on the evaluation results, selected styles for fine-tuning. The methodology for fine-tuning these design styles involved the following steps: 1) data preparation and preprocessing, 2) hyperparameter optimization, and 3) model training and construction. Utilizing the fine-tuned model thus constructed, we conducted image generation demonstrations. The research results revealed that design styles not well represented by the base model were effectively captured, and high-quality images were generated by the fine-tuned model. Notably, this fine-tuned model demonstrated the ability to represent images of specific design styles with a high degree of accuracy in capturing the characteristics and keywords associated with each style, compared to the base model. This implies that through fine-tuning image-generation AI, a wide range of applications can be inferred when aiming to create customized designs by considering these aspects. In conclusion, this study explores an efficient approach to interior design representation in the field of interior architecture by employing image-generation AI and proposes a method to effectively generate visualized images by training on design style keywords. Through this approach, our study can contribute to improving the interior design process by facilitating the generation of visualized images that reflect design styles. Furthermore, the study aims to suggest the potential for applying this approach not only to the field of interior architecture but also across various domains to achieve effective visualization
Effect of Euphorbia hirta Leaf and Root Extracts on the Early Growth of Cucurbit Crops
Authors: Akter Pervin , Begum Rabeya
Published: February 2024

The research investigates the allelopathic influence of Euphorbia hirta extracts on the germination and seedling development of various cucurbit species, including Bottle gourd (Sp. A), Winter melon (Sp. B), Ridged gourd (Sp. C), Sponge gourd (Sp. D), Bitter gourd (Sp. E), Snake gourd (Sp. F), Muskmelon (Sp. G), and Pumpkin (Sp. H). The experimental design incorporated the use of both aqueous and methanol extracts of E. hirta, applied at different concentrations, with distilled water employed as the control. The findings reveal that the methanolic extract of E. hirta significantly impedes both the germination process and the subsequent growth of seedlings for all cucurbit species tested. For instance, at 12 days post-treatment, control groups exhibited high germination percentages (78%, 90.67%, 95.33% and 90% for Sp. A, Sp. C, Sp. D and Sp. E, respectively), which drastically declined with a 20% methanol extract concentration (23.33%, 18.33%, and 17.67% and 18.33% for the same species, respectively). Similarly, shoot and root length measurements indicated that higher concentrations of both extracts suppressed growth, with methanol extract showing a stronger inhibitory effect compared to the aqueous extract. Interestingly, Lower extract concentrations occasionally boosted shoot growth, revealing a hormetic effect. This two-fold response, notably in Pumpkin and Snake Gourd, illuminates the complexity of allelopathic interactions. The study suggests E. hirta extracts could serve as eco-friendly herbicides, urging further research to isolate their active components. This could lessen synthetic herbicide use, promoting sustainable agriculture and environmental protection.
Cupriavidus Pauculus as A Cause of Late Onset Post-Operative Sinusitis
Authors: Kosaraju Kranthi , Hashim Mohammed Abdul Rahman Al , Alabandi Ali , Sarhan Ruba , Johani Ruba Al
Published: February 2024

Cupriavidus pauculus is an environmental gram-negative bacilli of low virulence, being increasingly reported to be associated with invasive infections in immunocompromised patients and device related infections. Often undetected or misidentified in most microbiology laboratories, identification of this organism requires increased suspicion and automated identification or molecular methods. We hereby describe a curious case of post-operative acute sinusitis caused by this rare yet emerging human pathogen.
Effects of Training and Development on Employees’ Performance in the Ministry of Niger Delta Development Abuja
Authors: O. Ambrose, N. , A Abdul Hamidu,
Published: February 2024

This study examines the effects of training and development initiatives on employee performance in the Ministry of Niger Delta Development, Abuja, Nigeria. Given its crucial role in driving socio-economic progress in the Niger Delta region, the Ministry's efficacy rests heavily on its workforce's competence. To explore the correlation between training, development, and employee performance; this research employed a survey-based approach is ascertain the relationship, a questionnaire was distributed among employees and management personnel of the Ministry to gather primary data. The data collected were analysed using descriptive statistical methods. The underpinning theoretical framework was the Human Capital Theory. The findings revealed that training and development exert a positive influence on the performance of MNDD employees. Furthermore, it was found that training and development initiatives enhanced comprehension of job responsibilities, roles, and pertinent policies in MNDD. As a recommendation, the study suggested the formulation of specialized training schemes aimed at augmenting employees' understanding of their distinct job responsibilities, roles, and pertinent Ministry policies.
Da Tsohuwar Zuma...: Fasalta Narambaɗa da Shata da Ɗanƙwairo ga Matasan Yanzu
Authors: Muhammad Alhaji Ibrahim
Published: February 2024

Arewacin Nijeriya wani yanki ne a ƙasar Hausa da ya haɗa tarin fasihai ta ɓangaren makaɗa da mawaƙa. Mafi yawanci suna gwada hikimarsu cikin harshen Hausa. Da wannan masaniyar aka nemi gwarazan mawaƙa uku (3) waɗanda aka yi ittifaƙin hikimominsu da zalaƙa irin tasu a awon baka, ta yi zarrar da babu mai tababa. Bayan dogon shaƙo wajen nazarin rayuwar mawaƙan baka, an so a ƙara bayani ga ayyukan da magabata suka yi kan Narambaɗa, domin mu taya shanu ciri. An dai fahimci har yanzu akwai buƙatar a ci gaba da nazari a cikin waƙoƙin tsofaffin mawaƙan baka na Hausa, domin a yaba da ƙwazonsu, a kuma amfana da tarin hikimomin da suka naɗe a cikin waƙoƙinsu.
The Use of Hausanized Arabic Words in Hausa Poetry: A Study of Professor Aliyu Muhammad Bunza's "EKOWAS ba mu Yarda da Yaƙi ba"
Authors: A Masama, A. , S. Auwal, A.
Published: February 2024

Allah the Almighty chose the Arabic language to be an immortal language among the ancient Semitic languages, and perhaps the secret is that it is the language of His clear book, and the language of His eternal message. History clear that there are religious and economic ties between the Arab countries and West Africa from since a long time ago, but historians mentioned that the Arabic language began to penetrate into the countries of West Africa through the trade that was taking place between the Arab countries and those quarters since the eleventh century AD, and as for the primacy of the Arabic language in Kanem Bornu and Hausa lands, as there were frequent narrations that it entered by Arab Muslim merchants through peace, so the people accepted Islam in their Arabic language since that era, until the Arabic language in those countries became the language of culture, writing, speeches, teaching, and even an official language before British colonialism. So this eloquent interest in the Arabic language left its far-reaching impact on the Hausa people, even their poetry, as you do not find a Hausa poet except that he borrowed in his poetry Arabic vocabulary that the Hausa people obsessed with, and among these poets is the poet Professor Aliyu Muhammad Bunza. From this point of view, this haste is an analytical linguistic study of a phenomenon of sociolinguistics titled: The use of Hausanized Arabic words in Hausa poetry: A case study of Professor Aliyu Muhammad Bunza's Poem "Ekowas ba mu yarda da Yaƙi ba". The importance of this article appears to be that, it is an applied, analytical linguistic study that would clarify the pioneering role played by the Arabic language in developing and expanding the vocabulary of the Hausa language, especially among poets.
Effects of the Implementation of the Employees’ Compensation Act in Federal Inland Revenue Service (FIRS) Abuja, Nigeria
Authors: M Dance, A. , D Amehojo, , S. Sani,
Published: February 2024

This study examines the effect of implementing the Employees' Compensation Act in the Federal Inland Revenue Service (FIRS) located in Abuja, Nigeria. The study assesses how the implementation of this Act affects the well-being of workers within the FIRS in Abuja. A total of 399 employees from the FIRS in Abuja participated in the survey. The findings of the study indicate that the respondents lack awareness regarding the compensation act within the FIRS. Additionally, the majority of the respondents expressed disagreement with the adequacy of compensation for FIRS employees who experience work-related disabilities. On the other hand, most respondents agreed that employees suffering from work-related mental stress are adequately compensated within the FIRS. One of the recommendations put forth is for the FIRS to establish effective communication strategies to ensure that all employees have a clear understanding of their rights and the compensation provisions available to them in cases of work-related disabilities. Achieving this objective could involve organizing regular workshops, training sessions, and informative campaigns.
The Challenges Of Rule-Based Learning Of The Comma Among Nigerian English Users
Authors: Ama Jacob Ado , Azi Joseph Nuhu
Published: February 2024

Learners of English in Nigeria hardly relate punctuation and meaning in written expressions or recognize the relationship between intonation and punctuation. There is a proliferation of incorrect use of punctuation marks, especially commas, among students and even graduates. The investigation therefore focuses on the use of the comma among students of the College of Education Gindiri. 40 students were interviewed and issued a questionnaire to find out why and how they use the comma. Data from the questionnaire and interviews were collected and analyzed. The investigation revealed that most of the students use the commas more in compliance with some kind of abstract rules than the need to enhance meaning. This explains why it is difficult for most students and graduates to correctly use the comma. It is taught or used largely based on rules; as such learners don’t realize its creative application to enhance meaning. The researchers recommend that approaches to the teaching of punctuation should de-emphasize the rule approach and focus instead on its output. Function as regards clarity of expression and information should be emphasized not mechanical functions.
Wasu Darussan La’akari a Zaɓaɓɓun Matanonin Adabin Baka Na Hausa
Authors: A. Bunza, U. , J Yusuf, , A. Aliyu,
Published: February 2024

Manufar wannan takarda ita ce nazarin labarun bakan Hausawa a matsayin taskar ilimi ga al’ummar yau. Nazarin yana da muhimmanci ne ganin labaran baka kamar sauran nau’o’in adabin bakan Bahaushe, ɗamfare suke da ilimin da zai iya zama madogara ga al’umma, amma duk da haka manazarta ba su faye ba su muhimmanci ba a wannan ƙarnin. Hanyyoyin da aka yi amfani da su wajen gudanar da binciken sun haɗa: karance-karance a littattafai da kuma yanar gizo tare da zaƙulo labaran da kuma yin nazarinsu. A ƙarshe, takardar ta gano cewa, labarun bakan Bahaushe suna da matuƙar muhimmanci, musamman wajen bayyana tunani da dabarun rayuwar al’ummar da ta gabata domin kasancewa madubi ga al’ummar yau.
Traditional Mechanisms of Conflict Resolution in Africa: A Pathway to Sustainable Peace in Nigeria
Authors: Austine Dr. Ejovi , Justus Iwegbu Chuks
Published: February 2024

This study delved into the traditional mechanisms of conflict resolution in Africa; A pathway to sustainable peace in Nigeria. It deployed the qualitative method of data collection and content analysis. The work was adopted as a tool for analysis of the Peace Process theory propounded by John Darby and Roger Macunity. It ascertained that disputes or disagreements are unarguably and necessarily an inevitable part of human existence flowing directly from communication, interaction and relationships which can occur at individual, national and even international levels. Alternative Dispute Resolution (ADR) mechanism is a basket of procedures outside the traditional process of litigation or strict determination of legal rights. It may also be elucidated as a range of procedures that serve as generally involve the intercession and assistance of a neutral and impartial third party. The traditional mechanisms of conflict resolution in Africa are alien to the Western world, this paper is of utmost importance to the Western world and also enriched their pool of literature. Nigeria is a country that is dominated by various ethnic groups anchored on diverse cultures, customs and traditions. It is therefore not surprising to see conflicts arise and despite the various attempts at resolving these conflicts through litigation, it remained unabated. The paper investigated the lessons learned from Traditional Mechanisms of Conflict resolution, it also interrogated its impact on conflict resolution and the way forward. In light of the lessons that were learnt and the impact of the traditional mechanisms of conflict resolution; suggestions on how to attain sustainable peaceful society were proffered. In conclusion, the study crystallized reforms on the alternative dispute resolution introduced through the traditional mechanism which includes amongst others that constitutional recognition should be given to traditional institutions of conflict resolution to enable quick dispensation of matters.
Impact of Vestibular Schools System in Enhancing Entrepreneurial Skills in Zamfara State
Authors: Muhammad Dr. Bello
Published: February 2024

Entrepreneurship is considered as an instrument for economic development in the society. The global demand for economic integration, made it compulsory to create sustainable ways for surviving as an entrepreneurial; so as to create employment opportunities, social and rural economic development and to encourage creativity. Efforts have been intensified by the Nigerian government and other stakeholders to introduce entrepreneurship course in universities and other institutions of learning in Nigeria, but vestibular schools are exempted. For that reason, the research intends to focus on the role of vestibular schools and scholars in enhancing entrepreneurial skills for the students of vestibular schools in Zamfara state. The research objectives are to create employment opportunities and investigate possible ways and solutions of small handwork in different methods for vestibular students in Zamfara state to earn a sustainable life. The research work was carried out in some selected vestibular schools in Zamfara, neighboring states and some selected small and medium enterprise places. The research concluded with the findings: the scholars, parents, guardians and government in Zamfara state have obligations to ensure that the youth are empowered to discharge their obligations to the society, and to have a better and sound for their future.
Betel Nuts, Health Policies, and Adolescent Health
Authors: Luong Jasper Hoi Chun , Kozlakidis Zisis , Cheong Io Hong , Wang Hui
Published: February 2024

Areca nut and betel quid (AN/BQ) products are largely scrutinized by the scientific community because of their toxicological and carcinogenic properties. However, at the same time there exists an ever-growing user base in low- and medium-income countries, whose users are responding to innovative products preparation processes and are initiated to AN/BQ products by their parents and family at a young age. This report compiles current cessation policies, implemented interventions, and comments on their corresponding effectiveness and/or potential effectiveness. The report also highlights the need for further research from both an adolescent health and a Chinese perspective, as data regarding the region with the second largest user group after India are largely unknown or unavailable for scientific review. Ultimately, recent studies involving analytical methods to observe how different cultivation environments, and/or processing methods change the chemical composition of the AN/BQ product have also presented a potential insight in better understanding and eventually regulating AN/BQ across all population groups, including adolescents. The rise of digital solutions may also encourage development of applications to track consumption and usage and distribution of AN/BQ products for policy makers to design targeted campaigns.
Brain Tumor Classification using Convolutional Neural Network
Authors: Ganesh Babu T.R. , Varsha V. , Yamuna Sri T. S , Kumari S. Priyanka
Published: February 2024

This study intuitively addresses a crucial need in the medical domain by introducing a customized Mobile-Net model for classifying brain cancer in medical imaging data. The paramount importance of accurate classification in cancer diagnosis and treatment planning cannot be emphasized further. This particular research primarily focuses on the practical application of semantic classification techniques to precisely identify and outline brain cancer zones in medical imaging data. By utilizing a Mobile-Net architecture, the developed model highlights outstanding performance with an accuracy score of 85%.
DESAFIOS ANESTÉSICOS EM CIRURGIA PLÁSTICA: DO PLANEJAMENTO À RECUPERAÇÃO PÓS-OPERATÓRIA
Authors: Basuino Letícia , Carvalho Leonardo Cavalcante de , Castro Heron de , Carvalho Priscila Medeiros Pizarro , Maranhão Luiza Maria Barbosa , Lemos Felipe Naves , Pinto Maria Queiroga , Martins Marco Antonio Miranda , Amorim Maria Laura Correria , Costa Marco Antônio Penachini da , Assumpção Érika Marquezan , Filho Carlos Augusto da Conceição Sena , Fernandes Karina Rabelo , Queiróz Júlia Leitão
Published: February 2024

A cirurgia plástica é uma especialidade médica que visa melhorar a aparência e a função de determinadas partes do corpo, contribuindo para a melhoria da qualidade de vida e autoestima dos pacientes. No entanto, os procedimentos cirúrgicos nessa área apresentam desafios anestésicos específicos que requerem uma abordagem cuidadosa e individualizada, desde o planejamento até a recuperação pós-operatória. Nesse contexto, é essencial compreender e enfrentar esses desafios de forma eficaz, garantindo a segurança e o conforto dos pacientes ao longo de todo o processo cirúrgico. Objetivo: Analisar os desafios anestésicos enfrentados em cirurgias plásticas, desde o planejamento até a recuperação pós-operatória. Metodologia:  Foram utilizadas as bases de dados Pubmed, Scielo e Medline, com artigos publicados entre os anos 2020 e 2024, disponíveis em inglês ou português. Considerações Finais: A utilização de protocolos atualizados, o monitoramento contínuo e a adaptação às necessidades específicas de cada caso clínico são pilares para o sucesso dos procedimentos cirúrgicos plásticos. Ao superar os desafios anestésicos com uma abordagem abrangente e interdisciplinar, é possível proporcionar aos pacientes resultados satisfatórios, minimizando os riscos e maximizando a qualidade da assistência médica.
The Contribution of Road Construction on Regional Economic Development in Indonesia
Authors: Sawitri Dhian
Published: February 2024

The development process in Indonesia's provinces is expected to contribute to regional economic growth. This research aims to evaluate road construction's impact on regional economic development and the spatial spillover effect in 34 Indonesian provinces. This paper constructs Spatial Autoregressive Model (SAR), Spatial Durbin Model (SDM), Spatial Autocorrelation Model (SAC), Spatial Error Model (SEM), and Generalised Spatial Random Effect Model (GSPRE) for 34 provinces in Indonesia from 2010 to 2020 to capture the effect of road construction on regional economic development and spatial spillover effect. Compared to other models, the spatial Durbin model has the minimum Akaike information criterion (AIC) value, indicating that it is the most suited model. The result indicates that road infrastructure has little impact on regional economic development. Moreover, road development has a negative value and negligible spatial spillover effect.
The Disaster Preparedness and Mitigation in Indonesia During the Covid-19 Pandemic in Indonesia: A Narrative Overview
Authors: Aisyah Hesty , Edison Elisa Daniati , Tasnim M
Published: February 2024

This paper presents the results of literature reviews on disaster mitigation preparation during the Pandemic Covid-19 since 2019. Of the 780 articles selected 6 articles on this topic published in national and international journals between Mei 2019 and June 2021 were included in the review. These articles are written in different disaster contexts, including events related to weather (floods and storms), natural disasters (earthquakes and tsunamis), and other events (such as pandemics and terrorist attacks). The main themes that arise include lack of communication between stakeholders, media sensationalism, the importance of choosing the most effective marketing messages, lack of disaster management plans, image damage, and goal reputation, and changes in tourist behavior after a crisis and disaster. This review identifies ways to increase the speed and effectiveness of responses to disasters, the importance of marketing relationships with loyal customers, and the need to quickly improve the image of the goal. Suggestions for future research arising from this review include the urgent need to encourage tourism operators to be involved with crisis readiness and disaster management strategies and the importance of getting a better understanding of consumer responses to disaster events.