American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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Physico-Mechanical Impact of Different Cem Ii 42.5 MPA Cement Brands On Hardened Concrets in Cameroon
Full text link Cement is defined as a hydraulic binder composed of finely ground powdered materials which, when mixed with water or a saline solution, form a plastic paste capable of binding various substances as it hardens. This study aims to highlight the impact of different class II cements (strength class 42.5 MPa) available on the Cameroonian market (ROBUST, CIMAF, DANGOTE, and MEDCEM) on the compressive strength of hardened concrete at different curing ages. To determine which cement brand provides the best compressive strength at maturity, a series of tests were carried out on samples from each brand, including consistency, setting time, density, compressive strength, and water absorption. The results support our hypothesis: cements are often used indiscriminately with identical dosages, based solely on their mass, in an attempt to achieve similar compressive strength outcomes. The study revealed that the finer the cement, the higher its compressive strength. Additionally, less dense cements produce lighter concretes but require more water for optimal workability, which affects the water-to-cement ratio. DANGOTE cement exhibited the highest compressive strength (29.09 MPa), followed by MEDCEM, and all the studied cements exceeded the target strength of 25 MPa at 28 days
The Impact of Formative Assessment Feedback on Secondary Students’ Attitude towards Science and Academic Achievement in Grade Nine Biology
Full text linkThe purpose of this research is to investigate the effect of formative feedback on secondary Biology students’ attitudes toward science and how formative feedback affect students academic scores in Biology at the grade 9 level. This research will explore how the academic performance of grade 9 Biology students can be improved using formative assessment feedback. The attitude of students toward science will be measured using an Assessment for learning tool (AFL). This research will also help the instructor to improve teaching practice and provides an opportunity for science students to maximize on their ability, hence improved their academic score in science.
This research used a quasi-experimental design, with intact classes using the embedded quantitative design methodology of an action investigation. The samples were selected using a convenience sampling technique. Data was collected using Two Instruments, Six Weeks and Science Attitude Scale (SAS), Two instruments, Six Weeks Test. Quantitative data will be collected using Science Attitude Scale (SAS). A positive relationship between academic success and formative feedback [r=.54, n=35, p > .001] has been found. The students in the treatment group performed significantly well on the Six Weeks Test, compared to the control group, t (61.83) = 3.58, P =.001. There was also, a statistically significant difference in the treatment group performance on the Science Attitude Scale (SAS), on the post-test compared to the pretest.
The use of formative feedback in the science classroom is a positive teaching method that can lead to higher students’ engagement which will result in an increase academic performance relative to the control group
Assessing the Nutritional Status and Nutrition Education Knowledge of Patients with Type 2 Diabetes in Bamenda Regional Hospital, North -West Region -Cameroon
Full text linkType 2 diabetes (formerly termed non-insulin dependent or adult-onset diabetes) occurs when the body does not produce enough insulin (relative insulin deficiency) or cannot use the insulin it produces effectively (insulin resistance) and it could be due to ?-cell dysfunction and dysregulated hepatic glucose production.Effective management of diabetes mellitus requires comprehensive nutrition education. However, the relationship between patients\u27 nutritional knowledge and their nutritional status remains underexplored in Cameroon, particularly in the Bamenda Regional Hospital. The aim of this study was to evaluate the nutrition education knowledge and nutritional status of diabetic patients at Bamenda Regional Hospital. A cross-sectional study was conducted involving 152 diabetic patients receiving care at the Bamenda Regional Hospital. Data collection was done through the administration of a structured questionnaire to obtain information on sociodemographic characteristics and nutrition education knowledge. Anthropometric measurements( weight and height) was carried out and used to calculate BMI (weight/height2). This was used to assess nutritional status following the standard procedures and compared with the World Health Organisation(WHO) classification standards. Dietary intake was assessed using 24 -hour recall and food frequency questionnaire methods.The data was analysed using SPSS (version 21).The level of significance was set at p-value< 0.05.
The study revealed that 62.2% of the diabetic patients were female, 53.9% were?45years,62.5% had low income .Moreover, 23.3% were overweight(BMI;24.9 - 29.9kg/?) and 58.6% were obese (BMI;? 30kg/?). A significant difference was observed between low income, nutritional status and nutrition education knowledge(p<0.05).: The study revealed that diabetes patients in Bamenda Regional Hospital could be having inadequate knowledge which led to poor food choices and financial limitations could have restricted access to healthy foods. Lifestyle modification is the best approach and this can be achieved through enhancing nutrition education programs tailored to local dietary practices which may improve patients\u27 nutritional status and diabetes management outcomes
Interaction between IDEs and AI Assistants in the Process of Writing Automated Tests
Full text linkThis paper looks at how modern, integrated development environments (IDEs) work with AI helpers to make it easier to write automated tests. The paper tries to see how far these kinds of tools take away the boring workload from engineers and better the quality of test thinking. Fast growth in using AI helpers in IDEs justifies this study: 74% of developers plan on still using ChatGPT, and 41% — GitHub Copilot. More than 80% of firms have put Copilot into everyday use, and 90% say it has raised job happiness. Meanwhile, only 31% of suggestions are taken up, and 17% of those stay in the codebase, showing that AI has a small degree of independence on automated tests. This work remains novel in a systematic analysis that intertwines quantitative survey data with Copilot usage telemetry, and crowdsourced info on mobile platform fragmentation, further coupled with an architectural review of AI-plugin integration within Android Studio and IntelliJ IDEA. The support covers Kotlin Multiplatform, Gradle scripts, Page Object, Gherkin, and cross-platform steps generation. Suggestion acceptance rates, flakiness metrics, and CI/CD licensing requirements have been integrated to evaluate the real-world impact of AI assistants. Findings at a high level indicate that helpful AI assistants in IDEs reduce the time it takes to build automated test scaffolds; helpful ones further automate locator choice considering Android/iOS fragmentation; they generate cross-platform scenarios in KMP projects and reduce flaky testing percentages through helpful wait-template recommendations. In return, though, suggestions are accepted by only about one-third of engineers; responsibility for quality and security of tests remains with the expert and therefore requires code review, static analysis, as well as license scanning. This article will help test developers, QA engineers, and automation architects who use AI helpers to make mobile and cross-platform testing better and more trustworthy
Key Challenges for Entrepreneurship in Nigeria’s Fish Farming Sector
Full text linkThe fish farming sector in Nigeria hold significant potential for contributing to the country’s food security and economic growth and development. However, entrepreneur’s in this sector face numerous challenges/constraints or limitations that obstruct the full realization of this opportunities. This paper explore the key challenges confronting entrepreneurship in Nigeria’s fish farming industry, focusing on areas such as Financial, Technical, and Ecological challenges. Additionally, issues related to Product, Market, Business, Regulatory/Policy and Climatic challenges were revealed. The study highlights how these challenges impact the growth and profitability of fish farming enterprises and suggest some strategic recommendations that could mitigate these challenges. Addressing these challenges is critical for enhancing the productivity and competitiveness of Nigeria’s fish farming sector, which in turn could stimulate broader economic development and poverty alleviation in the country
Global Influence of Transcendentalism on Social and Political Movements
Full text linkThis article explores the lasting impact of Transcendentalism, a nineteenth-century American philosophical and literary movement, on global social and political change. Rooted in the works of Ralph Waldo Emerson, Henry David Thoreau, and Margaret Fuller, Transcendentalism emphasized self-reliance, individual conscience, nonconformity, civil disobedience, and a deep reverence for nature. Though these ideas originated in America, they quickly transcended national boundaries, inspiring reform movements around the world. Emerson’s emphasis on spirituality and nature helped shape modern environmental ethics, while Thoreau’s essay “Civil Disobedience” became a cornerstone for nonviolent resistance movements led by figures such as Mahatma Gandhi, Martin Luther King Jr., and Nelson Mandela. Fuller’s feminist writings laid a foundation for gender equality movements, which influenced global conversations about women’s rights. Through case studies in abolitionism, civil rights, feminism, environmentalism, and anti-colonial struggles, the article illustrates how Transcendentalist values have shaped both historical and contemporary activism. The article also highlights gaps in current scholarship, particularly in understanding the movement’s role in modern ecological and human rights movements. Ultimately, it argues that Transcendentalism remains a powerful and relevant moral framework for social and political reform in the present day
Domain Driven Development — Changing the Philosophy of Working on a Project
Full text linkThe article examines domain-driven development (DDD) as a shift from technology-first delivery to business-first modeling with enforceable boundaries and contracts. The review integrates recent findings on bounded contexts, aggregate consistency, context mapping, and domain events with empirical results from microservice performance studies, event-driven pipelines, reactive execution, autoscaling, and overload control. In addition, the review positions CQRS as a complementary pattern to DDD: commands validate invariants within aggregate boundaries while queries rely on denormalized read models for independent evolution. The paper provides a decision aid for when CQRS improves throughput, traceability, and change isolation versus when a unified model remains simpler. The analysis consolidates a boundary-discovery workflow that couples collaborative modeling with data-assisted decomposition. A practitioner case with DDD reports shorter onboarding and faster delivery after establishing a stable ubiquitous language and context map. The manuscript includes an evidence-based interaction table, a governance table for documentation and operations, and a figure illustrating data-driven decomposition. The results target architects and leads who need reproducible criteria for partitioning, collaboration, runtime control, and team enablement across complex enterprise portfolios
Auto-Instrumenting Go Applications: A Study of Compile-Time and Runtime Instrumentation Using Opentelemetry
Full text linkThe study aims to provide a systematic comparison of automatic instrumentation methods for Go applications within a unified OpenTelemetry observability architecture. The research problem is that the Go language, being statically compiled and performance-oriented, lacks built-in mechanisms for dynamic telemetry injection, which makes it difficult to achieve complete observability without modifying the source code. To address this issue, the study applies methods of comparative architectural analysis, engineering modeling, and synthesis of experimental data presented in scientific research. The work compares instrumentation performed at compile time and during program execution. It is shown that the first approach ensures high semantic accuracy and metric predictability, while the second provides flexibility and continuous monitoring without requiring recompilation of the application. The study concludes that creating a hybrid observability model that combines the advantages of both approaches offers an effective balance between data precision and operational reliability. The significance of this research lies in forming conceptual foundations for the development of self-regulating observability systems, which can be applied in the design of telemetry infrastructures, optimization of DevOps processes, and enhancement of the resilience of industrial software systems
Automated Customer Support Systems in Service Companies: Analysis of AI Implementation
Full text linkThis study analyzes the potential of integrating artificial intelligence (AI) into customer support systems within service companies. The research is based on a review of the technological foundations of AI solutions, including natural language processing (NLP, NLU), interactive systems (chatbots, virtual and voice assistants), business process automation, and analytical tools for predictive modeling. Through statistical analysis and case studies from various industries—such as the banking sector, retail, and the implementation of the Pega platform—the study identifies key performance changes, including reduced response times, an increase in first-contact resolution rates, improved customer satisfaction, and lower service costs. The study presents an integrated model for evaluating the effectiveness of AI implementation, offering recommendations for optimizing customer support automation. By addressing a scientific gap, this research combines technical, economic, and ethical aspects of AI applications in customer service. The findings will be of interest to service company executives, customer support specialists, and IT directors seeking to enhance customer interactions through AI-driven automation. Additionally, the study provides valuable insights for analysts and researchers in digital transformation, examining the impact of artificial intelligence on business processes and user experience
Bridging Zero-Shot and Fine-Tuned Performance in Text Classification through Retrieval-Augmented Prompting
Full text linkLarge Language Models (LLMs) have shown promise in zero-shot and few-shot classification. Yet, their performance often falls short of classic fine-tuned encoders, especially in fine-grained or domain-specific settings. This study compares fine-tuned BERT-family models with zero-shot and few-shot prompting of LLMs (GPT-4o, Llama 3.3 70B, and Mistral Small 3) on two benchmarks: AG News (coarse-grained topic classification) and BANKING77 (fine-grained intent classification). Baseline results confirm that fine-tuned models outperform zero-shot LLMs by ~10-25 points in accuracy, with a larger gap on the fine-grained task. We then test training-free methods to improve LLM performance, focusing on retrieval-augmented few-shot prompting, example ordering, and Chain-of-Thought (CoT) reasoning. Our results show that retrieval-augmented prompting consistently boosts accuracy, especially on the BANKING77 dataset with many semantically similar examples, where GPT-4o even slightly surpasses the best fine-tuned encoder. Ordering demonstrations from least to most similar further improves accuracy, reflecting the impact of recency bias in in-context learning. By contrast, CoT prompting decreases accuracy, suggesting that explanation-based prompting is not universally helpful for classification. These findings demonstrate that careful example selection and ordering can substantially narrow the gap between zero-shot LLMs and fine-tuned encoders, offering a practical, training-free alternative in data-scarce scenarios