Articles

This study utilized a qualitative approach to completely ascertain Junior High School Social Studies teachers’ knowledge, strategies and tools used in formative assessment. This study employed a qualitative approach. The study, which was based on a qualitative paradigm, used a phenomenological research methodology to investigate the lived experience and acuity of formative assessment procedures. A selected sample of nine (9) Junior High School Social Studies teachers was chosen based on their academic credentials and professional teaching experience. A focus group discussion was employed to gather data, which was then thematically examined in light of the research questions. The results showed that the participants had a robust knowledge and were adept at using formative assessment strategies and tools in Social Studies lesson. It was recommended that pre-service and in-service teachers should be allowed to use formative assessment techniques sparingly. The National Teaching Council seminars ought to focus on how Junior High School Social Studies teachers can effectively use formative assessment techniques to enhance instruction.

<b>Objective:</b> To identify and evaluate the main functional assessment tools applied in the postoperative monitoring of patients undergoing total knee arthroplasty (TKA), and to synthesize the functional outcomes reported through these instruments in the current scientific literature. <b>Method</b><b>ology</b><b>:</b> A structured review was conducted following PRISMA 2020 guidelines. Thirty-one peer-reviewed studies were selected through a targeted manual search based on predefined eligibility criteria. Included studies evaluated functional recovery following TKA using validated outcome measures such as the WOMAC, KSS, KOOS, IKDC, SF-36, and SANE. Data extraction focused on the instruments used, patient population characteristics, and reported outcomes. A descriptive synthesis was compiled in Table 1. Additionally, 15 studies with quantitative data were analyzed using a forest plot to illustrate risk ratios (RR) and 95% confidence intervals (CI) for functional improvement. Risk of bias was assessed qualitatively based on methodological rigor, clarity of reporting, and validation of the outcome tools. <b>Results:</b> All included studies reported improvements in functional status following TKA. Most risk ratios ranged from 0.66 to 0.85, indicating a consistent reduction in the risk of postoperative functional limitation. High-quality studies demonstrated more precise effect estimates and greater internal validity. The SANE scale emerged as a valid and practical tool with high responsiveness, including in its culturally adapted Brazilian version. Despite heterogeneity in study design, the direction of effect remained consistent across all included studies. <b>Conclusion:</b> Validated functional assessment tools are essential for monitoring recovery after total knee arthroplasty. Instruments such as WOMAC and SANE demonstrate strong clinical utility and psychometric validity. Their systematic use enhances outcome comparability, supports individualized rehabilitation planning, and improves decision-making in orthopedic care.

Progress in the development and adoption of technological innovations is instrumental in enhancing the efficiency of production systems across the globe. Through the introduction of cost-efficient and high-performing technologies, countries can both reduce the resource use intensity of their economies and boost the global supply of essential products. The focus of this study is to analyze the application of advanced machinery and mechanisms within the agricultural sector, a primary industry that acts as a major contributor to the gross domestic product (GDP) of many nations. Specifically, this paper provides an in-depth review of the latest impact assessments based on analytical and modeling tools conducted on agricultural machinery and production technologies. Our findings highlight the positive role played by scientific progress and innovation in driving the competitiveness, growth and improved sustainability of the agricultural sector. Over the years, advanced technologies have accelerated the development and modernization of machinery, equipment, and processes in farming. Typically, modern machinery and equipment have enabled large-scale production on farms, enhancing the cost-efficient use of both land and labor, as well as the capacity and timeliness in performing essential agricultural operations. The rapid diffusion of technical advancements has further contributed to resource savings, productivity growth, and the overall transformation of agricultural value chains. Accordingly, the implementation of appropriate enabling conditions is of vital importance in encouraging the widespread integration of technologies in agriculture, not only boosting productivity along the agri-food chain but also yielding widespread social, economic, and environmental benefits.

Assessment is a vital aspect of curriculum practice. The study adopted explanatory mixed-method approach and sequential research design. The population for the study comprised all Social Studies teachers in Junior High Schools in the Ayensuano District. Convenient sampling techniques and census method were used to select the district, and all the one hundred and twenty-seven (127) teachers who teach Social Studies in the junior high schools in the district. The main instruments used for data collection and analysis were questionnaire and interview guide. The quantitative data was analysed both descriptive and inferential statistical tools. The qualitative data was transliterated and coded based on themes. Pre-set themes were used to generate the transcript (text) data based upon the research questions and discussed. The study concluded that authentic assessment practices by Social Studies teachers included the occasional guidance they give to their students about how to interpret topics and situations into relevant tasks with a clearly defined goal, and how to relate their knowledge in practical challenges. The study also revealed that refined essays, oral presentations, interviews, case study discussions, and live performances as authentic assessment strategies or tools Social Studies teachers used in assessing their students' learning outcomes in lessons. The study indicated that large class size, traditional assessment (examination) system, insufficient logistics and infrastructure, a paucity of funds to begin various activities and programs, a lack of motivation from school administrators, time constraints and difficulty in developing some authentic assessment tasks coupled with assessing some lessons using authentic assessment method are some of the criteria that influence the effectiveness of authentic assessment execution in teaching Social Studies courses. It is recommended that educational leaders should provide the resources to motivate Social Studies teachers to use authentic assessments for students’ learning in the classroom situation. It is also recommended that, Colleges of Education, Universities and National Teaching Council should organise professional development workshops and seminars to build the capacity of trained Social Studies teachers on the effective uses of authentic assessment practices.

Automated testing and software quality assurance (SQA) practices are essential for ensuring the reliability, scalability, and maintainability of modern software systems. This paper presents a review of widely used automated testing frameworks, including Driven, Data-Driven, Behavior-Driven Development (BDD), and Record/Playback approaches, outlining their methodologies, benefits, and limitations in different development contexts. In parallel, it examines established SQA techniques such as Test-Driven Development, static analysis, and white-box testing, which provide systematic methods for defect detection and quality improvement. The study further examines the role of practical tools, such as Selenium, TestNG, and JUnit, in supporting test automation and validation activities. In addition to highlighting technical capabilities, the paper identifies common challenges faced in automation, including incomplete requirements, integration complexities, and maintaining evolving test suites. Recommended best practices are provided to address these issues, offering guidance for organizations seeking to strengthen their software testing processes through structured frameworks, adaptive techniques, and reliable automation tools.

<b>Background</b>: There are currently numerous risk instruments available to aid in predicting the present or future chance of getting a cancer diagnosis. It aids in determining a person's likelihood of developing certain cancers by looking at various risk factors, including environmental, behavioral, and genetic. <b>Aim:</b> To analyze the effectiveness of cancer risk assessment techniques utilized in primary care settings. <b>Methods</b>: An integrative review of literature <b>Results</b>: Five (5) studies were met the criteria based on the inclusion and exclusion criteria. These tools demonstrated effectiveness in improving patient outcomes and serving as useful therapeutic tools in the primary care setting. <b>Conclusion:</b> Advantages that may aid clinicians in the primary care setting in validating the diagnosis and assisting patients in determining the early signs and symptoms in the diagnosis of cancer. The role of assessment tools can enhance the reliability and caliber of clinical judgment, which can enhance patient outcomes. <b>Implications</b>: The role of healthcare professionals, such as oncologists, nurses, and the healthcare team, on cancer risk assessment in the primary care setting across the lifespan is crucial to ensure a care plan tailored to each patient’s needs.

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.

This paper shows how Big Data Analytics (BDA) and Artificial Intelligence (AI) automation facilitate regulatory compliance in Finance. Regulatory compliance is essential in helping institutions to mitigate reputational, litigation, and financial risk. Existing literature reveals several preconditions for compliance. However, much of the literature has adopted an internal view of compliance without considering external regulatory frameworks. This research draws on the cognitive model of regulation that looks at regulatory compliance as a social construct. It uses a triangulation research method comprising literature review, interview of trade compliance experts, and questionnaire survey of compliance practitioners to understand how regulation affects compliance and what role ICTs play in implementing compliance. The findings of this study present a regulatory compliance framework comprising four cognitive stages and a conceptual regulatory compliance system that presents how BDA and AI automation are applied to mitigate regulatory complexity and enhance regulatory compliance. The conceptual regulatory compliance system shows how BDA and AI enable institutions to dynamically assess regulatory risk, automatically monitor compliance, and intelligently predict risk violations mitigating regulatory complexity and preventing producing unnecessary documents. It provides theoretical contributions to understanding regulatory evolution and compliance and practical implications for understanding how regulation evolves to be more complicated and elements of a regulatory compliance system mitigate proliferating regulations. Additionally, it provides avenues for future research into the relationship between competing regulatory mandates and how institutions cope with that. Regulations are important for ensuring compliance and governance in finance and to curb systemic risk. Complying with regulations is difficult due to their growing volume, complexity, and fragmentation. Institutions use large-scale Information and Communication Technologies (ICTs), such as Big Data Analytics (BDA) and Artificial Intelligence (AI) automation, to monitor compliance and mitigate regulatory complexity. However, less is known about how firms comply with regulation. Most literature does not thoroughly investigate regulatory elements nor explicitly relate them to compliance.