European Journal of Science, Innovation and Technology

A Review of the Global Effects of Cherax quadricarinatus on Native Biodiversity in Non-Native Ecosystems

Geofrey Makwelele — 2025-04-10

Cherax quadricarinatus, also known as the red claw crayfish, is an alien invasive species (AIS) in numerous freshwater ecosystems outside its native range in northern Australia and southern New Guinea. Its introduction to non-native ecosystems, often driven by aquaculture, accidental incidents and the pet trade, has significant ecological implications for native biodiversity. This species' adaptability to various environmental conditions and high reproductive rate facilitates its establishment and spread in new habitats. The presence of C. quadricarinatus in non-native ecosystems results in several detrimental effects on native biodiversity, including competition with indigenous crayfish and other aquatic species for resources, predation on native species, and alteration of habitat structures.

C. quadricarinatus competes directly with native crayfish species, often leading to a decline in native populations due to its aggressive behavior and more efficient resource utilization. This competition extends to other aquatic invertebrates and small vertebrates, resulting in reduced biodiversity and disruptions in local food webs. Predation by C. quadricarinatus on native fish, amphibians and invertebrates further exacerbates these impacts, often leading to declines in vulnerable native populations. In addition, the burrowing behavior of C. quadricarinatus causes physical alterations to habitats, such as increased sedimentation and destruction of aquatic vegetation, which are crucial for the survival of many native species. These habitat modifications can lead to long-term changes in ecosystem structure and function, affecting water quality and availability of resources. Hybridization with native crayfish species can occur, leading to genetic dilution and loss of unique genetic traits in native populations. C. quadricarinatus crayfish can carry diseases and parasites that may infect native species.

Enhancing Evacuation Planning in High-Risk Areas: A Case Study of Laurel, Batangas, Philippines, Post-2020 Taal Volcano Eruption

Claire Maurene Gratil-Peñamante — 2025-04-11

Effective evacuation planning is vital for safeguarding lives and minimizing damage during natural disasters. This study explores the challenges and opportunities associated with evacuation planning in Laurel, Batangas, Philippines, following the 2020 eruption of Taal Volcano. Anchored in the framework of the UNDRR’s Making Cities Resilient Campaign, the research focuses on evaluating evacuation route management and emergency shelter placement in a high-risk volcanic setting.

The study critically reviews historical eruption data and past evacuation responses, identifying key issues such as delayed response times, transportation limitations, and the area’s complex terrain. It further assesses the adequacy of existing evacuation centers and proposes improvements to optimize accessibility and safety.

Utilizing Geographic Information Systems (GIS) and spatial analysis, the research maps local street networks to identify optimal rescue points and alternative evacuation routes, particularly for the most vulnerable zones. These findings contribute to enhancing disaster preparedness and community resilience.

The study underscores the importance of developing context-specific evacuation strategies that reflect the unique characteristics of volcanic hazards. By addressing existing gaps in evacuation planning and integrating spatial tools, this research aims to support more effective and adaptive disaster risk reduction efforts in Laurel and other similarly vulnerable communities.

Artificial Intelligence-Driven Diagnostics: Transformative Innovations in Telemedicine for Precision Healthcare

Bongs Lainjo — 2025-04-11

With AI's assistance in diagnostics, telemedicine has transformed the concept of diagnosis, creating remarkable opportunities for its advancement in accuracy, speed, and accessibility. This study examines cutting-edge diagnostic technologies that utilize AI to enhance patient outcomes and explores the potential of these advanced technologies to eliminate challenges such as late and incorrect diagnoses and unequal access to diagnostic services. A combination of focus group interviews and questionnaires was employed with key stakeholders, including clinicians and patients, along with a structured review of case studies showcasing AI applications in telehealth. The study demonstrates that diagnostic accuracy and efficiency can be significantly improved across various medical fields, such as cardiology, dermatology, and radiology. The use of AI in diagnosing health issues has been shown to reduce diagnostic errors while facilitating early interventions through machine learning and decision-support systems; late interventions have also been improved in underserved and remote regions. Despite these advancements, challenges persist, including issues related to inadequate infrastructure, algorithm-related problems, and concerns regarding patient privacy and data security. To address these challenges, the study proposes recommendations such as developing culturally sensitive databases, establishing ethical management systems for AI, and fostering public-private collaboration to enhance technological support. These findings illustrate how diagnostic AI in telemedicine can shift healthcare from the current paradigm, where patients’ experiences are often subjective, to the concept of precision healthcare for all.

Classification and Prediction of Lumbar Spondylolisthesis using a Bagging Classifier

Deepika Saravagi — 2025-04-11

Lumbar spondylolisthesis is a degenerative spinal condition that may lead to considerable pain and impairment. Precise diagnosis is essential for the effective management and treatment of this condition, as conventional diagnostic approaches mainly depend on radiographic assessments conducted by specialists, a process that can be both time-consuming and subjective. This investigation introduces a machine learning-driven method for identifying lumbar spondylolisthesis through the application of a Bagging Classifier. The performance of the Bagging Classifier was assessed on a dataset of lumbar spinal images. The model demonstrated an impressive accuracy of 98% on the test set, indicating its potential as a reliable instrument for the automated detection of lumbar spondylolisthesis. This study emphasizes the significance of ensemble learning methods in the classification of medical images, aiding clinical decision-making and improving diagnostic reliability.

Shaping the Future: Civil Society, Party Politics and Child Social Protection in South Africa, 1994-2024

Isaac Chinyoka — 2025-04-16

South Africa’s social grants for poor families with children provide a model child social protection system in the developing world. This article identifies the distinguishing characteristics, traces the evolution and explains the child welfare regime in South Africa. The paper applies a political economy approach and concludes that domestic civil society and party politics were central in shaping a regime that is expansive, relatively generous and poverty targeted. This work is important because it shows the significance of a comprehensive child grants system in reducing child poverty and the centrality of legislating children’s social security rights as a pre-requisite for social policy reforms.

The Role of Gender in Technology Adoption and Diffusion in the United States

Ngozi Caleb Kamalu — 2025-04-17

The purpose of this paper is to explore gender differences between men and women in the use of information and communication technology, with special focus on the frequency and intensity of use. Put in another way, to determine whether gender makes a difference in computer and internet use between men and women. Further, it explores whether differences in internet and computer use, “gender bias” or “gender gap” in technology use does indeed exist between the genders. Moreover, this research helps to inform whether such disparities have changed in recent years, reasons for concern, by how much, factors that are responsible and what should be done to eliminate gender-gap in technology adoption and usage. While reviewing numerous and available literature on gender and technology adoption and diffusion, necessary to contribute to knowledge on the subject, this research concludes that there exists gender-gap in computer and internet use in favor of males over females because of differences in factors such as socialization, modeling, anxiety or levels of comfort in technology-use and gender-related stereotypes. This research concludes with the recommendation of proper mitigation policies, steps and actions necessary to drastically reduce or eliminate the gender gap in technology adoption and usage that include but not limited to early socialization for girls, early exposure of girls in STEM fields, involvement of more females in technology training and deployment of high skilled and achieving females in engineering and computer fields as role models and mentors for aspiring females in the computer fields and related industries.

Gender and Regional Disparities in Child Malnutrition in Vietnam: Evidence from National Data

Trinh Thi Thu Trang — 2025-04-19

Child malnutrition remains a pressing public health concern in Vietnam, with notable disparities across genders and regions. This study aims to examine the trends and inequalities in malnutrition among children under five between 2017 and 2023. Using secondary data on three key malnutrition indicators—underweight, stunting, and wasting—this research applies descriptive analysis and multiple linear regression models to explore the effects of time, gender, and indicator type on malnutrition prevalence. The findings reveal a gradual decline in overall malnutrition rates over time; however, significant differences persist between male and female children, particularly in stunting and underweight rates. Regional disparities also contribute to the unequal distribution of malnutrition. Regression analysis confirms that indicator type has a statistically significant effect on malnutrition rates, while time and gender effects are less pronounced. These results highlight the need for more targeted, gender-sensitive, and region-specific nutrition interventions to promote equity in child health outcomes in Vietnam.