Journal of Prospective Researches

Breast cancer is a complex, diverse disease that has an effect on people around the world. The rise of breast cancer is associated with hormonal imbalance and emphasizes the importance of understanding complex relationships between hormones and breast tissue. This intensive study intends to detect the importance of hormonal breast cancer imbalance, and analyzes how to affect the growth of large hormones such as estrogen and progesterone. Review checks how hormone imbalance affects the breast tissue. In addition, it discusses the importance of hormone samples for clinical and medical decisions, as well as considering hormonal imbalance as a method to estimate the risk of breast cancer. The study also provides observations of benefits, boundaries and ongoing research projects to adapt to the effectiveness of hormone therapy and remove resistance in the treatment of breast cancer. In this sense, it is important to study the role of hormone dynamics in pathophysiology for breast cancer for the development of our knowledge of the situation and the development of more adapted, effective treatment. Understanding how hormones affect the initiation, spread and resistance of cancer treatment, it is necessary to improve the patient\\'s results and reduce the global stress of breast cancer. The purpose of this review is to provide a comprehensive analysis of how hormonal changes affect breast cancer pathophysiology and the clinical importance of major clinical-pathological features. It tries to improve the results of the treatment by integrating existing data and providing possible paths for the patient\\'s satisfaction with sequence, hormone -focused therapy.

Laser tissue interaction for photobiomodulation is significantly dependent on the skin pigmentation due to the difference in melanin concentration. This affects the optical absorption and the depth of penetration of the laser beam. The computational model developed was aimed at analyzing the interactions between the diode laser beam of wavelength 635 nm and the light and dark layers of epidermis skin tissue. The power output of the laser was varied from 2 to 20 mW. The absorption coefficient of optical power (mW·mm⁻³) was determined in respect to different depths of epidermis skin tissues (0.015 to 0.1025 mm). The findings indicated that the rate of absorption was greater in dark skin compared to light skin because the volume absorbed optical power was 2.39 times higher due to a higher concentration of melanin in the dark skin. Absorption of energy occurred at a lesser depth of 0.019 to 0.0317 mm in dark skin but occurred at a greater depth in light skin. There was a strong correlation between the rate of energy absorbed and power, where R²>0.98. The findings revealed that there were marked variations in the energy absorption mechanisms among various skin types, with far-reaching implications for fine-tuning the process of photobiomodulation. Notably, no biological effect was assessed in the study.

The present paper conducts an in-depth investigation into the types of speech acts utilized in F. Scott Fitzgerald’s short story “The Freshest Boy,” utilizing J. R. Searle’s (1976) classification of speech acts as the primary analytical framework. The main research question focuses on how these speech acts unveil the psychological aspects of the protagonist’s character within the narrative. Employing a qualitative analytical methodology, the study merges speech act theory with Freudian psychoanalytic concepts, specifically analyzing selected textual data. Findings indicate that the protagonist’s speech acts transcend mere communicative functions; they act as reflections of deeper psychological states and internal conflicts. The study’s significance is underscored by its interdisciplinary approach, integrating pragmatic analysis with psychoanalytic interpretation, thus providing enriched insights into character construction and development in literary contexts.

Low-level ionising radiation is a persistent environmental factor in many freshwater ecosystems, particularly in mineral springs and areas affected by historical uranium mining. However, its ecological effects under natural field conditions remain poorly quantified. This study investigates the influence of naturally occurring radioactive materials (NORM) and technologically enhanced naturally occurring radioactive materials (TE-NORM) on benthic diatom communities using field observations from mineral springs and a former uranium mining wetland in Southern Iraq. Diatom assemblages were analysed using morphological and genetic approaches and related to environmental and radiological parameters. Results indicate that radioactivity contributes to the structuring of diatom communities by influencing species dominance and composition, while overall species richness is primarily controlled by physico-chemical conditions. Low-level radioactivity therefore acts as a chronic ecological constraint rather than an acute stressor.

Teaching culture and fostering intercultural communicative competency has received dramatic attention lately as it is the aim of the current study. It is recognized that mere knowledge of culture and the skills required to develop intercultural communicative competency is hardly enough. Extensive, focused, and organized practice is in order. The study utilizes narrative conversational activities as basis for the tasks to enhance intercultural communicative competence. The population consists of 72 learners evenly divided into 2 groups; group A is randomly chosen to serve as the experimental group. The two-group pretest-posttest design is used in the study for statistical assessment of the hypotheses proposed. The results show efficiency of the procedures adopted as the statistical treatment shows a significant improvement in ICC.

Plastics have been increasingly used to supplement or partially replace natural and traditional materials including paper, wood and in some uses, even metals because they are inexpensive, highly flexible, strong, and non-biodegradable. Increased production of plastic, worldwide distribution, and the lack of degradability resulted in plastic accumulations in the environment and pose serious environmental and biological health risks. Therefore, creating appropriate measures for the cleanup environment may be a top priority in our attention. Recently, breakdown of polymers by biological systems has attracted the interest of Numbers of researchers. Different fungal species are shown to be able to break down plastic polymers and use them as a carbon source. This feature is exploited as an environmentally friendly approach to manage plastic waste. Various fungal strains have been shown to be effective in this field, including Aspergillus flavus, Phanerochaete chrysosporium, Aspergillus niger, Fusarium sp., Mucor sp., Cephalosporium sp., and others. Biodegradation mechanisms of polymers include biodeterioration, followed by polymer fragmentation, microbial assimilation, and ultimately mineralization. This review highlights the type of plastics, the fungal role in degradation of plastic, the biodegradation process of plastic polymers, the environmental conditions that influence the biodegradation process of plastic polymers as well as the enzymes used by fungi in the biodegradation process of plastic polymers and other methods of measuring and analyzing the biodegradation process of plastic polymers.

This study investigates the effect of AI-enhanced grammar instruction on the achievement, attitudes, and perceptions of EFL students at university level. The participants of the study were 60 third-year EFL students (EG = 30 and CG = 30) enrolled in a grammar course at the University of Mosul. Both intact classes received the same instructional time, unit sequence, and teacher-led instruction. The experimental group was taught with simplified explanations, contextualized examples, and practice materials generated via Gemini AI tool and subsequently reviewed, edited, and aligned with the mandated textbook by the instructor, whereas the control group received textbook-based explanations and examples. Baseline equivalence was established for gender, age, a general grammar screening test, and pre-attitudes. Outcomes were measured using a unit-based grammar achievement test, a pre-post attitudes questionnaire, and perceptions questionnaire. The experimental group outperformed the control group on grammar achievement (M=34.26 vs 25.44; d=0.98; p<.001), reported more positive perceptions (M=88.35 vs 62.91; d=1.81; p<.001), and showed larger attitude gains (ΔM=27.59 vs 5.88; d=2.42; p<.001). The findings suggest that generative AI can enhance tertiary EFL grammar learning when used as an instructor-mediated materials-development tool within a teacher-led, curriculum-aligned instruction.

This study aims to analyze the role of behavioral capabilities in enhancing the orientations of educational institutions toward digital maturity. Digital transformation has become an inevitable necessity within a dynamic business environment characterized by complexity and competitiveness, which requires attention to human and behavioral dimensions alongside technology. The study adopted a descriptive-analytical approach based on relevant theoretical literature and applied studies, and conducted a field survey on a sample of leaders in educational institutions in the region undergoing digital transformation. A total of 250 questionnaires were distributed, and 201 were returned. AMOS.V.24 was used for statistical analysis. The findings showed that behavioral capabilities—such as flexibility and innovation—contribute directly to formulating and implementing effective digital strategies and increase the institution’s ability to adapt to changes. The results also indicated that flexibility affects the digital strategy and digital infrastructure, and that innovation also contributes to strengthening the digital maturity strategy and the digital infrastructure required by educational institutions to enhance their digital maturity. This, in turn, increases responsiveness to modern technologies and reduces resistance to change. The study recommends developing training and developmental programs that focus on strengthening the behavioral capabilities of leaders and staff alike, as these capabilities represent a pivotal element for achieving digital maturity and ensuring the sustainability of the institution’s competitive advantage.

Animal-assisted therapy (AAT) has gained increasing attention due to its potential to enhance physical, psychological, and social well-being. In recent years, its application has expanded within clinical rehabilitation settings, including neurological care. This systematic review aims to critically evaluate the effectiveness of animal-assisted interventions in adults with neurological disorders. A comprehensive literature search was conducted across PubMed, Scielo, Embase, and PEDro databases to identify clinical studies examining the impact of AAT on neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria focusing on adult populations, clinical trial designs, and clearly reported outcome measures. From the initially identified studies, seventeen clinical trials met the eligibility criteria and were included in the final analysis. Overall findings indicate that animal-assisted therapy contributes positively to multiple outcome domains, including motor performance, physical functioning, psychological health, and behavioral outcomes across various neurological disorders. The evidence synthesized in this review suggests that animal-assisted therapy represents a promising complementary approach in neurological rehabilitation. These findings provide occupational therapists and rehabilitation professionals with supportive evidence to integrate AAT alongside conventional therapeutic strategies to improve patient outcomes.

In the realm of computational optimization, the Minimum Spanning Tree (MST) remains a cornerstone algorithm for solving complex networking and sequence-related problems. This research explores a novel application of MST within the field of Automated Process Planning (APP). By leveraging the "Delta Volume" decomposition method, we represent the material removal process as a structured graph of interdependent data nodes. The core contribution of this work lies in the development of a specialized algorithmic framework, implemented via MATLAB, which treats machining volumes as a discrete search space. Our approach utilizes MST-based logic to navigate this space and determine the most efficient execution sequence, effectively transforming a traditional manufacturing challenge into a graph-theory optimization problem. By integrating these computational techniques, the study provides a robust automated solution that bridges the gap between geometric modeling and intelligent process sequencing, ensuring both precision and algorithmic efficiency.

Surface defects in solar panels have a detrimental impact on the performance and reliability of the system. In this research paper, we proposed an automatic fault detection model for solar panels using a deep learning model based on the VGG16 (CNN) architecture with transfer learning and data augmentation. A dataset that has a total of 869 images for six fault categories (Clean, Dusty, Bird-drop, Electrical-damage, Physical-damage, Snow) was used. Two models were utilized for analysis: (Model 1) the baseline VGG16 which is the VGG16 architecture pre-trained on the original dataset scope; and (Model 2) the enhanced VGG16 which incorporates data augmentation, freezing of early layers and dropout regularizing. Model 2 achieved a higher accuracy at 98.76% on validation set compared to Model 1 with an accuracy of 82.49% indicating that comprising transfer learning along and pertinent augmentation positively enhances the classification accuracy. The proposed approach is capable of being scaled up and replicated in real-world applications of smart automated solar panel inspection.