Future University in Egypt

Abstract Background Hot flashes represent a major and clinically significant global health challenge, affecting a large proportion of women in midlife, with a higher burden observed in African regions and low-income settings compared with high-income countries. These vasomotor symptoms are also common after menopause and are particularly prevalent among breast cancer patients receiving endocrine therapy, where they can substantially impair quality of life and adherence to treatment. Although hormone replacement therapy is effective in relieving vasomotor symptoms, its contraindication in hormone receptor–positive breast cancer creates a clear therapeutic gap and highlights the urgent clinical need for well-validated, safe, and effective non-hormonal treatment strategies that can be integrated into routine breast cancer care. Main body. Hot flashes, a common and distressing symptom in cancer patients, result from estrogen withdrawal and involve hypothalamic thermoregulatory changes as well as altered serotonergic and noradrenergic signaling. Although many interventions are being explored, relatively few have demonstrated clear, safe, and effective clinical benefits. Non-hormonal management strategies include lifestyle modifications, complementary therapies, and pharmacological treatments. Behavioral approaches such as relaxation, yoga, and paced breathing may offer modest and variable relief, while complementary interventions like acupuncture, vitamin E, soy, and phytoestrogens have shown mixed results. Pharmacological options, including SSRIs (e.g., paroxetine, escitalopram), SNRIs (e.g., venlafaxine, desvenlafaxine), and gabapentin, have shown more consistent reductions in hot flash frequency and severity (25–69%) in clinical studies, though potential interactions with tamoxifen should be carefully considered. This review aims to critically evaluate current non-hormonal treatments options for hot flashes, focusing on key endpoints reported in recent clinical trials, and to propose prospective future directions. The review specifically addresses women receiving hormonal therapy for breast cancer. Conclusion Pharmacological non-hormonal treatments currently provide the most reliable relief from hot flashes, supporting adherence to therapy and improving quality of life in breast cancer patients. Behavioral and complementary interventions may supplement care, but further research is needed to refine long-term strategies, ensure safety, and enhance patient adherence.

Pretrained multilingual End-to-End (E2E) Automatic Speech Recognition (ASR) models demonstrate remarkable capabilities but struggle with domain-specific terminologies and underrepresented dialects. Fine-tuning requires expensive paired audio-transcript data, creating barriers to practical adaptation. This paper investigates language model (LM) integration via shallow fusion as an efficient, text-only adaptation method. We introduce Confidence Gated Fusion (CGF), a novel approach that dynamically determines the LM weight during decoding based on ASR model uncertainty, eliminating the expensive validation-set-dependent grid search required by traditional shallow fusion. We validate our approach using OpenAI’s Whisper across multiple model sizes on Arabic ASR, evaluating Modern Standard Arabic (MSA), Egyptian dialect (EGY), and a specialized judiciary domain. Integrating a domain-specific LM achieved up to 40.92% relative WER reduction on dialectal speech and 32.96% on the judiciary domain. Our CGF method achieved comparable performance to tuned baselines while requiring no hyperparameter optimization, with particular advantages in specialized domains (28.92% relative WER reduction on judiciary) and on smaller models where static weights often lack robustness.

This paper presents a novel, accessible pipeline for the prediction and prevention of motion-related joint injuries in multiple individuals. Current methodologies for biomechanical analysis often rely on complex, restrictive setups such as multi-camera systems, wearable sensors, or markers, limiting their applicability in everyday environments. To overcome these limitations, we propose a comprehensive solution that utilizes only single-camera 2D images. Our pipeline comprises four distinct stages: (1) extraction of 2D human pose keypoints for multiple persons using a pretrained Human Pose Estimation model; (2) a novel ensemble learning model for person-view classification—distinguishing between front, back, and side perspectives—which is critical for accurate subsequent analysis; (3) a view-specific module that calculates body-segment angles, robustly handling movement pairs (e.g., flexion–extension) and mirrored joints; and (4) a pose assessment module that evaluates calculated angles against established biomechanical Range of Motion (ROM) standards to detect potentially injurious movements. Evaluated on a custom dataset of high-risk poses and diverse images, the end-to-end pipeline demonstrated an 87% success rate in identifying dangerous postures. The view classification stage, a key contribution of this work, achieved a 90% overall accuracy. The system delivers individualized, joint-specific feedback, offering a scalable and deployable solution for enhancing human health and safety in various settings, from home environments to workplaces, without the need for specialized equipment.

This study presents the exponential distribution optimization (EDO) technique for tuning the proportional-integral-derivative (PID) controller parameters in combined load frequency control (LFC) and automatic voltage regulation (AVR) for both single- and two-area electrical power system models. The objective is to enhance system performance by minimizing frequency and voltage deviations. The proposed approach leverages widely adopted objective functions, namely, the integral time squared error (ITSE) and the integral time absolute error (ITAE) to effectively reduce control errors while improving system stability and transient response. The EDO algorithm iteratively refines PID parameters by selecting optimal values from evolving solution populations, with system disturbances introduced to evaluate robustness under varying operating conditions. Simulation results demonstrate significant improvements, including a 16.52% reduction in LFC undershoots and a 33.3% improvement in AVR steady-state error in single-area systems, and up to 13.2% faster settling times in two-area systems. The EDO-based controller achieves faster dynamic responses, smoother operation, and complete elimination of steady-state errors while maintaining balanced frequency overshoot and minimized transient oscillations. Comparative analyses confirm the method’s superiority over established optimization techniques. A comprehensive robustness further validates the reliability and effectiveness of the EDO-tuned PID controller, underscoring its strong potential for ensuring power system stability.