Moebius syndrome (MBS) is a rare congenital cranial nerve disorder characterized by unilateral, bilateral symmetrical, or asymmetrical facial (VII) and abducens (VI) nerve palsies. Genetics and rhombencephalon vascular disturbances from intrauterine environmental exposures have been attributed to its development. It can present with various orofacial abnormalities. Although the diagnosis is purely clinical, certain characteristic features are present in the brain's images. With no cure, it is essential to devise management on a personalized basis. We discuss etiology, presentation, diagnostic approaches, and effective management in the existing literature. This comprehensive review examines the clinic-pathological aspects of Moebius syndrome. The authors employed the PUBMED base index to identify pertinent literature and reference it according to research keywords. Findings suggest the most popular etiology is the theory of intrauterine vascular disruption to the brainstem during embryogenesis, followed by the genetic hypothesis. Intrauterine environmental exposures have been implicated as potential risk factors. Facial and abducens nerve palsies are the most common presenting features. However, clinical manifestations of lower cranial nerves (IX, X, XI, XII) may be present with orthopedic anomalies and intellectual deficiencies. The diagnosis is clinical with minimal defined diagnostic criteria. Characteristic radiological manifestations involving the brainstem and cerebellum can be observed in imaging studies. With no definitive treatment options, a multidisciplinary approach is employed to provide supportive care. Despite radiological manifestations, Moebius syndrome is diagnosed clinically. Although incurable, a multidisciplinary approach, with personalized rehabilitative measures, can manage physical and psychological deficits; however, standard guidelines need to be established.

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The concept of hybrid high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) grid systems brings a massive advantage to reduce AC line loading, increased utilization of network infrastructure, and lower operational costs. However, it comes with issues, such as integration challenges, control strategies, optimization control, and security. The combined objectives in hybrid HVAC–HVDC grids are to achieve the fast regulation of DC voltage and frequency, optimal power flow, and stable operation during normal and abnormal conditions. The rise in hybrid HVAC–HVDC grids and associated issues are reviewed in this study along with state-of-the-art literature and developments that focus on modeling robust droop control, load frequency control, and DC voltage regulation techniques. The definitions, characteristics, and classifications of key issues are introduced. The paper summaries the key insights of hybrid HVAC–HVDC grids, current developments, and future research directions and prospects, which have led to the evolution of this field. Therefore, the motivation, novelty, and the main contribution of the survey is to comprehensively analyze the integration challenges, implemented control algorithms, employed optimization algorithms, and major security challenges of hybrid HVAC–HVDC systems. Moreover, future research prospects are identified, such as security algorithms’ constraints, dynamic contingency modeling, and cost-effective and reliable operation.

Energy saving and different working voltage applications are widely employing matrix converters. Matrix converters are used to convert three phase AC input voltages to three phase AC output voltages of required magnitude and frequency. Pulse width modulation is a technique used for converting three phase AC input to three phase AC output. In this research paper, pulse width modulation based matrix converter will be designed to acquire voltage of desired magnitude and frequency. Voltage transfer ratio will be maximized in this technique so that any desired voltage magnitude could be achieved. Secondly, harmonics will be made reduced in this design. Simulations will be performed in Matlab and characteristics of output waveform will be observed with respect to characteristics of input waveforms.

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