Optimal Function Research Articles

Optimizing wind-catching butterfly roofs to improve cross natural ventilation performance in apartment buildings

Abstract This study aims to enhance cross natural ventilation performance in multi-level double-loaded apartments by employing a wind-catching butterfly roof, specifically designed for apartments with a center-to-center opening configuration. A 2k full-factorial design of experiment (DOE) method with a center point is utilized for design optimization. Nine configurations, determined by roof height h and roof angle α, are assessed using Computational Fluid Dynamics (CFD). After the initial DOE assessment, three additional butterfly roof designs with varying α values are explored. The dimensionless flow rate (DFR) serves as the objective function for optimization. The optimal design features a butterfly roof with h = 0.01m and α = 30°, demonstrating improvements of 2.5%−4.9% in DFR and 5.3%−9.0% in the pressure coefficient C p compared to scenarios without a butterfly roof. Butterfly roofs also increased the velocity magnitudes in the rooms of the leeward block, with configurations h = 0.01m and α = 35° and 37.5° showing the most significant increases of 58.5% − 86.9% on levels 1 to 3. The discharge coefficient C d induced by all butterfly roof configurations are within 0.6 − 0.7 on levels 1 to 3, while the C d values for levels 4 to 6 are within 0.5 − 0.6. Although butterfly roofs can enhance indoor air quality, they require further research to meet all recommended guidelines. These findings provide valuable insights for architects and urban planners aiming to reduce the environmental footprint of apartment buildings. This research presents a comprehensive evaluation of the potential of butterfly roofs to enhance apartment ventilation and offers recommendations for future design improvements.

COMPARISON OF CLASSICAL AND QUANTUM COMPUTING FOR PARTICLE SWARM OPTIMIZATION

The article explored and delved into the advanced computational strategies of Particle Swarm Optimization (PSO) by contrasting classical and quantum computing paradigms. The advantages of quantum computing lie in its potential to solve computationally complex problems exponentially faster than classical computers. One of the advantages of Particle Swarm Optimization is its ability to find optimal solutions in complex search spaces. The research centers around the performance of PSO algorithms, as a part of the biological swarm optimization algorithms, when applied to a set of single-objective optimization functions, namely the Sphere, Rosenbrock, Booth, and Himmelblau functions. Utilizing a controlled setup of 100 particles, iterating 100 times across various dimensions tailored to each function, our study reveals that quantum Particle Swarm Optimization, implemented via Q# programming language and tested in Azure Quantum Workspace, consistently surpasses classical PSO in precision and convergence to global minima, despite the increased computational demands and error sensitivity inherent to quantum computations. The classical approach facilitated through Python programming language and leveraging deterministic pseudorandom number generators demonstrates robustness and lower computational costs but does not achieve the quantum's level of accuracy. The paper highlights the potential of quantum PSO to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces, paving the way for future applications where quantum advantages can be fully realized. The analysis goes further to discuss the implications of these findings for the future of optimization in various industries, including logistics, engineering, and finance, where optimization plays a critical role. The potential of quantum Particle Swarm Optimization to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces is particularly notable. It suggests that quantum computing could soon transform the landscape of computational optimization, providing solutions that are not only quicker but also more accurate.

Exploring the evolutionary landscape and structural resonances of ferritin with insights into functional significance in plant

The mineral iron plays a crucial role in facilitating the optimal functioning of numerous biological processes within the cellular environment. These processes involve the transportation of oxygen, energy production, immune system functioning, cognitive abilities, and muscle function. However, it is crucial to note that excessive levels of iron can result in oxidative damage within cells, primarily through Fenton reactions. Iron availability and toxicity present significant challenges that have been addressed through evolution. Ferritin is an essential protein that stores iron and is divided into different subfamilies, including DNA-binding proteins under starvation (Dps), bacterioferritin, and classical ferritin. It plays a critical role in maintaining cellular balance and protecting against oxidative damage. This study delves into ferritin's evolutionary dynamics across diverse taxa, emphasizing structural features and regulatory mechanisms. Insights into ferritin's evolution and functional diversity are gained through phylogenetic and structural analysis in bacterial Dps, bacterioferritin, and classical ferritin proteins. Additionally, the involvement of ferritin in plant stress responses and development is explored. Analysis of ferritin gene expression across various developmental stages and stress conditions provides insights into its regulatory roles. This comprehensive exploration enhances our understanding of ferritin's significance in plant biology, offering insights into its evolutionary history, structural diversity, and protective mechanisms against oxidative stress.

Spiral-refraction mutation prairie dog algorithm: Optimization framework for engineering design of interconnected multimachine power system

Many real-world engineering problems, characterized by high-dimensionality, nonlinearity, nonconvexity, and multi-modality, demand advanced optimization methods. Traditional algorithms may struggle with these challenges. Prairie dog optimization (PDO) was proposed for function optimization but faces limitations in balancing exploration and exploitation due to two reasons. The first one is related to diversity features which may not be efficient, and the second one is related to having no leading mechanism for direct search feature towards the promising regions. With that in mind, this study introduces an enhanced PDO variant, improved PDO (IPDO), addressing PDO's drawbacks through two key strategies: refraction-based learning (RBL) and spiral search learning (SSL). RBL enhances exploration by generating refraction solutions, while SSL conducts deep local searches around the best solution, strengthening exploitation. IPDO's performance is evaluated on benchmarks, IEEE CEC2017/CEC2020 test suites, and real-world constraint engineering optimization problems. Comparative analyses, including statistical measures, convergence analysis, and boxplot analysis, demonstrate IPDO's superiority. Besides, the IPDO is applied to estimate more promising parameters of power system stabilizer utilized in an interconnected multimachine power system using Western System Coordinating Council three-machine, nine-bus power system. Results illustrate that the IPDO harvests the better estimation among the other methods, making it to be an efficient and powerful tool for dealing with the efficient operation of an interconnected multimachine power system which is a challenging real-world engineering problem.

Optimizing Green Design: Evaluating Environmental Satisfaction of End Users in Bhimber's Dhq Outpatient Department for Sustainable Future Usage & Optimum Functionality

Optimizing Green Design: Evaluating Environmental Satisfaction of End Users in Bhimber's Dhq Outpatient Department for Sustainable Future Usage & Optimum Functionality

An inverse problem of reconstructing the unknown coefficient in a third order time fractional pseudoparabolic equation

In this paper, we have considered the problem of reconstructing the time dependent potential term for the third order time fractional pseudoparabolic equation from an additional data at the left boundary of the space interval. This is very challenging and interesting inverse problem with many important applications in various fields of engineering, mechanics and physics. The existence of unique solution to the problem has been discussed by means of the contraction principle on a small time interval and the unique solvability theorem is proved. The stability results for the inverse problem have also been presented. However, since the governing equation is yet ill-posed (very slight errors in the additional input may cause relatively significant errors in the output potential), the regularization of the solution is needed. Therefore, to get a stable solution, a regularized objective function is to be minimized for retrieval of the unknown coefficient of the potential term. The proposed problem is discretized using the cubic B-spline (CB-spline) collocation technique and has been reshaped as a non-linear least-squares optimization of the Tikhonov regularization function. The stability analysis of the direct numerical scheme has also been presented. The MATLAB subroutine $lsqnonlin$ tool has been used to expedite the numerical computations. Both perturbed data and analytical are inverted and the numerical outcomes for two benchmark test examples are reported and discussed.

Can I trust my AI friend? The role of emotions, feelings of friendship and trust for consumers' information-sharing behavior toward AI

Research background: AI devices and robots play an increasingly important role in consumers’ everyday life, by accompanying the consumer all day long. This presence has several utilitarian and social benefits, but at the same time the optimal functioning of AI requires personal information from the consumer. Purpose of the article: Starting from the premise that people share more information with friends, we have tested empirically whether an emotional behavior of AI can evoke the same emotions in the relationship between consumers and their AI devices, leading to a higher self-disclosing behavior. Methods: To validate the proposed hypotheses, three mediation models were tested using structural equation modelling in Smart-PLS 3.3.3, based on data collected with the help of an online survey. Findings & value added: We prove empirically that AI’s emotional behavior can increase consumers’ trust, it can evoke feelings of friendship and it can determine a higher perceived control over the shared private information, thus leading to lower perceived threats regarding the consumers’ vulnerability and exposure related to sharing of private data. These results have important implications for designing consumer-AI interactions.

Review of respiratory sleep disorders in children with genetic and metabolic diseases

Sleep is an important physiological process for the optimal functioning of the human body. Respiratory sleep disorders in children can be found in any age group with a prevalence of 2-5%. Genetic and metabolic syndromes in children associate a higher prevalence of sleep pathology. Polysomnography represents the gold standard investigation for evaluation and monitoring of breathing disorders associated with sleep. Early detection of sleep pathology in children and periodic monitoring plays an important role in improving quality of life of these patients.

Multi-objective parameter optimization of large-scale offshore wind Turbine's tower based on data-driven model with deep learning and machine learning methods

The tower plays a crucial role in wind turbine systems. However, the design and optimization of configuration parameters have traditionally been lacking in intelligent methods. This study proposes a multi-objective parameter optimization framework that incorporates artificial intelligence models. Specifically, the diameters and thicknesses of the tower are the design parameters that strongly influence two conflicting optimization objectives: mass and top deflection. The nonlinear relationship between these parameters is predicted using surrogate models, such as the Convolutional Neural Network (CNN), Back-propagation Neural Network (BPNN), and Support Vector Machine (SVM), which serve as optimization functions. Additionally, the solutions must meet the requirements for frequency, stress, and buckling. In this study, two reference wind turbines, namely, IEA-15-240 and IEA-22-280, are selected as case studies, and the open-source software WISDEM is utilized to construct the training and testing datasets. Bayesian optimization is used to fine-tune the hyperparameters. Results show that the CNN model outperforms others with larger datasets. Leveraging Deep Learning in the design of offshore wind turbines can significantly reduce mass and deflection while maintaining integrity and performance.

Comparative Analysis of Impact Strength among Various Polymeric Materials for Orthotic Production.

Orthotic devices play an important role in medical treatment, addressing various pathologies and promoting patient recovery. Customization of orthoses to fit individual patient morphologies and needs is essential for optimal functionality and patient comfort. The advent of additive manufacturing has revolutionized the biomedical field, offering advantages such as cost reduction, increased personalization, and enhanced dimensional adaptability for orthotics manufacturing. This research focuses on the impact strength of nine polymeric materials printed by additive manufacturing, including an evaluation of the materials' performance under varying conditions comprising different printing directions (vertical and horizontal) and exposure to artificial sweat for different durations (0 days, 24 days, and 189 days). The results showed that Nylon 12 is good for short-term (24 days) immersion, with absorbed energies of 78 J and 64 J for the vertical and horizontal directions, whereas Polycarbonate (PC) is good for long-term immersion (189 days), with absorbed energies of 66 J and 78 J for the vertical and horizontal directions. Overall, the findings contribute to a better understanding of the suitability of these materials for biomedical applications, considering both short-term and long-term exposure to physiological and environmental conditions.

Functional optimization of the design of adaptive furniture to improve the accessibility and comfort of people with impaired musculosical system

In today's world, there is a growing need to create an inclusive environment that meets the needs of people with limited mobility. This applies not only to architecture and public spaces at the level of macro- and meso-ergonomics, but also to adaptive furniture at the level of micro-ergonomics. The study is devoted to the formalization of a number of requirements for the modeling of adaptive furniture for people with musculoskeletal disorders. Particular attention is paid to aspects in the development of furniture that not only provide comfort, safety and functionality, but also represent an aesthetically pleasing design that contributes to the integration of ASPA into the social environment. Ergonomic requirements are considered, taking into account the anthropometric and biomechanical features of such users with the help of digital models (DHM). Using DHM, you can study the interaction of users with furniture, simulate different scenarios of their use, and evaluate the parameters of the quality of comfort and safety. It has been established that the integration of DHM with modern three-dimensional modeling technologies allows optimizing furniture designs, making them maximally adapted to the needs of ASPA. The purpose of the study is to develop a methodology for designing adaptive furniture, which ensures an increase in the standard of living of users thanks to individually configured ergonomic. In order for ASPAs to be able to work comfortably and productively, the projected development of the interpenetration of modern technical innovations in this field with the subsequent unification in a separate direction of designing inclusive furniture and equipment, with the division into separate groups for different types of disabilities, becomes quite relevant.

A New Alternating Suboptimal Dynamic Programming Algorithm with Applications for Feature Selection

Feature selection is predominantly used in machine learning tasks, such as classification, regression, and clustering. It selects a subset of features (relevant attributes of data points) from a larger set that contributes as optimally as possible to the informativeness of the model. There are exponentially many subsets of a given set, and thus, the exhaustive search approach is only practical for problems with at most a few dozen features. In the past, there have been attempts to reduce the search space using dynamic programming. However, models that consider similarity in pairs of features alongside the quality of individual features do not provide the required optimal substructure. As a result, algorithms, which we will call suboptimal dynamic programming algorithms, find a solution that may deviate significantly from the optimal one. In this paper, we propose an iterative dynamic programming algorithm, which invertsthe order of feature processing in each iteration. Such an alternating approach allows for improving the optimization function by using the score from the previous iteration to estimate the contribution of unprocessed features. The iterative process is proven to converge and terminates when the solution does not change in three successive iterations or when the number of iterations reaches the threshold. Results in more than 95% of tests align with those of the exhaustive search approach, being competitive and often superior to the reference greedy approach. Validation was carried out by comparing the scores of output feature subsets and examining the accuracy of different classifiers learned on these features across nine real-world applications, considering different scenarios with various numbers of features and samples. In the context of feature selection, the proposed algorithm can be characterized as a robust filter method that can improve machine learning models regardless of dataset size. However, we expect that the idea of alternating suboptimal optimization will soon be generalized to tasks beyond feature selection.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm−1 to 0.233 mm−1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm−1 before to 0.205 mm−1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

Vascular remodelling in cardiovascular diseases: hypertension, oxidation, and inflammation.

Optimal vascular structure and function are essential for maintaining the physiological functions of the cardiovascular system. Vascular remodelling involves changes in vessel structure, including its size, shape, cellular and molecular composition. These changes result from multiple risk factors and may be compensatory adaptations to sustain blood vessel function. They occur in diverse cardiovascular pathologies, from hypertension to heart failure and atherosclerosis. Dynamic changes in the endothelium, fibroblasts, smooth muscle cells, pericytes or other vascular wall cells underlie remodelling. In addition, immune cells, including macrophages and lymphocytes, may infiltrate vessels and initiate inflammatory signalling. They contribute to a dynamic interplay between cell proliferation, apoptosis, migration, inflammation, and extracellular matrix reorganisation, all critical mechanisms of vascular remodelling. Molecular pathways underlying these processes include growth factors (e.g., vascular endothelial growth factor and platelet-derived growth factor), inflammatory cytokines (e.g., interleukin-1β and tumour necrosis factor-α), reactive oxygen species, and signalling pathways, such as Rho/ROCK, MAPK, and TGF-β/Smad, related to nitric oxide and superoxide biology. MicroRNAs and long noncoding RNAs are crucial epigenetic regulators of gene expression in vascular remodelling. We evaluate these pathways for potential therapeutic targeting from a clinical translational perspective. In summary, vascular remodelling, a coordinated modification of vascular structure and function, is crucial in cardiovascular disease pathology.

Mechanical loading and orthobiologic therapies in the treatment of post-traumatic osteoarthritis (PTOA): a comprehensive review.

The importance of mechanical loading and its relationship to orthobiologic therapies in the treatment of post-traumatic osteoarthritis (PTOA) is beginning to receive attention. This review explores the current efficacy of orthobiologic interventions, notably platelet-rich plasma (PRP), bone marrow aspirate (BMA), and mesenchymal stem/stromal cells (MSCs), in combating PTOA drawing from a comprehensive review of both preclinical animal models and human clinical studies. This review suggests why mechanical joint loading, such as running, might improve outcomes in PTOA management in conjunction with orthiobiologic administration. Accumulating evidence underscores the influence of mechanical loading on chondrocyte behavior and its pivotal role in PTOA pathogenesis. Dynamic loading has been identified as a key factor for optimal articular cartilage (AC) health and function, offering the potential to slow down or even reverse PTOA progression. We hypothesize that integrating the activation of mechanotransduction pathways with orthobiologic treatment strategies may hold a key to mitigating or even preventing PTOA development. Specific loading patterns incorporating exercise and physical activity for optimal joint health remain to be defined, particularly in the clinical setting following joint trauma.

Selective Capturing of the CO2 Emissions Utilizing Ecological (3-Mercaptopropyl)trimethoxysilane-Coated Porous Organic Polymers in Composite Materials.

Capturing carbon dioxide (CO2) is still a major obstacle in the fight against climate change and the reduction of greenhouse gas emissions. To address this problem, we employed a simple Friedel-Crafts alkylation to investigate the effectiveness of porous organic polymers (POPs) based on triphenylamine (TPA) and trihydroxy aryl terms derived from chloranil (CH), designated as TPA-CH POP. We then treated the TPA-CH POP with (3-mercaptopropyl)trimethoxysilane (3-MPTS), forming a TPA-CH POP-SH nanocomposite to enhance CO2 capture. Utilizing FTIR, solid-state NMR, SEM, TEM, along with XPS techniques, the molecular makeup, morphological characteristics, as well as physical features of TPA-CH POP and the TPA-CH POP-SH nanocomposite were thoroughly explored. Upon scorching to 800 °C, the TPA-CH POP-SH nanocomposite demonstrated more thermal durability over TPA-CH POP, achieving a char yield of up to 71.5 wt.%. The TPA-CH POP-SH nanocomposite displayed a 2.5-times better CO2 capture, as well as a comparable adsorption capacity of 48.07 cm3 g-1 at 273 K. Additionally, we found that the TPA-CH POP-SH nanocomposite exhibited an improved CO2/nitrogen (N2) selectivity versus the original TPA-CH POP. Typical enthalpy changes for CO2 capture were somewhat increased by the 3-MPTS coating, indicating greater binding energies between CO2 molecules and the adsorbent surface. Our outcomes demonstrate that a TPA-CH POP composite coated with MPTS is a viable candidate for effective CO2 capture uses. Our findings encourage the investigation of different functional groups and optimization strategies.

BIOM-04. ALTERED IMMUNE ACTIVATION AND REDUCED LYMPHOPOIESIS IN TREATMENT NAÏVE PEDIATRIC BRAIN TUMOUR PATIENTS

Abstract Pediatric central nervous system (CNS) tumors are the most common solid tumor in children. Despite improvements in diagnostic and therapeutic approaches over time, treatment effectiveness remains unsatisfactory. Optimal immune function is critical to protect against development and dissemination of cancer and to offer the ideal conditions for the success of anticancer treatment. Here we analyzed the peripheral immunological features of patients affected by pediatric brain tumors, the process of de novo T- and B-cell development and its association with the lymphopenia often observed in these patients. Eighty-six steroids-naïve pediatric patients with CNS tumors at the onset of the disease were included in the study. Brain tumors were classified into low-grade gliomas (LGG: n=44, 51%), pediatric-type diffuse high-grade gliomas (pHGG: n=16, 19%), medulloblastoma (n =9, 10%), and other CNS tumors (n =17, 20%). Patient’s peripheral immunological status was analyzed by flow-cytometry and by evaluating T-cell receptor excision circles (TRECs) and kappa-deleting excision circles (KREC) to quantify T- and B-cell neogenesis. Although all patients showed tendencies towards low lymphocyte counts, this effect was particularly evident in pHGG patients. T-cells of pHGG patients displayed a shift from naïve to effector memory phenotype compared to age-matched controls. On the other hand, LGG patients were characterized by an expansion of the T-central memory pool and higher expression of T-cell activation markers. Both patient cohorts exhibited decreased frequency of B-cells with activated phenotype. Interestingly, pHGG patients exhibited reduced thymic function as revealed by reduced levels of the recent thymic emigrant population and TRECs. In addition, LGG and pHGG patients were characterized by lower KREC values, suggesting suboptimal B-cell formation. At diagnosis, pediatric patients with brain tumor show alterations in their immunological status and reduced thymic and medullary lymphopoiesis, particularly pronounced in pHGG, which may contribute to explain the systemic lymphopenia characterizing these patients.

HEALTH PROMISING NUTRITIONAL MILLETS (KSHUDRA DHANYA) FOR CARDIOVASCULAR DISEASES AND ITS MODIFIABLE RISK FACTORS: AN EV-IDENCE-BASED OVERVIEW.

Cardiovascular disease is a leading cause of death worldwide in today's era along with rising risk factors such as diabetes, hypertension, dyslipidemia, malnutrition, etc. Acharyas treats Diet as 'Mahabhaishajya' (the superior medicine). Shuka Dhanya Varga (group of Cereals) is reviewed for their properties for Santarpanottha (allevia-tion of heart diseases by excessively nourishing bodily tissues), heart diseases, and other modified risk factors. This review aims to concisely outline the nutritional essentials and therapeutic characteristics of millets for car-diovascular diseases and the alteration of risk factors associated with heart health. In this review, we have tried to summarize the essential nutrition and therapeutic properties of millets for cardiovascular diseases and modi-fied risk factors for heart diseases. Millets offer numerous health benefits, including the reduction of cardiovas-cular disease and its associated risk factors contributing to mortality. Millets serve as a great source of essential nutrients and trace elements, playing a pivotal role as catalysts in supporting the optimal functioning of the car-diovascular system. Moreover, trace elements provided by millets help the cardiovascular system primarily function as catalysts and are a good source. This comprehensive review draws from various authentic sources, including Ayurvedic texts, medical books, and published articles from reputable scientific sources like PubMed, Google Scholar, and Sci-ence Direct. This article reviews the various authentic sources from Ayurveda texts, medical books, and pub-lished articles from scientific sources such as PubMed, Google Scholar, Science Direct, etc. The primary focus of this review is extensive research on the benefits of nutrition, the balance of Ayurvedic Doshas (fundamental elements), and a comprehensive evaluation of therapeutic properties. Essentially, the principles are adopted from value-added research of nutritional benefits and research along with Ayurveda dosha balancing and centred re-view of therapeutic properties. The evidence-based review of research shows that Kshudra Dhanya (millets) have been proven for anti-atherosclerotic, anti-hypertensive, hypoglycemic, and anti-dyslipidemia properties.

QOL-20. A REVIEW OF THE LITERATURE ON THE REHABILITATION OF PATIENTS WITH POSTERIOR FOSSA SYNDROME

Abstract BACKGROUND Posterior fossa syndrome (also recently described as pediatric postoperative cerebellar mutism syndrome) is a well described phenomenon, in which a patient develops a spectrum of speech and motor/coordination deficits generally in the acute stage after posterior fossa tumor resection. While most patients recover well, some patients evidence significant deficits years after diagnosis, and there is some evidence that minor long-term deficits may remain. While its frequency of occurrence is relatively low, and efforts have been made to identify strategies to decrease the rate of Posterior Fossa Syndrome, the fundamental interventions for functional optimization and rehabilitation of patients are still being discussed and researched. METHODS Along with reviewing the most up-to-date literature on the rehabilitation management of patients with posterior fossa syndrome, we will discuss current a survey based study designed by a group of representatives from the American Academy of Physical Medicine and Rehabilitation (AAPM&R) Pediatric Cancer Rehabilitation subcommittee, to better understand the standards of how the syndrome is managed by physiatrists in the US. This survey distributed via email, posting on an AAPM&R online community, as well as social media sites. RESULTS While literature is limited, there is evidence for the practice of therapy services, bracing, and in admission for acute inpatient rehabilitation to guide treatment for patients with posterior fossa syndrome. Results from the above survey study will be discussed, which preliminarily shows a consistent reliance on therapy services and a broader heterogeneity to frequency and treatment preferences among physicians in the US. CONCLUSION Posterior Fossa Syndrome can be a challenging and debilitating disorder for pediatric brain tumor patients. Further research into treatment options and outcomes is necessary to guide future management.

Occlusal Cant-A Narrative Review

Occlusal cant, indeed, plays a significant role in smile aesthetics. It refers to the asymmetry or tilt of the occlusal plane, which can affect the balance and harmony of the smile. The dynamic display zone, which includes lateral, vertical, and anteroposterior characteristics, as well as the cant of the occlusal plane, are important factors to take into account when creating a smile or designing mechanotherapy (treatment utilizing mechanical devices). Ensuring an aesthetic plane of occlusion involves aligning the teeth and jaws in a way that enhances the overall appearance of the smile. This may involve orthodontic treatment, dental restorations, or other interventions aimed at achieving symmetry and balance in the occlusal plane. By addressing occlusal cant as part of smile design and mechanotherapy, orthodontists can help patients achieve optimal smile aesthetics and function This review article decribes diagnosis ,etiology, methods of detecting occlusal cant and treatment alternatives.