Application of Neoformalism for Analysis of two Iranian Distinguished films

Neoformalism is one of the recent film criticism approaches which appeared in the 1980s and 1990s. This approach uses a new strategy in addition to achievements gained using prior methods. In this article, the specifications of this approach are studied on the basis of works of founders of neoformalism including David Bordwell and Kristin Thompson and issues such as definition of form and narration, differentiation of plot and story, principles of form, definition of device, function and motivation are expressed. According to the definition given by neoformalist, form is the overall system of relations between the elements in the whole film and narration is the plot's way of distributing story information in order to achieve specific effects. The set of all the events in a narrative, both those presented explicitly and those inferred by the viewer, constitutes the story and the term plot is used to describe everything visibly and audibly present in the film before us. A large number of Iranian filmmakers and critics believe that too much attention to content and lack of attention to form is one of the most important problems of Iranian cinema that has emerged in recent years. Also, neoformalism emphasizes the importance of analysis of form and aesthetical principles of cinema. Therefore, this approach is appropriate for studying contemporary Iranian films. Then. this approach has been used for analyzing of two important films of Majid Majidi including Children of Heaven and Baran as two distinguished Iranian contemporary films. These two films awarded the best film prize from Fajr festival in 1997 and 2001. Majidi has yet to achieve this prize again. Results of this neo-formalistic analysis show that in these two films, the form is developed to present main themes including family affection, dedication and love. Majidi in Children of Heaven has presented an attractive narration with sufficient rise and fall. He has used some devices such as overhead shot, slow motion and sound design and has applied form principles in order to represent the theme of dedication. In Baran he has taken a step further and has created a form that is consonant to theme of film, by using appropriate devices and form principles. Due to this film he rose up from ordinary form of Iranian cinema. In Baran, theme of love has been expressed with a mystic and spiritual aspect and Majidi has used Iranian mysticism and Sufism and especially has used Rumi’s works for expression of romantic story of film. Therefore, through directing of these two films Majidi gained more knowledge about cinematic form and expression and obtained more strength and ability for creating consonant form to themes of his films

Application of Neoformalism for Analysis of two Iranian Distinguished films

Neoformalism is one of the recent film criticism approaches which appeared in the 1980s and 1990s. This approach uses a new strategy in addition to achievements gained using prior methods. In this article, the specifications of this approach are studied on the basis of works of founders of neoformalism including David Bordwell and Kristin Thompson and issues such as definition of form and narration, differentiation of plot and story, principles of form, definition of device, function and motivation are expressed. According to the definition given by neoformalist, form is the overall system of relations between the elements in the whole film and narration is the plot's way of distributing story information in order to achieve specific effects. The set of all the events in a narrative, both those presented explicitly and those inferred by the viewer, constitutes the story and the term plot is used to describe everything visibly and audibly present in the film before us. A large number of Iranian filmmakers and critics believe that too much attention to content and lack of attention to form is one of the most important problems of Iranian cinema that has emerged in recent years. Also, neoformalism emphasizes the importance of analysis of form and aesthetical principles of cinema. Therefore, this approach is appropriate for studying contemporary Iranian films. Then. this approach has been used for analyzing of two important films of Majid Majidi including Children of Heaven and Baran as two distinguished Iranian contemporary films. These two films awarded the best film prize from Fajr festival in 1997 and 2001. Majidi has yet to achieve this prize again. Results of this neo-formalistic analysis show that in these two films, the form is developed to present main themes including family affection, dedication and love. Majidi in Children of Heaven has presented an attractive narration with sufficient rise and fall. He has used some devices such as overhead shot, slow motion and sound design and has applied form principles in order to represent the theme of dedication. In Baran he has taken a step further and has created a form that is consonant to theme of film, by using appropriate devices and form principles. Due to this film he rose up from ordinary form of Iranian cinema. In Baran, theme of love has been expressed with a mystic and spiritual aspect and Majidi has used Iranian mysticism and Sufism and especially has used Rumi’s works for expression of romantic story of film. Therefore, through directing of these two films Majidi gained more knowledge about cinematic form and expression and obtained more strength and ability for creating consonant form to themes of his films

Diagnosis of Neurodegenerative Diseases Using Higher Order Statistical Analysis of Electroencephalography Signals

The field of signal processing has many applications, one of which is in the field of biomedical engineering where it has improved the performance of biomedical devices and the accuracy of medical diagnosis. One of the areas that have benefited from this field is the diagnosis of Parkinson’s disease (PD). This disease is one of the most common neurodegenerative diseases of the central nervous system. Nearly one million Americans suffer from PD, and this number goes up to over ten million people worldwide. The main symptoms of PD include bradykinesia, rest tremor, rigidity, and impaired balance. There are many types of biomedical data that have been used in the diagnosis process of PD, however, most of the applied biomedical signals rely on the presence of motor symptoms which means in most cases, by the time that the patients are diagnosed, they are likely to have lost the majority of the dopaminergic neurons in their brains. One of the signals that have been used for the diagnosis of PD is electroencephalography (EEG). The neurons in the brain communicate with each other through electrical potentials that appear at the synapses. EEG is a noninvasive method that collects the small voltages that appear on the scalp caused by large clusters of neurons using multiple electrodes; therefore, EEG recordings are multi-channel signals where each channel is corresponding to a specific region of the brain. There are two main types of EEG signals, background EEG, and event-related potential (ERP). Background EEG is the signal collected during the rest state and contains the regular activity of the brain when it is not provoked, whereas ERP is the changes in the background EEG, resulting from a specific stimulus. While background EEG signals are better suited for diagnosis purposes, they are highly nonlinear, non-stationary and non-Gaussian signals; hence, to extract relevant information from them, advanced methods of signal processing are required. The background EEG is a random signal which indicates that regular features such as time locked features or peaks of the signal do not carry much information. For random signals, statistics is usually the most appropriate method for analysis. The word statistic is usually used to refer to first and second order statistics. Higher order statistics (HOS) is defined as a more general term that covers higher order of statistical features. the field of HOS analysis is usually employed for highly complex signals, where the first and second order statistics failed to adequately define the system. Due to the highly random nature of background EEG signals, HOS has been employed by many researchers for more detailed analysis. In this study, a range of HOS features have been used to improve the diagnosis performance of PD patients from healthy control (HC) and classification of stages of PD after a positive diagnosis. A detailed analysis of the features was performed to find the best combination for this application and a number of new HOS features were developed to improve the performance of the model. Concurrently, based on previous research a spectral analysis of the data was performed to investigate the effect of PD on brain rhythms where HOS features were extracted from multiple rhythms and used separately in the diagnosis process. The classification stage was performed by a range of conventional, ensemble and deep learning algorithms while employing the leave-one-trial-out (LOTO), leave-one-subject-out (LOSO) cross validation (CV) methods. To preserve the balance of the data, a new CV approach, leave-two-subjects-out (LOTO) was also employed (one from each class). For diagnosis of PD, a comparison between the features extracted from different brain rhythms and different classifiers was performed. The result was then compared to several deep learning methods and other state-of-the-art approaches. The performance of the PD stage classification was also compared to other studies in this field. Together these two methods create a unified hierarchy model to diagnose and identify the stages of PD

Diagnosis of Neurodegenerative Diseases Using Higher Order Statistical Analysis of Electroencephalography Signals

The field of signal processing has many applications, one of which is in the field of biomedical engineering where it has improved the performance of biomedical devices and the accuracy of medical diagnosis. One of the areas that have benefited from this field is the diagnosis of Parkinson’s disease (PD). This disease is one of the most common neurodegenerative diseases of the central nervous system. Nearly one million Americans suffer from PD, and this number goes up to over ten million people worldwide. The main symptoms of PD include bradykinesia, rest tremor, rigidity, and impaired balance. There are many types of biomedical data that have been used in the diagnosis process of PD, however, most of the applied biomedical signals rely on the presence of motor symptoms which means in most cases, by the time that the patients are diagnosed, they are likely to have lost the majority of the dopaminergic neurons in their brains. One of the signals that have been used for the diagnosis of PD is electroencephalography (EEG). The neurons in the brain communicate with each other through electrical potentials that appear at the synapses. EEG is a noninvasive method that collects the small voltages that appear on the scalp caused by large clusters of neurons using multiple electrodes; therefore, EEG recordings are multi-channel signals where each channel is corresponding to a specific region of the brain. There are two main types of EEG signals, background EEG, and event-related potential (ERP). Background EEG is the signal collected during the rest state and contains the regular activity of the brain when it is not provoked, whereas ERP is the changes in the background EEG, resulting from a specific stimulus. While background EEG signals are better suited for diagnosis purposes, they are highly nonlinear, non-stationary and non-Gaussian signals; hence, to extract relevant information from them, advanced methods of signal processing are required. The background EEG is a random signal which indicates that regular features such as time locked features or peaks of the signal do not carry much information. For random signals, statistics is usually the most appropriate method for analysis. The word statistic is usually used to refer to first and second order statistics. Higher order statistics (HOS) is defined as a more general term that covers higher order of statistical features. the field of HOS analysis is usually employed for highly complex signals, where the first and second order statistics failed to adequately define the system. Due to the highly random nature of background EEG signals, HOS has been employed by many researchers for more detailed analysis. In this study, a range of HOS features have been used to improve the diagnosis performance of PD patients from healthy control (HC) and classification of stages of PD after a positive diagnosis. A detailed analysis of the features was performed to find the best combination for this application and a number of new HOS features were developed to improve the performance of the model. Concurrently, based on previous research a spectral analysis of the data was performed to investigate the effect of PD on brain rhythms where HOS features were extracted from multiple rhythms and used separately in the diagnosis process. The classification stage was performed by a range of conventional, ensemble and deep learning algorithms while employing the leave-one-trial-out (LOTO), leave-one-subject-out (LOSO) cross validation (CV) methods. To preserve the balance of the data, a new CV approach, leave-two-subjects-out (LOTO) was also employed (one from each class). For diagnosis of PD, a comparison between the features extracted from different brain rhythms and different classifiers was performed. The result was then compared to several deep learning methods and other state-of-the-art approaches. The performance of the PD stage classification was also compared to other studies in this field. Together these two methods create a unified hierarchy model to diagnose and identify the stages of PD

Jump Technique versus Seton Method for Anal Fistula Repair: A Randomized Controlled Trial

Background The treatment of anal fistula has been a conundrum for surgeons over the years. Various methods such as fistulotomy, fistulectomy, seton, ligation of the intersphincteric fistula tract (LIFT), advancement flaps, fibrin glue, and plugs are well-known techniques. Yet, they may be followed by several considerable complications, including incontinency and recurrence. Methods In this study, the outcomes of the “Jump” and “Seton” techniques are compared. A randomized controlled trial consisting of 130 cases with cryptoglandular anal fistula randomly sorted into two groups was conducted. Group A underwent the “Jump technique” while group B underwent the “Seton technique.” Outcomes, incontinency and recurrences in particular, were evaluated after a year of treatment. Data were analyzed by Fisher Exact, Chi-Square and Mann Whitney Tests. Results Group A with 65 cases underwent the “Jump technique” while group B with 65 cases underwent the “Seton Method.” Recurrence was reported in 12 (20%) cases in group A and 10 (15.6%) cases in group B (). Overall incontinence was reported in 3 (4.6%) cases in group A and 18 (27.7%) cases in group B (). The total St. Mark’s scores for incontinency of group A () and group B () significantly differed (). Conclusions The “Jump technique”, named after a runner who jumped over hurdles, has obviated these complications. The “Jump technique” had satisfactory results and can be utilized as a first-line approach for all types of fistulas. Moreover, it can be redone for cases with recurrences without affecting the continence, paving the way to change the technique during operations