bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction of Combined Teaching Evaluation System Based on STATA Analysis
Authors: Li Yutian
Published: November 2022

Designing a reasonable combined teaching scheme is not only the need to achieve teaching optimization, but also a reasonable choice to improve the overall level of teaching efficiency. The existing researches lack an evaluation system to judge the rationality of their designs. Therefore, this paper takes music teaching as an example to carry out the research on the construction of combinatorial teaching evaluation system based on STATA analysis. Firstly, the implementation process of the combined teaching strategy is provided here, and the construction of the combined teaching evaluation system is carried out from seven aspects, namely teaching in fun, emotional experience, student engagement, elicitation and induction, teaching according to students’ aptitude, creative development and vividness and intuitiveness. The entropy weight method is adopted to assign the weight of the combined teaching evaluation index of music major courses. Based on stata15.1 software tool, regression analysis is carried out on the main factors that affect the effect of combined teaching, and the relevant regression analysis model is constructed to realize the effect prediction of short-term combined teaching scheme in the case of small samples. The experimental results verify the effectiveness of the evaluation system and analysis model.
Online Course Learning Resource Recommendation Based on Difficulty Matching
Authors: Jia Ligang
Published: November 2022

The current methods for online course learning resource recommendation tend to continuously push similar items to students. To make students more satisfied with the overall recommendation service, it is necessary to explore the recommendation methods for online course learning resources based on difficulty matching. Therefore, this paper devises an online course learning resource recommendation method based on difficulty matching. Firstly, an online course learning resource recommendation approach was developed based on the autoencoder, in the light of difficulty matching. The flowchart of learning resource recommendation was presented, and the proposed algorithm was described in details. Then, the authors depicted the flow of the online course learning resource recommendation model, which considers difficulty matching, and proposed to capture the hierarchy of resources by assigning difficulty labels to texts, such that the proposed recommendation model outputs interpretable recommendation results. Finally, the authors designed a model that can discover the influence of the difficulty of learned resources on the difficulty of unlearned learning resources, and recommended online course learning resources considering difficulty matching. The effectiveness of the proposed model is verified through experiments.
CRITIQUE OF DESIGN CHALLENGE OF FLYING ROBOTS
Authors: Maity Ritu , Mishra Ruby , Pattnaik Prasant Kumar
Published: November 2022

Flying robots popularly known as drones or UAVs are emerging technologies of the current era. A significant amount of research work has been undertaken in this area in the last few years. Considering the current scenario where aerial vehicles are taking a major part of the market it is important to have an effective and robust design of flying robots. This paper aims to examine the categories of flying robots based on the features that include a range from petite to large and its body structure, wing designs, tail design, propulsion system, and gripper mechanisms along with the associated materials and manufacturing techniques. Again the work is intended to review the respective challenges faced by each category. Mostly the challenges faced by flying robots are design challenges, material selection, and fabrication challenges which are discussed in the paper. In this paper, we have summarized various designs of flying robots developed to date as well as we have focused on major features to be taken care of while designing flying robots. This paper has tried to focus on different design aspects and challenges faced by flying robots so that further research can be carried out to develop effective flying robots in the future.
Ethical Phone for a Fair, Circular, and Sustainable Future
Authors: Romagnoli Sara , Brunelli Sofia , Bruscino Camilla , Ciotti Alessio , Pietro Sofia Di Giò , Dumontel Tommaso , Fusari Chiara , Nacca Francesca
Published: November 2022

Fairphone is a Dutch company that produces ethical mobile phones relying on a circular business model. The company was born after a campaign condemning the “bloody minerals” sourced by the mobile phone industry and the huge amount of e-waste this industry produces. For what attains the reduction of e-waste, Fairphone is implementing both slowing and closing resource loop strategies to reduce the tendency of rapid smartphone replacements. Fairphone is also engaging in complex supply chain management operations to assure that all its partners provide high quality social standards. Given the complexity of the business model, Fairphone has created a unique network of stakeholders, with customers playing a pivotal role, each providing fundamental help. Given the innovative and holistic approach Fairphone is implementing, the company could also generate positive effects in the growing field of smart cities. The chapter is dedicated to the analysis of Fairphone's business case and the positive impacts the company could generate within smart cities.
Circular Economy in the Textile Industry
Authors: De Giovanni Pietro
Published: November 2022

This chapter elaborates on the application of circular economy systems in the textile industry, which is historically influenced by seasonality effects that induce a search for circular solutions independent of environmental constraints and regulations. This chapter shows that circular economy systems can create business opportunities as well as important cannibalization effects when consumers are confronted with new and used textile goods. Hence, it highlights how the use of digital technologies can help to support the mitigation of such issues..
Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis
Authors: Rajalingam B.  , Priya R.  , Bhavani R.  , Santhoshkumar R.
Published: November 2022

Image fusion is the process of combining two or more images to form a single fused image, which can provide more reliable and accurate information. Over the last few decades, medical imaging plays an important role in a large number of healthcare applications including diagnosis, treatment, etc. The different modalities of medical images contain complementary information of human organs and tissues, which help the physicians to diagnose the diseases. The multimodality medical images can provide limited information. These multimodality medical images cannot provide comprehensive and accurate information. This chapter proposed and examines some of the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods. The hybrid multimodal medical image fusion algorithms are used to improve the quality of fused multimodality medical image. An experimental result of proposed hybrid fusion techniques provides the fused multimodal medical images of highest quality, shortest processing time, and best visualization.
A Fusion-Based Approach to Generate and Classify Synthetic Cancer Cell Image Using DCGAN and CNN Architecture
Authors: Chatterjee Ahan , Roy Swagatam
Published: November 2022

The most talked about disease of our era, cancer, has taken many lives, and most of them are due to late prognosis. Statistical data shows around 10 million people lose their lives per year due to cancer globally. With every passing year, the malignant cancer cells are evolving at a rapid pace. The cancer cells are mutating with time, and it's becoming much more dangerous than before. In the chapter, the authors propose a DCGAN-based neural net architecture that will generate synthetic blood cancer cell images from fed data. The images, which will be generated, don't exist but can be formed in the near future due to constant mutation of the virus. Afterwards, the synthetic image is passes through a CNN net architecture which will predict the output class of the synthetic image. The novelty in this chapter is that it will generate some cancer cell images that can be generated after mutation, and it will predict the class of the image, whether it's malignant or benign through the proposed CNN architecture.
Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique
Authors: Kugunavar Sneha , Prabhakar C. J.
Published: November 2022

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.
Medical Image Zooming by Using Rational Bicubic Ball Function
Authors: Abdul Karim Samsul Ariffin , Zulkifli Nur Atiqah Binti , Shafie A'fza Binti , Sarfraz Muhammad , Ghaffar Abdul , Nisar Kottakkaran Sooppy
Published: November 2022

This chapter deals with image processing in the specific area of image zooming via interpolation. The authors employ bivariate rational cubic ball function defined on rectangular meshes. These bivariate spline have six free parameters that can be used to alter the shape of the surface without needed to change the data. It also can be used to refine the resolution of the image. In order to cater the image zomming, they propose an efficient algorithm by including image downscaling and upscaling procedures. To measure the effectiveness of the proposed scheme, they compare the performance based on the value of peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Comparison with existing schemes such as nearest neighbour (NN), bilinear (BL), bicubic (BC), bicubic Hermite (BH), and existing scheme Karim and Saaban (KS) have been made in detail. From all numerical results, the proposed scheme gave higher PSNR value and smaller RMSE value for all tested images.
Dental Image Segmentation Using Clustering Techniques and Level Set Methods
Authors: Sathees Prabha
Published: November 2022

Segmentation is necessary for dental images for finding the parts of the teeth, surrounding tissues, and bones. The human identification system in dental methodology is a tedious and time-consuming process. The automatic identification system is the best solution for dental diagnosis and dental treatment systems. Choosing an appropriate region of interest with high accuracy and success rate is a challenging one. This can be attained with the help of proper segmentation methodologies. The segmentation techniques proposed for the root canal treatment are analyzed and compared. Clustering techniques and level set methods with different edge maps are implemented for the proper analysis of segmentation in dental images. Finally, the integration of coherence-enhanced diffusion filtering in basic level set segmentation methodology seems to be effective in improving the segmentation performance of dental images.
Bioinspired Inference System for MR Image Segmentation and Multiple Sclerosis Detection
Authors: Zouaoui Hakima , Moussaoui Abdelouahab
Published: November 2022

Multiple sclerosis (MS) is a chronic autoimmune and inflammatory disease affecting the central nervous system (CNS). Magnetic resonance imaging (MRI) provides sufficient imaging contrast to visualize and detect MS lesions, particularly those in the white matter (WM). A robust and precise segmentation of WM lesions from MRI provide essential information about the disease status and evolution. The proposed FPSOPCM segmentation algorithm included an initial segmentation step using fuzzy particle swarm optimization (FPSO). After extraction of WM, atypical data (outliers) is eliminated using possibilistic C-means (PCM) algorithm, and finally, a Mamdani-type fuzzy model was applied to identify MS. The objective of the work presented in this paper is to obtain an improved accuracy in segmentation of MR images for MS detection.
P2P-COVID-GAN
Authors: Abirami Nandhini , Vincent Durai Raj , Kadry Seifedine
Published: November 2022

Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.
Biomedical Image Processing Software Development for Shoulder Arthroplasty
Authors: Sadeghi Majid Mohammad , Kececi Emin Faruk , Bilsel Kerem , Aralasmak Ayse
Published: November 2022

Shoulder arthroplasty is an important operation for the treatment of shoulder joints, with an increasing rate of operations per year around the world. Although this operation is generally achieved successfully, there are a number of complications which increase the risks in the operation. Preoperative planning for a surgery can help reduce the amount of risks resulting from complications and increase the success rate of the operation. Three-dimensional visualization software can be helpful in preoperative planning. This chapter aims to provide such software to help reduce the risks of the operation by visualizing 3D joint anatomy of the specific patient for the surgeon, and letting surgeons observe the geometrical properties of the joint.
Image Segmentation Using Contour Models
Authors: Kavitha G.  , Muthulakshmi M.  , Latha M.
Published: November 2022

Image segmentation is an important task in image processing, which is widely used in medical applications such as abnormality detection and after treatment progress monitoring. Conventionally, texture, region, and edge information are used for segmentation. Recently, the majority of image segmentation uses contour-based models. The problem of efficient segmentation in medical images is of great importance in disease diagnosis. Medical images suffer from weak boundaries, and placement of initial contour is a major issue. Level method is an effective method for segmentation of image as it has ability to tackle complex geometries. It helps to detect the precise location of the target region and help to prevent the boundary leakage problem. This chapter presents an overview of the advanced region and edge-based level set segmentation algorithms and their application in the dental x-ray images. Computer-aided diagnosis from x-ray images are of interest to clinicians in detection and accurate decision making. Case studies of multiple region segmentation from dental x-rays are presented.
Histopathological Image Analysis in Medical Decision Making
Authors: Prakash R. Meena , Shantha Selva Kumari R.
Published: November 2022

Digital pathology is one of the significant methods in the medicine field to diagnose and treat cancer. The cell morphology and architecture distribution of biopsies are analyzed to diagnose the spread and severity of the disease. Manual analyses are time-consuming and subjected to intra- and inter-observer variability. Digital pathology and computer-aided analysis aids in enormous applications including nuclei detection, segmentation, and classification. The major challenges in nuclei segmentation are high variability in images due to differences in preparation of slides, heterogeneous structure, overlapping clusters, artifacts, and noise. The structure of the proposed chapter is as follows. First, an introduction about digital pathology and significance of digital pathology techniques in cancer diagnosis based on literature survey is given. Then, the method of classification of histopathological images using deep learning for different datasets is proposed with experimental results.
Retinal Blood Vessel Extraction From Fundus Images Using Improved Otsu Method
Authors: Dash Jyotiprava , Bhoi Nilamani
Published: November 2022

In the present time, the identification of blood vessels is a basic task for diagnosis of various eye abnormalities. So, this article offers an instinctive approach for identification of blood vessels in ophthalmoscope images. This approach includes three different phases: pre-processing, vessel extraction and post-processing for getting a final vessel segmentation outcome. In the presented method, formerly log transformation and contrast limited adaptive histogram equalization are used for the enhancement of retinal images. The enhanced image is then filtered using a morphological opening operation and subsequently the optic disk is removed. The second phase includes the application of the improved Otsu method on the pre-processed image for the identification of blood vessels. Lastly, the resultant vessel-segmented image is obtained by using the morphological cleaning operation. The proposed method is fast, time efficient, and gives consistent accuracy for all retinal images. It is more robust and easier to implement compared to other traditional methods. The performance of the presented method is evaluated using ten different mathematical measures. It achieves average sensitivity, specificity and accuracy of 0.710, 0.982 and 0.956 for the digital retinal images for vessel extraction (DRIVE) database, 0.738, 0.982 and 0.954 for the structure analysis of the retina (STARE) database and 0.737, 0.964 and 0.949 for the child heart and health study in England (CHASE_DB1) database. The presented method also performs better in segmenting thin vessels by giving average accuracies of 0.964, 0.954 and 0.965 for DRIVE, STARE and CHASE_DB1 databases respectively.
A Software for Thorax Images Analysis Based on Deep Learning
Authors: Almulihi Ahmed H. , Alharithi Fahd S. , Mechti Seifeddine , Alroobaea Roobaea , Rubaiee Saeed
Published: November 2022

People suspected of having COVID-19 need to know quickly if they are infected, so that they can isolate themselves, receive treatment, and inform those with whom they have been in close contact. Currently, the formal diagnosis of COVID-19 infection requires laboratory analysis of blood samples or swabs from the throat and nose. The lab test requires specialized equipment and takes at least 24 hours to produce a result. For this reason, in this paper, the authors tackle the problem of the detection of COVID-19 by developing an open source software to analyze chest x-ray thorax images. The method is based on supervised learning applied to 5000 images. However, deep learning techniques such as convolutional neural network (CNN) and mask R-CNN gives good results comparing with classic medical imaging. Using a dynamic learning rate, they obtained 0.96 accuracy for the training phase and 0.82 for the test. The results of our free tool are comparable to the best state of the art open source systems.
Mitigating Data Imbalance Issues in Medical Image Analysis
Authors: Banik Debapriya , Bhattacharjee Debotosh
Published: November 2022

Medical images mostly suffer from data imbalance problems, which make the disease classification task very difficult. The imbalanced distribution of the data in medical datasets happens when a proportion of a specific type of disease in a dataset appears in a small section of the entire dataset. So analyzing medical datasets with imbalanced data is a significant challenge for the machine learning and deep learning community. A standard classification learning algorithm might be biased towards the majority class and ignore the importance of the minority class (class of interest), which generally leads to the wrong diagnosis of the patients. So, the data imbalance problem in the medical image dataset is of utmost importance for the early prediction of disease, specifically cancer. This chapter attempts to explore different problems concerning data imbalance in medical diagnosis. The authors have discussed different rebalancing strategies that offer guidelines for choosing appropriate optimal procedures to train the samples by a classifier for an efficient medical diagnosis.
A Primitive Survey on Ultrasonic Imaging-Oriented Segmentation Techniques for Detection of Fetal Cardiac Chambers
Authors: Prabha V. Punya , Sriraam N.
Published: November 2022

Recognition of presence of fetal cardiac chambers through ultrasonic Doppler imaging poses a huge challenge for the clinical community. The four-chamber view and outflow tracts are found to be a potential identity marker for presence of all heart chambers as well as current states of fetal heart. Given the cine loop ultrasonic imaging sequences, computer-aided diagnostic tools have been developed to detect and measures the chambers through automated mode. Segmentation and region of interest identification process contribute significantly towards the presence of heart chamber and presence of abnormality. This study provides a primitive survey towards the ultrasonic imaging-oriented segmentation techniques for detection/recognition of all four fetal cardiac chambers. The challenges for the biomedical community were also reported.
Laser Metal Deposition (LMD) Toolpaths with Adaptive Capability for Complex Repairs and Coating Geometries
Authors: Ortiz Igor , Alvarez Piera , Montealegre Maria Angeles
Published: November 2022

Inside Direct Energy Deposition (DED) processes is the Laser Metal Deposition (LMD) technology. Industries that can implement this technology approach are from automotive to energy sectors where critical parts suffer due to operation cycles, weather or hazardous environments etc. LMD process can be applied for coating, repair and build near net shape geometries. One of the main problems of LMD applied in the coating or in the repair is the dealing with these different types of geometries, to achieve an adherent and homogeneous coating. The current calculation of toolpaths in LMD software is based on a mathematical algorithm that relies on subtractive processes such as machining. The main drawback of using this type of toolpaths is that in this case they do not take into account the overlap between adjacent machining toolpaths. While for machining this parameter is not relevant, in the LMD process, the overlap between two contiguous laser tracks is a critical point to have an adequate process with the required quality. Talens Systems has developed a new Software, Azala software is able to calculate these strategy toolpaths for advanced repairs and coatings in any type of geometry. Beside taking into account the overlapped between contiguous laser clads, the calculated toolpaths have integrated the main laser process in LMD (laser power, process speed, powder flow). The objective of this work is to validate the Azala software developed using a piece with complex geometry on a laboratory scale. The developed software brings the possibility to automate repair and coating, where the LMD process provides a value-added opportunity to reduce production costs due to the repair of value-added components.
Suppress Short Channel Effects on Split Channel-Cylindrical GAA TFET Using Buried Oxide Layer
Authors: Dhake Pratiksha , Ghosh Jyotirmoy , Joshi Mayuresh , Mathew Ribu , Beohar Ankur
Published: November 2022

The idea of a buried oxide layer (BOx) in a split channel Gate All Around-Tunnel Field Effect Transistor (GAA-TFET) was investigated in this paper. This work examined the impact of buried oxide layer on the device's performance. With the BOx layer present and channel length of 20nm, the channel area of the TFET device investigated in this study is divided equally on the same side. The doping concentration has been transferred to the split channel on the drain side. The device’s performance was examined using numerical simulation utilizing simulation software of CAD devices. The final results which incorporate the buried oxide layer were being compared to the uniform split channel GAA-TFET. The parameters like ON current (Ion),OFF current (Ioff), subthreshold swing (SS) and electric-field (E) intensity are observed and compared with silicon (Si) based GAA-TFET and Indium phosphide (InP) based GAA-TFET. It is found that InP based GAA-TFET with buried oxide layer is more advanced device design than the others with Ion and Ioff of 3.02 x 10-05 A/um and 2.09 x 10-22 A/um, respectively.
Optimization of Process Variables for Prediction of Penetration Depth of HSLA Steel Welds Using Response Surface Methodology
Authors: Pathak Deepak , Kumar Dilip , Singh Rudra Pratap , Balu Vincent
Published: November 2022

The statistical model is created for predicting penetration depth in an alternating current-based additional axial magnetic field controlled shielded metal arc welding of ASTM A 516 Gr.70 steel. The design for the trials is developed using the Placket-Burman design and response surface methodology. The created model determines the optimum process variables for getting excellent penetration depth. The input variables (current, magnetic field density, and magnetic frequency) are chosen for a response like penetration depth. This model can predict the main effects and the interacting effects of three process variables. The findings reveal that a higher current value with a low magnetic field density leads to deeper penetration and vice versa. Furthermore, a greater penetration depth is achieved at lower magnetic field density and higher magnetic frequency. With a desirability of 98.8%, the optimum process variables are 110 A, 0 mT, and 60 Hz. The predicted response values produced from the regression equation based upon process variables are extremely similar to the observed output, demonstrating the usefulness of second-order regression equations. For improved joint efficiency, a high level of penetration is needed.
Intracoronary Transplantation of Autologous Haematopoietic Stem Cells in Ischaemic End-Stage Left Ventricular Failure
Authors: Athauda arachchi P. M.
Published: November 2022

The prevalence of end-stage heart failure is rising despite presence of successful reperfusion strategies for treating acute myocardial infarction and advancements made in pharmacological and device therapy optimized for heart failure management. The non-availability of a mature cardiac transplantation programme and absence of left and right ventricular assist devices in many parts of the world has renewed interests in potential alternative options. We demonstrate the preliminary observational data (not a trial) of a beneficial effect of intra coronary autologous bone marrow derived peripheral haemopoietic stem cell transplantation to improve myocardial contractility. Systematic and cellular level analysis on these cells will be required to optimize therapeutic utility of this emerging regenerative medical speciality.
Anemia and iron in internal medicine: an Italian survey and a review on iron intravenous therapy in medical patients
Authors: Montagnani Andrea , Frasson Stefania , Gussoni Gualberto , Dentali Francesco , Fontanella Andrea , Manfellotto Dario
Published: November 2022

In Italy, Internal Medicine Units hospitalize approximately 1,300,000 patients, often elderly and comorbid. The prevalent diagnoses are respiratory diseases, heart failure, or pneumonia. As a matter of fact, anemia is probably underestimated in the compilation of the official discharge forms (SDO) according to ICD-9 diagnostic codes. We promoted a survey among the Members the Italian Scientific Society of Internal Medicine (FADOI) with the aim to investigate the prevalence of anemia and iron deficiency, over than certain aspects related to the therapeutic management of patients with anemia. Furthermore, we performed a review summarizing current evidence for iron intravenous therapy in these patients. According to the survey, anemia is present in around half of the patients hospitalized in Internal Medicine, and about a quarter of them shows iron metabolism alterations. In the evaluation of iron metabolism, the dosage of ferritin is the most requested exam, whereas transferrin saturation is less considered. By focusing on some categories of patients, the awareness of the usefulness of intravenous iron therapy in patients with heart failure seems to be sufficiently common (76% of physicians), while it seems lower (60%) in the management of patients with chronic kidney disease (CKD) and anemia. Finally, more than 75% of the physicians answered that, in their hospital, there are few outpatients’ offices or diagnostic pathways dedicated to patients with anemia. Anemia due to absolute or functional iron deficiency is particularly prevalent in Internal Medicine inpatients. For this reason, an accurate evaluation of iron profile and an adequate iron therapy is mandatory in these patients. Recent studies show that, in patients with heart failure, intravenous iron therapy is an effective way of improving patients’ health, regardless of the presence of anemia. Similarly, iron therapy results fundamental to optimize erythropoiesis-stimulating agent efficacy in patients with chronic renal failure. In the next future, other therapeutic aspects of intravenous iron therapy will be probably clarified by several interesting ongoing studies focused on these patients.
Implications of Corona Virus Disease-19 pandemic on cytopathology laboratory—Challenges and solutions in resource-limited settings
Authors: Chandra Smita , Kusum Anuradha , Gaur Dushyant Singh
Published: November 2022

Background:

Corona Virus Disease-19 (COVID-19) disease which is still showing peaks and plateau with emerging new mutational viral variants has led to the modification of working of hospitals and medical institutes. Cytopathology laboratories geared up with the changing situation to cope up with not only the COVID-19 patients but also patients of other diseases, particularly oncology patients. It was also important to adopt changes in the cytopathology residency program so as to cope up with the changing scenario.

Aim:

The present study was conducted to study the various measures adopted and the changes made in the cytopathology laboratory during COVID-19 infection in a resource-limited setting while catering to a large population of COVID-19-positive patients in the north Himalayan region of India. It was also intended to study the methods adopted for effective cytopathology residency training during corona times.

Setting and Design:

The study was conducted from March 2020 to December 2021 in the cytopathology lab, and all the samples were analyzed and compared with the samples received in pre-corona times from March 2018 to December 2019. The different methods adopted during corona times for effective cytopathology residency training were also analyzed.

Results:

The total number of samples received in the cytopathology laboratory in pre-corona times from March 2018 to December 2019 were 6822, and samples received from March 2020 to December 2021 were 6567 with decrease of 1.9% of cases. There was increase in 17.4% for the samples of the respiratory tract received in the lab with an increase in cases of infections including aspergillosis, mucormycosis, and tuberculosis. There was 10.3% increase in cytological diagnosis of malignant cases during corona times. Use of 90% alcohol for fixation rather than air drying of smears, avoiding of pneumatic tubes, judicious use of personal protective equipment (PPE) kits along with access to the digital cytology slide database were included in the changes made in the lab.

Conclusions:

There was a marginal decrease in cytopathological diagnosis during COVID-19 with substantial increase in the malignancy rate of 10.3% in cytological diagnosis of different organs. Diagnosis of mucormycosis, aspergillosis, and tuberculosis infections of respiratory tract cytology increased during this period. Judicious use of limited available resources led not only to smooth functioning of the cytopathology lab but also prevented any infection in the working health personals. Newer online resources were explored and modified for continuous training of the cytopathology residents. Emotional support with effective communication was the key to alleviate any psychological stress among all health professionals in the cytopathology laboratory.
Clinical profile of dermatitis neglecta with special emphasis on psychiatric comorbidities: A case series of 22 patients from Eastern India
Authors: Ghosh Sudip K. , Sarkar Sharmila , Mondal Satarupa , Das Sisir
Published: November 2022

Dermatitis neglecta (DN), first described by Poskitt et al. in 1995, is a dermatosis resulting from insufficient frictional cleansing of the skin. Lesions result from self-neglect and may have several underlying etiologies, including psychiatric conditions. There is a dearth of any formal study or large case series on this subject. Moreover, the psychological aspects of DN mostly remain unexplored. We report here the clinical profile and psychiatric comorbidities of a group of patients with DN from eastern India. Sporadic cases of underlying psychological problems like cognitive deficit and executive dysfunction, schizophrenia, severe anxiety disorder, and depression have been reported in the existing literature. Associated psychiatric disorder was present in 72.7% of the patients of this case series that include several underlying psychiatric disorders like depression, schizophrenia, somatic symptom disorder, social anxiety disorder, intellectual disability, bipolar disorder, Alzheimer’s disease, and alcohol dependence. In this article we would like to emphasize that DN is frequently associated with psychiatric disorder and management of the underlying psychiatric condition with psychotropic medications; psychotherapy is of immense importance.
A review on status of incarcerated prisoners with an unsound mind and need for remedies to protect their constitutional rights
Authors: Kathane Prachi J. , Tripathi Mahesh A. , Tiwari Sudhanshu Shekher
Published: November 2022

There is limited research in India focusing on the criminal justice system issues involving a special category of victims and accused suffering from unsoundness of mind. According to the Mental Health Care Act, 2017, they need immediate mental health services, as denial of the same is a violation of their fundamental right. Despite several advancements, people with unsound mind serving their sentences in the prisons constitute a major lot of the total prison population in India, despite the fact that they are the most helpless targets of human rights violations. Section 84 of the Indian Penal Code provides a constitutional right to an accused with unsound mind to seek defense against prosecution; but due to lack of awareness and acceptance and also due to outdated testing methods, the law is not under frequent practice and not exercised to its complete worth. By compromising on the procedure established by law, they are kept under trial in judicial custody. Thus, there is an urgent need to identify and address such victims and provide them with right care and protection that the constitution entitles them. This review article is an attempt to explore and highlight this issue, to raise a voice for identification of such violations, to provide means to deliberate medicolegal aid, and lastly to find out probable solutions, recommendations, and suggestions for a better administration of justice by providing legal security to the protection of basic human rights of people with an unsound mind.
Ultrasound-guided dry needling of masticatory muscles in trigeminal neuralgia – A case series of 35 patients
Authors: Vas L , Phanse S , Pawar KS , Pai R , Pattnaik M
Published: November 2022

Background:

Trigeminal neuralgia (TGN) is considered a sensory neuropathy. However, reports of pain on chewing/speaking suggest a masticatory myofascial involvement.

Objective:

To examine the effect of ultrasound-guided dry needling (USGDN), which deactivates myofascial trigger points in masticatory, neck, and facial muscles on TGN symptoms.

Methods:

Charts of 35 patients treated for TGN were retrospectively reviewed. Treatment was USGDN alone or combined with trigeminal ganglion/mandibular nerve pulsed radiofrequency (PRF), followed by yoga mudras to stretch masticatory and facial muscles. Patients were followed for 1–8 years. Outcome parameters were reduction of medications with reduction in neuralgic attack frequency and Numeric Rating Scale (NRS) score.

Results:

23 patients (65.7%) received USGDN alone, 12 patients (34.3%) received PRF treatment before USGDN. A significant reduction in the mean (SD) NRS (5.7 [1.2] vs 8.8 [1.6]; P < .001) and neuralgic attack frequency (47 [27] vs 118 [70] attacks/day; P < .001) was seen after PRF compared with baseline, respectively. Following USGDN, the mean (SD) NRS further decreased significantly to 1.0 (0.9) (P < .001). USGDN alone produced a similar improvement in the NRS (8.9 [1.5] at baseline reduced to 0.6 [0.7] post-USGDN; P < .001). Patients in both groups reported a cessation in neuralgic attacks after USGDN. Post-USGDN, 18/27 patients completely discontinued medication, with the mean (SD) carbamazepine dose significantly reducing from 716.7 (260.9) mg/day at baseline to 113.0 (250.2) mg/day post-USGDN (P < .001).

Conclusion:

Decisive relief of TGN by USGDN suggests neuromyalgia involving masticatory muscles. Prospective, controlled studies could confirm these findings.
Критерий цикличности обобщенного крестового графа с помощью минимальных запрещенных миноров
Authors: Ilyutko Viktor Petrovich , Ilyutko Denis Petrovich
Published: November 2022

В своей работе Дж. Джилен и С. Оум нашли классы минимальных запрещенных миноров для цикличности простого графа с точностью до шарнирных преобразований и для эйлеровости дельта-матроида. Классы эквивалентности циклических простых графов по шарнирным преобразованиям и дельта-матроиды возникают при исследовании эйлеровых циклов на крестовых графах (4-графах с крестовой структурой). Полученные Джиленом и Оумом результаты полностью опираются на некоторые леммы из их совместной работы, которые, как мы покажем ниже, не совсем верны.
В настоящей работе рассматриваются обобщенные крестовые графы, которые появляются при описании поворачивающих обходов на крестовых графах. Для таких графов мы ищем критерий цикличности, повторяя и дополняя рассуждения Джилена и Оума, а также подправляя неправильно сформулированные утверждения. В результате мы получаем тот же самый список, состоящий из 166 неэквивалентных графов, минимальных запрещенных миноров для цикличности обобщенного крестового графа.
Библиография: 14 названий.
Конечные группы бимероморфных автоморфизмов неунилинейчатых трехмерных кэлеровых многообразий
Authors: Prokhorov Yuri Gennadievich , Shramov Constantin Aleksandrovich
Published: November 2022

Доказывается свойство Жордана для групп бимероморфных автоморфизмов компактных трехмерных кэлеровых многообразий неотрицательной кодаировой размерности и положительной иррегулярности.
Библиография: 32 названия.
Реализация геодезических потоков с линейным интегралом биллиардами с проскальзыванием
Authors: Vedyushkina Viktoriya Viktorovna , Zav'yalov Vladimir Nikolaevich
Published: November 2022

При помощи биллиардов с проскальзыванием на столах-комплексах удалось промоделировать произвольный геодезический поток на проективной плоскости и бутылке Клейна, имеющий линейный по компонентам импульса дополнительный первый интеграл. Требуемый стол кругового топологического биллиарда с проскальзыванием строится алгоритмически. При этом линейные интегралы геодезических потоков удается свести к одному каноническому интегралу плоского кругового биллиарда.
Библиография: 36 названий.
Критерий сходимости квантовой относительной энтропии и его использование
Authors: Shirokov Maksim Evgenievich
Published: November 2022

Получены критерий и необходимое условие сходимости (локальной непрерывности)
квантовой относительной энтропии. Рассмотрены некоторые приложения этих результатов. В частности, доказано сохранение локальной непрерывности квантовой относительной энтропии при действии вполне положительных линейных отображений.
Библиография: 29 названий.
От стабилизирующих состояний к фидуциальным состояниям, задающим симметричную информационно-полную положительную операторнозначную меру
Authors: Feng Lingxuan , Luo Shunlong
Published: November 2022

В формализме стабилизатора квантовых вычислений теорема Готтесмана-Книлла показывает, что универсальные отказоустойчивые квантовые вычисления требуют ресурса, который называется магичностью (отсутствие стабилизации). Стабилизирующие состояния выступают как "классические состояния", а для подлинно квантовых вычислений необходимы другие состояния. В этом контексте основными проблемами являются описание, обнаружение и количественная оценка магических состояний. В квантовых измерениях заметную роль играют симметричные информационно-полные положительные операторнозначные меры (SIC-POVM) из-за их структурной симметрии и замечательных свойств. Однако их существование для любой размерности, хотя и подтверждается многими теоретическими и числовыми данными, остается открытой проблемой (гипотеза Заунера). Стандартный метод построения SIC-POVM связан с орбитой группы Гейзенберга-Вейля для фидуциального состояния. Возникает вопрос: как соотносятся стабилизирующие и фидуциальные состояния? Доказано, что они находятся в крайних положениях относительно значения $p$-нормы характеристической функции квантового состояния. Это открывает простой путь от стабилизирующих к фидуциальным состояниям, количественно показывающий, что они максимально удалены друг от друга, и позволяет переформулировать гипотезу Заунера в терминах экстремумов $p$-норм характеристических функций. Также представлен удобный критерий магичности состояния и рассмотрены некоторые интересные открытые проблемы.
Резонансное взаимодействие бризеров в системе Манакова
Authors: Raskovalov Anton Aleksandrovich , Gelash Andrei Aleksandrovich
Published: November 2022

С помощью метода одевания построены многополюсные решения фокусирующей системы Манакова в присутствии постоянного фона. Рассмотрен важный случай вырожденных решений, когда полюсы одевающей функции попарно сливаются. При определенном выборе констант интегрирования такие решения описывают слияние либо распад пульсирующих солитонов - бризеров, для которых выполняется условие резонанса волновых векторов и частот. Изучены различные случаи таких резонансных взаимодействий.
A Heatmap Approach for Master Data Management Programs
Authors: Ericsson AnnMarie , Berndtsson Mikael
Published: November 2022

Master data management programs are large by nature since the aim is to provide the entire enterprise with a shared trusted view of the organisation’s most critical data assets. In this paper, we present what dimensions and activities a master data management program in a large organisation should consider and how to monitor such a program once it is up and running. A heatmap approach is used to visualize the inherent complexity of a master data management program. Our approach is derived from participating in four different master data management programs in four different global organisations during 2007-2020.
Development of 3D-based Learning Modules for University Students
Authors: Farihah Farihah , Sitompul Harun , Ampera Dina , Tanjung Samsidar , Jahidin Ismail
Published: November 2022

Some lecturers need suitable learning media and strategies in the learning process so that learning is effective. Previously, lecturers only used book media, handouts, and whiteboards as media, and only certain materials used media. It is also known that the obstacles in using media because there are still many lecturers who do not have the expertise to develop and use media and are not even familiar with computer technology, so learning strategies are implemented conventionally. The learning system is centered on lecturers delivering learning materials with lectures and further demonstrations of lecturers giving assignments or exercises to students. This research aims to know whether the development of 3D-based learning modules with direct learning strategies in Pattern Construction courses is worth using for students in the Department of Fashion Education at Universitas Negeri Medan and to find out whether module learning based on 3D with direct learning strategies in the Pattern Construction course is effectively used by the students.