Miracle-dz/UniFAR-FTUs huggingface dataset:
ARCHETYPES OF SOCIAL INSTITUTIONS

by oligarchic clans can be considered delinquent. At the same time, new formations are actions that correspond to the chosen general European guidelines and the revival of Ukrainian values and beliefs. The author assumes the following boundaries of delinquency: on the one hand – heroism. On the other – villainism. There are transitional forms between them. The author emphasises that a villain can be understood as a clear enemy, the struggle of intrapersonal multidirectional interests and motives (one's enemy), and a reincarnated hero (antihero, villain). The author tries to summarise the characteristics of the hero accordingly. Heroes and villains are distinguished by their values in their activities, which they are carriers of. The author characterises the modern Ukrainian hero's values (universal human values, European values, and values of a just, fair war). This work contributes to the consolidation of society around selected values. The author sees further work in the study of the characteristics of the archetypes of the hero and the villain in the post-war period.
Sentiment Analysis using Logistic Regression Approach on E-commerce

— In recent times, the e commerce market has shown immense growth. These companies have innovated their ideas and have made it so easy for customers to get anything at just one click. It has made lives of people simpler due to its level of convenience. Amazon is one such widely known E-commerce platform, which sells millions of products worldwide. Thus, the growth of E-commerce gave value to customer needs and opinions that gave rise to an important aspect of online shopping known as ‘User Reviews. ’ User reviews are customer suggestions and opinions about the product that help other customers make decisions. Such review systems form the backbone of E-commerce. The field of sentiment analysis is used frequently to evaluate the text data and extract sentiment elements from it. Every day a massive volume of textual data is produced by online business websites -user ratings, recommendations, messages, and suggestions. Sentiment analysis is performed in python using the nltk module. The NLTK module is used to perform tasks in natural language processing. The main aim of the paper is to understand and analyze the Amazon User Review Dataset. We have used logistic regression algorithm for analyzing the dataset. It is a machine learning algorithm that is used for classification problems. We have also used different visualization techniques that will help showcase various informative statistical trends and provide us with insights about the Amazon Review system. These insights will help in exploring the possible improvements that could be made to satisfy the customers.
C OMPARING D ETECTION M ETHODS FOR P AUSE -I NTERNAL P ARTICLES

: This study investigates different machine learning architectures for classifying pause-internal phonetic particles (PINTs), such as ﬁller particles (FPs), breath noises complementary to silences, and tongue clicks. Many of these PINTs co-occur, and by modeling them simultaneously, the aim is to improve the classi-ﬁcation accuracy for the surrounding PINTs as well. An annotated subset from a German spontaneous speech corpus was used for modeling. Mel-frequency cepstral coefﬁcients were used as inputs to model PINTs with three kinds of neural networks: a general neural network, a convolutional neural network, and a recurrent neural network. The models used the same hyperparameters, number of layers, and number of neurons for those layers, so that the focus was put onto the model architecture. The recurrent neural network was expected to perform the best since it is able to capture temporal information; however, all models performed similarly. The models performed best at classifying silent segments, followed by inhalations and exhalations. However, all models failed to accurately classify FPs and clicks, indicating that modeling PINTs simultaneously doesn’t always improve accuracy for surrounding PINTs. These ﬁndings suggest that accurate classiﬁcation is more dependent on annotation quantity and quality than model architecture. The main contributions of this paper are the classiﬁcation of multiple PINTs simultaneously, and the improvement of PINTs classiﬁcation for the German language.
Novel-fosfamide monotherapy or in combination with doxorubicin versus doxorubicin alone in patients with advanced soft tissue sarcoma: A pooled analysis of randomized clinical trials

Background: Novel-fosfamides (NFOs) belong to active metabolites of ifosfamide that bypass the generation of toxic byproducts. In this analysis, we aimed to comprehensively assess the benefits and risks of NFO monotherapy or in combination with doxorubicin (DOX) versus single-drug DOX in previously untreated patients with advanced soft-tissue sarcoma (ASTS). Methods: Online PubMed, Web of Science, Embase, and Cochrane CENTRAL databases were systematically searched on April 26, 2022. Objective response rate and disease control rate were primary outcomes. Overall survival (OS), progression-free survival (PFS), and grade ≥ 3 treatment-related adverse events were secondary outcomes. Results: In all, 3 randomized clinical trials with a total of 1207 ASTS patients were eligible. DOX plus NFO combination therapy showed higher risk ratios of objective response rate (1.50, 95% CI 1.20–1.68, P = .0003) and disease control rate (1.15, 95% CI 1.05–1.27, P = .0030) compared with DOX monotherapy. Nevertheless, NFO-based monotherapy and combination therapy were found no improvements on OS (hazard ratio 0.93, 95% CI 0.52–1.65, P = .8050) and PFS (hazard ratio 0.88, 95% CI 0.54–1.43, P = .6088) against DOX. More incidences of grade 3 or worse anemia, thrombocytopenia, stomatitis, diarrhea, constipation, and febrile neutropenia were observed in NFO-based treatments. Conclusion: Adding NFO to DOX as first-line therapy improved the responses in ASTS patients but did not prolong OS and PFS. Grade 3 or worse treatment-related adverse events should be treated with caution during the NFO-based therapies.
Detection and Segmentation of Defects in Industrial CT Images Based on Mask R-CNN

For defect detection and segmentation of industrial computed tomography (CT) images, existing methods regard them as two separate tasks, that is, the detection and the segmentation are not integrated. This paper improves mask regions with convolutional neural networks (Mask R-CNN), and the improved network is used to complete the detection and segmentation of industrial CT image defects at the same time. In this study, the three most common defects in industrial CT images are cracks, bubbles, and slags, which are taken as the research objects. The experimental results showed that the network can achieve a detection mAP value of 0.98 and a segmentation accuracy of 0.96 for three typical CT image defects (12 × 12 pixels ~ 35 × 35 pixels). The method proposed in this paper has high accuracy, in both the defect detection and segmentation of industrial CT images. At the same time, the algorithm also has good robustness and generalization ability. It can be applied to the detection and segmentation of various defects and has great practical significance.
Chapter One. Japan’s population growth during the past 100 years

In this chapter, the period of population change during the past 135 years is divided into four demographically distinctive eras. They are: (1) the period between 1870 and 1945, here called the first stage of demographic transition, (2) the period between 1945 and around 1960, the second stage of demographic transition, (3) the period between around 1960 and the mid-1970s, the era of 'population bonus', and (4) the period after the mid-1970s, the era of declining fertility to below replacement level. Demographically, the fifteen years just after World War II were an important turning-point in modern Japan, despite being a relatively short period. This is because Japan substantively completed both fertility transition and epidemiological transition in this period, decelerated its population increase and started population ageing. The main demographic cause of phenomenal fertility decline is the postponement of marriage and childbearing. Keywords: fertility transition; Japanese population
Effect of semaglutide 2.4 mg once weekly on 10‐year type 2 diabetes risk in adults with overweight or obesity

In the Semaglutide Treatment Effect in People with obesity (STEP) trials, once‐weekly subcutaneous semaglutide 2.4 mg plus lifestyle intervention reduced body weight and improved cardiometabolic parameters in adults with obesity (or overweight with weight‐related comorbidities). Effects on the risk of developing type 2 diabetes (T2D) require investigation.
Precision Machine Design

This book is a comprehensive engineering exploration of all the aspects of precision machine design - both component and system design considerations for precision machines. It addresses both theoretical analysis and practical implementation providing many real-world design case studies as well as numerous examples of existing components and their characteristics. Fast becoming a classic, this book includes examples of analysis techniques, along with the philosophy of the solution method. It explores the physics of errors in machines and how such knowledge can be used to build an error budget for a machine, how error budgets can be used to design more accurate machines. Supporting software (MS Excel spreadsheets on 5.25' diskette) enable the reader to study 'What if' design scenarios based on formulas presented in the book.
Deeb and Rosenbaum ( [9] ) wrote about intravasation into the epidural venous plexus as an adverse effect of myelography, causing transient pulmonary symptoms

By 1979 the new dye, Amipaque, had redefined the myelography market, which was a source of concern to Alcon, no doubt. During this period, Newton at Lafayette, sent an internal memo concerning a conversation he had had with a physician enquiring on behalf of his daughter. He mentioned saying that &quot;retained Pantopaque should present no problem&quot; but &quot;would gradually be eliminated&quot;. He recalled also saying &quot;We had not seen inflammatory responses in animals&quot;. Newton has also mentioned that in the UK, Pantopaque was routinely being left in the subarachnoid space as standard practice.
A Multimodal Approach: Acoustic-Linguistic Modelling for Neural Extractive Speech Summarisation on Podcasts

Podcasts, a contemporary medium of audio-only content, have rapidly progressed in consumption and generation across the internet. Along with the accelerated pace of its popularity in recent years, effectively publicising the podcast shows has become a need for all podcast creators, listeners and streaming platforms. To improve the overall visibility of podcast contents and enhance user engagement, a summary of an episode has become a need for the users or utilising in searching and recommendation systems, which can be a replacement of or in addition to keywords, manual descriptions and transcripts. Since manual summarisation for podcast episodes takes ample time, automatic summarisation becomes a valuable task. Specifically, in the context of automatic summarisation task for spoken documents, we need to consider that the extracted salient information relies on what is said but also how it is said. In the wake of this, this thesis investigates summarisation models for podcasts and proposes a multimodal approach exploiting acoustic and linguistic features. Accordingly, we aimed to explore how to automatically generate an extractive summary from a podcast episode in a multimodal way. For our research, we have employed a lexical-only pre-trained transformer model (i.e. SentenceBERT) for embedding sentences in the transcripts. In this work, speech summarisation of podcasts is defined as a classification task; in respect to that, our purpose was to extract meaningful sentences from the transcribed text where the importance is predicted by combining acoustic and linguistic information. To build an experimental setup for analysing the impact of acoustic features, we have integrated a two-layer multilayer perceptron on the top layer of the SentenceBERT model. Feature projection, ranking and selection were also performed for feature importance analysis of acoustic information. After projection and selection of acoustic features, our proposed multimodal model outperforms the baseline (text-only) and achieved moderately better ROUGE scores; with this project, we aim not to find a complete solution for the automatic summarisation of podcast episodes, but to understand the critical part of the puzzle linked to incorporating acoustic features into podcast summarisation.
Coverage Optimization using Generalized Voronoi Partition

In this paper a generalization of the Voronoi partition is used for optimal deployment of autonomous agents carrying sensors with heterogeneous capabilities, to maximize the sensor coverage. The generalized centroidal Voronoi configuration, in which the agents are located at the centroids of the corresponding generalized Voronoi cells, is shown to be a local optimal configuration. Simulation results are presented to illustrate the presented deployment strategy.
Multimodal Analysis of Acoustic and Linguistic Features in Entrepreneurial Pitches using Deep Learning

Multimodal Analysis of Acoustic and Linguistic Features in Entrepreneurial Pitches using Deep Learning by Pepijn van Aken Acquiring early-stage investments for the purpose of developing a business is a fundamental aspect of the entrepreneurial process, which regularly entails pitching the business proposal to potential investors. Previous research suggests that business viability data and the perception of the entrepreneur play an important role in the investment decision-making process. This perception of the entrepreneur is shaped by verbal and non-verbal behavioural cues produced in investor-entrepreneur interactions. This study explores the impact of such cues on decisions that involve investing in a startup on the basis of a pitch. A multimodal approach is developed in which acoustic and linguistic features are extracted from recordings of entrepreneurial pitches to predict the likelihood of investment. The acoustic and linguistic modalities are represented using both hand-crafted and deep features. The capabilities of deep learning models are exploited to capture the temporal dynamics of the inputs. The findings show promising results for the prediction of the likelihood of investment using a multimodal architecture consisting of acoustic and linguistic features. Models based on deep features generally outperform hand-crafted representations. Across multiple explainable models, consistent features are found to be important predictors. An early fusion multimodal model consisting of deep representations of the two modalities has been proven to be most predictive.
Bipolar Depression : The Search for Diagnosis and Treatment

M ajor depressive episodes associated with bipolar I disorder (bipolar depression) can be difficult to diagnose, which can prove challenging for clinicians and frustrating for patients. Patients can wait up to 10 years or longer from the time of symptom onset to receiving an accurate diagnosis.1,2 According to one study, patients undergo a mean of 3.5 differential diagnoses and see 4 healthcare professionals before finally receiving a diagnosis of bipolar disorder.1 Misdiagnosis is a frequent challenge. In a study of patients in a community sample who screened positive for bipolar disorder, nearly half (49%) had previously received no diagnosis, and nearly a third (31.2%) had received an incorrect diagnosis of major depressive disorder. Only 19.8% received a diagnosis of bipolar disorder.3 Consistent with this finding, unipolar depression, at 60%, is the most common misdiagnosis among patients later diagnosed with bipolar disorder. Anxiety disorder is the second most common misdiagnosis, at 26%. 1 Available at CLINICIANREVIEWS.COM A F R O N T L I N E M E D I C A L C O M M U N I C AT I O N S P U B L I C AT I O N
A review of airborne laser bathymetry for mapping of inland and coastal waters

column. Additional reflections from the air-waterinterface are used to reconstruct the 3D-shape of the water surface. Exact knowledge of the height and tilt of the water surface is a precondition to correct the raw measurement due to refraction effects at the air-water-interface (Westfeld et al. 2017; Birkebak et al. 2018; Richter et al., 2018). The measurable depth of this active remote sensing technique mainly depends on the optical properties of the water body (i.e. turbidity). State-of-the-art bathymetric sensors can measure depths in the range of 1 to 3 times the Secchi
Modeling of Mixed-Pixel Clumping Index From Remote Sensing Data and Its Evaluation

The clumping index (CI) is a canopy structure parameter that describes the dispersion or grouping of leaves. Previously, it has been estimated based on the normalized difference between hotspot and darkspot (NDHD), which is derived from multi-angle remote sensing data. However, currently it is impossible to derive CI from NDHD for a large area at a spatial resolution finer than 275 m since such fine multi-angle data are unavailable. In this study, an algorithm of the mixed-pixel clumping index (MPCI) was implemented, and an MPCI map of China's landmass at 1 km resolution was derived from the HJ-1A/1B data at 30 m resolution. The MPCI map was compared with the previous NDHD CI derived from the moderate resolution imaging spectroradiometer (MODIS). The correlation of these two datasets was greater than 0.9, and the mean bias was approximately 0.1. Indirectly, the MPCI map was applied to an effective leaf area index (LAI) product to derive true LAI. Using the MODIS LAI product as a reference, we found that the coefficient of determination was improved from 0.72 to 0.80, and the root mean squared error was reduced from 0.53 to 0.35 m2/m2 after the effective LAI is corrected by this MPCI map, suggesting that this MPCI map is comparable to the NDHD CI. Although our algorithm is currently tested at 1 km resolution, potentially, it can be applied to higher spatial resolution than 275 m for mapping LAI and carbon cycle modeling before these multi-angle data at higher resolution are available.
Environmental Regulation promotes Green Technological Diversification: Evidence from Chinese Cities

: Accelerating the development of green technologies is essential to achieve a green transition, but green technologies tend to be more radical and complex. It means that they require significant efforts to scale and we need to understand all possible levers of green technological change. In this paper, we investigate whether environmental regulation can provide opportunities for path-breakthrough and complex technology diversification during the green transition process. The analysis is based on patenting activities in Chinese cities from 2003 to 2016. Our results show that cities with tighter environmental regulations are more likely to branch into new green technology spaces. In addition, environmental regulations help cities enter less related and more complex green domains. This study provides significant policy implications for the green transition literature.
Dynamic matching

We consider a dynamic matching model with random arrivals – a dynamic version of the bipartite matching model. As in the static setting, it is based on a bipartite graph. In the discrete-time dynamic model there are arrivals of units of ‘supply’ and ‘demand’ that can wait in queues located at the nodes in the network. A control policy determines which items are matched at each time. The theory of matching has a long history in economics, mathematics, and graph theory [7], with applications found in many other areas such as chemistry and information theory. Most of the work is in a static setting. The dynamic model has received recent attention in [5, 3, 6]. The most compelling application is organ donation: United Network for Organ Sharing (UNOS) offers kidney paired donation (KPD). This is a transplant option for candidates who have a living donor who is medically able, but cannot donate a kidney to their intended candidate because they are incompatible (i.e., poorly matched) [1]. In this and many other applications, data arrives sequentially and randomly, so that matching decisions must be made in real-time, taking into account the uncertainty of future requirements for supply or demand. The choice of matching decisions can be cast as an optimal control problem for a dynamic matching model.
Social Support among Parents of Children with Congenital Heart Disease, Parents of Children with Other Diseases and Parents of Healthy Children

The study reported here compared social support experiences among parents of children born with congenital heart disease PCCHD (n=1 092), parents of children with other diseases PCOD (n=112), and parents of healthy children PHC (n=293). In addition, factors related to social support among the parents were identified. The parents completed a questionnaire about such areas as availability of social support, financial strain, and psychological distress. The design was cross-sectional and data were collected over 20 consecutive days. The univariate analysis indicated that availability of social attachment was low among all parents and moderate concerning interaction/integration, with no significant differences between PCCHD, PCOD, and PHC. In addition, the univariate analysis indicated that mothers within all parent groups had lower availability of social support than fathers, with the lowest availability among mothers of children with CHD. The multivariate analysis indicated, however, that extra time devoted to care giving, financial instability (e.g. unemployment and financial burden of children's diseases), psychological distress, and hopelessness accounted for the variation in availability of social support more than gender, children's diseases, and their severity. The study corroborated previous findings and may have provided new insights into factors that may be associated with social support experiences among PCCHD. Implications for interventions are discussed.
An evaluation of agri-environmental programmes and Westsik's crop rotation experiments.

In spite of extensive knowledge, both environmental and cost-effectiveness of agrienvironmental programs show great variations and are often considered unsatisfactory in many countries of EU. From a scientific point of view, the design and implementation of these programs are driven rather by income than environmental objectives. The best known and most remarkable example of continuous production in Hungary is the Westsik’s crop rotation experiment established in 1929, which is still in use to study the effects of manure treatments, develop models and predict likely effects of different cropping systems on soil properties and crop yields. In this respect, Westsik’s crop rotation experiment is an efficient tool for studying the effects of agri-environmental programmes as it models various possible methods of nutrient management (fallow, straw, farmyard and green manure), and gives assistance in answering the questions related to the sustainability of agrienvironmental projects, where farmers must adopt technologies which serve agricultural and environmental goals.