Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the Utility of Low Resolution Brain Imaging:: Treatment of Infant Hydrocephalus

As low-field MRI technology is being disseminated into clinical settings, it is important to assess the image quality required to properly diagnose and treat a given disease. In this post-hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. Images were degraded in terms of resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in LMIC for assessment of clinical utility in treatment planning for hydrocephalus. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of a useful image for hydrocephalus treatment planning. For images with 128x128 resolution, a contrast-to-noise ratio of 2.5 has a high probability of being useful (91%, 95% CI 73% to 96%; P=2e-16). Deep learning enhancement of a 128x128 image with very low contrast-to-noise (1.5) and low probability of being useful (23%, 95% CI 14% to 36%; P=2e-16) increases CNR improving the apparent likelihood of being useful, but carries substantial risk of structural errors leading to misleading clinical interpretation (CNR after enhancement = 5; risk of misleading results = 21%, 95% CI 3% to 32%; P=7e-11). Lower quality images not customarily considered acceptable by clinicians can be useful in planning hydrocephalus treatment. We find substantial risk of misleading structural errors when using deep learning enhancement of low quality images. These findings advocate for new standards in assessing acceptable image quality for clinical use.
Component analysis of volatile oil from fruits of Trachyspermum ammi by headspace solid-phase microextraction GC-MS

Objective:To utilize headspace solid-phase microextraction(HS-SPME) combined with gas chromatography-mass spectrometry technology(GC-MS) as a new method to analyze the volatile components of the fruit of Trachyspermum ammi(L.) Sprague(Uighur traditional medicine). Methods:Four extraction fibers,PDMS,CAR/PDMS,PDMS/DVB and DVB/CAR/PDMS,were chosen for solid phase microextraction to extract the volatile components which were further isolated and identified by GC-MS.The relative mass fraction of each component was determined by the area normalization method.The results from HS-SPME-GC-MS were compared with those obtained from conventional hydrodistillation GC-MS(HD-GC-MS). Results: The volatile components of fruits of T.ammi were effectively extracted by the DVB/CAR/PDMS fiber.The main components were γ-terpinene(26.21%),p-cymene(23.58%) and thymol(20.02%). Conclusions:Twenty-two volatile compounds were identified with HD and HS-SPME,and the main volatile components are consistent.The components can be rapidly obtained by HS-SPME method which is conducive to extract the volatile compounds,specially for monoterpenes and sesquiterpenes compounds.This method is applicable for rapid analysis and quality control of volatile components of Trachyspermum ammi(L.) Sprague fruits.
Double layered solutions to the extended Fisher- Kolmogorov P.D.E

We construct double layered solutions to the extended FisherKolmogorov P.D. E. , under the assumption that the set of minimal heteroclinics of the corresponding O.D. E. satisfies a separation condition. The aim of our work is to provide for the extended Fisher-Kolmogorov equation, the first examples of two-dimensional minimal solutions, since these solutions play a crucial role in phase transition models, and are closely related to the De Giorgi conjecture.
Giving Without Sacrifice?

Once we are made aware of the extent and severity of global poverty, it is difficult to avoid the conclusion that we ought to do something to help. Unfortunately, the gap between what we ought to do and what we actually put into action is famously wide. We have trouble doing what we ought even when we are the ones who stand to benefit: we know that we should get more exercise or save more for the future, and yet we fall short of even these commitments. In cases of like the above, it is the lure of immediate gratification that diverts us from doing what we should. When it comes to donating money or time to the benefit of others, the perceived importance of self-interest can similarly exceed its bounds. If we are rightly convinced that we ought to help those in need, we can nonetheless be inspired to hold back by the worry that this will require us to make a substantive sacrifice, even if we recognise this sacrifice as being scarcely comparable to the benefit that we could achieve for others. Ethical reasoning may be relatively ineffectual against this worry and its demotivational force. We prefer, therefore, to confront it in a more head-on fashion. In the following, we present empirical evidence to show that joining Giving What We Can will most likely requires no substantive sacrifice on your part, and may even be better, from a purely self-interested perspective, than spending your income in the common fashion. This, we hope, will help to close the gap between your actions and your knowledge of what should be done. It is undeniable that giving away 10% of your income will reduce the amount of money that you have available to spend on yourself. However, money surely has no intrinsic value: it is something we accumulate not for its own sake, but for what we can get with it. Any sensible self-interested worry that fuels our reluctance to donate 10% of our income to the good of others must therefore be a worry that we will not be as happy as we might have been had we kept such money for ourselves. It is the merit of this worry that is at issue in the following. Of course, different people have different relationships with money, and draw their happiness from different sources. We cannot tailor our approach to suit the peculiarities of every possible situation, and what is provided in the following is therefore limited in this respect. In order to determine exactly how your wellbeing depends upon a certain level of income, some personal judgment is called for. You should, however, be aware of your own fallibility. Psychologists have studied our accuracy in predicting the effect of various outcomes on happiness: these predictions are
SUMDocS: Surrounding-aware Unsupervised Multi-Document Summarization

Multi-document summarization, which summarizes a set of documents with a small number of phrases or sentences, provides a concise and critical essence of the documents. Existing multi-document summarization methods ignore the fact that there often exist many relevant documents that provide surrounding background knowledge, which can help generate a salient and discriminative summary for a given set of documents. In this paper, we propose a novel method, SUMDocS ( Surrounding-aware Unsupervised Multi-Document Summarization ), which incorporates rich surrounding ( topically related ) documents to help improve the quality of extractive summarization without human supervision . Speciﬁcally, we propose a joint optimization algorithm to unify global novelty (i.e. , category-level frequent and discriminative ), local consistency (i.e. , locally frequent, co-occurring ), and local saliency (i.e. , salient from its surroundings ) such that the obtained summary captures the characteristics of the target documents. Extensive experiments on news and scientiﬁc domains demonstrate the superior performance of our method when the unlabeled surrounding corpus is utilized.
Analysing Social Needs of the Ukrainian Ex-Combatants: Looking on The Lived Experience

© 2021 The Authors. This article is licensed under a Creative Commons Attribution 4.0 License Abstract. Since 2014, Ukraine experiences armed conflict on the territory of its eastern part. The Ukrainian government had announced the anti-terrorist operation (ATO) to combat the separatists. This paper reports on research exploring the social needs of ex-combatants in Ukraine and pathways for social reintegration available to them. The research data reached theoretical saturation by interviewing ten ex-combatants and 11 representatives of different services (social workers, psychologists, psychotherapists, priests etc.). Additionally, field notes and grey literature were also considered for the analysis. To triangle, the data received from an interview the analysis of national legal acts on social protection of ex-combatants were analysed.
Texture Transfer Method for Bio-inspired Surface Fabrication Using Image Processing

Surface texture is an important aspect on precision parts fabrication, since it defines the quality of such products. It is also opens the possibility to enhance the product value by adding functional surface texture to the fabricated parts. Various types of surface texture were developed by creating structured shape on the parts surface. Most of the surface texture design was done on CAD (computer aided design) software, this process spent too much time especially to design complex surface texture. In this paper, we develop a rapid and fast technique to acquire complex surface texture data by using image processing technique. A complex surface texture of human skin was selected to transfer to metal surface for micro-mold process also presented on this paper.
The COVID-19 Global Fear Index, Realized Volatility and Jump

This paper subjects the global fear index (GFI) for the COVID-19 pandemic to an empirical investigation to determine its relationship with realized volatility, continuous volatility, and jump volatility for seven international indices. The results show evidence of a positive relationship between the measures of realized volatilities and the global fear index, which is persistent in periods of high volatility, and a negative relationship in periods of low volatility. It is observed that when serial dependence, systemic risk and alternative measure of volatility are considered the significance of the COVID-19 global fear index and volatility relationship decreases significantly.
In Vitro Regeneration and Rapid Multiplication of Tuberose

An experiment was conducted at Tissue Culture Laboratory, Department of Biotechnology, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh from July 2011 to March 2012 for In Vitro regeneration of tuberose. Experiment consisted ten combinations of different concentrated KIN and IAA as supplemented with MS medium. Early shoot (10.5 days), leaf (14.6 days) and root (16.0 days) initiation were found from MS+0.5 mg/L IAA +1.0 mg/L KIN. Maximum number of leaves (2.99/plantlet), length of leaf (5.48 cm), number of roots (5.69/plantlet) and length of root (3.49 cm) were also found from MS+0.5 mg/L IAA +1.0 mg/L KIN.
Detection and Attribution of Extreme Temperature Using an Analogue-based Dynamical Adjustment Technique

Recent studies have highlighted the importance of internal variability in decadal trends and variability of regional-scale temperature and precipitation (Deser et al. 2012; Hawkins et al. 2015). Understanding and potentially reducing the uncertainties in climate change projections arising from internal variability has been a focal point of climate research over the last decade (Bindoff et al. 2013). The superposition of anthropogenically forced climate change with this unforced internal variability complicates attempts to detect and attribute climate change (Thompson et al. 2009). In the Northern Hemisphere mid to high latitudes, the stochastic variability of atmospheric circulation is a major contributor to this internal variability (Smoliak et al. 2015; Deser et al. 2015). Beyond decadal trends in temperature or precipitation, recent research has focused on diagnosing the driving factors behind individual extreme events, such as heat waves, cold snaps, or floods (Herring et al. 2015). It is therefore of interest to estimate the contribution of atmospheric circulation to a given trend or anomaly in temperature or precipitation.
Automated quantification of myocardial tissue characteristics from native T1/cmss/bx/n/13 1 mapping using neural networks with Bayesian inference for uncertainty-based quality-control

Background: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T1 mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies.
SMART HOME ANTI-THEFT DETECTION USING IOT AND AI

–This paper addresses the problem involved incurrent socio-economic life where in automation using the techniques of IOT and Artificial intelligence (AI) take up the responsibility of providing security to our homes by providing two way authentication to the users. The proposed system verifies the person identity by verifying the person’s face as a key and also asks for inputting the right password to access the door. The owner can even monitor and control the system remotely with the help of IOT. Artificial Intelligence provides framework for real-time decisions in terms of face detection and recognition. The model also alerts the owner by sending an email containing video clip of the intruder. If the intruder tries to breach the security by breaking the lock or damaging the system, an alarm is raised to intimate the neighbours about the activity.
Examining the Inter-Sectoral Relationship, Productivity and Inclusive Growth of Pakistani and Indonesian Economies

Inclusive growth is progressing the diverse patterns, backgrounds, and sectors of an economy. As an equitable economy has more potential for prosperity so reducing the inequalities and enhancing the productivity of workers in all the sectors of the economy is a core concern for emerging policies. Sectoral interlinkages and patterns of production are the basis for economic growth and essential for welfare outcomes. Therefore, the study analyzes the sectoral integration of Pakistan and Indonesian economies using the dataset from 1980 to 2019. For this purpose, the productive efficiency of workers is focused on three major sectors of economies i.e., services, manufacturing, and agriculture sector. In this study, we have used the VAR model to assess the integration and causal relationship among sectors and found that the per capita value addition of labor is relatively higher in the manufacturing sector of Pakistan and Indonesia. More than 36 percent of employed labor is in the agriculture sector of Pakistan but it has a slow growth rate of only 0.97 percent in 2019. Indonesia has the second-highest employment in the agriculture sector (i.e., 3.6 percent) but the lowest per capita value-added. This indicates slow development and high deprivations in the agriculture sector of both economies particularly in terms of opportunities. The services sector of Pakistan is categorized as a major sector in terms of employment with the highest growth rate that is approximately 3.7 percent whereas the Indonesian services sector has also employed a large share of total employment i.e., 48.9 percent in 2019. But it is found that the value-added production of Indonesia has been lower in services than in the industrial sector. We found a positive association of the services sector with agriculture is found in both economies but there is a negative relationship between agriculture and industry for Pakistan. Therefore, it is suggested to focus the skill development programs aligned with sectoral requirements and provide incentives for efficient allocation of employment across sectors to get the benefits of growth in a broad base.
Agency and Resilience-foundational elements of adolescent well-being

Agency and resilience are critical to adolescent well-being. As a domain of the adolescent wellbeing framework, adolescent agency and resilience are intrinsically linked to a sense of purpose, fulfillment and identity and cuts across all other domains within the adolescent well-being framework. The ability of adolescents to make and act on their choices and meet individual and collective aspirations is essential for their holistic well-being. Adolescents’ agency and resilience are influenced by various intersecting socio-structural factors including deep-rooted social and gender norms and unequal power structures, that adversely affect the potential of adolescents. Acknowledging the extent of prevailing inequalities, and that adolescents are not a monolithic group, allows us to explore opportunities to commit to inclusive, gender transformative and adolescent responsive efforts that target adolescent well-being and rights. Several programmatic interventions seek to strengthen adolescents’ individual and collective agency, as well as their resilience, by working directly with adolescents, creating an enabling community environment and leveraging the influence and accountability of duty bearers and service providers to strengthen institutional responsiveness to adolescents’ well-being needs. These programs consider adolescents’ rights, voice and choices, as well as the impact of the social, including the gendered, economic, cultural and political influences in their lives. Currently, there is no universally agreed upon framework or set of indicators or tools for measuring agency and resilience. A significant number of frameworks have been developed and tested in the Global North; however, they require adaptation for multicultural and diverse contexts. Further development of shorter, more youth-friendly, mixed-methods approaches are required, particularly for measurement frameworks which support standardized rights and empowerment-based programming and population-level measurement of participation among adolescents.
Improving of Organizational Performance Using Lean Supply Chain Management Practices: The Mediating Role of Supply Chain Collaboration

Purpose Surviving in the face of growing globalization, competition and rapid changes in consumer expectations would mean that, firms have to adopt modern business strategies that are flexible and capable of sustaining performance improvements among firms. The aim of this paper is to establish the effect of lean supply chain management (LSCM) approach on performance improvement among firms through the influence of supply chain collaboration (SCC). Design/Approach/Methodology This conceptual paper recommends an inclusive model that incorporate LSCM and SCC dimensions as well as organizational performance, underpinned by theories such as the Dynamic Capabilities (DCT) and Relational View (RV). The proposed model is to establish the direct and indirect effects of LSCM approach on firms’ performance through the influence of SCC (intra and inter firm) dimensions in the manufacturing context, as the main goal of LSCM implementation which will subsequently lead to improvements in organizational performance. Business Management and Strategy ISSN 2157-6068 2021, Vol. 12, No. 2 36 Originality: The study effectively combines the DCT and RV to develop a comprehensive model involving the three broad operations and supply chain management concepts. Though there may be similar studies on LSCM practices among manufacturing firms, limited studies have explored similar model or work has limited discussion especially on exploring the mediating roles of individual dimensions of SCC in the LSCM practices – organizational performance among manufacturing firms. This is a novelty as it provides deeper insights on the mediating effects of each dimension of SCC in the relationship between LSCM practices and organizational performance. The proposed theoretical model will serve as a potential blueprint for the successful implementation of LSCM practices.
Neural Network Based Detection of Driver’s Drowsiness

The primary purpose of this paper was to propose a way to alert sleepy drivers in the act of driving. Most of the traditional methods to detect drowsiness are based on behavioral aspects while some are intrusive and may distract drivers, while some require expensive sensors/hardware. Therefore, in this paper, driver’s drowsiness detection system is developed and implemented to aid drowsy drivers from falling asleep and to prevent accidents. The system takes images from the device as input. Using these image templates, the trained model starts execution and predicts/classifies whether the face of the person in the image is drowsy or alert. The proposed model is able to achieve accuracy of 99.93% using CNN on trained image dataset.
A Survey on Big Data Processing Frameworks for Mobility Analytics

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.
Face Recognition based Security System Using Raspberry PI

— Most doors square measure controlled by persons with the employment of keys, security cards, parole or pattern to open the door. The aim of this paper is to assist users for improvement of the door security of sensitive locations by victimization face detection and recognition. Face may be a advanced flat structure and desires sensible computing techniques for detection and recognition. This paper is comprised primarily of 3 subsystems: particularly face detection, face recognition and automatic door access management. Face detection is that the method of police investigation the region of face in a picture. The face is detected by victimization the viola Jones technique and face recognition is enforced by victimization the Principal part Analysis (PCA). Face Recognition supported PCA is usually brought up because the use of Eigenfaces.If a face is recognized, it's better-known, else it's unknown. The door can open mechanically for the better-known person because of the command of the microcontroller. On the opposite hand, alarm can ring for the unknown person. Since PCA reduces the size of face pictures while not losing vital options, facial pictures for several persons may be hold on within the info. though several coaching pictures square measure used, machine potency cannot be remittent considerably. Therefore, face recognition victimization PCA may be a lot of helpful for door security system than alternative face recognition schemes.