Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of the Methodist Children's Home GAP Program

Methodist Children’s Home’s (MCH) GAP Program provides services for grandparents raising their grandchildren. This single case study explores whether the services provided by the GAP Program will reduce the risk factors for grandparents raising their grandchildren while increasing their ability to care for the child in a way that will promote well-being in today’s complex world. The literature shows some challenges for these grandparents include: role ambiguity, limited legal rights, lack of resources, inadequate living arrangements, employment and retirement, health issues, and outdated parenting skills and disciplinary methods. Protective factors for grandparents include: adaptive coping skills, self-care, positive relationships, support, and access to resources. The well-being of the grandchildren can be negatively affected by the level of attention they are receiving from their grandparents, maladaptive behaviors and coping skills, a sense of fear of abandonment and feeling unwanted, as well as a lack of support. Protective factors for these children include strong support systems, stability, feeling loved, and being around family. Being placed with their grandparents can provide feltsafety, improvements in school, as well as positive personality and behavioral characteristics. Successful interventions for this population include in-home services, case management, and group interventions. This single case study supports that the GAP Program has the ability to reduce the stress levels for grandparents and increase the competence in parenting. An Evaluation of the Methodist Children’s Home GAP Program A Thesis Presented to The Faculty of the School of Social Work Abilene Christian University In Partial Fulfillment Of the Requirements for the Degree Master of Science in Social Work By Ashten Wiedebusch
Leveraging Stragglers in Coded Computing with Heterogeneous Servers

With the increasing sizes of models and datasets, it has become a common practice to split machine learning jobs as multiple tasks. However, stragglers are inevitable when running a job on multiple servers. Compared to replicating each task on multiple servers, running coded tasks can tolerate the same number of stragglers with much fewer servers. However, additional results of tasks running on stragglers are typically disregarded in existing schemes of coded computing, incurring a waste of the resources on such servers. In this paper, we leverage the results of partially finished tasks. In existing designs that utilize partially finished tasks, they have only considered servers with homogeneous performance. However, in a typical distributed infrastructure, e.g., a cloud, servers with heterogeneous configurations are common. Therefore, we propose Spinner which can efficiently utilize the results of partially finished tasks even on heterogeneous servers. Spinner works with existing coding schemes for matrix multiplication, a fundamental operation in various machine learning algorithms, and can efficiently assign the workload based on the performance of the corresponding server. Furthermore, Spinner can equivalently adapt the coding scheme for heterogeneous servers, aligned with the expected workload assigned to each server, and thus save the complexity of decoding. Combining the two strategies together, we demonstrate in our experiments that Spinner can improve the time of matrix multiplication by up to 84.0% and thus improve the time of linear regression by 40.7%.
Human Emotion Recognition using GSR and EEG

Human beings are capable of expressing thousands of emotions. The complexity, intensity and involvement with that emotion also vary with the situation. Various mood disorders can be recognized by the differentiated characteristics and features. The process of analyzing human emotional state is known as emotion recognition. In this paper the main focus is on analyzing a human's emotional states in a particular period of time. So, a dimmed tendency of different psychological states are observed over a short span of time in a wide range of age groups. The main concentration is on the four major parameters such as GSR, BPM, Body Temperature, and EEG (Electroencephalogram) for detecting the emotional states. To get an accurate result GSR (Galvanic Skin Resistance) is used depending on the principle of Electro-dermal activity (EDA) which shows various electrical signal attributes with changes in human activities (which involves sweating in time of anxiousness etc.). BPM (Beats Per Minute) which acts simultaneously to give a precise heart rate count at that particular time. EEG adds up for collecting exact brain signals of the subject using electrodes revealing the psychological state. Body temperature acts as a helping hand when measured along with, as different emotions leads to the secretion of hormones which causes a slight variation in the body temperature.
Individual response to the end of Daylight Saving Time is largely dependent on habitual sleep duration

The end of Daylight Saving Time provides a valuable opportunity to study the effects of a small, 1-h shift in local clock time in a naturalistic setting. Previous research suggests a delay in adjusting activity patterns by at least one week, possibly more. This study was designed to investigate the importance of prior sleep habits in predicting adaptation to the new schedule. One hundred and twenty participants completed the Pittsburgh Sleep Diary for six days before and six days after the October, UK transition (2010). A subsample (n = 35) also wore activity watches throughout the study. Adherence to the external clock by adjusting bed times and rise times was apparent on the first night, but difficulties in following this timing on subsequent nights, along with deterioration in sleep latency and efficiency, point to a delay in full adjustment, particularly for those who habitually sleep for less than 7.5 h/night.
AUTHENTICATION WITH ENHANCED PRIVACY USING FILE STREAM SEQUENCING TECHNIQUE

: Authentication is the most common and essential Service of Information Security. User authentication protocol plays a vital role in payment schemes. The main concept of oPass is to free users from having to remember or type any passwords into conventional computers for authentication. Unlike generic user authentication, oPass involves a component which is used to generate one-time passwords and a communication channel, mail ids which is used to transmit authentication messages. In our opinion, it is difficult to thwart password reuse attacks from any scheme where the users have to remember something. We also state that the main cause of stealing password attacks is when users type passwords to untrusted public computers. In order to make the communication more secure, the user logins by providing the graphical password. This graphical password is stored in the database when the user registers his/her account. This graphical password is limited to a certain pixels. When the user logins, he/she has to provide the graphical password of the given pixels. If the graphical password provided matches with the password stored in database, then the user is recognized as an authorized user. Else the user’s access will be denied since he is an authorized person. We propose RACE, a Report-based payment scheme for MWNs. The nodes submit lightweight payment reports (instead of receipts) to the AC to update their credit accounts, and temporarily store undeniable security tokens called Evidences. The reports contain the alleged charges and rewards of different sessions without security
Prediction of Heart Disease Using Enhanced Association Rule Based Algorithm

The risk of coronary illness is increasing at a fast pace and it's been scowling at us for a considerable length of time, making us doubt each subtle element of our confounded way of life decisions, eating regimen and level of physical movement. It's been a main executioner in the West and has now forcefully advanced toward India. As indicated by government information, the pervasiveness of heart disappointment in India because of coronary illness, hypertension, corpulence, diabetes and rheumatic coronary illness ranges from anyplace between 1.3 to 4.6 million, with a yearly frequency of 491,600 to 1.8 million. But because of impreciseness of the diagnosis tools less than 68 per cent of heart diagnosis yield correct results. To make the odds go higher, this research presents a novel algorithm whose key idea is germinated from classical association rule mining.
Sugar composition of ergot honeydews

Honeydews of several Claviceps spp. , Sphacelia spp. , and Sphacelia-like fungi were analyzed for differences in their sugar composition. Sugars were separated for comparative and preparative purposes by gel filtration and paper chromatography. The structures of pure sugars were determined with the use of periodate, enzymes, deuterium labeling, nuclear magnetic resonance spectroscopy, and methylation analysis by combined gas–liquid chromatography (glc) and mass spectrometry. Three sugars, 6 kestose, O-β-D-Fruf-(2 → 1)-O-β-D-Fruf-(2 → 6)-O-α-D-Glcp-(1 → 2)-β-D-Fruf, and O-β-D-Fruf-(2 → 6)-O-β-D-Fruf-(2 → 1)-O-α-D-Glcp-(6 → 2)-β-D-Fruf, not previously detected in ergot are reported.Sugar analysis of honeydews from infected hosts indicates that synthesis of the major sugars is controlled by the parasite and that sugar composition is distinct for each fungus. There were a number of taxonomic subgroupings that could be formed on the basis of honeydew constituents.
CLD and PFT States of the Art Series Airway Function Measurements and the Long-Term Follow-Up of Survivors of Preterm Birth With and Without Chronic Lung Disease

This seventh paper in a review series on different aspects of chronic lung disease followingpretermbirth focuseson thecurrent knowledgeof respiratory symptoms, airway function, airway hyperresponsiveness, and exercise capacity from childhood to adulthood. This paper further considers the long-term implications of these studies for both future research and clinical practice. Pediatr Pulmonol. 2006; 41:497–508. 2006 Wiley-Liss, Inc.
An Efficient Approach for Classification and Identification of Plant Leaf Diseases Based on Deep Neural Networks

-Plant sicknesses are primary purpose for massive damage and economic losses in the agricultural field. Identification of ailment inside the plant might be very essential for better farming results. The principal motive is to correctly identify leaf illnesses with the usage of neural networks. The feature extraction approach plays an essential role. Feature extraction techniques such as the SURF (Speeded-UP Robust Features) feature extraction technique is used for extracting the features. The input image in RGB form is converted to HSV (Hue Saturation Value) form for the segmentation process. The leaves with ailment are then classified on the premise in their similarity with the training dataset of disease samples formerly defined by using the feature extraction techniques. The proposed model implementation is done with the use of MATLAB and the proposed version result is the name of the disease present in the plant leaf. Here Back Propagation Neural Network and neural network classifier (PatternNet) is used for achieving the result.
The features of the predictive computing modeling power system load in terms of reforming energy market

Розвиток енергосистеми країни, її реформа є одним з ключових питань не тільки для розвитку національної економіки, а й для національної безпеки. Україна не залишається осторонь від глобальних тенденцій інтеграції енергосистем, підвищення енергоефективності, збільшення частки поновлюваних джерел в структурі енергоспоживання. Тому сегмент енергетичного ринку займає особливе місце на енергетичному ринку як одна з найпотужніших експортно-орієнтованих галузей. Тому значна увага приділяється формуванню ефективної інфраструктури ринку електроенергії, оптимізації її системи розподілу і генерації, впровадження системи її раціонального використання і зниження цін на електроенергію. Саме тому завдання стратегічного та оперативного управління навантаженням на енергосистему важливі як для суб'єктів господарювання, так і для державних і регіональних органів влади. Відсутність ефективної науково обгрунтованої системи аналізу та прогнозування навантаження мережі, вироблення і споживання електроенергії є одним з факторів, які суттєво ускладнюють проведення реформ в енергетичному секторі. Саме тому створення системи підтримки прийняття рішень на основі підсистеми прогнозного моделювання є особливо своєчасним і актуальним. Пропонована система є універсальною і може використовуватися в різних країнах світу, так як вона адаптована з урахуванням національних особливостей енергетичного ринку країни, при її створенні використовувався досвід провідних наукових установ та енергогенеруючих компаній, крім цього ефективність системи апробована на численних експериментах.
EFFICIENT TRANSIENT TOPOLOGY OPTIMIZATION THROUGH DYNAMIC SUBSTRUCTURING

This paper presents an efficient topology optimization methodology for dynamic structural design problems. The key to the method is to utilize dynamic substructering, or component mode synthesis, i.e. the Craig-Bampton reduction method to speed up the transient analysis. The dynamic substructuring concept is used for both the forward (the state equation) and the backward (adjoint equation). The developed optimization scheme is demonstrated on design problems for linear elastic Mindlin plates subject to impact loads, where the goal is to minimize the deflection in a specified region.
Snow cover detection in Estonia from SAR images using machine learning methods

: Usability of optical satellite data for monitoring snow cover can be limited in regions with frequent high cloud coverage. Synthetic aperture radar (SAR) could theoretically be used to monitor snow regardless of clouds or lack of illumination. There are several factors that complicate the task in Estonia such as dense vegetation and quickly changing snow conditions. So far most studies on using SAR for snow detection have been done in mountainous regions and over short time periods. The aim of this study was to test applicability of a method that combines most common features for snow detection extracted from SAR images in a machine learning model. This method had shown good transferability in mountain regions, however the modelling results on Estonian data were unsatisfactory. Analysis of features derived from SAR images revealed poor separability of snow free and snow covered classes. This suggest the main issue is with the feature extraction methods rather than machine learning. Possibly the processing chain could be optimized for Estonia and other regions with ﬂat topography and predominantly dense vegetation. This thesis did not result in a usable model, but could serve as a basis for further studies.
Cadmium stress induces production of thiol compounds and transcripts for enzymes involved in sulfur assimilation pathways in Arabidopsis

Summary One of the defense systems against toxic heavy metals in plants is to employ cysteine-rich chelating compounds, phytochelatins, which are polymeric forms of glutathione. In the present study cadmium stressed Arabidopsis was employed in order to investigate the response of genes involved in glutathione biosynthesis via cysteine and production of thiol compounds. The results showed a 13-fold increase in transcripts for ATP sulfurylase, 6 to 10-fold for APS reductase and 2-fold for sulfite reductase, all of which are involved in cysteine synthesis. In addition, a 3-fold increase in total thiols was noted, mainly of glutathione, phytochelatins and cysteine. It is suggested that, to cope with toxic heavy metals, plants activate the sulfur assimilation pathway by increasing transcription of related genes to provide an enhanced supply of glutathione for phytochelatin biosynthesis.
Improving Agile Processes with Customized Mission-based Practices. Case Study

There is no silver bullet when it comes to software development approaches, methodologies and practices, since every IT business is unique. Selecting a suitable software development model is one of the recipes for effective IT company functioning and growth. However, quite often in order to keep up with company scaling the initially chosen model has to be revisited. In this paper, a case study about a new Agile software development framework adoption for scaling engineering at Pipedrive will be presented. The main research goal is to find out what the impact of such software development process redesign is.
Ghostbusting: mitigating spectre with intraprocess memory isolation

Spectre attacks have drawn much attention since their announcement. Speculative execution creates so-called transient instructions, those whose results are ephemeral and not committed architecturally. However, various side-channels exist to extract these transient results from the microarchitecture, e.g., caches. Spectre Variant 1, the so-called Bounds Check Bypass, was the first such attack to be demonstrated. Leveraging transient read instructions and cache-timing effects, the adversary can read secret data. In this work, we explore the ability of intraprocess memory isolation to mitigate Spectre Variant 1 attacks. We demonstrate this using Executable and Linkable Format-based access control (ELFbac) which is a technique for achieving intraprocess memory isolation at the application binary interface (ABI) level. Additionally, we consider Memory Protection Keys (MPKs), a recent extension to Intel processors, that partition virtual pages into security domains. Using the original Spectre proof-of-concept (POC) code, we show how ELFbac and MPKs can be used to thwart Spectre Variant 1 by constructing explicit policies to allow and disallow the exploit. We compare our techniques against the commonly suggested mitigation using serialized instructions, e.g., lfence. Additionally, we consider other Spectre variants based on transient execution that intraprocess memory isolation would naturally mitigate.
Covalent Coupling of Antibodies to Self-Assembled Monolayers of Carboxy-Functionalized Poly(ethylene glycol): Protein Resistance and Specific Binding of Biomolecules†

We report the synthesis, film formation, protein resistance, and specific antigen binding capability of a carboxy-functionalized poly(ethylene glycol) alkanethiol [HOOC−CH2−(OCH2−CH2)n−O−(CH2)11−SH, n = 22−45]. Despite its polymeric character, the molecule is found to form a densely packed self-assembled monolayer on polycrystalline gold, if adsorbed from dimethylformamide solution. Due to its chain length distribution, the carboxy tailgroups are expected to be partially buried within the film and, thus, do not affect the protein repulsive characteristics of the ethylene glycol moieties when exposed to fibrinogen and immunoglobulin G (IgG). However, if activated by N-hydroxysuccinimide and N-(3-dimethylaminopropyl)-N-ethylcarbodiimide hydrochloride, antibodies can be covalently coupled to the monolayer. While resistance to nonspecific fibrinogen and IgG adsorption is maintained for this biologically active layer, it exhibits high specific antigen binding capacity. The performance of this highly selective .
A DIMENSIONAL ANALYSIS OF SUPERSATURATED TOTAL DISSOLVED GAS DISSIPATION

Elevated levels of total dissolved gas (TDG) may occur downstream of dam discharges, leading to increased incidence of gas bubble disease in fish. Accelerating the dissipation of supersaturated TDG in the downstream river can mitigate this negative problem. However, developing effective mitigation techniques is hampered by limitations in present models of TDG dissipation processes. Furthermore, data useful for modelling the dissipation of supersaturated TDG through the free surface in natural rivers are limited. Past studies indicated that the TDG dissipation process is quantitatively different from the reaeration process, and TDG behavior is quantitatively different from dissolved oxygen. However, a correct parameterization of the TDG dissipation process is still missing. The paper presents a novel dimensional analysis of the dissipation of supersaturated TDG. This approach can provide a relationship between the TDG dissipation coefficient and some classical fluid mechanics index-numbers. This dimensional analysis considers some key parameters for the dissipation process both water and TDG properties as well as flow characteristics, including turbulence. These parameters are water kinematic viscosity, TDG molecular diffusivity and vertical turbulent diffusivity, and channel width. The application of dimensional analysis pointed out that the TDG dissipation coefficient is a function of the Schmidt number, the aspect ratio of the channel, and the shear Reynolds number. The dimensional analysis was then verified using both field data collected in some large natural rivers and reservoirs in Sichuan and experimental data in laboratory flume at State Key Laboratory of Hydraulics and Mountain River Engineering of Sichuan University. The analysis revealed the key role of turbulence in controlling the TDG dissipation while the importance of gas/water characteristics remains still unclear and needs further investigations.
RECENT ADVANCES IN CULTURING THE UNCULTURABLE BACTERIA

The diversity of micro-organisms like bacteria is poorly studied and categorized. Preserving them like plants and animals is extremely difficult as these tiny organisms cannot be observed with the naked eye. Moreover these organisms cannot be classified solely based on their phenotypic characters therefore various methods are being employed to access the diversity of these unculturables. The attempts are being made to understand the biology of the unculturable bacteria by using tools of metagenomics, creating artificial environment in the laboratory, co-culturing in combinations with microfludics devices etc. While metagenomic study provides culture independent access to whole microbial communities based on their genetic material recovered directly from an environment, it is highly difficult to get information about physiology of an organism. The present review discusses the reasons for un-cultivability of these organisms and advances in molecular ecology to culture the same.
Owen, Alison and Grogan, Sarah and Clark-Carter, David and Buckley, Emily (2019) The impact of an appearance-focussed facial-ageing intervention on adolescents’ attitudes towards sun protection and UV exposure. British Jour-

The study investigated British adolescents’ experiences of an appearance-focussed, facial-ageing, morphing intervention designed to increase sun protection intentions. The study population consisted of 237 adolescents, 60 of whom were randomly allocated to participate in the appearance-focused intervention condition and 176 to a control condition. Participants completed questionnaires at baseline and immediately post-intervention. Adolescents who had participated in the appearance-focussed intervention had significantly greater intentions to use sun protection, significantly more negative sun risk beliefs, lower sun benefit attitudes and higher perceived sun damage susceptibility after viewing the information given than participants in the control group. It is concluded that appearance-focussed interventions may be a useful tool to use when educating young people about the importance of protecting their skin from UV exposure.