Miracle-dz/UniFAR-FTUs huggingface dataset:
Crude Ethanol Extract of Pithecellobium ellipticum as a Potential Lipid-Lowering Treatment for Hypercholesterolaemia

If left untreated, hypercholesterolaemia can lead to atherosclerosis, given time. Plants from the Fabaceae family have shown the ability to significantly suppress atherosclerosis progression. We selected four extracts from Pithecellobium ellipticum, from the Fabaceae family, to be screened in a 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA reductase) assay. The ethanol extract, at a concentration of 500 μg/mL, exhibited superior inhibition properties over the other extracts by demonstrating 80.9% inhibition, while 0.223 μg/mL of pravastatin (control) showed 78.1% inhibition towards enzymatic activity. These findings led to the fractionation of the ethanol extract using ethyl acetate : methanol (95 : 5), gradually increasing polarity and produced seven fractions (1A to 7A). Fraction 7A at 150 μg/mL emerged as being the most promising bioactive fraction with 78.7% inhibition. FRAP, beta carotene, and DPPH assays supported the findings from the ethanol extract as it exhibited good overall antioxidant activity. The antioxidant properties have been said to reduce free radicals that are able to oxidize lipoproteins which are the cause of atherosclerosis. Phytochemical screenings revealed the presence of terpenoid, steroid, flavonoid, and phenolic compounds as the responsible group of compound(s), working individually or synergistically, within the extract to prevent binding of HMG-CoA to HMG-CoA reductase.
Multi-Access RF Frequency Dissemination Based on Round-Trip Three-Wavelength Optical Compensation Technique Over Fiber-Optic Link

In this paper, we propose a tree-like topology networking scheme based on round-trip three-wavelength optical compensation technique over fiber-optic link. This scheme simplifies the system complexity of the local site, improves the scalability of the frequency dissemination network, and reduces the influence of Rayleigh backscattering on the local and remote sites. The feasibility of the scheme is verified by a set of fiber-optic frequency dissemination network, with a transmission frequency of 1 GHz. The network consists of two remote sites with distances of 20 and 55 km far from the local site, and the frequency compensation system is optimized. The experimental results show that the fractional frequency stability of the 20 and 55 km fiber links can reach 8.6 × 10−15@1s, 7.6 × 10−18@104s, and 1.3 × 10−14@1s, and 8.1 × 10−18@104s. The limitations of electrical amplifiers, WDM, and group velocity dispersion on long-term stability and network capacity are discussed. Our system demonstrates that the stable sharing of frequency signals from ultra-stable frequency sources at any site within a large metropolitan area is achievable.
Menthol Enhances the Desensitization of Human α3β4 Nicotinic Acetylcholine Receptors

The α3β4 nicotinic acetylcholine receptor (nAChR) subtype is widely expressed in the peripheral and central nervous systems, including in airway sensory nerves. The nAChR subtype transduces the irritant effects of nicotine in tobacco smoke and, in certain brain areas, may be involved in nicotine addiction and/or withdrawal. Menthol, a widely used additive in cigarettes, is a potential analgesic and/or counterirritant at sensory nerves and may also influence nicotine’s actions in the brain. We examined menthol’s effects on recombinant human α3β4 nAChRs and native nAChRs in mouse sensory neurons. Menthol markedly decreased nAChR activity as assessed by Ca2+ imaging, 86Rb+ efflux, and voltage-clamp measurements. Coapplication of menthol with acetylcholine or nicotine increased desensitization, demonstrated by an increase in the rate and magnitude of the current decay and a reduction of the current integral. These effects increased with agonist concentration. Pretreatment with menthol followed by its washout did not affect agonist-induced desensitization, suggesting that menthol must be present during the application of agonist to augment desensitization. Notably, menthol acted in a voltage-independent manner and reduced the mean open time of single channels without affecting their conductance, arguing against a simple channel-blocking effect. Further, menthol slowed or prevented the recovery of nAChRs from desensitization, indicating that it probably stabilizes a desensitized state. Moreover, menthol at concentrations up to 1 mM did not compete for the orthosteric nAChR binding site labeled by [3H]epibatidine. Taken together, these data indicate that menthol promotes desensitization of α3β4 nAChRs by an allosteric action.
“Not Only Defended But Also Applied”: The Perceived Absurdity of Bayesian Inference

The missionary zeal of many Bayesians of old has been matched, in the other direction, by an attitude among some theoreticians that Bayesian methods were absurd—not merely misguided but obviously wrong in principle. We consider several examples, beginning with Feller's classic text on probability theory and continuing with more recent cases such as the perceived Bayesian nature of the so-called doomsday argument. We analyze in this note the intellectual background behind various misconceptions about Bayesian statistics, without aiming at a complete historical coverage of the reasons for this dismissal.
Memory Hierarchy Aware I/O Scheduling under Contention for Hybrid Storage Based HPC

Scientific computing involved with handling enormous computation and processing huge volume of data poses a serious challenge to High Performance Computing (HPC). The significant progress of computational power further widens the historical gap between processing speed and storage latency. Introducing solid-state-drives (SSDs) as a burst buffer or cache to memory hierarchy in hybrid storage based HPC can effectively improve the I/O performance. Meanwhile, increasing the number of processes to get greater parallelism also can reduce the final execution time. However, these methods are unable to adequately utilize the system resources when there is competition for system resources like SSDs, I/O network. In this paper, we first analyze the effects of I/O congestion between different applications and SSDs contention between different processes. Then, a memory hierarchy aware I/O scheduling method, which is able to detect contentions and schedules I/O to relieve this situation, is proposed to improve overall I/O performance. The theoretical analysis and extensive experiments prove that our method is effective and efficient. The results from the real-world scientific simulations show this I/O scheduling technique achieves up to 20% execution performance improvement.
Analysis of Task Success Rate for Classifying 2D-Touch and 3D-Touch through Threshold

Since input interfaces using a touchscreen device appeared, some of 2D-Touch methods have been available such as Single Tap, Long Press, and Pinch Zoom, etc. Later, a technology called Force Touch or 3D-Touch was introduced that receives inputs for 2D-touch and touch force. In other words, 3D-touch is an interface that has Z coordinates as strength of touch force in addition to the X and Y coordinates on a screen used in 2D-Touch. Apple has manufactured iPhone 6S and later models which are 3D-Touch interface in use and it implemented force separation with two levels: peek and pop. The forces intensity for pick and pop interactions are divided into 200g and 400g respectively based on the maximum input of 400g that the 3D-Touch screen can measure the pressure.In order to define the range of both touch and peek in two levels provided by Apple, we first should specify an area where touch and peek areas can be distinguished. Previous researches show that the criterion for separating two areas is 200ms. In addition, when the strength is up to 66g, we may identify an action as touch and as force when the strength is more implying that 66g is a boundary for two areas. We need to perform experiments to make sure whether a task can be succeeded with the defined threshold. Then, we may be able to define new ranges for peek and touch and for peek and pop. Furthermore, we will search those ranges as large as possible and provide a basis for how to apply them.
Extended Reliability Analysis of Fault-Tolerant FPGA-based Robot Controller

The reliability of safety-critical systems is very important especially in case of electronic systems which are working in environment with increased occurrence of faults. As an example, space, aerospace or medical systems can serve. Fault tolerance is one of the techniques the goal of which is to avoid the impact of faults on such systems. Lots of fault tolerance techniques exists and new ones are under investigation. This paper is targeted mainly to Field Programmable Gate Arrays (FPGAs) which are also the target technology of many fault tolerant techniques. It is important to evaluate and test these techniques. This paper is the continuation of our previously published research results which presents experimental approach to evaluate such fault tolerance techniques by monitoring the impact of faults in the experimental electro-mechanical system utilizing robot navigation in a maze. However, in this paper, we research and compare similarities of the theoretical estimation to various methods of the SEU injection approaches. The theoretical estimation is calculated using known equations. The impact of artificially faults injected into the electronic controller, in which Triple Modular Redundancy is applied, is monitored and used for statistic reliability analysis. This approach serves as a tool for the fast reliability evaluation during the development process of fault tolerance systems.
A New X‐Ray Flare from the Galactic Nucleus Detected with XMM‐Newton

The compact radio source Sgr A*, believed to be the counterpart of the massive black hole at the Galactic nucleus, was observed to undergo rapid and intense flaring activity in X-rays with Chandra in October 2000. We report here the detection with XMM-Newton EPIC cameras of the early phase of a similar X-ray flare from this source, which occurred on 2001 September 4. The source 2–10 keV luminosity increased by a factor ≈20 to reach a level of 4 1034 erg s–1 in a time interval of about 900 s, just before the end of the observation. The data indicate that the source spectrum was hard during the flare and can be described by simple power law of slope ≈0.7. This XMM-Newton observation confirms the results obtained by Chandra, suggests that, in Sgr A* , rapid and intense X-ray flaring is not a rare event and therefore sets some constraints on the emission mechanism models proposed for this source.
Land-Grant Extension: Defining Public Good and Identifying Pitfalls in Evaluation.

Land-grant extension is an ongoing example of higher educa tion outreach and community engagement. Population, food, climate, and geographic isolation all factor into the importance of producing and facilitating agricultural knowledge. This quali tative study took place in a geographically isolated region with potential food security issues to highlight the function and role of agricultural extension as a provider of public good. A key component of the study includes the limitation of evaluation methods in documenting extension as a public good.
Acetogenic and Sulfate-Reducing Bacteria Inhabiting the Rhizoplane and Deep Cortex Cells of the Sea Grass Halodule wrightii

ABSTRACT Recent declines in sea grass distribution underscore the importance of understanding microbial community structure-function relationships in sea grass rhizospheres that might affect the viability of these plants. Phospholipid fatty acid analyses showed that sulfate-reducing bacteria and clostridia were enriched in sediments colonized by the sea grasses Halodule wrightii andThalassia testudinum compared to an adjacent unvegetated sediment. Most-probable-number analyses found that in contrast to butyrate-producing clostridia, acetogens and acetate-utilizing sulfate reducers were enriched by an order of magnitude in rhizosphere sediments. Although sea grass roots are oxygenated in the daytime, colorimetric root incubation studies demonstrated that acetogenic O-demethylation and sulfidogenic iron precipitation activities were tightly associated with washed, sediment-free H. wrightii roots. This suggests that the associated anaerobes are able to tolerate exposure to oxygen. To localize and quantify the anaerobic microbial colonization, root thin sections were hybridized with newly developed 33P-labeled probes that targeted (i) low-G+C-content gram-positive bacteria, (ii) cluster I species of clostridia, (iii) species of Acetobacterium, and (iv) species of Desulfovibrio. Microautoradiography revealed intercellular colonization of the roots by Acetobacteriumand Desulfovibrio species. Acetogenic bacteria occurred mostly in the rhizoplane and outermost cortex cell layers, and high numbers of sulfate reducers were detected on all epidermal cells and inward, colonizing some 60% of the deepest cortex cells. Approximately 30% of epidermal cells were colonized by bacteria that hybridized with an archaeal probe, strongly suggesting the presence of methanogens. Obligate anaerobes within the roots might contribute to the vitality of sea grasses and other aquatic plants and to the biogeochemistry of the surrounding sediment.
Compressibility Loss for Neural Network Weights

In this paper we apply a compressibility loss that enables learning highly compressible neural network weights. The loss was previously proposed as a measure of negated sparsity of a signal, yet in this paper we show that minimizing this loss also enforces the non-zero parts of the signal to have very low entropy, thus making the entire signal more compressible. For an optimization problem where the goal is to minimize the compressibility loss (the objective), we prove that at any critical point of the objective, the weight vector is a ternary signal and the corresponding value of the objective is the squared root of the number of non-zero elements in the signal, thus directly related to sparsity. In the experiments, we train neural networks with the compressibility loss and we show that the proposed method achieves weight sparsity and compression ratios comparable with the state-of-the-art.
Analysis of distributed energy management scheme for residential consumers using non-cooperative game

Demand response (DR) technique has resulted in fundamentally shifting the way energy demand is managed in the smart grid. Most of the previous studies focus only on single user energy management. However, it affects the system parameters adversely. Now-a-days, DR schemes focus on finding a solution that works well for individuals as well as overall system behavior. In this paper, a distributed energy management scheme is analyzed to achieve a flat load profile and minimize the energy cost of each user. A real-time price based quadratic cost function is considered to charge the customers. In simulation results, it is shown that the peak as well as the total electricity cost of the system is reduced. Individual energy cost is also minimized.
Desirability of control: psychometric properties and relationships with locus of control, personality, coping, and mental and somatic complaints in three Dutch samples

Desire for control (DC) or need for control has frequently been proposed as an important personality characteristic and as a possible crucial moderator within the fields of achievement, psychological adaptation, stress, and health. However, unlike locus of control, hardly any effort has been undertaken to assess the construct. An exception is the work of Burger and Cooper on the Desirability of Control Scale (1979). In the present study, the psychometric properties of a Dutch version of the DC scale were tested in three different samples (total N = 1044). Furthermore, relationships between DC, its subscales, and various psychological personality and outcome measures were examined. The results show that the psychometric properties of the original DC scale could be successfully transposed to the Dutch version. Furthermore, factor analyses (PCA) led to the construction of three reliable subscales: ‘control others’ (desire to be in charge of and control others), ‘control self’ (desire to control one's own life), and ‘relinquish control’ (desire to leave others in control). The pattern of relationships with convergent and outcome variables supports the interpretation of these subscales. For ‘control others’, as for the total DC scale (i.e. a total of all 20 items), the emphasis of these relationships was on dominance, active coping, and psychological adjustment. ‘Control self’ was mainly related to self‐sufficiency and independence, while ‘relinquish control’ was clearly associated with passive coping and poor psychological adjustment. In summary, the DC scale appears to be a psychometrically sound instrument to assess desire for control and its subdimensions. Copyright © 2002 John Wiley & Sons, Ltd.
A Comparative Study of Supervised and Unsupervised Classifiers Utilizing Extractive Text Summarization Techniques to Support Automated Customer Query Question-Answering

Customer service majorly involves a one-way kind of communication where the organization usually controls the point of interaction through either a call center, helpdesk email address, or even a postal address. The challenges faced by this model are 1) response time (time it takes a customer to get a response about an inquiry they have made) and 2) response rate (rate at which customer inquiries are retrieved and attended to). This paper looks at the use of machine learning algorithms and classifiers, utilizing extractive text summarization techniques for semantic and key phrase extraction of customer queries to facilitate customer response retrieval from a Frequently Asked Questions database. A comparative study of two text summarization approaches (supervised and unsupervised) is carried out by implementing a prototype of an automated agent to respond to customer queries in an electronic media domain.The study illustrates the use of machine learning; text summarization techniques to develop tools that can assist organizations manage their customer interactions effectively and implement robust, efficient, and effective electronic media enabled customer support mechanisms.
Collective Anomaly Detection Using Big Data Distributed Stream Analytics

The majority of today's data is creating in a form of streams by diverse application domains and the sizes of these data are becoming very large in order to detect unusual events or predict intelligent decision. This often poses a significant challenging task to process and discover unusual events from such dynamic behaviours. Importantly, real time detection in streaming application is a critical concern to detect anomalous event, this is due to a potential infinite size of the data and an evolving behaviour of the event stream. In this paper, we proposed a novel distributed real time Collective Anomaly detection by; a) defining event stream snapshot model, b) designing a novel distributed Collective event stream detection, c) evaluates the experimental result in terms of the accuracy of the detection rates and the detection processing runtime performance. The experimental evaluation is demonstrated that our method is capable to detect a collection of events from high volumes of unbounded streams in real-time.
Accurate Power Sharing With Harmonic Power for Islanded Multibus Microgrids

In an islanded multibus microgrid, accurate power sharing is more difficult than in a traditional single-bus microgrid due to its complex inner structure. This paper proposes an enhanced power sharing control scheme to share accurately dominant harmonic powers for the multibus microgrids with the aid of adaptive regulation of the virtual impedances at dominant frequencies. The proposed control method provides accurate harmonic power sharing even when the microgrid configuration or the load condition is changed. Moreover, a simple secondary controller has also been proposed to remove the frequency/voltage deviations without additional voltage measurement at the point of common coupling. The proposed control scheme can be implemented directly in any type of the microgrid structure (single bus or multibus) without any information about the detailed microgrid configuration, feeder impedances, and the load powers, which decreases complexity and system cost. The proposed control method is theoretically investigated, and its feasibility and effectiveness are validated via simulation and experiment.
Rejection Sensitivity and Depression: Indirect Effects Through Problem Solving

Objective: Rejection sensitivity (RS) and deficits in social problem solving are risk factors for depression. Despite their relationship to depression and the potential connection between them, no studies have examined RS and social problem solving together in the context of depression. As such, we examined RS, five facets of social problem solving, and symptoms of depression in a young adult sample. Method: A total of 180 participants completed measures of RS, social problem solving, and depressive symptoms. We used bootstrapping to examine the indirect effect of RS on depressive symptoms through problem solving. Results: RS was positively associated with depressive symptoms. A negative problem orientation, impulsive/careless style, and avoidance style of social problem solving were positively associated with depressive symptoms, and a positive problem orientation was negatively associated with depressive symptoms. RS demonstrated an indirect effect on depressive symptoms through two social problem-solving facets: the tendency to view problems as threats to one’s well-being and an avoidance problem-solving style characterized by procrastination, passivity, or overdependence on others. Conclusions: These results are consistent with prior research that found a positive association between RS and depression symptoms, but this is the first study to implicate specific problem-solving deficits in the relationship between RS and depression. Our results suggest that depressive symptoms in high RS individuals may result from viewing problems as threats and taking an avoidant, rather than proactive, approach to dealing with problems. These findings may have implications for problem-solving interventions for rejection sensitive individuals.
Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

Evolving Internet-of-things applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This letter presents a novel method based on a cascaded two-stage machine learning approach for highly accurate and robust localization in indoor environments using adaptive selection and combination of radio frequency (RF) features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on $k$-nearest neighbor machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments. Received signal strength, channel transfer function, and frequency coherence function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.