Miracle-dz/UniFAR-FTUs huggingface dataset:
Book Review: III. Ministry Studies: Ethical Dilemmas in Church Leadership: Case Studies in Biblical Decision Making

Written by Michael R. Milco, Pastor of Families and Small Groups at Moody Memorial Church in Chicago, this volume is intended t~ be helpful to evangelical ministe~s ill local church settings facing a vanety moral issues related to their roles as church leaders. The author begins by suggesting that moral dilemmas be analyzed in terms of a four level "DecisionMaking Tower" which calls for accurately describing the dilemma and then identifying the relevant personal values, theological principles and primary loyalties. Aft~r dismissing experientialism, legalism, antinomianism, and Pharisaism as misinterpretations of the [udeoChristian love ethic, he briefly discusses Kant's categorical imperative, Mill's utilitarianism, cultural perspectives, and the influence of personality. The chapters which follow present case studies dealing with sexual abuse, AIDS, infidelity, deception, confidentiality, financial mismanagement, crisis pregnancies and homosexuality. Two cases are presented under each heading, and each is followed by observations and advice about how ministers may encounter such cases. Two or more biblical passages which seen relevant to ~e case, but often suggesting alternative and sometimes contradictory actions, are then cited and discussed. Those alternatives are treated briefly and
Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing.
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models

Bayesian nonparametric methods based on the Dirichlet process (DP), gamma process and beta process, have proven effective in capturing aspects of various datasets arising in machine learning. However, it is now recognized that such processes have their limitations in terms of the ability to capture power law behavior. As such there is now considerable interest in models based on the Stable Processs (SP), Generalized Gamma process (GGP) and Stable-beta process (SBP). These models present new challenges in terms of practical statistical implementation. In analogy to tractable processes such as the finite-dimensional Dirichlet process, we describe a class of random processes, we call iid finite-dimensional BFRY processes, that enables one to begin to develop efficient posterior inference algorithms such as variational Bayes that readily scale to massive datasets. For illustrative purposes, we describe a simple variational Bayes algorithm for normalized SP mixture models, and demonstrate its usefulness with experiments on synthetic and real-world datasets.
The requirement of Ras and Rap1 for the activation of ERKs by cAMP, PACAP, and KCl in cerebellar granule cells

In cerebellar granule cells, the mitogen‐activated protein kinase (MAPK) or extracellular signal‐regulated kinase (ERK) cascade mediates multiple functions, including proliferation, differentiation, and survival. In these cells, ERKs are activated by diverse stimuli, including cyclic adenosine monophosphate (cAMP), pituitary adenylate cyclase activating protein (PACAP), depolarization induced by elevated extracellular potassium (KCl), and the neurotrophin brain‐derived neurotrophic factor. Extensive studies in neuronal cell lines have implicated the small G proteins Ras and Rap1 in the activation of ERKs by cAMP, PACAP, and KCl. However, the requirement of Ras and Rap1 in these pathways in cerebellar granule cells has not been addressed. In this study, we utilize multiple biochemical assays to determine the mechanisms of action and requirement of Ras and Rap1 in cultured cerebellar granule cells. We show that both Ras and Rap1 can be activated by cAMP or PACAP via protein kinase (PKA)‐dependent mechanisms. KCl activation of Ras also required PKA. Using both adenoviral and transgenic approaches, we show that Ras plays a major role in ERK activation by cAMP, PACAP, and KCl, while Rap1 also mediates activation of a selective membrane‐associated pool of ERKs. Furthermore, Rap1, but not Ras, activation by PKA appears to require the action of Src family kinases.
Utilizing dedicated short range communications (DSRC) in high power grid connected electric drive vehicle (GCEDV) wireless charging applications

A method of locating a charging target device (vehicle) in a parking lot scenario by the evaluation of Received Signal Strength Indication (RSSI) of the Dedicated Short Range Communications (DSRC) signal and Global Positioning System (GPS) data is proposed in this paper. A metric call Location Image (LI) is defined based on the RSSI received from each charger and the physical location of the parking associated to that chager. The central parking lot processor logs the GPS coordinates and LI received from the vehicle. Each pairing attempt by a vehicle loads a new LI into the central processors database. Utilizing the LI and the proposed methods the vehicle will achieve expedited charger to system pairing while in the company of multiple chargers.
Multiple Binary Codes for Fast Approximate Similarity Search

One of the fast approximate similarity search techniques is a binary hashing method that transforms a real-valued vector into a binary code. The similarity between two binary codes is measured by their Hamming distance. In this method, a hash table is often used when undertaking a constant-time similarity search. The number of accesses to the hash table, however, increases when the number of bits lengthens. In this paper, we consider a method that does not access data with a long Hamming radius by using multiple binary codes. Further, we attempt to integrate the proposed approach and the existing multi-index hashing (MIH) method to accelerate the performance of the similarity search in the Hamming space. Then, we propose a learning method of the binary hash functions for multiple binary codes. We conduct an experiment on similarity search utilizing a dataset of up to 50 million items and show that our proposed method achieves a faster similarity search than that possible with the conventional linear scan and hash table search. key words: similarity search, binary hash, approximate nearest neighbor search, machine learning, image retrieval
Low cost automatic object segmentation by detecting a signature motion within an optical flow signal

Induced motion provides a cheap and reliable way of segmenting an object from the background within an image. As such it has potential applications to a wide variety of machine vision systems. This paper presents a system that segments an object from the background by inducing a sinusoidal motion and then searching for the signal in the resulting optical flow. The effectiveness of the system is evaluated using ROC curves and shown to be reliable and robust in the presence of distractor motions.
Selective Test Response Collection for Low-Power Scan Testing with Well-Compressed Test Data

A new test application scheme is proposed for low-power scan testing, which is able to compress test data significantly. A combination of a scan architecture and an existent test compression scheme can compress test data even better. Test power can be reduced greatly based on the new test application scheme, according to which only a subset of scan flip-flops shifts a test vector or captures test responses in any clock cycle. Test response data can be another important problem. A new test response compaction scheme called selective test response collection is proposed to reduce test response data. Selective test response collection combines with a structure-based test response compactor, according to which many test response data can be dropped. Experimental results show that the proposed test application scheme can efficiently reduce test power, compress test stimulus data, and compact test response data while test application cost can be well-controlled.
Stability and Queueing Analysis of IEEE 802.11 Distributed Coordination Function

A widely adopted two-dimensional Markov chain model of the IEEE 802.11 DCF was introduced by Bianchi to characterize the backoff behavior of a single node under a saturated traffic condition. Using this approach, we propose a queuing model for the 802.11 DCF under a non-saturated traffic environment. The input buffer of each node is modeled as a Geo/G/1 queue, and the packet service time distribution is derived from Markov state space of 802.11 DCF with the underlying scheduling algorithm. The DCF defines two access mechanisms, namely the Basic access mechanism and the request-to-send/clear-to-send (RTS/CTS) access mechanism. Based on our model, performance analyses of both schemes are studied with probabilistic exponential backoff scheduling. We obtain the characteristic equation of network throughput and expressions of packet queueing delay. Specifically, we obtain the stable throughput and bounded delay regions with respect to the retransmission factor according to the basic queueing analysis. For both access schemes, the bounded delay region is a subset of the stable throughput region. Our results show that the RTS/CTS access mechanism is more stable and performs better than the Basic access mechanism. The analysis in this paper is verified by simulation results.
Functional Neuroanatomical Correlates of The Frontal Assessment Battery Performance in Alzheimer Disease

Background/Objectives: We aimed to elucidate the functional neuroanatomical correlates of Frontal Assessment Battery (FAB) performances by applying [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) to a large population of patients with Alzheimer disease (AD). Methods: The FAB was administered to 177 patients with AD, and regional cerebral glucose metabolism (rCMglc) was measured by FDG-PET scan. Correlations between FAB scores and rCMglc were explored using both region-of-interest-based (ROI-based) and voxel-based approaches. Results: The ROI-based analysis showed that FAB scores correlated with the rCMglc of the dorsolateral prefrontal cortices. Voxel-based approach revealed significant positive correlations between FAB scores and rCMglc which were in various cortical regions including the temporal and parietal cortices as well as frontal regions, independent of age, gender, and education. After controlling the effect of global disease severity with Mini-Mental State Examination score, significant positive correlation was found only in the bilateral prefrontal regions. Conclusions: Although FAB scores are influenced by temporoparietal dysfunction due to the overall progression of AD, it likely reflects prefrontal dysfunction specifically regardless of global cognitive state or disease severity in patients with AD.
Sustainable vehicles-based alternatives in last mile distribution of urban freight transport: A Systematic literature review

The advent of new technologies in last mile deliveries is about to cause a disruption in the traditional business model applied in urban cargo transportation, thus presenting innumerous research opportunities in this field of knowledge. In this context, identifying new operation models and vehicles that could be applied for last mile deliveries in urban areas becomes crucial. Therefore, this paper aims to identify, through a systematic literature review, the main types of vehicles addressed in the literature that could be used in the last mile of urban freight distribution in order to increase the sustainability of this type of operation. The results indicate a trend for the implementation of smaller and lighter vehicles for last mile deliveries in urban areas: 47% of the studies suggest, among other alternatives, the use of bicycles and tricycles; while 53% of the articles support the use of light commercial vehicles. Another trend observed in this type of distribution, indicated in 64% of the studies, is the shift from conventional (fossil fuels) to alternative sources of energy (electricity).
Multimodal stimulation for a P300-based BCI

em it f.aloise@hsantalucia. Abstract. Present-day non-invasive Brain Computer Interfaces (BCI) determine the intent of the user from a variety of different electrophysiological signals. Among others, these signals include P300 potentials recorded from the scalp. In the present study, we investigate the influence of different stimulus modalities (visual, tactile, and auditory) during sessions of P300-based BCI. In all the subjects (n=8), on-line classification displayed the highest percentage (93 %) with visual stimulus modality with respect to the audio (70%) and tactile (68%) modalities of stimulation. The elicitation of P300 was however, affected by the type of stimulus modality thus, preventing the generalization of feature classification in the same subject during P300-based BCI control performed under different modalities of stimulation.
Crystal structure of 3-amino-1-(4-hydroxyphenyl)-1H-benzo[f]chromene-2-carbonitrile

In the title compound, C20H14N2O2, the hydroxybenzene ring is almost perpendicular to the mean plane of the naphthalene ring system, making a dihedral angle of 85.56 (4)°. The 4H-pyran ring fused with the naphthalene ring system has a flattened boat conformation. In the crystal, O—H⋯N and N—H⋯O hydrogen bonds link the molecules into a supramolecular layer in the bc plane; N—H⋯π interactions also contribute to this arrangement. The layers are linked by weak by C—H⋯π and π–π [inter-centroid separation = 3.8713 (7) Å] interactions.
Approximate parameterization by planar rational curves

We describe a method for approximate parameterization of a planar algebraic curve by a rational Bézier (spline) curve. After briefly discussing exact methods for parameterization and methods for rational interpolation, we describe a new technique for rational parameterization. Our approach is based on the minimization of a suitable--nonlinear objective function, which takes both the distance from the curve and the positivity of the weight function (i.e., the numerator of the rational parametric representation) into account. The solution is computed by using an SQP-type optimization technique. In addition, we use a region--growing--type approach in order to obtain a good initial solution, which is crucial for the convergence of the nonlinear optimization procedure.
PCBA demand forecasting using an evolving Takagi-Sugeno system

This paper investigates the use of using an evolving fuzzy system for printed circuit board (PCBA) demand forecasting. The algorithm is based on the evolving Takagi-Sugeno (eTS) fuzzy system, which has the ability to incorporate new patterns by changing its internal structure in an on-line fashion. We argue that these capabilities could aid in forecasting dynamic demand patterns such as those experienced in the electronic manufacturing (EMS) industry. An eTS fuzzy system is implemented in the R statistical programming language and is tested on both synthetic and real-world data. To our knowledge, this is one of the first applications of an evolving fuzzy system to forecast product demand. The results indicate that the evolving fuzzy system outperforms competing approaches for the application considered.
Two-way traceability and conflict debugging for AspectLTL programs

Tracing program actions back to the concerns that have caused them and blaming specific code artifacts for concern interference are known challenges of AOP and related advanced modularity paradigms. In this work we address these challenges in the context of AspectLTL, a temporal-logic based language for the specification and implementation of crosscutting concerns, which has a composition and synthesis-based weaving process whose output is a correct-by-construction executable artifact. When a specification is realizable, we provide two-way traceability information that links each allowed or forbidden transition in the generated program with the aspects that have justified its presence or elimination. When a specification is unrealizable, we provide an interactive game proof that demonstrates conflicts that should be fixed. The techniques are implemented and demonstrated using running examples.
Application specific slicing for MVNO through software-defined data plane enhancing SDN

In this paper, we show our prototype implementation of our proposal in the real MVNO connecting customized smartphones so that we can identify applications from the given traffic with 100% accuracy using deeply programmable nodes developed in FLARE project. In addition, we propose a new method of identifying applications from the traffic of unmodified smartphones by machine learning using the training data collected from the customized smartphones. We show that a simple machine learning technique such as random forest achieves about 80% to 96% of accuracy in application identification.
Movement Strategies for Learning in Visual Recognition

In this paper we study the role of movement strategies during learning in object recognition models. We show that a simple model, the RBF, can outperform a more complex hierarchical model, the HMAX, when rotation and scale invariance are provided by the training phase. Moreover, we assess the exploitation of temporal information by the RBF using optic flow. The results show that the RBF model can only exploit the temporal information using optic flow when the training and testing trajectories are the same. This work exemplifies the idea that the complexity of the neural mechanisms in object recognition can be understood not only in the brain but also in the interaction between brain, body and environment.
Nanoyeast and Other Cell Envelope Compositions for Protein Studies and Biosensor Applications

Rapid progress in disease biomarker discovery has increased the need for robust detection technologies. In the past several years, the designs of many immunoaffinity reagents have focused on lowering costs and improving specificity while also promoting stability. Antibody fragments (scFvs) have long been displayed on the surface of yeast and phage libraries for selection; however, the stable production of such fragments presents challenges that hamper their widespread use in diagnostics. Membrane and cell wall proteins similarly suffer from stability problems when solubilized from their native environment. Recently, cell envelope compositions that maintain membrane proteins in native or native-like lipid environment to improve their stability have been developed. This cell envelope composition approach has now been adapted toward stabilizing antibody fragments by retaining their native cell wall environment. A new class of immunoaffinity reagents has been developed that maintains antibody fragment attachment to yeast cell wall. Herein, we review recent strategies that incorporate cell wall fragments with functional scFvs, which are designed for easy production while maintaining specificity and stability when in use with simple detection platforms. These cell wall based antibody fragments are globular in structure, and heterogeneous in size, with fragments ranging from tens to hundreds of nanometers in size. These fragments appear to retain activity once immobilized onto biosensor surfaces for the specific and sensitive detection of pathogen antigens. They can be quickly and economically generated from a yeast display library and stored lyophilized, at room temperature, for up to a year with little effect on stability. This new format of scFvs provides stability, in a simple and low-cost manner toward the use of scFvs in biosensor applications. The production and “panning” of such antibody cell wall composites are also extremely facile, enabling the rapid adoption of stable and inexpensive affinity reagents for emerging infectious threats.
Efficacy and Safety of Once- versus Twice-Daily Carbamazepine Extended-Release Capsules for the Treatment of Manic Symptoms in Patients with Bipolar I Disorder.

OBJECTIVE To compare the efficacy and safety of carbamazepine extended-release capsules (CBZ-ERC) administered twice daily (BID) versus once daily for the treatment of manic symptoms associated with bipolar I disorder in adults. DESIGN This was a Phase IIIb, randomized, double-blind, parallel-group, multicenter, 12-week study. Subjects were randomized (1:1) to CBZ-ERC once daily at bedtime (QHS) or BID. Dosing was initiated at CBZ-ERC 200mg/d and titrated to achieve an optimal dose (target dose, 800mg/d; maximum dose, 1600mg/d). The primary efficacy outcome variable was the Young Mania Rating Scale (YMRS). The Hamilton Rating Scale for Depression, 21-item version (HAM-D(21)), Montgomery-Åsberg Depression Rating Scale (MADRS), Clinical Global Impressions Scale-Bipolar Version (CGI-BP), and time to remission were secondary outcome variables. Safety measures included recording of adverse events, physical examination, vital signs (blood pressure, pulse rate, and weight), and clinical laboratory and electrocardiogram (ECG) parameters. RESULTS BID and QHS dosing were equally effective in improving symptoms of bipolar disorder, as measured with the YMRS, HAM-D(21), MADRS, and CGI-BP. Both BID and QHS dosing significantly improved total scores on the YMRS, HAM-D(21), and MADRS at all time points without statistically significant differences between groups. All three components of the CGI-BP improved during the study, and a large percentage of subjects in both groups achieved remission without significant differences between groups. Both CBZ-ERC regimens appeared to be safe and well tolerated. CONCLUSION These results suggest QHS dosing may be a safe and effective alternative to BID dosing of CBZ-ERC for treating manic episodes for many adults with bipolar I disorder, although additional studies are needed to confirm this finding.
Thrombin detection by means of an aptamer based sensitive coating fabricated onto LMR-based optical fiber refractometer

An optical fiber sensor using an aptamer receptor has been developed for the detection of thrombin. The sensitive coating consists of an aptamer-polymer structure fabricated using the layer-by-layer electrostatic self assembly technique onto novel optical fiber refractometers based on lossy mode resonances (LMRs). LMR based refractometers enable to overcome the traditional drawbacks of surface Plasmon resonance (SPR) based devices, such as the utilization of polarized light for example. The refractometer is based on an indium tin oxide thin layer deposited by DC-sputtering onto the cladding removed multimode fiber. These refractometers enable to monitor the variations of the surrounding medium refractive index (RI). Hence, RI variations of the outer film induced by thrombin can be referenced to the LMR wavelength shift. The described configuration has enabled the detection of low concentrations of thrombin (100nM) as well as obtaining a maximum wavelength shift of 50 nm at higher (1μM) concentrations.