Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Joint Cooperative Routing, MAC and Physical Layer with QoS-aware Traffic-based Scheduling for Wireless Sensor Networks

Compare the WSN with other conventional network system, the changing or charging the existing battery of sensor node within WSN is a tough job. Therefore, since last decade the effort has been made to design and introduce a large number of communication protocol for WSN with given concern on the performance parameter of energy efficiency and still the key requirements within WSN domain, that how to incrementally expands the energy minimization consuming techniques of sensor battery. The other parameters include latency, fairness, throughput and delivery ratio. In this work, we propose a novel joint cooperative routing, medium access control (MAC) and physical layer protocol with traffic differentiation based QoSaware for wireless sensor network (WSNs). This is referred to as a Joint Routing, MAC and Physical layer protocol (J-RMP). By blending the classical layered approach and combining routing, MAC and physical layer functions, the proposed J-RMP protocol achieves a solution for energy efficiency in WSNs. Convention ally, the problem of energy efficient protocol design is considered independently at respective layer of protocol stack. This paper has taken a holistic approach of finding solution by addressing the possible energy gain in Routing, MAC and Physical layer together. Firstly, it is seen that communication path with the large number of short hop substantially minimizes energy consumption. This phenomenon is used while instantaneous network information is collected with minimum overhead through the control packets for selecting next hop. Based on the updated network knowledge the next hop neighbor is chosen with reduced control overhead. Further, we describe how J-RMP protocol uses the approach for finding the constrained shortest path for forwarding packets [1], which results in load balancing in WSNs and provide mathematical analysis of node forwarding path determination within network. Finally, we compare our proposed method with existing protocols by ns-2 simulator and confirm that J-RMP outperform in some cases in term of sensitive traffics. The experimental results have proved that the traffic load has been balanced to a certain traffic, and energy efficiency has been achieved by properly adjusting the applied theory. The outcome supports the theory, and proves the effectiveness of our proposed scheme. This kind of protocol should have significant implications for various WSNs application where energy constraint is the main point of concerns.
Analysis of Antimicrobial Potential of Silver Nanoparticles Synthesized by Fucoidan Isolated from Turbinaria conoides

The present study investigates the antibacterial potential of fucoidan synthesized silver nanoparticles (FAgNps) against the chosen human and fish pathogens. The fucoidan used for the synthesis of FAgNps was isolated from a brown sea weed Turbinaria conoides. Minimum Inhibition Concentration (MIC) and Minimum Bactericidal Concentration (MBC) method were performed to analyze the antimicrobial efficacy. ATR-FTIR analysis revealed the presence of polar functional groups in the fucoidan molecule capable of synthesizing and stabilizing silver nanoparticles. The fucoidan synthesized nanoparticles were mostly spherical, ranging in size from 5 to 20 nm and with antibacterial activity against gram negative being more distinct and pronounced than gram positive. This study highlighted the prospect of using fucoidan for the synthesis of antimicrobial silver nanoparticles against human and fish microbial pathogens, making them relevant in various medicinal units and antimicrobial control system. Further study is required to get the better understanding of mode of action of fucoidan synthesized silver nanoparticles (FAgNps) against the pathogens
Evaluating the potential role of pleiotropy in Mendelian randomization studies

Abstract Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another (‘vertical pleiotropy’), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways (‘horizontal pleiotropy’). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.
Quantitative imaging of chromatin decompaction in living cells

Chromatin organization is highly dynamic and regulates transcription. Upon transcriptional activation, chromatin is remodeled and referred to as “open”, but quantitative and dynamic data of this decompaction process are lacking. Here, we have developed a quantitative superresolution microscopy assay in living yeast cells to visualize chromatin decompaction using the GAL7-10-1 locus as a model system. Upon transcriptional activation of these three clustered genes, we detect an increase of the mean distance across this locus by ~100 nm. This decompaction is linked to active transcription but is not sensitive to the histone deacetylase inhibitor trichostatin A or to deletion of the histone acetyl transferase Gcn5. By contrast, the deletion of SNF2 (encoding the ATPase of the SWI/SNF chromatin remodeling complex) or the deactivation of the histone chaperone complex FACT lead to a strongly reduced decompaction. Our assay quantifies and visualizes for the first time the chromatin conformation changes brought about by transcriptional activation and suggests that nucleosome remodeling and eviction activities are major contributors to chromatin reorganization. Abbreviations TSA trichostatin A ORF open reading frame qPCR quantitative PCR SE standard error SPB spindle pole body GST Glutathione S transferase smFISH single molecule fluorescence in situ hybridization Pol II RNA polymerase II
The role of aviation networks for urban development

City officials are continuously working to attract airlines willing to fly to new destinations. The inherent expectation is that a more extensive aviation network stimulates economic growth. This paper investigates empirically the causal implication of this hypothesis. Using data on non-stop flights by origin and destination over the period 1984-2001, we propose a new measure for a city’s connectivity to the national aviation network. We then use this measure to investigate its contribution to local economic development, as reflected by the growth in population, in total employment, in percapita income and new firm entry. To ensure causality, we use instrumental variable methods that exploit geography and weather patterns at destination cities as a way to capture the exogenous variation in the likelihood to add new travel routes. Our results suggest that a city’s air connectivity, resulting from an extensive local aviation network, has a positive effect on population size, employment level and on the number of businesses established in that location. JEL: O18, R1, R4
Credit Prediction with Neural Network Algorithm

One of the most crucial problems which are faced by financial institution is high level of non-performing loan. Many analysts have attempted to solve this problem by various strategy and method, but this problem still persists until now on. With regard to the spreading of credit cooperation, now it is time for the credit cooperation manager to minimize the level of non-performing loan to under 5 % as the Indonesia bank stated. On the other hand numerous credit cooperations still cannot hire an analyst because it is expensive, while the decision to approve or not a credit is a key point which determines the probability of non-performing loan. Some requirements which relevant to the credit approval are: amount of loan, time, earning, number of family and personal credit track record. This research uses the data from the “Artha Abadi” Jepara credit cooperation from the year of 2010 to 2014, the number of credit data as many as 2010 which consist of performing and nonperforming loan. This research use the data mining classification method with the model of neural network which is deployed to assess the data processing accuracy with the rapid miner and then continued with the measurement use the confusion matrix, ROC curve. The result of the neural network algorithm after the test of confusion matrix, ROC curve and T-test show a very high accuracies value, and the dominant value of AUC and algorithm. The value of accuracy is 96.95 % with the number of AUC equal to 0.981 and the result of the T test show that the neural network algorithm is the most dominant result. Finally, this research reveals that the high level accuracy of the credit analysis can be done with the relatively cheap cost of computation. Key words: Neural Network, Accuracy, Confusion Matrix, ROC Curve and T-Test
Summing Logarithmic Expansions for Singularly Perturbed Eigenvalue Problems

Strong localized perturbations of linear and nonlinear eigenvalue problems in a bounded two-dimensional domain D are considered. The effects on an eigenvalue $\lambda _0 $ of the Lapla-cian, and on the fold point $\lambda _{c0} $ of a nonlinear eigenvalue problem, of removing a small subdomain $D_\epsilon $, of “radius” $\epsilon $, from D and imposing a condition on the boundary of the resulting hole, are determined. Using the method of matched asymptotic expansions, it is shown that the expansions of the eigenvalues and fold points for these perturbed problems start with infinite series in powers of $( - 1/\log [ \epsilon d( \kappa ) ] )$. Here $d( \kappa )$ is a constant that depends on the shape of $D_\epsilon $ and on the precise form of the boundary condition on the hole. In each case, it is shown that the entire infinite series is contained in the solution of a single related problem that does not involve the size or shape of the hole. This related problem is not stiff and can be solved numerically.
Unusual Stability of Polycyclic Aromatic Hydrocarbon Radical Cations in Amorphous Water Ices up to 120 K: Astronomical Implications

The PAH radical cation quaterrylene+ (QTR+, C40H20) is found to be stable in amorphous water ice up to 120 K. A careful, 30 day, slow warm-up of water ice containing QTR+from 20 to 190 K revealed that QTR+ does not show any sign of reactivity up to 120 K, the temperature at which the phase transition of amorphous to crystalline water ice begins. At higher temperatures the absorption due to the QTR radical cation diminishes rapidly but persists until the water ice itself sublimes around 170 K. From the absence of evidence for the recovery of neutral QTR upon warm-up or reactions with other trapped reaction intermediates in the ice, we infer that QTR+ reacts with the water ice itself during warm-up above 120 K. Earlier we found that PAH ionization is quantitative in water ice and PAH ionization energy is lowered by up to 2 eV. Some implications of these unusual findings for cosmic ice chemistry and physics are briefly discussed.
Energy Management Systems for Electrified Powertrains: State-of-the-Art Review and Future Trends

Energy management systems (EMSs), implemented in the electronic control unit (ECU) of an actual vehicle with electrified powertrain, are a much simpler version of the theoretically developed EMS. Such simplification is done to accommodate the EMS within the given memory constraint and computational capacity of the ECU. The simplification should ensure reasonable performance compared to theoretical EMS under real-life driving scenarios. The process of simplification must be effective to create a versatile and utilitarian EMS. Hence, it is comprised of rigorous analysis of results obtained from theoretical EMS under various driving scenarios. This review paper broadly categorizes most of the reported utilitarian EMSs into three major categories and discusses the processes of simplification associated with each category. The utilitarian EMSs are classified based on their dependence on either online computation or offline pre-computation or even both for spewing control decisions. The paper delineates the chronological steps of a utilitarian EMS development, starting from theoretical background, process of simplification, validation through model-in-the-loop, software-in-the-loop, hardware-in-the-loop simulation, dynamometer test, and on-road performance validation. Future trends of on-board EMSs are also discussed before the conclusion.
Source Memory in Korsakoff Syndrome: Disentangling the Mechanisms of Temporal Confusion

BACKGROUND Korsakoff syndrome (KS), most frequently resulting from alcohol dependence (ALC), is characterized by severe anterograde amnesia. It has been suggested that these deficits may extend to other memory components, and notably source memory deficits involved in the disorientation and temporal confusion frequently observed in KS. However, the extent of this source memory impairment in KS and its usefulness for the differential diagnosis between ALC and KS remain unexplored. METHODS Nineteen patients with KS were compared with 19 alcohol-dependent individuals and 19 controls in a source memory test exploring temporal context confusions ("continuous recognition task"). Episodic memory and psychopathological comorbidities were controlled for. RESULTS While no source memory deficit was observed in ALC, KS was associated with a significant presence of temporal context confusion, even when the influence of comorbidities was taken into account. This source memory impairment did not appear to be related to performances on episodic memory or executive functions. CONCLUSIONS Patients with KS displayed source memory deficits, as indexed by temporal context confusions. The absence of a relationship with episodic memory performances seems to indicate that source memory impairment is not a mere by-product of amnesia. As ALC was associated with preserved source memory, the presence of temporal context confusion may serve as a complementary tool for the differential diagnosis between ALC and KS.
Cartels, Competition and Public Procurement

Stefan Weishaar explores the ways in which economic theory can be used to mitigate the adverse effects of bid rigging cartels. The study sheds light on one of the vital issues for achieving cost-effective public procurement – which is itself a critical question in the context of the global financial crisis. The book comprehensively examines whether different laws deal effectively with bid rigging and the ways in which economic theory can be used to mitigate the adverse effects of such cartels. The employed industrial economics and auction theory highlights shortcomings of the law in all three jurisdictions – the European Union, China and Japan – and seeks to raise the awareness of policymakers as to when extra precautionary measures against bid rigging conspiracies should be taken.
Educating Citizens as well as Professionals: Using Service-Learning to Enhance the Civic Element of Social Work Education

Schools of social work have put considerable energy into civic engagement and community partnership. Despite the attention paid to the civic mission of the university and/or of the profession, however, very little attention has been paid to the civic education of social work students. It will be argued here that social work education must include discussions about citizenship and democracy, about participating in our communities apart from our work. Service-learning, with its emphasis on civic learning and a complementary focus on social justice, provides both a lens and a pedagogy for accomplishing this.
AN EMPIRICAL-INVESTIGATION OF THE MARKET FOR AUDIT SERVICES IN THE PUBLIC-SECTOR

This study investigates the structure of audit fees paid by the 100 North Carolina county governments during the period 1980-84. The data analysis is designed to document the characteristics of this specific audit environment and to facilitate two kinds of comparisons with prior results for other markets for audit services. First, we relate differences in audit fees to differences in the financial and political characteristics of the county governments. Second, we investigate the structure of audit fees in the years surrounding a change in auditor. The relations between fees and the financial and political factors are addressed by a cross-sectional analysis of the counties' mean audit fees. The results indicate statistically significant associations between audit fees and political factors, consistent with results reported elsewhere for state governments (Baber [1983]), and between audit fees and outstanding debt, consistent with results reported for municipal governments (Rubin [1987]). A cross-sectional, time-series analysis of audit fee changes indicates that audit fees for initial engagements that follow an auditor switch are significantly less than fees for engagements that do not follow an auditor switch. These results contradict prior results for
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Purpose Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction. Methods We collected 12 datasets (3496 patients) from prior studies on post‐(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non‐)small‐cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built‐in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open‐source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100‐repeated nested fivefold cross‐validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre‐selection based on other datasets) or estimating the best classifier for a dataset (set‐specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection). Results Random forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set‐specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively. Conclusion Random forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection.
The maize lilliputian1 (lil1) gene, encoding a brassinosteroid cytochrome P450 C-6 oxidase, is involved in plant growth and drought response

Background and Aims Brassinosteroids (BRs) are plant hormones involved in many developmental processes as well as in plant-environment interactions. Their role was investigated in this study through the analysis of lilliputian1-1 (lil1-1), a dwarf mutant impaired in BR biosynthesis in maize (Zea mays). Methods We isolated lil1-1 through transposon tagging in maize. The action of lil1 was investigated through morphological and genetic analysis. Moreover, by comparing lil1-1 mutant and wild-type individuals grown under drought stress, the effect of BR reduction on the response to drought stress was examined. Key Results lil1-1 is a novel allele of the brassinosteroid-deficient dwarf1 (brd1) gene, encoding a brassinosteroid C-6 oxidase. We show in this study that lil1 is epistatic to nana plant1 (na1), a BR gene involved in earlier steps of the pathway. The lill-1 mutation causes alteration in the root gravitropic response, leaf epidermal cell density, epicuticular wax deposition and seedling adaptation to water scarcity conditions. Conclusions Lack of active BR molecules in maize causes a pleiotropic effect on plant development and improves seedling tolerance of drought. BR-deficient maize mutants can thus be instrumental in unravelling novel mechanisms on which plant adaptations to abiotic stress are based.
Faculty Perceptions of Organizational Leadership at Christian Colleges and Universities with Missions of Servant Leadership.

This study examined differences in faculty perceptions of organizational leadership at Christian institutions with servant leadership missions. The study evaluated faculty members‘ perceptions based on the independent variables of employment status (fullor part-time/adjunct), number of years employed at the institution, and attendance at an orientation about mission. A sample of 860 faculty members from 11 institutions participated in the online Organizational Leadership Assessment (OLA) measuring the servant leadership dimensions of Values People, Develops People, Builds Community, Displays Authenticity, Provides Leadership, Shares Leadership, and the Overall score. An independent samples t test indicated that part-time faculty participants hold significantly higher perceptions of organizational leadership‘s alignment with the six dimensions and overall. A one-way analysis of variance measured differences in three ranges of years employed at the institution: 0 to 5 years; 6 to 15 years; and more than 15 years. The ANOVA indicated that perceptions for faculty with the fewest years of experience were significantly higher than those with the greatest number of years of experience for all six dimensions and overall. For some dimensions, faculty perceptions in the range of 0 to 5 years were significantly higher than those in the 6 to 15 years range. For some dimensions, perceptions in the 6 to 15 years range were significantly higher than those in the range of more than 15 years. An independent samples t test indicated that attendance at an orientation about mission made no significant difference in faculty perceptions for any of the dimensions of servant leadership and overall.