Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Phenolic Contents and Antioxidant and Antibacterial Activities of Extracts from Four Varieties of Iranian Date Palm (Phoenix dactylifera L.) Seeds

Background and objective: Every year, large quantities of date palm seeds are produced as byproducts in date processing and packaging industries, which is discarded or used as low-value materials for animal feeds and composts. However, these bioresources may include potentials to produce high-value added products in food industries. The major aim of the current study was to assess phenolic profiles and contents and antioxidant and antibacterial activities of four Iranian date palm seed extracts, namely Zahedi, Kabkab, Mazafati and Rabbi. Material and methods: Total phenolic contents, phenolic compounds profile and antioxidant and antibacterial activities of extracts from four Iranian date palm seeds were assessed using Folin-Ciocalteu, reversed-phase high-performance liquid chromatography, 2, 2-diphenyl-1picrylhydrazyl radical scavenging, agar disc diffusion and broth microdilution methods, respectively. Results and conclusion: Total phenolic contents varied 1480-3380 mg GAE 100 g-1 dw. cinnamic, chlorogenic, caffeic and 3, 5-dihydroxybenzoic acid included the primary phenolic compounds, respectively. Of the varieties, Kabkab and Mazafati seed extracts with IC50 values of 16.56 and 22.6 µg ml-1 demonstrated the highest and lowest radical scavenging activity, respectively. Results obtained from disc diffusion method revealed that all extracts included inhibitory effects against Staphylococcus aureus, but not against Escherichia coli. Minimum inhibitory concentration and minimum bactericidal concentration of the extracts ranged 1.563.125 and 3.125-12.5 mg ml-1 for Staphylococcus aureus, respectively. Based on the findings, Iranian date seeds are good sources of extractable phenolic compounds with notable antioxidant activities, which can be used as natural additives in formulations of various products such as functional foods and dietary supplements. Furthermore, these seeds can be converted to value added products through biotechnological processes. Conflict of interest: The authors declare no conflict of interest.
A 6800- $\mu$ m2 Resistor-Based Temperature Sensor With ±0.35 °C (3 $\sigma$ ) Inaccuracy in 180-nm CMOS

This paper describes a compact resistor-based temperature sensor that has been realized in a 180-nm CMOS process. It occupies only 6800 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}^{2}$ </tex-math></inline-formula>, thanks to the use of a highly digital voltage-controlled oscillator (VCO)-based phase-domain sigma–delta modulator, whose loop filter consists of a compact digital counter. Despite its small size, the sensor achieves ±0.35 °C (3<inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>) inaccuracy from −35 °C to 125 °C. Furthermore, it achieves 0.12 °C (1<inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula>) resolution at 2.8 kSa/s, which is mainly limited by the time-domain quantization imposed by the counter.
LATENT SEMANTIC INDEXING BASED ON VSM

The traditional vector space model of information retrieval technology,just statistical key words in the document frequency,search results do not reflect the relevance of the document.In order to solve semantic keyword search when the problem of mining potential,a vector space model based on Latent Semantic Indexing improved algorithm,comparative experiments show that the algorithm can effectively improve the retrieval precision.
Therapeutic effect of chitosan on remineralisation of enamel carious lesions by bioglass-based biomaterials

1 Figure Legends . 7 Table Legends . 18 Acknowledgements . 19 Chapter 1 Literature review . 20 1.1 Dental hard tissues . 21 1.1.1 Enamel . 21 1.1.2 Dentine . 24 1.1.3 Caries . 27 1.1.4 Dental erosion . 33 1.2 Remineralisation of enamel caries . 37 1.2. 1 Fluoride . 37 1.2. 2 Casein phosphopeptide-amorphous calcium phosphate . 39 1.2. 3 Bioactive glass . 41 1.2. 4 Chitosan . 47 1.2. 5 Amelogenin proteins . 51 1.3 Characterisation methods . 54 1.3. 1 Topography . 54 1.3. 2 Composition analysis . 63 1.3. 3 Mechanical analysis . 69 1.4 Overall aims . 75 1.5 Objectives . 75 Chapter 2 Characterisations of artificial white spot lesions formed by an acidic gel demineralisation model . 77 2.
Generative versus Sampling-Based Approaches to Variability of Class Imbalance in Visual Anomaly Detection

Data sets for visual anomaly detection are often stratified such that every stratum or batch in the data set suffers from imbalance of different magnitude. A common approach to this detection task is to use supervised inductive learning from labeled or partially labeled image data to simultaneously solve the task of segmenting the anomaly and classifying it. Many representations and algorithms for these learning tasks exhibit some preference (inductive bias) towards balanced data from each class and thus perform better with balanced data sets than imbalanced. Such representations and algorithms are sensitive to not only the aggregate degree of class imbalance but its within-stratum variation. This includes learning representations such as deep learning for intermediate visual features. Several oversampling-based techniques have been proposed to mitigate the skewness of the data. However, most of the synthetic oversampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) or Adaptive Synthetic Sampling (ADASYN) are suitable only for the low dimensional data which limits their application in visual anomaly detection. Recently, deep generative models such as Variational Autoencoders (VAE) or Generative Adversarial Networks (GAN) have been established as effective approaches to augment high-dimensional image data. However, the literature lacks a detailed study of the learning process in a data set augmented to cope with variable imbalance across strata. We carried out an experiment to analyze the training phase and the final classifier performance when the more imbalanced batch is augmented using different approaches to achieve the same data ratio as the less imbalanced batch. We identified the classification on merged batches as baseline and compared the performance of the classifier on data sets augmented by simple oversampling, an adaptation of SMOTE, and a GAN-based generative model. Our results indicate that the GAN-based augmentation is capable of avoiding overfitting and leads to better performance.
DCM: A duty cycle based multi-channel MAC protocol for wireless sensor networks

Limitations of current multi-channel MAC protocols include requiring of strict time synchronization, existing large number of multi-channel hidden terminals, and lacking single-hop broadcast support and sleep mechanism, i.e. duty cycling. This paper proposes an asynchronous duty cycle based multi channel MAC protocol, called DCM, for wireless sensor net works. Instead of requiring time synchronization among nodes, DCM takes advantage of receiver-initiated cross channel handshake mechanism to make a channel reservation for data transmission, and employs receiver adjustment mechanism to tackle the problem of the loss of broadcasting data and to improve broadcast reliability. According to theoretical analysis using Queueing theory, this paper obtains the aver age number of available channels. The simulation and testbed experimental results show that DCM reduces the number of multi-channel hidden terminals effectively, and achieves significant improvement in network throughput with increasing benefit as traffic loads increase while maintaining higher energy efficiency.
Structured Process For Writing, Revising, And Assessing Multiple Choice Quizzes

A structured process is presented for developing or revising a multiple-choice quiz. A multiplechoice checklist form was created based on the best practices found in educational measurement books. The multiple-choice checklist form serves as a guide for an instructor to revise an old quiz or develop a new quiz. The effectiveness of the multiple-choice quiz checklist form is determined based on an assessment and evaluation process. This paper considers the development a ‘new’ quiz for bending stress in a sophomore level fundamentals of mechanics course. Four instructors used the multiple-choice checklist form to develop a new quiz and five instructors developed a new multiple-choice quiz without the checklist form. Independent reviewers are used to carry out a quantitative evaluation of the new quizzes developed with and without the multiple-choice checklist form. The assessment form is based on the multiple-choice checklist form. The results of the assessment process show that the proposed multiple-choice quiz checklist form is a valuable tool for instructors to develop more effective quizzes.
An Efficient Machine Learning Technique to Classify and Recognize Handwritten and Printed Digits of Sudoku Puzzle

In this paper, we propose a convolutional neural network model to recognize and classify handwritten and printed digits present in Sudoku puzzle, which is captured using smartphone camera from various magazines, and printed papers. Sudoku puzzle grid is detected using various image processing and filtering techniques such as adaptive threshold. The system described in the paper is thoroughly tested on a set of 100 Sudoku images captured with smartphone cameras under varying conditions. The system shows promising results with 98% accuracy. Our model can handle more complex conditions often present on images that were taken with phone cameras and the complexity of mixed printed and handwritten digits.
Nitrogen nutrition and use efficiency in rice as influenced by crop establishment methods, cyanobacterial and phosphate solubilizing bacterial consortia and zinc fertilization

ABSTRACT A field experiment was conducted at the ICAR-Indian Agricultural Research Institute, New Delhi during wet season of 2013 and 2014, involving three crop establishment methods (CEMs) viz. puddled transplanted rice (PTR), system of rice intensification (SRI), and aerobic rice system (ARS). Three rates of nitrogen (N) and phosphorus (P) application (0%, 75%, and 100% of recommended dose of N and P) (RDN) (120 kg N ha–1 and 25.8 kg P ha–1) and two sources of N and P (fertilizer and cyanobacterial-bacterial inoculation) were tested with and without zinc (Zn) fertilization in all CEMs. The concentration of N in PTR and SRI was significantly higher than ARS with higher uptake of 10.3 and 11.1 kg ha–1 in PTR and SRI over ARS. Treatment with 100% RDN led to significantly higher N availability and uptake than 75% RDN and absolute control, i.e. 14.5 and 32.0 kg ha–1, respectively. Application of Anabaena-Pseudomonas biofilm formulation (MC2) in conjunction with 75% RDN increased total uptake of N by 12.7 kg ha–1 compared to 75% RDN only. A positive correlation was found between N concentration and acetylene reductase activity at 70 days after sowing (DAS) (R2 = 0.52) and 100 DAS (R2 = 0.38) based on mean of 2 years study. Zn fertilization significantly increased N concentration in rice straw and milled rice irrespective of the crop establishment methods. Our study signifies the importance of microbial inoculation, optimal N fertilization along with SRI and PTR as more effective crop establishment methods for deriving greater benefits in terms of N nutrition in rice.
Why Plan Generation and Plan Execution Are Inseparable

People usually think of the planning problem as the problem of identifying a sequence of actions that when executed will achieve a goal. However, the purpose of planning is not actually the plan, but the achievement of the goals through the use of the plan. Perhaps a more appropriate formulation of the planning problem to think of it as the problem of achieving a goal through the execution of a sequence of actions. The issue is that due to the complexities and uncertainties in the real world it is rarely the case that one can simply formulate a plan and then execute it with guaranteed success. Instead, planning may require executing actions to gain additional information (i.e., sensing) recovering from action failures due to uncertainty in the world or an incomplete world model. In previous papers I described a planner called Sage that has been applied to the problem of information gathering (Knoblock 1994; 1995; 1996). The Sage planner supports simultaneous action execution, tightly integrates planing and execution, and provides replanning, planning for asynchronous goals, and sensing in a unified framework. The execution model is based on the execution framework that was developed for IPEM (Ambros-Ingerson 1987). Sage extended this framework to support simultaneous execution and planning in parallel with the action execution and is then applied to the information gathering task. Information gathering task requires selecting, integrating, and retrieving data from distributed and heterogeneous information sources in order to satisfy a query. The relevant data must be selected from numerous, possibly overlapping or replicated sources. Integrating the information may be costly, especially when combining data from different sites. Retrieving the information may be time consuming due to the distribution of data and the contention for limited resources. Integration of plan generation and execution for this task results in a much more flexible and robust solution. The remainder of this paper first reviews the integration of planning and execution in Sage and then examines some of the issues that arise in both generating executable plans and executing those plans for the information gathering task.
Eligibility of BPMN Models for Business Process Redesign

Business process redesign (BPR) is an organizational initiative for achieving competitive multi-faceted advantages regarding business processes, in terms of cycle time, quality, cost, customer satisfaction and other critical performance metrics. In spite of the fact that BPR tools and methodologies are increasingly being adopted, process innovation efforts have proven ineffective in delivering the expected outcome. This paper investigates the eligibility of BPMN process models towards the application of redesign methods inspired by data-flow communities. In previous work, the transformation of a business process model to a directed acyclic graph (DAG) has yielded notable optimization results for determining average performance of process executions consisting of ad-hoc processes. Still, the utilization encountered drawbacks due to a lack of input specification, complexity assessment and normalization of the BPMN model and application to more generic business process cases. This paper presents an assessment mechanism that measures the eligibility of a BPMN model and its capability to be effectively transformed to a DAG and be further subjected to data-centric workflow optimization methods. The proposed mechanism evaluates the model type, complexity metrics, normalization and optimization capability of candidate process models, while at the same time allowing users to set their desired complexity thresholds. An indicative example is used to demonstrate the assessment phases and to illustrate the usability of the proposed mechanism towards the advancement and facilitation of the optimization phase. Finally, the authors review BPMN models from both an SOA-based business process design (BPD) repository and relevant literature and assess their eligibility.
Electromagnetic Wave Coverage via Ray Tracing on GPUs

Electromagnetic Wave Coverage via Ray Tracing on GPUs by Mads Buvik Sandvei With the appearance of libraries such as NVIDIA’s CUDA programming environment and and NIDIA’s OptiX ray tracing framework, we can make use of the processing power of modern GPUs to perform compute-intensive simulations. Simulation of electromagnetic waves such as radio waves based on a physically accurate model of propagation, using the method known as ray tracing, is theoretically capable of producing far more realistic coverage maps than those produced by traditional methods such as Longley-Rice which is based on statistics. However, using the ray tracing approach causes the computational cost of the simulation to become enormous, and performing a full simulation on the CPU alone could take hours, if not days. This thesis gives an overview of the technique of ray tracing and theory surrounding the propagation of electromagnetic waves. We then use this to build a radio wave simulation application which performs all simulation on the GPU. Each wave is reflected and refracted based on dielectric properties of the medium(s), although the exact values used might not precisely correspond to the precise dielectric properties of any actual terrains, and interference caused by differences in phase is accurately accounted for. Finally, upon intersection with terrain or receiver, the signal strength is stored in a buffer mapped onto the terrain which can then be visualized. In conclusion, the thesis argues that the results obtained show promise for great improvements on signal coverage calculation. No explicit comparison between Longley-Rice and the presented method is made, as no expectancy is made that the method has incorporated enough detail about radio wave propagation and enviornmental effects for the simulation to compare. The thesis instead hopes to show the potential in generating coverage maps by means of ray tracing. NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
Developing a decision-support process for landscape conservation design

Planning for sustainable landscapes is hampered by uncertainty in how species will respond to conservation actions amidst impacts from landscape and climate change. Planning decisions, including tradeoffs among competing species objectives, are complex. We developed a decision-support framework that integrates dynamic-landscape metapopulation models (DLMPs) and structured decision making (SDM) to help guide landscape conservation design. With this framework, we demonstrated that planning for viable populations across broad scales can be achieved under global change. Furthermore, the integration of DLMPs with SDM enabled decisions to be more objective and transparent, and thus, more defensible.
Increasing detection efficiency of border mental disorders based on adaptive decomposition and cepstral analysis of speech signals.

The detection accuracy of borderline mental disorders depends on correct processing of speech signals. The main reason of low accuracy and large errors in measurements is associated with the use of inefficient and non-adaptive methods for processing of non-stationary speech signals. In this paper, the authors propose a method for increasing the detection efficiency of borderline mental disorders based on adaptive decomposition technology for non-stationary signals, namely, improved complete ensemble empirical mode decomposition with adaptive noise and mel-frequency cepstral analysis. A block diagram for the method and a brief mathematical description are presented. The research results are presented, on the basis of which it was concluded that the method proposed by the authors can successfully be tested in remote monitoring systems of psychogenic disorders to accelerate the treatment process.
HiggsHunters - a citizen science project for ATLAS

Since the launch of HiggsHunters.org in November 2014, citizen science volunteers have classified more than a million points of interest in images from the ATLAS experiment at the LHC. Volunteers have been looking for displaced vertices and unusual features in images recorded during LHC Run-1. We discuss the design of the project, its impact on the public, and the results of how the human volunteers performed relative to the computer algorithms in identifying displaced secondary vertices. People were better than existing algorithms at identifying displaced vertices for some masses and lifetimes, and showed good ability to recognize unexpected new features in the data.
Data structure splicing

Data structure splicing (DSS) refers to reorganizing data structures by merging or splitting them, reordering fields, inlining pointers, etc. DSS has been used, with demonstrated benefits, to improve spatial locality. When data fields that are accessed together are also collocated in the address space, the utilization of hardware caches improves and cache misses decline. A number of approaches to DSS have been proposed, but each addressed only one or two splicing optimizations (e.g., only splitting or only field reordering) and used an underlying abstraction that could not be extended to include others. Our work proposes a single abstraction, called Data Structure Access Graph (D-SAG), that (a) covers all data-splicing optimizations proposed previously and (b) unlocks new ones. Having a common abstraction has two benefits: (1) It enables us to build a single tool that hosts all DSS optimizations under one roof, eliminating the need to adopt multiple tools. (2) It avoids conflicts: e.g., where one tool suggests to split a data structure in a way that would conflict with another tool’s suggestion to reorder fields. Based on the D-SAG abstraction, we build a toolchain that uses static and dynamic analysis to recommend DSS optimizations to developers. Using this tool, we identify ten benchmarks from the SPEC CPU2017 and PARSEC suites that are amenable to DSS, as well as a workload on RocksDB that stresses its memory table. Restructuring data structures following the tool’s suggestion improves performance by an average of 11% (geomean) and reduces cache misses by an average of 28% (geomean) for seven of these workloads.
Low Latency Neural Networks using Heterogenous Resources on FPGA for the Belle II Trigger

One of the major components of the Belle II trigger system is the neural network trigger. Its task is to estimate the z-Vertex particle tracks observed in the experiments drift chamber. The trigger is implemented on FPGAs to ensure exibility during operation and leverage their IO capabilities. Meanwhile the implementation has to estimate the vertex in a few hundred nanoseconds to fulfil the requirements of the experiment. A first version of that trigger was operational during the first collisions. While it was able to estimate the vertex, it had some drawbacks regarding the possible throughput and timing closure. These are the focus of this work, which modifies the original design to allow two networks running in parallel and less routing congestion. We conducted a rescheduling of multiply and accumulate which are the basic operations in such networks. While the original design tried to parallelize as much as possible, the rescheduling tries to reduce the number of parallel data transmission by reusing processing modules. This way resource consumption was reduced by 40% for DSPs. To further increase the throughput by operating an additional network in parallel, we investigated the balanced use of SRAM-LUTs and DSPs for multiply and accumulate operations. With the found balancing ratio the trigger is able to operate two neural networks in parallel on the targeted FPGA within the required latency.