nils-herrmann/clean_scirepeval huggingface dataset:
Bioaccumulation of nutrients and metals in sediment, water, and phoomdi from Loktak Lake (Ramsar site), northeast India: phytoremediation options and risk assessment.

In order to determine the potential of phoomdi to accumulate nutrients and metals, 11 dominant species belonging to 10 different families, sediment, and water were analyzed for a period of 2 years from the largest freshwater wetland of north-east India, Loktak (Ramsar site). Results revealed nutrient (TN and TP) and metal (Fe, Mn, Zn, and Cu) compartmentalization in the order phoomdi > sediment > water. Iron concentrations in water (0.37 ± 0.697 to 0.57 ± 1.010 mg L(-1)) and sediments (81.8 ± 0.45 to 253.1 ± 0.51 mg kg(-1)) show high metal discharge into the wetland. Metal accumulation in phoomdi ranged up to 212.3 ± 0.46-9461.4 ± 1.09 mg kg(-1) for Fe; 85.9 ± 0.31-3565.1 ± 0.87 mg kg(-1) for Mn; 9.6 ± 0.41-85.39 ± 0.58 mg kg(-1) for Zn; and 0.31 ± 0.04-9.2 ± 0.04 mg kg(-1) for Cu, respectively. High bioaccumulation factors (BAF) for metals (S. cucullata, 5.8 × 10(4) Fe, 3.9 × 10(4) Mn, and 1.7 × 10(4) Cu, and O. javanica, 4.9 × 10(3) Zn) and nutrients (S. polyrhiza, 9.7 × 10(2) TN, and Z. latifolia, 7.9 × 10(4) TP) revealed good accumulation in phoomdi compared to the wetland water column and indicate their potential to maintain a safe environment of Loktak. Further, the paper analyzed the health hazard of metals via phoomdi wild edible consumption, with the results confirming potential risk. Thus, the paper showed the need of in-depth monitoring and ample management strategies to ensure nutritional safety conditions of locals from the metals.
Avoiding honeypot detection in peer-to-peer botnets

A botnet is group of compromised computers that are controlled by a botmaster, who uses them to perform illegal activities. Centralized and P2P (Peer-to-Peer) botnets are the most commonly used botnet types. Honeypots have been used in many systems as computer defense. They are used to attract botmasters to add them in their botnets; to become spies in exposing botnet attacker behaviors. In recent research works, improved mechanisms for honeypot detection have been proposed. Such mechanisms would enable bot masters to distinguish honeypots from real bots, making it more difficult for honeypots to join botnets. This paper presents a new method that can be used by security defenders to overcome the authentication procedure used by the advanced two-stage reconnaissance worm (ATSRW). The presented method utilizes the peer list information sent by an infected host during the ATSRW authentication process and uses a combination of IP address spoofing and fake TCP three-way handshake. The paper provides an analytical study on the performance and the success probability of the presented method. We show that the presented method provide a higher chance for honeypots to join botnets despite security measures taken by botmasters.
EXPLORING THE CONSUMERS ’ ACCEPTANCE OF ELECTRONIC RETAILING USING TECHNOLOGY ACCEPTANCE MODEL

Following the approach of Pavlou (2003), we investigate key drivers for consumers̀ electronic retailing (e-tailing) acceptance that are integrated into TAM (technology acceptance model). TAM is one of the better known models for explaining the intention to use technology and it integrates two perceptions: usefulness and ease of use. Th e main purpose of this paper is to explore the relationship between perceived ease of use and perceived usefulness in accepting e-tailing among Croatian consumers. In particular, the focus of the paper is on the areas which infl uenced customer commitment and loyalty in e-tailing. Th e results of the quantitative study among Croatian consumers show that electronic retailing, e.g. purchasing via the Internet, would enhance consumers̀ eff ectiveness in getting product information and their eff ectiveness in purchasing products.
Metallurgical characterization of controlled memory wire nickel-titanium rotary instruments.

To improve the fracture resistance of nickel-titanium (NiTi) files, manufacturers have introduced new alloys and developed new manufacturing processes for the fabrication of NiTi files. This study aimed to examine the phase transformation behavior and microstructure of NiTi instruments from a novel controlled memory NiTi wire (CM wire).



Instruments of EndoSequence (ES), ProFile (PF), ProFile Vortex (Vortex), Twisted Files (TF), Typhoon (TYP), and Typhoon™ CM (TYP CM), all size 25/.04, were examined by differential scanning calorimetry (DSC) and x-ray diffraction (XRD). Microstructures of etched instruments were observed by optical microscopy and scanning electron microscopy with x-ray energy-dispersive spectrometric (EDS) analyses.



The DSC analyses showed that each segment of the TYP CM and Vortex instruments had an austenite transformation completion or austenite-finish (A(f)) temperature exceeding 37°C, whereas the NiTi instruments made from conventional superelastic NiTi wire (ES, PF, and TYP) and TF had A(f) temperatures substantially below mouth temperature. The higher A(f) temperature of TYP CM instruments was consistent with a mixture of austenite and martensite structure, which was observed at room temperature with XRD. All NiTi instruments had room temperature martensite microstructures consisting of colonies of lenticular features with substantial twinning. EDS analysis indicated that the precipitates in all NiTi instruments were titanium-rich, with an approximate composition of Ti(2)Ni.



The TYP CM and Vortex instruments with heat treatment contribute to increase austenite transformation temperature. The CM instrument has significant changes in the phase transformation behavior, compared with conventional superelastic NiTi instruments.
Field assessment of Serious Games for Entrepreneurship in Higher Education

The potential of Serious Games (SGs) in education is widely recognized, and their adoption is significant in particular in children instruction. However, the deployment rate of SGs in higher education (HE) and their proper insertion in meaningful curricula is still quite low. This paper intends to make a first step in the direction of a better characterization of the pedagogical effectiveness of SGs in HE, by providing a qualitative analysis based on our field experience using three games for entrepreneurship, that we have studied in the light of two well established pedagogical paradigms, such as the Revised Bloom’s taxonomy and the Kolb’s Learning stages. In general, we observe that SGs address several goals of the Bloom’s taxonomy, in particular at the lower levels. Moreover, the cyclical nature of the business simulations can be directly mapped to the sequential steps described by Kolb. However, our analysis also shows that SGs have still to significantly evolve in order to become an effective and efficient tool that could be successfully and reliably used in HE. In the light of our experience, we also propose a schema for a proper integration of SGs by supporting different goals in different steps of a formal education process, Our study finally suggests directions for future research in the field.
Analysis and test procedures for NOR flash memory defects

Widespread use of non-volatile memories, especially flash memories, in diverse applications such as in mobile computing and systemon-chip is becoming a common place. As a result, testing them for faults and reliability is drawing considerable interest of designers and researchers. One of the most predominant failure modes for which these memories must be tested is called disturb faults. In this paper, we first analyze different defects that are responsible for disturb faults using a 2-dimension device simulator. We determine the impact of various defects on cell performance and develop a methodology based on channel erase technique to detect these defects. Our tests are efficient and can be converted to march tests prevalently used to test memories. We also propose a very low cost design-for-testability approach that can be used to apply the test technique developed in this paper.
Impact of Ambulance Dispatch Policies on Performance of Emergency Medical Services

In ambulance location models, fleet size and ambulance location sites are two critical factors that emergency medical service (EMS) managers can control to ensure efficient delivery of the system. The ambulance relocation and dispatch policies that are studied in dynamic ambulance relocation models also significantly contribute to improving the response time of EMS. In this paper, we review dynamic ambulance relocation models from the perspective of dispatch policies. The connection between the reviewed ambulance dispatch policies and real-life policies is highlighted. Our ambulance model is based on the modified maximal covering location problem (MCLP). It is used to examine the commonly used dispatch policy and the proposed method of free-ambulance exploitation to further improve urgent call response time. Simulation results show that the proposed method can reduce the response time of urgent calls, especially during low-ambulance-supply period. We also compared the performance of EMS with and without reroute-enabled dispatch.
The role of mindfulness in a contextual cognitive-behavioral analysis of chronic pain-related suffering and disability

An increasing number of studies consider the specific processes by which distressing sensations, thoughts, and emotional experiences exert their influence on the daily functioning of those who suffer with chronic pain. Clinical methods of mindfulness and the processes that underlie them appear to have clear implications in this area, but have not been systematically investigated to this point in time. The purpose of the present study was to examine mindfulness in relation to the pain, emotional, physical, and social functioning of individuals with chronic pain. The present study included 105 consecutive patients attending a clinical assessment for treatment of chronic pain. Each completed a standardized battery of questionnaires, including a measure of mindfulness, the Mindful Attention Awareness Scale [Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. . Correlation analyses indicated that mindfulness was unrelated to age, gender, education, or chronicity of pain, but was significantly related to multiple measures of patient functioning. In multiple regression analyses, after controlling for patient background variables, pain intensity, and pain-related acceptance, mindfulness accounted for significant variance in measures of depression, pain-related anxiety; physical, psychosocial, and "other" disability. In each instance greater mindfulness was associated with better functioning. The combined increments of variance explained from acceptance of pain and mindfulness were at least moderate and, in some cases, appeared potentially meaningful. The behavioral processes of mindfulness and their accessibility to scientific study are considered.
Distributed Detection of Single-Stage Multipoint Cyber Attacks in a Water Treatment Plant

A distributed detection method is proposed to detect single stage multi-point (SSMP) attacks on a Cyber Physical System (CPS). Such attacks aim at compromising two or more sensors or actuators at any one stage of a CPS and could totally compromise a controller and prevent it from detecting the attack. However, as demonstrated in this work, using the flow properties of water from one stage to the other, a neighboring controller was found effective in detecting such attacks. The method is based on physical invariants derived for each stage of the CPS from its design. The attack detection effectiveness of the method was evaluated experimentally against an operational water treatment testbed containing 42 sensors and actuators. Results from the experiments point to high effectiveness of the method in detecting a variety of SSMP attacks but also point to its limitations. Distributing the attack detection code among various controllers adds to the scalability of the proposed method.
A 6-bit active phase shifter for Ku-band phased arrays

This paper presents a 6-bit active phase shifter in 0.13-μm SiGe BiCMOS technology for Ku-band phased arrays based on a phase interpolation technique. An input Marchand balun and a RC poly phase filter are used for high I/Q accuracy wideband quadrature generation with high linearity and an integrated current-mode DAC is used for controlling the amplitude of I/Q path signal to achieve 6-bit phase resolution. The phase shifter operates in the 12–18 GHz range. The simulation results show an rms phase error less than 2.61°. The average insertion gain ranges from −11.16 dB to −20.39 dB with an rms gain error less than 0.4 dB for 64 phase states. The phase shifter achieves an input P1dB of 9.98 dBm at 16 GHz for 0°-phase state. The core area of the phase shifter is 1.1×0.9 mm2, with a total current consumption of 13.4 mA from a 3.3 V supply voltage.
Sparse Spectrum Gaussian Process Regression

We present a new sparse Gaussian Process (GP) model for regre ssion. The key novel idea is to sparsify thespectral representation of the GP. This leads to a simple, practical algorithm for reg ession tasks. We compare the achievable trade-offs between pr edictive accuracy and computational requirements, and show that these are typically superior to existing state-of-the-art sparse approximations. We discuss both the weight space and function spac e representations, and note that the new construction implies priors over functions which are al ways stationary, and can approximate any covariance function in this class.
Diagnosing wind turbine faults using machine learning techniques applied to operational data

Unscheduled or reactive maintenance on wind turbines due to component failures incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it's needed. By continuously monitoring turbine health, it is possible to detect incipient faults and schedule maintenance as needed, negating the need for unnecessary periodic checks. To date, a strong effort has been applied to developing Condition monitoring systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine's Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. In this paper, data is obtained from the SCADA system of a turbine in the South-East of Ireland. Fault and alarm data is filtered and analysed in conjunction with the power curve to identify periods of nominal and fault operation. Classification techniques are then applied to recognise fault and fault-free operation by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show success in predicting some types of faults.
An efficient centralized scheduling algorithm in IEEE 802.15.4e TSCH networks

standard has gained a lot of attention within the Industrial Internet of Things research community due to its effectiveness in improving reliability and providing ultra-low power consumption for industrial applications, and in which its communication is orchestrated by a schedule. Despite its relevance, the standard leaves out of its scope in defining how the schedule is built, updated and maintained. This open issue is one of the trending topics in the IETF 6TiSCH WG, that still need to be addressed. This work focuses on scheduling in TSCH networks in a centralized manner where the gateway makes time and frequency slot allocation. This paper formulates the scheduling problem as a throughput maximization problem and delay minimization problem. We propose a graph theoretical approach to solve the throughput maximization problem in a centralized way. The combinatorial properties of the scheduling problem are addressed by providing an equivalent maximum weighted bipartite matching (MWBM) problem to reduce the computational complexity and also adopting the Hungarian algorithm in polynomial time. Simulation results are provided to evaluate the performance of the proposed scheme.
Distributed Time Series Analytics

In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitoring, finance, IoT applications, news feeds, social networks, data centre monitoring and so on. Availability of such large scale time series data highlights the importance of of scalable data management, efficient querying and analysis. Meanwhile, in the online setting time series carries invaluable information and knowledge about the real-time status of involved entities or monitored phenomena, which calls for online time series data mining for serving timely decision making or event detection. In addition, in many scenarios data generated from sensors (environmental, RFID, GPS, etc.) are noisy and non-stationary in nature. In this thesis we aim to address these important issues pertaining to scalable and distributed analytics techniques for massive time series data. Concretely, this thesis is centered around the following three topics: Managing and Querying Model-View Time Series. As the number of sensors that pervade our lives significantly increases (e.g., environmental sensors, mobile phone sensors, IoT applications, etc.), the efficient management of massive amount of time series from such sensors is becoming increasingly important. The infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Traditional raw sensor data management systems based on relational databases lack scalability to accommodate large scale sensor data efficiently. Thus, distributed key-value stores in the cloud are becoming a prime tool to manage sensor data. On the other hand, model-view time series management, which stores the data in the form of modeled segments, brings the additional advantages of data compression and value interpolation. However, currently there are no techniques for indexing and/or query optimization of the model-view sensor time series data in the cloud. In Chapter 2, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index consists of two interval indices on the time and sensor value dimensions respectively, each of which has an in-memory search tree and a secondary list materialized in the key-value store. We show that the proposed KVI-index enables to perform efficient query processing upon modeled segments. Mining Correlations in Streaming Time Series. The dramatic increase in the availability of data streams fuels the development of many
A 14-mW 6.25-Gb/s Transceiver in 90-nm CMOS

This paper describes a 6.25-Gb/s 14-mW transceiver in 90-nm CMOS for chip-to-chip applications. The transceiver employs a number of features for reducing power consumption, including a shared LC-PLL clock multiplier, an inductor-loaded resonant clock distribution network, a low- and programmable-swing voltage-mode transmitter, software-controlled clock and data recovery (CDR) and adaptive equalization within the receiver, and a novel PLL-based phase rotator for the CDR. The design can operate with channel attenuation of -15 dB or greater at a bit-error rate of 10-15 or less, while consuming less than 2.25 mW/Gb/s per transceiver.
Systematic Thinking Fostered by Illustrations in Scientific Text

In 2 experiments, students who lacked prior knowledge about car mechanics read a passage about vehicle braking systems that either contained labeled illustrations of the systems, illustrations without labels, labels without illustrations, or no labeled illustrations. Students who received passages that contained labeled illustrations of braking systems recalled more explanative than nonexplanative information as compared to control groups, and performed better on problem solving transfer but not on verbatim recognition as compared to control groups. Results support a model of meaningful learning in which illustrations can help readers to focus their attention on explanative information in text and to reorganize the information into useful mental models.
Performance indicators for an objective measure of public transport service quality

The measurement of transit performance represents a very useful tool for ensuring continuous increase of the quality of the delivered transit services, and for allocating resources among competing transit agencies. Transit service quality can be evaluated by subjective measures based on passengers’ perceptions, and objective measures represented by disaggregate performance measures expressed as numerical values, which must be compared with fixed standards or past performances. The proposed research work deals with service quality evaluation based on objective measures; specifically, an extensive overview and an interpretative review of the objective indicators until investigated by researchers are proposed. The final aim of the work is to give a review as comprehensive as possible of the objective indicators, and to provide some suggestions for the selection of the most appropriate indicators for evaluating a transit service aspect.
Advanced control strategies for balancing LED usage of AC LED driver

This paper presents two enhanced methods to balance LEDs energy usage in the AC LED Driver. While conventional AC LED driver has energy consumption imbalance, the proposed control methods provide equally distributed energy usage. First method is to apply the appropriate firing voltage that is the threshold voltage for equalizing energy consumption of LEDs. This method improves balancing energy consumption within a rectified line cycle. The other method is to change the order of LEDs turning on sequence by every two rectified line cycle. These two approaches are compared with mathematical analysis in terms of the energy consumption of each LED. A prototype driver with three 12 W-LED strings is built. The proposed method 1 and method 2 achieve 0.0030 J and 0.0033 J standard deviation of energy consumption, respectively, compared to the 0.0070 J deviation for the conventional approach.
Actor-Oriented Design of Scientific Workflows

Scientific workflows are becoming increasingly important as a unifying mechanism for interlinking scientific data management, analysis, simulation, and visualization tasks. Scientific workflow systems are problem-solving environments, supporting scientists in the creation and execution of scientific workflows. While current systems permit the creation of executable workflows, conceptual modeling and design of scientific workflows has largely been neglected. Unlike business workflows, scientific workflows are typically highly data-centric naturally leading to dataflow-oriented modeling approaches. We first develop a formal model for scientific workflows based on an actor-oriented modeling and design approach, originally developed for studying models of complex concurrent systems. Actor-oriented modeling separates two modeling concerns: component communication (dataflow) and overall workflow coordination (orchestration). We then extend our framework by introducing a novel hybrid type system, separating further the concerns of conventional data modeling (structural data type) and conceptual modeling (semantic type). In our approach, semantic and structural mismatches can be handled independently or simultaneously, and via different types of adapters, giving rise to new methods of scientific workflow design.