nils-herrmann/clean_scirepeval huggingface dataset:
Mediterranean Wild Edible Plants: Weeds or "New Functional Crops"
Venue: Molecules
Year: 2018

The Mediterranean basin is a biodiversity hotspot of wild edible species, and their therapeutic and culinary uses have long been documented. Owing to the growing demand for wild edible species, there are increasing concerns about the safety, standardization, quality, and availability of products derived from these species collected in the wild. An efficient cultivation method for the species having promising nutraceutical values is highly desirable. In this backdrop, a hydroponic system could be considered as a reproducible and efficient agronomic practice to maximize yield, and also to selectively stimulate the biosynthesis of targeted metabolites. The aim of this report is to review the phytochemical and toxic compounds of some potentially interesting Mediterranean wild edible species. Herein, after a deep analysis of the literature, information on the main bioactive compounds, and some possibly toxic molecules, from fifteen wild edible species have been compiled. The traditional recipes prepared with these species are also listed. In addition, preliminary data about the performance of some selected species are also reported. In particular, germination tests performed on six selected species revealed that there are differences among the species, but not with crop species. "Domestication" of wild species seems a promising approach for exploiting these "new functional foods"
My partner's stories: relationships between personal and vicarious life stories within romantic couples
Venue: Memory
Year: 2018

In this paper, we examined relationships and differences between personal and vicarious life stories, i.e. the life stories one knows of others. Personal and vicarious life stories of both members of 51 young couples (102 participants) based on McAdams' Life Story Interview (2008) were collected. We found significant positive relationships between participants' personal and vicarious life stories on agency and communion themes and redemption sequences. We also found significant positive relationships between participants' vicarious life stories about their partners and those partners' personal life stories on agency and communion, but not redemption. Furthermore, these relationships were not explained by similarity between couples' two personal life stories, as no associations were found between couples' personal stories on agency, communion and redemption. These results suggest that the way we construct the vicarious life stories of close others may reflect how we construct our personal life stories.
From fundamentals to applications: a toolbox for robust and multifunctional MOF materials.
Venue: Chemical Society reviews
Year: 2018

In recent years, metal organic frameworks (MOFs) have been regarded as one of the most important classes of materials. The combination of various metal clusters and ligands, arranged in a vast array of geometries has led to an ever expanding MOF family. Each year, new and novel MOF structures are discovered. The structural diversity present in MOFs has significantly expanded the application of these new materials. MOFs show great potential for a variety of applications, including but not limited to: gas storage and separation, catalysis, biomedicine delivery, and chemical sensing. This review intends to offer a short summary of some of the most important topics and recent development in MOFs. The scope of this review shall cover the fundamental aspects concerning the design and synthesis of MOFs and range to the practical applications regarding their stability and derivative structures. Emerging trends of MOF development will also be discussed. These trends shall include multicomponent MOFs, defect development in MOFs, and MOF composites. The ever important structure property application relationship for MOFs will also be investigated. Overall, this review provides insight into both existing structures and emerging aspects of MOFs.
Electrophoretic Interpretation of PEGylated NP Structure with and without Peripheral Charge.
Venue: Langmuir the ACS journal of surfaces and colloids
Year: 2015

Anchoring poly(ethylene glycol) (PEG) to inorganic nanoparticles (NPs) permits control over NP properties for a variety of technological applications. However, the core shell structure tremendously complicates the interpretation of the ubiquitous z potential, as furnished by electrophoretic light scattering, capillary electrophoresis or gel electrophoresis. To advance the z potential and the more fundamental electrophoretic mobility as a quantitative diagnostic for PEGylated NPs, we synthesized and characterized Au NPs bearing terminally anchored 5 kDa PEG ligands with univalent carboxymethyl end groups. Using the electrophoretic mobilities, acquired over a wide range of ionic strengths, we developed a theoretical model for the distributions of polymer segments, charge, electrostatic potential, and osmotic pressure that envelop the core: knowledge that will help to improve the performance of soft NPs in fundamental research and technological applications.
Modification of silicalite 1 with ammonium compounds aimed at preparation of acidic catalyst for acetalization of glycerol with acetone
Venue: Applied Catalysis A: General
Year: 2019

Post synthesis modification of silicalite 1 (MFI) with solutions of ammonium compounds (NH4F, NH4Cl, NH4NO3, NH4OH) without any additional alkaline agent followed by further thermal treatment appeared a new, facile, and cheap method to generate the acidic sites in starting material. The number and strength of resulting acid sites depend on the nature of anion in the applied ammonium salt. Used procedure resulted in partial removal of external silanol groups and in the formation of acidic internal isolated as well as hydrogen bonded OH groups (e.g. silanol nests) The novel acidic sites show a high catalytic activity for acetalization of glycerol with acetone and high selectivity towards 2,2 dimethyl 1,3 dioxolane 4 methanol (solketal) The modified silicalite 1 indicates also high stability with time on stream. The proposed reaction mechanism considers a combined contribution of both Lewis and very weak Bronsted acid sites.
Semi Supervised Multimodal Relevance Vector Regression Improves Cognitive Performance Estimation from Imaging and Biological Biomarkers
Venue: Neuroinformatics
Year: 2013

Accurate estimation of cognitive scores for patients can help track the progress of neurological diseases. In this paper, we present a novel semi supervised multimodal relevance vector regression (SM RVR) method for predicting clinical scores of neurological diseases from multimodal imaging and biological biomarker, to help evaluate pathological stage and predict progression of diseases, e.g. Alzheimer's diseases (AD) Unlike most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG PET, and CSF. Considering that the clinical scores of mild cognitive impairment (MCI) subjects are often less stable compared to those of AD and normal control (NC) subjects due to the heterogeneity of MCI, we use only the multimodal data of MCI subjects, but no corresponding clinical scores, to train a semi supervised model for enhancing the estimation of clinical scores for AD and NC subjects. We also develop a new strategy for selecting the most informative MCI subjects. We evaluate the performance of our approach on 202 subjects with all three modalities of data (MRI, FDG PET and CSF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our SM RVR method achieves a root mean square error (RMSE) of 1.91 and a correlation coefficient (CORR) of 0.80 for estimating the MMSE scores, and also a RMSE of 4.45 and a CORR of 0.78 for estimating the ADAS Cog scores, demonstrating very promising performances in AD studies.
An adaptive and configurable protection framework against android privilege escalation threats
Venue: Future Gener. Comput. Syst.
Year: 2019

Android is a successful mobile platform with a thriving application ecosystem. However, despite its security precautions like permission mechanism, it is still vulnerable to privilege escalation threats and particularly confused deputy attacks that exploit the permission leak vulnerabilities of Android applications. Worse, most existing detection and protection techniques have become costly and unresponsive in current Android dynamic permission environments. In this paper, we propose a configurable Android security framework to prevent the exploitation of permission leak vulnerabilities of third party applications via confused deputy attacks. Our framework collects the runtime states of applications and enforces policy and capability based access control to restrain riskful inter application communications, so as to provide more responsive, adaptive, and flexible application protection. Besides, our framework provides users with a flexible runtime policy configuration together with a complementary security mechanism to mitigate risks induced by inappropriate policies. Additionally, we present a sophisticated access decision cache system with a proactive maintenance method that ensures the efficiency and dependability of decision services. Theoretical analysis and experimental evaluation demonstrate that our approach provides configurable and effective protections for third party applications against permission leak vulnerabilities at small performance and usability costs.
What Information Propagates among the Public when an Initial Coin Offering (ICO) is Initiated? A theory driven approach
Venue: HICSS
Year: 2019

Since the popularity of blockchain based cryptocurrency investments has increased among the public, people have directly purchased cryptocurrency through the cryptocurrency market or joined initial coin offering (ICO) projects. This research explores what informational cues are captured before, during, and after ICO projects that can be considered as signals and a fulfillment of information asymmetry. We adopted two theoretical underpinnings to achieve our research goal agency and signaling theory. Using information from Twitter, we selected the best performing ICO project based on the highest return on investment (ROI) Then, we extracted 5,085 tweets related to the selected ICO project. Tweets are categorized by pre ICO, during and post ICO, by topic, and dispersion. Analyzing the tweets, we found multiple categories of informational cues for each ICO project. Implications and limitations are discussed.
Identification and analysis of moderator variables: investigating the customer satisfaction loyalty link
Year: 2008

Research on the relationship between customer satisfaction and customer loyalty has advanced to a stage that requires a more thorough examination of moderator variables. Limited research shows how moderators influence the relationship between customer satisfaction and customer loyalty in a service context; this article aims to present empirical evidence of the conditions in which the satisfaction loyalty relationship becomes stronger or weaker.  Using a sample of more than 700 customers of DIY retailers and multi group structural equation modelling, the authors examine moderating effects of several firm related variables, variables that result from firm/employee customer interactions and individual level variables (i.e. loyalty cards, critical incidents, customer age, gender, income, expertise) Findings The empirical results suggest that not all of the moderators considered influence the satisfaction loyalty link. Specifically, critical incidents and income are important moderators of the relationship between customer satisfaction and customer loyalty.  Several of the moderator variables considered in this study are manageable variables.  This study should prove valuable to academic researchers as well as service and retailing managers. It systematically analyses the moderating effect of firm related and individual level variables on the relationship between customer satisfaction and loyalty. It shows the differential effect of different types of moderator variables on the satisfaction loyalty link.
Knowledge based model for geomaterials in the Ancient Centre of Naples (Italy) towards an integrated approach to cultural heritage
Venue: Digit. Appl. Archaeol. Cult. Heritage
Year: 2020

An integrate approach, aimed at allowing the access to a large dataset, concerning Cultural Heritage, is proposed. The research, developed within Databenc SNECS (High Tech District for Cultural Heritage Social Network of Historic Center Entities) project, aimed to represent and promote the knowledge of Campania historical centres through paradigms of technological intelligence. An innovative ICT (Information and Communication Technologies) architecture was designed to contain data concerning geomaterials (and relative decay) used in the historical site of Saints Marcellino and Festo (Naples, Italy) along with other ones from selected monuments of the Ancient Centre of Naples. Data, collected into a no relational database, were processed to realize a 3D model whose prototype will be useful for preserving Cultural Heritage. A further interface, a geo touristic application for mobile devices, NAppSTONES (Naples Application STONES) was developed. This latter, promoting the cultural heritage of the Ancient Centre of Naples, highlighted the close relationship between its geological and urban setting.
Spatial Variability Mapping of Soil Nutrient through Geo informatics Technology
Year: 2020

Spatial distribution patterns of soil properties, techniques such as conventional statistics and geo statistics were widely applied (Saldana et al. 1998, McGrath and Zhang, 2003, Sepaskhah et al. 2005, Liu et al. 2006) and based on the theory of a regionalized variable (Matheron1963) geo statistics provides advanced tools to quantify the spatial features of soil parameters and allows for spatial interpolation to be conducted. The research International Journal of Current Microbiology and Applied  (2020)  http:/www.ijcmas.com
In depth comparison of deep artificial neural network architectures on seismic events classification
Year: 2020

One of the most challenging tasks in volcanic data analysis is the classification of seismic events. By knowing them, it is possible to take decisions in advance, providing benefits for the neighboring societies as, for instance, how such events may impact users' life and cropland areas. Although there are several approaches to perform such task, Deep Neural Networks (DNN) have been barely considered to deal with seismic signals, as discussed in our related work. In this sense, we started our research with a wide set of experiments to analyze the DNN performance while discriminating seismic activities through two common network architectures: a 2D Convolutional Neural Networks (CNN) and the Long Short Term Memory (LSTM) network. In attempt to draw a parallel of DNN and classical supervised learning strategies, we extended our study by comparing those two architectures against the Multilayer Perceptron (MLP) which would be the simplest and most common baseline to take into account. Our MLP network was implemented after combining a basic architecture with elements from DNN models as dropout, flatten, and batch normalization layers. From this first analysis, we confirmed the need of additional neural network layers to obtain good classification results for seismic events, given the MLP required more DNN based pipeline operations to improve its overall performance, making it comparable to the previous results obtained by our research group. As a natural extension, we designed a new DNN architecture by assessing additional network layers specifically devoted to extract features from seismic signals, thus improving the overall classification of Volcano activities involving the following events: Volcano Tectonic (VT) Long Period (LP) Tremor (TR) and Tectonic (TC) Such design motivated the study of current DNN architectures tackling similar problems to classify raw signals from which SoundNet was taken as the most prominent candidate, thus leading to a new CNN architecture here proposed and referred to as SeismicNet. As our final contribution, SeismicNet provided classification results among the best in the literature without demanding explicit signal pre processing steps though.
Progress and Strategies for Testing of Materials for Solar Panels
Year: 2017

Accelerated testing is key to confident launch of a new product. However, for new products like solar panels, the best approach is not always clear. The challenge for materials manufacturers is that test times can be long. Also, small coupon testing may not predict the behavior in the full size module, but testing of the full size module is too expensive. As a result, solar panel test standards like IEC 61215 are useful, but are not sufficient. Material manufacturers have needed to define their own test protocols. This presentation will review some historical data (e.g. data show that manufacturers are making great progress toward reducing encapsulant discoloration) and describe advances in material testing (for example, new techniques are being demonstrated on how to more quantitatively assess adhesion, detect tendency for delamination, and understand how encapsulant properties affect other properties like cracking of cells) The International PV Quality Assurance Task Force has been researching climate specific weathering tests toward the goal of defining international standards that would simplify qualification and quality assurance testing for materials. The status of these tests and the strategies for how to organize these standards to best meet the needs of the industry will be discussed.
Insights from the study of complex systems for the ecology and evolution of animal populations
Venue: Current zoology
Year: 2020

The interactions among individuals can often create population dynamics that are fundamentally deterministic yet display unpredictable dynamics. Animal populations can, therefore, be thought of as complex systems. Complex systems display properties such as nonlinearity and uncertainty and show emergent properties that cannot be explained by a simple sum of the interacting components. Any system where entities compete, cooperate, or interfere with one another may possess such qualities, making animal populations similar on many levels to complex systems. Some fields are already embracing elements of complexity to help understand the dynamics of animal populations, but a wider application of complexity science in ecology and evolution has not occurred. We review here how approaches from complexity science could be applied to the study of the interactions and behavior of individuals within animal populations and highlight how this way of thinking can enhance our understanding of population dynamics in animals. We focus on 8 key characteristics of complex systems: hierarchy, heterogeneity, self organization, openness, adaptation, memory, nonlinearity, and uncertainty. For each topic we discuss how concepts from complexity theory are applicable in animal populations and emphasize the unique insights they provide. We finish by outlining outstanding questions or predictions to be evaluated using behavioral and ecological data. Our goal throughout this article is to familiarize animal ecologists with the basics of each of these concepts and highlight the new perspectives that they could bring to variety of subfields.
An Efficient Semiautomatic Active Contour Model of Liver Tumor Segmentation from CT Images
Venue: Asset Analytics
Year: 2019

Computed tomography (CT) image is one of the most extensively used imaging modalities for revealing and analyzing tumors as it has a higher spatial resolution, faster imaging speed relatively lower cost compared to MRI. The liver tumor volumetry requires the tumor segmentation in three dimensions which is performed manually tracing the tumor regions on slices which are tiresome and prolonged also, and the volume of manual demarcation is subjective. The major challenging task in liver tumor segmentation is due to the significant variation in location, shape, intensity and texture. This makes it difficult to develop a universal computer algorithm that is applicable for all cases. In this paper, an efficient semiautomatic technique for segmentation of liver tumor from CT images is proposed. The technique is based on semiautomated segmentation approach based on active contour segmentation using the level set method. The proposed approach consists of mainly four stages. In the first stage, the region of interest (ROI) image is initialized, which contains the liver tumor region in the CT image extraction using seed points; in the second stage, resampling and segmentation of ROI is done for reducing the noise and enhancing the boundaries; in the third stage, threshold values adjustment and seed point calculations are carried out; and in the fourth stage, the post processing is done to extract and refine the liver tumor boundaries. The proposed technique is compared with the region growing algorithms that require filling holes and removing small connected components that can be achieved by using binary morphological operations: opening to remove small connected components and closing to fill holes. The liver tumors detected by the scheme were compared with those manually traced by experts, used as the ground truth results. The study was evaluated on two datasets of tumors. The proposed scheme obtained the Dice similarity coefficient 0.938 and the Jaccard similarity coefficient 0.883. The mean surface distance, the median surface distance and the maximal surface distance were 0.025, 0.712 and 2.828 mm, respectively. With the proposed scheme, the time requisite is reduced significantly and proves to be helpful to the observer in better visualizing the probable tumors inside the liver. The proposed method represents the 3D reconstruction of segmented tumors within the liver.
Energi Kinetik Alat Kebugaran Lat Pull Down untuk Lampu LED dan Pemandu
Year: 2015

This research builds a fitness equipment Lat Pull Down which is modified to produce alternative energy. This tool works by pulling a lever connected to a gearbox. Rotor and generator are connected to the gearbox and will rotate with angular velocity at about 22 rad/s, thus, can generate electromotive force of about 202 V. This AC voltage will be converted by a full wave rectifier using Schottky diodes into 19,8 VDC. An electronic switch, then, will automatically connect the DC voltage, which is still fluctuative, to either an LM2577 or LM2576 chip that will increase or decrease the voltage, respectively, into a stable voltage of 13,8 V. The energy will be stored in the 12 V accumulator and can be used as an energy source for the 2,5 W LED and microcontroller based guidance system. This system can generate energy up to 2,367 Wh each day. Penelitian ini merancang sebuah alat kebugaran jenis Lat Pull Down yang dimodifikasi untuk menghasilkan energi listrik alternatif. Alat ini bekerja dengan cara menarik tuas yang terhubung dengan gearbox. Rotor dan generator yang terhubung dengan gearbox tersebut akan berputar dengan kecepatan sudut sekitar 22 rad/s dan dapat menghasilkan gaya gerak listrik sebesar 202 V. Tegangan yang dihasilkan akan dikonversi oleh sebuah penyearah gelombang penuh yang menggunakan diode Schottky menjadi tegangan searah sebesar 19,8 V. Besarnya tegangan searah ini masih fluktuatif, sehingga harus dinaikkan/diturunkan agar menjadi tegangan stabil 13,8 V. Sebuah saklar elektronik akan memilih secara otomatis IC LM2577, untuk menaikkan tegangan, atau IC LM2576, untuk menurunkan tegangan. Kemudian energi akan disimpan di dalam akumulator 12 V dan dapat digunakan sebagai sumber energi bagi LED 2,5 W dan sistem pemandu berbasis mikrokontroler. Energi yang dihasilkan oleh sistem ini dalam setiap harinya adalah 2,367 Wh.