nils-herrmann/clean_scirepeval huggingface dataset:
Guidelines for Conducting Mixed methods Research: An Extension and Illustration
Venue: J. Assoc. Inf. Syst.

In this paper, we extend the guidelines of Venkatesh et al. (2013) for mixed methods research by identifying and integrating variations in mixed methods research. By considering 14 properties of mixed methods research (e.g. purposes, research questions, epistemological assumptions) our guidelines demonstrate how researchers can flexibly identify the existing variations in mixed methods research and proceed accordingly with a study design that suits their needs. To make the guidelines actionable for various situations and issues that researchers could encounter, we develop a decision tree to map the flow and relationship among the design strategies. We also illustrate one possible type of mixed methods research in information systems in depth and discuss how to develop and validate metainferences as the outcomes of such a study.
A robust fuzzy optimization model for carbon efficient closed loop supply chain network design problem: a numerical illustration in electronics industry

Environmentalism has become an important global issue in the present century. During the recent years, the closed loop green supply chain management has been increasingly a center of attention as a result of governmental laws and legislations (regarding the environmental effects of any activity) and the soaring consumers' expectations. In this study, effort has been made to investigate a facility location/allocation model for a multi product closed loop green supply chain network consisting of manufacturing/remanufacturing and collection/inspection centers as well as disposal center and markets. To design the network, we have proposed a mixed integer linear programming model capable of reducing the network total costs. The model has been so developed as to consider such environmental objectives as reducing the rate of carbon dioxide emission in the environment throughout the network in question. Moreover, the model has been developed using a robust fuzzy programming approach to investigate the effects of uncertainties of the variable costs, as well as the demand rate, on the network design. To solve the bi objective programming model, use has been made of the e constraint approach and a numerical illustration of Copiers Industry is used to show the applicability of the proposed optimization model. Results have revealed that the model is capable of controlling the network uncertainties as a result of which a robustness price will be imposed on the system.
p53 Proteoforms and Intrinsic Disorder: An Illustration of the Protein Structure Function Continuum Concept
Venue: International journal of molecular sciences

Although it is one of the most studied proteins, p53 continues to be an enigma. This protein has numerous biological functions, possesses intrinsically disordered regions crucial for its functionality, can form both homo tetramers and isoform based hetero tetramers, and is able to interact with many binding partners. It contains numerous posttranslational modifications, has several isoforms generated by alternative splicing, alternative promoter usage or alternative initiation of translation, and is commonly mutated in different cancers. Therefore, p53 serves as an important illustration of the protein structure function continuum concept, where the generation of multiple proteoforms by various mechanisms defines the ability of this protein to have a multitude of structurally and functionally different states. Considering p53 in the light of a proteoform based structure function continuum represents a non canonical and conceptually new contemplation of structure, regulation, and functionality of this important protein.
On the Sensitivity of Optimal Wave Energy Device Geometry to the Energy Maximizing Control System
Venue: IEEE Transactions on Sustainable Energy

Wave energy converters (WECs) are traditionally designed independently of control systems. This usually involves matching the device frequency response to the predominant sea spectrum of a specific site. Energy maximizing control systems are subsequently employed to improve the energy conversion of the device for sea states other than the design sea state. It is well known that some control techniques have the ability to change the characteristics of a wave energy device, allowing it to improve the power absorption for frequencies other than the device natural frequency. However, while the controllers do their best to broaden the device frequency response, they can be limited in their capability to produce an overall optimal system. A different approach is studied in this paper, where iteration is performed between geometry optimization and control design. The aim is to investigate the implications of geometry optimization in the absence of control and the ultimate effect of incorporating the control design at the geometry optimization stage. Unconstrained control strategies are considered, namely, latching, declutching, and a model predictive control like algorithm. It is shown that different optimal WEC geometries can be obtained depending on whether the device is controlled or not and the type of controller employed. The differences in overall power absorption characteristics of the optimized geometries are presented.
Somatostatin expressing neurons in cortical networks
Venue: Nature Reviews Neuroscience

Somatostatin expressing GABAergic neurons constitute a major class of inhibitory neurons in the mammalian cortex and are characterized by dense wiring into the local network and high basal firing activity that persists in the absence of synaptic input. This firing provides both GABA type A receptor (GABAAR) and GABABR mediated inhibition that operates at fast and slow timescales. The activity of somatostatin expressing neurons is regulated by brain state, during learning and in rewarded behaviour. Here, we review recent advances in our understanding of how this class of cells can control network activity, with specific reference to how this is constrained by their anatomical and electrophysiological properties.
Early Somatostatin Interneuron Connectivity Mediates the Maturation of Deep Layer Cortical Circuits
Venue: Neuron

The precise connectivity of somatostatin and parvalbumin cortical interneurons is generated during development. An understanding of how these interneuron classes incorporate into cortical circuitry is incomplete but essential to elucidate the roles they play during maturation. Here, we report that somatostatin interneurons in infragranular layers receive dense but transient innervation from thalamocortical afferents during the first postnatal week. During this period, parvalbumin interneurons and pyramidal neurons within the same layers receive weaker thalamocortical inputs, yet are strongly innervated by somatostatin interneurons. Further, upon disruption of the early (but not late) somatostatin interneuron network, the synaptic maturation of thalamocortical inputs onto parvalbumin interneurons is perturbed. These results suggest that infragranular somatostatin interneurons exhibit a transient early synaptic connectivity that is essential for the establishment of thalamic feedforward inhibition mediated by parvalbumin interneurons.
Somatostatin and Somatostatin Containing Neurons in Shaping Neuronal Activity and Plasticity
Venue: Front. Neural Circuits

Since its discovery over four decades ago, somatostatin (SOM) receives growing scientific and clinical interest. Being localized in the nervous system in a subset of interneurons somatostatin acts as a neurotransmitter or neuromodulator and its role in the fine tuning of neuronal activity and involvement in synaptic plasticity and memory formation are widely recognized in the recent literature. Combining transgenic animals with electrophysiological, anatomical and molecular methods allowed to characterize several subpopulations of somatostatin containing interneurons possessing specific anatomical and physiological features engaged in controlling the output of cortical excitatory neurons. Special characteristic and connectivity of somatostatin containing neurons set them up as significant players in shaping activity and plasticity of the nervous system. However, somatostatin is not just a marker of particular interneuronal subpopulation. Somatostatin itself acts pre and postsynaptically, modulating excitability and neuronal responses. In the present review, we combine the knowledge regarding somatostatin and somatostatin containing interneurons, trying to incorporate it into the current view concerning the role of the somatostatinergic system in cortical plasticity.
Hot Topics in Clinical Oral Implants Research: Recent Trends in Literature Coverage
Venue: Dentistry journal

This systematic review looks at thematic trends in clinical research publications on dental implants. For this purpose, MEDLINE electronic searches as well as additional hand searches of six main journals in the field were conducted. A total of 2875 clinical studies published between 2001 and 2012 met the inclusion criteria and were subjected to statistical analysis. Hot topics in dental implant literature included immediate loading (14.3% bone substitutes (11.6% cross arch full bridges (8.0% and immediate implant placement (7.5% A significant increase in scientific interest for immediate loading +6.3% p 0.001) platform switching +2.9% p 0.001) guided implant surgery +1.9% p 0.011) growth factors (p 0.014, +1.4% piezoelectric surgery +1.3% p 0.015) and restorative materials +0.7% p 0.011) was found. A declining scientific interest in onlay grafting 0.3% p 0.042) was recorded. The findings regarding current clinical oral implants research tie in with better informed consumers and increased patient demands. Our results demonstrate an increasing interest in techniques that avoid complicated procedures such as bone grafting and that reduce treatment duration.
Perspective: Web based machine learning models for real time screening of thermoelectric materials properties

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. Carrete et al. Phys. Rev. X 4, 011019 (2014) and experimental researchers generally do not directly use computation to drive their own synthesis efforts. To bridge this practical gap between experimental needs and computational tools, we report an open machine learning based recommendation engine (http:/thermoelectrics.citrination.com) for materials researchers that suggests promising new thermoelectric compositions based on pre screening about 25 000 known materials and also evaluates the feasibility of use.
Effect of Polyelectrolyte Stiffness and Solution pH on the Nanostructure of Complexes Formed by Cationic Amphiphiles and Negatively Charged Polyelectrolytes.
Venue: The journal of physical chemistry. B

The interaction between amphiphiles and polyelectrolytes has been widely investigated in recent years due to their potential application in industry and medicine, with special focus on gene therapy. The cationic lipid dioleoyl trimethylammonium propane, DOTAP, and the oppositely charged polyelectrolytes, sodium poly(acrylic acid) and sodium poly(styrenesulfonate) form multilamellar complexes in water. Because of the different molecular stiffness of the two polyelectrolytes, they form different nanostructured complexes. Also, because of the different ionization behavior of the two polyelectrolytes, pH differently affects the complexation of the polyelectrolytes with didodecyldimethylammonium bromide (DDAB) another cationic surfactant. We used cryogenic temperature transmission electron microscopy (cryo TEM) and small angle X ray scattering (SAXS) to compare the nanostructures formed. Our results show that although the basic nanostructures of the complexes are always lamellar (multilamellar or unilamellar) the morphology of the complexes is affected by the polyelectrolyte rigidity and the solution pH.
Competency Assessment for Entry level Sport Ticket Sales Professionals

With the emergence of outbound sales philosophies taking hold in sport organizations, sales managers are seeking talent to fill sales positions with candidates launching their careers in the sport industry. However, observers have noted that the infusion of sport ticket sales personnel has not been accompanied by an appropriate professional development and evaluation protocol, which has contributed to high turnover rates. As sport management education programs modernize their curriculum to meet industry needs and sales managers attempt to reduce turnover, a better understanding of the competencies required of entry level sport ticket sales professionals is required. The purpose of this research was to identify the competencies required for success in an entry level sport ticket sales position and the extent to which sales managers find it difficult to train salespeople on those competencies. Nineteen sport sales managers served as expert panelists on a study that employed the Delphi method. The result of the study is a 40 item competency model comprised of the following eight global competency areas: knowledge and skill development, relationship building, communication skills, opening the sale, consultative or adaptive sales, closing, maximizing each call, and service and education. Implications for sales managers, human resource managers, sport sales educators, and academic scholarship in sport sales are discussed.
A multi sink coordination framework for low power and lossy networks
Venue: 2016 International Conference on Industrial Informatics and Computer Systems (CIICS)

Low power and lossy networks (LLNs) consist of devices having limited battery power, less memory, reduced processing capability. RPL (Routing Protocol for low power and Lossy networks) is an approved Internet Engineering Task Force (IETF) standard routing protocol for IPv6 based LLNs. RPL uses a proactive routing approach and the sink or controller node within the network creates a Directed Acyclic Graph (DAG) of the network. In a RPL network each node has a path to the sink node and the sink has routing paths to all nodes within the network. In case of multiple sink nodes within the network, nodes attach themselves with DAG of closet sink depending on the objective function used by RPL. In a multi sink environment sink nodes can coordinate for evenly distributing the network load, increasing network life time, resource sharing and quick data collection. Currently, RPL does not provide any coordination framework that can define message exchange between different sink nodes for enhancing the network performance. In this paper, we propose a novel sink to sink coordination framework in RPL network for enhancing overall network throughput and increasing overall network lifetime. The proposed coordination framework uses the network management messages issued by RPL to achieve message exchange between multiple sink nodes. Simulation analysis has shown that the proposed coordination framework enhance network performance of RPL in a multi sink network.
Micro fibrillated cellulose reinforced eco friendly polymeric resin from non edible 'Jatropha curcas' seed waste after biodiesel production

Eco friendly polymeric resin with desirable mechanical and physical properties was developed from non edible protein extracted from 'Jatropha curcas' (Jatropha) seed cake, so far considered as an agro waste after oil extraction for bio diesel conversion. A green, facile and cost effective water based casting and evaporation method was applied to fabricate Jatropha Protein (JP) based resin sheets. High molecular weight and the presence of reactive amino acids (a high content of arginine) in JP provide the basis to form a sustainable polymeric material. Also, JP resins were found to display diverse mechanical properties ranging from brittle and rigid to ductile and soft depending on the external modifiers such as plasticizer, cross linker and reinforcing element used. Experimental studies using 10% sorbitol as a plasticizer, 10% glyoxal as a cross linker and 20% microfibrillated cellulose (MFC) as a reinforcing element rendered JP resin with promising mechanical, thermal and physico chemical properties. A favorable comparison between the modified JP and various polymers opens up possibilities for a sustainable alternative from non edible protein based agro wastes that can reduce the dependency on biobased polymers from edible sources and petroleum based non degradable polymers for applications such as fiber reinforced composites.
Effects of several temporomandibular disorders on the stress distributions of temporomandibular joint: a finite element analysis
Venue: Computer methods in biomechanics and biomedical engineering

The aim of this study was to evaluate stress distributions in the temporomandibular joints (TMJs) with temporomandibular disorders (TMDs) for comparison with healthy TMJs. A model of mandible and normal TMJs was developed according to CT images. The interfaces between the discs and the articular cartilages were treated as contact elements. Nonlinear cable elements were used to simulate disc attachments. Based on this model, seven models of various TMDs were established. The maximum stresses of the discs with anterior, posterior, medial and lateral disc displacement (ADD, PDD, MDD and LDD) were 12.09, 9.33, 10.71 and 6.07 times magnitude of the identically normal disc, respectively. The maximum stresses of the posterior articular eminences in ADD, PDD, MDD, LDD, relaxation of posterior attachments and disc perforation models were 21, 59, 46, 21, 13 and 15 times greater than the normal model, respectively. TMDs could cause increased stresses in the discs and posterior articular eminences.
Efficient Decision Making under Uncertainty for Proactive Self Adaptation
Venue: 2016 IEEE International Conference on Autonomic Computing (ICAC)

Proactive latency aware adaptation is an approach for self adaptive systems that improves over reactive adaptation by considering both the current and anticipated adaptation needs of the system, and taking into account the latency of adaptation tactics so that they can be started with the necessary lead time. Making an adaptation decision with these characteristics requires solving an optimization problem to select the adaptation path that maximizes an objective function over a finite look ahead horizon. Since this is a problem of selecting adaptation actions in the context of the probabilistic behavior of the environment, Markov decision processes (MDP) are a suitable approach. However, given all the possible interactions between the different and possibly concurrent adaptation tactics, the system, and the environment, constructing the MDP is a complex task. Probabilistic model checking can be used to deal with this problem since it takes as input a formal specification of the stochastic system, which is internally translated into an MDP, and solved. One drawback of this solution is that the MDP has to be constructed every time an adaptation decision has to be made to incorporate the latest predictions of the environment behavior. In this paper we present an approach that eliminates that run time overhead by constructing most of the MDP offline, also using formal specification. At run time, the adaptation decision is made by solving the MDP through stochastic dynamic programming, weaving in the stochastic environment model as the solution is computed. Our experimental results show that this approach reduces the adaptation decision time by an order of magnitude compared to the probabilistic model checking approach, while producing the same results.
Structured decision making for managing pneumonia epizootics in bighorn sheep

Good decision making is essential to conserving wildlife populations. Although there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. To illustrate the application of this process to a management need, we present an SDM based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep (Ovis canadensis) in Montana. Pneumonia epizootics are a major challenge for managers due to long term impacts to herds, epistemic uncertainty in timing and location of future epizootics, and consequent difficulty knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd specific objectives, and predicted costs and benefits of each alternative. Decision analyses for 2 example herds revealed that meeting management objectives necessitates specific approaches unique to each herd. The analyses showed how and under what circumstances the alternatives are optimal compared to other approaches and current management. Managers can be confident that these decisions are effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes, and risk tolerance.
Model Based Simulation at Runtime for Self Adaptive Systems
Venue: 2016 IEEE International Conference on Autonomic Computing (ICAC)

Modern software systems are subject to uncertainties, such as dynamics in the availability of resources or changes of system goals. Self adaptation enables a system to reason about runtime models to adapt itself and realises its goals under uncertainties. Our focus is on providing guarantees for adaption goals. A prominent approach to provide such guarantees is automated verification of a stochastic model that encodes up to date knowledge of the system and relevant qualities. The verification results allow selecting an adaption option that satisfies the goals. There are two issues with this state of the art approach: i) changing goals at runtime (a challenging type of uncertainty) is difficult, and ii) exhaustive verification suffers from the state space explosion problem. In this paper, we propose a novel modular approach for decision making in self adaptive systems that combines distinct models for each relevant quality with runtime simulation of the models. Distinct models support on the fly changes of goals. Simulation enables efficient decision making to select an adaptation option that satisfies the system goals. The tradeoff is that simulation results can only provide guarantees with a certain level of accuracy. We demonstrate the benefits and tradeoffs of the approach for a service based telecare system.