Formal and relational governance in IT outsourcing: substitution,complementarity and the role of the psychological contract

This study aims to contribute to the literature on IT outsourcing governance by advancing our understanding of mechanisms of substitution and complementarity between formal and relational governance. Our study illustrates certain conditions under which substitution and complementarity can occur and depicts a two‐way causal relationship between them. Our examination further provides a more in‐depth assessment of relational governance by using the concept of the psychological contract. Our results demonstrate that the psychologically binding nature of the psychological contract makes it a particularly forceful substitute to formal governance, while its associations with other relationship aspects make it a forceful complement.     

Effect of Extraction Method on the Yield and Quality of Mullet Roe Oil

Striped mullet (Mugil cephalus) roe is used for the production of high nutritional and added-value delicacies. Its lipid fraction is rich in polyunsaturated fatty acids (PUFA) and bioactive compounds. This study examines scalable oil extraction methods for mullet roe oil extraction. Namely, solvent extraction (SE) using ethanol in two different temperatures, supercritical fluid extraction (SFE) using CO₂-ethanol mixture (SFE-E) in two different temperatures, expeller oil press (EP) extraction, expeller oil press combined with ethanol extraction (EP-SE) and wet reduction (WR) are examined. The methods are evaluated with regard to the oil yield and recovery, the oil oxidation and the composition in fatty acids, and polar compounds and unsaponifiable matter. EP-SE and SE provide the highest oil recovery for tested extraction temperatures (76% and 65% respectively), followed by SFE-E (46%) and EP (36%). Extracted oils present high PUFA content (28.5–33.9%). The type of extraction process and the process variables affect oil oxidation as well as the concentration of polar compounds and unsaponifiable matter. In terms of oxidation levels, 85% of the extracted oil samples were within the limits set by the Codex Alimentarius Commission. The potential of the examined methods for industrial mullet roe oil production is discussed. Practical applications : Oil rich in polyunsaturated fatty acids was extracted from stripped mullet roe. The work proposes several scalable extraction methods using mild conditions which could be applied to obtain edible and high nutritional value mullet roe oil with high recovery reaching up to 76%. The same methods could be employed also for mullet roe by-products. The obtained results improve the knowledge regarding the potential of roe valorization for oil extraction as well as the effect of the extraction method on the oil yield, main composition features and the quality characteristics of oil extracted by mullet roe. This research could offer new opportunities for the food industry for fish roe valorization for high nutritional quality oil production.     

Multi‐responsive polymeric microcontainers for potential biomedical applications: synthesis and functionality evaluation

Temperature and pH responsive poly(N‐isopropylacrylamide‐co‐methacrylic acid) (P(NIPAAm‐co‐MAA)) microcontainers with encapsulated magnetic nanoparticles in the shell were prepared by a two‐stage distillation precipitation polymerization. PMAA@Fe₃O₄/P(NIPAAm‐co‐MAA) core–shell nanoparticles were synthesized by the second‐stage polymerization of NIPAAm, MAA and N, N′‐methylenebisacrylamide as crosslinker in the presence of magnetic nanoparticles and PMAA as core. These novel triple‐functional microcontainers were prepared by selective removal of the PMAA core in water. Daunorubicin hydrochloride (DNR) was loaded into the microcontainers and the release profile was studied by UV–visible spectroscopy. The synthesized nanostructures were characterized with transmission and scanning electron microscopy, X‐ray diffraction and Fourier transform infrared spectroscopy. The magnetic properties were evaluated by vibrating sample magnetometry. The shrink and swelling behavior was studied by dynamic light scattering. Copyright © 2012 Society of Chemical Industry     

Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models

In this work the radial basis function neural network architecture is used to model the dynamics of Distributed Parameter Systems (DPSs). Two pure data driving schemes which do not require knowledge of the governing equations are described and compared. In the first method, the neural network methodology generates the full model of the system that is able to predict the process outputs at any spatial point. Past values of the process inputs and the coordinates of the specific location provide the input information to the model. The second method uses empirical basis functions produced by the Singular Value Decomposition (SVD) on the snapshot matrix to describe the spatial behavior of the system, while the neural network model is used to estimate only the temporal coefficients. The models produced by both methods are then implemented in Model Predictive Control (MPC) configurations, suitable for constrained DPSs. The accuracies of the modeling methodologies and the efficiencies of the proposed MPC formulations are tested in a tubular reactor and produce encouraging results.     

The effect of basil (Ocimum basilicum L.) on soil organic matter biodegradation and other soil chemical properties

The effects of basil (Ocimum basilicum L.) on soil organic matter biodegradation, nutritional mineral elements and bacterial colonies were studied in the laboratory. The air‐dried basil plant tissues incorporated at five different rates (0, 1, 2, 3, and 4 g per 50 g of soil) resulted in increases in organic carbon mineralization, mineral nitrogen forms in organic phosphorus and available potassium. The level of available forms of manganese and zinc was increased at all the rates of added basil whereas copper was increased at the two upper rates. Also, the addition of basil resulted in a decrease in soil bacterial colonies. The results of this study indicated that the basil could be used as a cover crop for suppressing weed or pathogens in organic soils, with a positive effect on soil productivity. In addition, the incorporation of basil in soil could reduce the number of bacterial colonies. Copyright © 2007 Society of Chemical Industry     

Influence of salinity on lead and cadmium accumulation by the salt cedar (Tamarix smyrnensis Bunge)

BACKGROUND: The removal of heavy metals from polluted soils through the use of suitable plants has attracted much interest over recent decades. In this study Tamarix smyrnensis Bunge has been investigated for the characterization of its metal tolerance and ability to accumulate Pb and Cd, in order to evaluate its effectiveness as a cleanup tool for phytoextraction applications. For this purpose, two hydroponic experiments were performed, one with lead at concentration 100 ppm and a second with cadmium at concentration 5 ppm and at three different salt concentrations (0, 100, 200 mmol L^?1 NaCl). RESULTS: The experimental results showed that Pb and Cd accumulation in shoots ranged from 150–270 ppm and 7.5–42 ppm, respectively, and salinity was found to increase metal accumulation in shoots. However, the presence of high metal and salt concentrations affected negatively the health and finally the survival of the plants. CONCLUSION: T. smyrnensis is neither a Pb nor a Cd hyperaccumulator; however, metal accumulation levels in shoots considered together with its high biomass production suggest that it could be used for phytoextraction applications. Furthermore, salinity has a positive influence on Pb and Cd accumulation in harvestable parts of the plant when it remains in low concentrations. Copyright © 2009 Society of Chemical Industry     

Implementing Equitable Water Allocation in Transboundary Catchments: The Case of River Nestos/Mesta

Border areas comprise inevitably problematic fields especially when the countries are bound to share common water resources. The conflict potential is rather high and quite often countries are directed to the negotiation table in order to form tranboundary agreements. These agreements aim to the settlement of tensions and conflicts originating from the management of shared water resources. Often a starting point of the conflict resolution is the water allocation scheme between the interested parts. The definition of entitlements over shared water resources in not an easy task and it requires the use of specific decision tools in order to reach the maximum level of objectivity. According to the Water Framework Directive (WFD) 2000/60, management of transboundary catchments shall be based on existing structures set by international agreements, such as the UN Convention on the Protection and Use of Transboundary Watercourses and International Lakes. The main aim of this paper is the implementation of a water allocation tool based on representative indicators characterizing the involved parties while placing emphasis on environmental protection. The variables used are proposed by the UN Convention on the Law of the Non-navigational Uses of International Watercourses incorporating both natural and socio-economic aspects. These factors are quantified for each country and a weight factor is assigned to each one depending on its importance and suitability for describing the environmental and socio-economic situation in each country. As an illustration example the transboundary river Nestos/Mesta case is presented.     

Differencing logical UML models

UMLDiff is a heuristic algorithm for automatically detecting the changes that the logical design of an object-oriented software system has gone through, as the subject system evolved from one version to the next. UMLDiff requires as input two models of the logical design of the system, corresponding to two of its versions. It produces as output a set of change facts, reporting the differences between the two logical-design versions in terms of (a) additions, removals, moves, renamings of model elements, i.e., subsystems, packages, classes, interfaces, attributes and operations, (b) changes to their attributes, and (c) changes to the relations among these model elements. In this paper, we detail the underlying metamodel, the UMLDiff algorithm and its heuristics for establishing lexical and structural similarity. We report on our experimental evaluation of the correctness and robustness of UMLDiff through a real-world case study.     

Characteristics of the Protocols Used in Electrical Pulse Stimulation of Cultured Cells for Mimicking In Vivo Exercise: A Systematic Review,Meta-Analysis,and Meta-Regression

While exercise benefits a wide spectrum of diseases and affects most tissues and organs, many aspects of its underlying mechanistic effects remain unsolved. In vitro exercise, mimicking neuronal signals leading to muscle contraction in vitro, can be a valuable tool to address this issue. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for this systematic review and meta-analysis, we searched EMBASE and PubMed (from database inception to 4 February 2022) for relevant studies assessing in vitro exercise using electrical pulse stimulation to mimic exercise. Meta-analyses of mean differences and meta-regression analyses were conducted. Of 985 reports identified, 41 were eligible for analysis. We observed variability among existing protocols of in vitro exercise and heterogeneity among protocols of the same type of exercise. Our analyses showed that AMPK, Akt, IL-6, and PGC1a levels and glucose uptake increased in stimulated compared to non-stimulated cells, following the patterns of in vivo exercise, and that these effects correlated with the duration of stimulation. We conclude that in vitro exercise follows motifs of exercise in humans, allowing biological parameters, such as the aforementioned, to be valuable tools in defining the types of in vitro exercise. It might be useful in transferring obtained knowledge to human research.     

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.