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The purpose of this paper is to identify major developments in corporate innovation. The author focuses on the behavioral differences between MNEs from developed and emerging markets in the way they locate their R&D activities.

With the help of descriptive statistics, the paper identifies major trends in the global distribution of innovative activity. The novel source of patent statistics, Patent Cooperation Treaty (PCT) applications, is used as a proxy for innovative effort by leading MNEs. This paper is among the first attempts to analyze the global geography of innovation based on PCT statistics.

The analysis underscores differences in the patenting activities of MNEs from emerging and advanced markets. It confirms that innovative activity by major MNEs remains largely home-based, which contradicts the premise of the global nature of corporate innovation. At the same time, the growing importance of China as a research center attracts MNEs from a variety of developed markets. Emerging MNEs also file patent applications domestically. Most Chinese R&D subsidiaries of MNEs from advanced economies in our sample do not pursue technological specialization, as they produce patents in the same technological areas as the corporate headquarters or other subsidiaries.

A number of assumptions about the innovation geography of major MNEs were empirically tested. An attempt was made to fill the gaps in our understanding of innovation strategies pursued by MNEs in emerging markets. The author uses the concept of MNEs as meta-integrators to explain the observed dynamics. Its explanatory power is more convincing as applied to our data than the concept of national systems of innovation.

The purpose of this paper is to propose a method to reduce the non-linear distortion of a transistor to its input and output ports to aid distortion contribution analysis (DCA). This is especially needed when the internal structure of a device model is complex.

The non-linear distortion generated by all non-linear sources inside a device model are reduced to transistor i/o ports by LMSE fitting techniques. Simulations of an LDMOS power transistor are used to compare the reduced distortion results with the actual non-linear sources.

It is shown, that device models where the current sources are split by intermediate nodes cause superficial results, when distortion contributions are calculated as a superposition of contributions from individual non-linear sources. The proposed iterative fitting technique works.

Some non-quasistatic effects and the transfer functions from external terminals to internal controlling nodes are not covered.

The analysis is a step toward a generic non-linear distortion contribution simulation tool that would aid the designers to develop more linear analog circuits.

The concept of DCA itself is fairly new. This paper makes a step to represent the distortion sources in a canonical way.

The purpose of this paper is to propose methods to improve the least square error polynomial fitting of multi-input nonlinear sources that suffer from strong correlating inputs.

The polynomial fitting is improved by amplitude normalization, reducing the order of the model, utilizing Chebychev polynomials and finally perturbing the correlating controlling voltage spectra. The fitting process is estimated by the reliability figure and the condition number.

It is shown in the paper that perturbing one of the controlling voltages reduces the correlation to a large extend especially in the cross-terms of the multi-input polynomials. Chebychev polynomials reduce the correlation between the higher-order spectra derived from the same input signal, but cannot break the correlation between correlating input and output voltages.

Optimal perturbations are sought in a separate optimization loop, which slows down the fitting process. This is due to the fact that each nonlinear source that suffers from the correlation needs a different perturbation.

The perturbation, harmonic balance run and refitting of an individual nonlinear source inside a device model is new and original way to characterize and fit polynomial models.

The noise and dust particles caused by the construction transport are by most stakeholders experienced as disturbing. The purpose of this study is to explore how sonification can support visualization in construction planning to decrease construction transport disturbances.

This paper presents an interdisciplinary research project, combining research on construction logistics, internet of things and sonification. First, a data recording device, including sound, particle, temperature and humidity sensors, was implemented and deployed in a development project. Second, the collected data were used in a sonification design, which was, third, evaluated with potential users.

The results showed that the low-cost sensors used could capture “good enough” data, and that the use of sonification for representing these data is interesting and a possible useful tool in urban and construction transport planning.

There is a need to further evolve the sonification design and better communicate the aim of the sounds used to potential users. Further testing is also needed.

This study introduces new ideas of how to support visualization with sonification planning the construction work and its impact on the vicinity of the site. Currently, urban planning and construction planning focus on visualizing the final result, with little focus on how to handle disturbances during the construction process.

Showing the potentials of using low-cost sensor data in sonification, and using sonification together with visualization, is the result of a novel interdisciplinary research area combination.

Sports and physical activity are widely recommended, both as guidelines and in clinical practice, because of their broad range of positive effects on health, depression, anxiety, and psychological well-being. While several studies have examined the anti-depressive and anxiolytic effects of physical activity in clinical populations, and fewer studies have focused on the nonclinical populations, the relationship between tennis and well-being has not been clearly investigated. This study was carried out with 76 student volunteers from Kocaeli University (Turkey) who had chosen tennis lessons as their University. The tennis exercise program consisted of 90-minute basic tennis skills lessons for 13 weeks. At the beginning and at the end of the study, the students were given the Symptom Checklist-90-Revised (SCL-90-R), the Beck Anxiety Inventory (BAI), and the Beck Depression Inventory (BDI) scales, and were evaluated by the DeWitt-Dugan Tennis Service Test, the DeWitt-Dugan Speed Test, and the Dyer Backboard Tennis Test. Upon evaluating the students' pre- and post-test scores, we concluded that their BDI and BAI scores had significantly decreased, with the most significant decreases seen in several sub-scores of the SCL-90-R; their tennis skills, meanwhile, increased significantly. This study shows that partaking in tennis exercise once a week decreases depression and anxiety symptoms and enhances well-being in healthy young people.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

The aim of this research is to underscore the pivotal role of warehouse management in the current turbulent global landscape exacerbated by the confluence of a health crisis and geopolitical instability in Europe. In today's interconnected global economy, the turbulence of the global supply chain causes a lack of its resilience among companies. Facing this critical crisis context, companies are refocusing on business processes and outsourcing support processes such as logistics. In this paper we have empirical and methodological objectives. Methodologically, we employ a qualitative research approach utilizing action research in a collaborative framework that involves academics and practitioners. The purpose of this methodology is to empirically investigate warehouse outsourcing as a solution for enhancing a company's performance and agility within the crisis context.

The authors’ action research based on case study approach is conducted through an immersion within the ALCL French multinational company located in Morocco. The authors mobilize the theory of constraints, which allows us to set up a process of identification and optimization of managerial constraints (Goldratt, 1990). The approach allows to set up a retroactive loop to increase the performance of the constraint.

The study shows that ALCL has a storage over-dimension constraint due to the decrease of physical flows caused by the global crisis. The results of action research protocol show that the optimization of warehousing constraint is achieved by the total outsourcing of the process.

The study provides new insights into how action research can improve management practices within companies and explore concrete solutions to the logistical challenges faced by businesses.

The present study aims to explore and compare consumer perception and scientific evidence related to food quality and food safety aspects of organic versus conventional vegetables.

Primary data on consumer perception were gathered in 2006‐2007 through a consumer survey with Flemish adults (n=529) and compared with scientific evidence from literature. Consumers of organic and conventional vegetables were selected by means of a convenience sampling procedure. Subjects were asked to complete a self‐administered questionnaire concerning the perception of the nutritional and toxicological value of organic relative to conventional vegetables. Data processing and analysis included descriptive analysis (frequency distributions), data reduction (Cronbach's alpha test, factor analysis), bivariate analysis (correlations, t‐test, ANOVA) and multivariate analysis (stepwise multiple regression).

It was found that organic vegetables are perceived as containing less contaminants and more nutrients, and as such, being healthier and safer compared to conventional vegetables. However, not enough evidence is currently available in the literature to support or refute such a perception, indicating a certain mismatch between consumer perception and scientific evidence. The gap between perception and evidence is larger among older consumers with children. The perception is stronger when the consumption frequency is higher, but is independent of gender, place of residence (rural or urban), education and income level. Also non‐users, on average, perceive that organic vegetables have a nutritional and toxicological advantage over conventional vegetables.

A non‐probability convenience sampling method was applied which limits generalisation of the findings beyond the sample characteristics.

This paper is original in comparing consumer perception and scientific facts related to both nutritional and safety aspects of organic versus conventional vegetables.

The increasing demand for organic foods is explained mainly by consumers' concerns about the quality and safety of foods and their perception that organically produced foods are healthier and safer than conventional foods. Based on internationally available concentration data of organic and conventional vegetables (carrots, tomatoes, lettuce and spinach) and potatoes, the paper aims to investigate the scientific validity of nutrition claims as “no vegetable/potato has higher amounts of nutrient X than organic vegetables/potatoes” and “no vegetable/potato has lower amounts of contaminant Y than organic vegetables/potatoes”.

Detailed nutrient and contaminant databases were developed for organic and conventional vegetables separately. Non‐parametric (Mann‐Whitney test) methods were used to detect significant differences between both types of vegetables. A chi‐square test was used to compare the incidence of pesticide residues in organic and conventional vegetables.

From a nutritional and toxicological point of view, organic vegetables and potato in general are not significantly better than conventional vegetables and potatoes. For some nutrients and contaminants organic vegetables and potatoes score significantly better but for others they score significantly worse. Therefore, it becomes difficult to justify general claims indicating a surplus value of organic over conventional vegetables and potatoes. More data from controlled paired studies are needed to reconsider the use of claims for these organic plant foods in the future.

Only a limited number of studies comparing the nutrient and/or contaminant concentration of organic and conventional vegetables are available (“paired studies”). Additionally, the majority of the studies are of moderate or poor quality. The implication is that more of those paired studies are heavily needed. Another limitation of the study is the fact that most pesticide residue data originated from the USA, the EU and Australia.

So far only few studies compared both nutrient and contaminant contents between organic and conventional plant foods. This paper covers therefore an important, not well‐explored research sub area.

The academic community has warned that predatory journals may attempt to capitalize on the confusion caused by the COVID-19 pandemic to further publish low quality academic work, eroding the credibility of scholarly publishing.

This article first chronicles the risks of predatory publishing, especially related to misinformation surrounding health research. Next, the author offers an empirical investigation of how predatory publishing has engaged with COVID-19, with an emphasis on journals related to virology, immunology and epidemiology as identified through Cabells' Predatory Reports, through a content analysis of publishers' websites and a comparison to a sample from DOAJ.

The empirical findings show that there were 162 titles related to these critical areas from journals listed on Cabells with a range of infractions, but most were defunct and only 39 had published on the pandemic. Compared to a DOAJ comparison group, the predatory journal websites were less likely to mention slowdowns to the peer review process related to the pandemic. Furthermore, another 284 predatory journals with COVID-19 engagement were uncovered from the initial exploration. These uncovered journals mostly centered on medical or biological science fields, while 42 titles came from other broader fields in social science, other STEM or humanities.

This study does not prove that predatory publications have released misinformation pertaining to COVID-19, but rather it exemplifies the potential within a complex academic publishing space. As these outlets have proven to be vectors of misleading science, libraries and the broader educational community need to stay vigilant as information intermediaries of online research.