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Research summary: In this study, we revisit the relationship between corporate social performance (CSP) and corporate financial performance (CFP) by conducting a replication of Waddock and Graves (1997). Using 1990 KLD ratings as the CSP measure, the original study reports a positive bidirectional relationship between CSP and CFP. However, our replication analyses with a larger sample over a longer time period indicate that the findings of the original study may not be generalizable to different samples. We argue that our replication casts doubt on the original study and can serve as a starting point to reconsider the CSP-CFP relationship. Based on the findings of our replication, we discuss the differences between the replication results and the original findings, and then suggest several approaches to revise and extend the original study. Managerial summary: Advocates of corporate social performance (CSP) have long argued that \"doing good leads to doing well.\" However, the evidence to support this argument is not strongly convincing, and managers hence doubt whether better CSP leads to improved corporate financial performance (CFP). In this article, we directly examine the relationship between CSP and CFP. Our article reports that CSP may not have a positive influence on CFP. Instead, our article shows the complexity of the relationship between CSP and CFP. Therefore, we cannot simply argue that doing good will necessarily lead to doing well.

Thermal energy storage (TES) is the most suitable solution found to improve the concentrating solar power (CSP) plant’s dispatchability. Molten salts used as sensible heat storage (SHS) are the most widespread TES medium. However, novel and promising TES materials can be implemented into CSP plants within different configurations, minimizing the TES costs and increasing the working temperature to improve the thermal performance of the associated power block. The first objective of this review is to provide an overview of the most widespread CSP technologies, TES technologies and TES-CSP configurations within the currently operational facilities. Once this information has been compiled, the second aim is to collect and present the existing European and North American TES-CSP Research and Development (R&D) projects within the last decade (2011–2021). Data related to these projects such as TES-CSP configuration path, TES and CSP technologies applied, storage capacity, power block associated and the levelized cost of electricity (LCOE) of the commercial up-scaling project are presented. In addition, project information such as location, research period, project leader and budget granted are also extracted. A timeline of the R&D projects launched from 2011 is built, showing the technology readiness level (TRL) achieved by the end of the project.

Purpose This paper aims to develop a scale to measure climate strategy proactivity (CSP) displayed by corporate in developing countries to attain competitive advantage (CA) and sustain it through sources of sustained competitive advantage (SOSCA). It attempts to derive sound operational definitions of CSP, CA and SOSCA through horizontal analysis of previous studies and discussions with experts and practitioners to construct a measurement scale. Design/methodology/approach Research design includes broad stages, namely, item generation, scale development and assessment of psychometric properties of the scale starting with review of literature and discussions with experts and practitioners, which were followed by a pilot and full study carried out through a cross-sectional, self-administered online survey questionnaire and assessment using suitable tools and techniques. Findings Findings of the study comprise development of sound operational definitions and construction of a valid and reliable measurement scale of CSP displayed by corporate to attain CA with strong psychometric properties, which is expected to be useful in developing countries not only for researchers and academics but also for the practitioners and organizations. Research limitations/implications The information was self-reported by respondents through climate strategy proactivity questionnaire (CSPQ) and consequently can be at risk to have been influenced by bias. Nonetheless, this CSPQ scale devoid of conceptual problems can be used in developing countries in future empirical studies with the caution that its reliability and validity require further tests. Practical implications Using the CSPQ scale will help managers in developing countries in enhancing their awareness with the provision of these conceptually clear comprehensive operational definitions of CSP, CA and SOSCA with respect to the conceptual nature and the latent expressions, and draw an extensively enhanced scope of climate-conscious strategy to bring about CA. Originality/value With the data collected from a sample representing different stakeholders of Indian companies from across the country, CSPQ scale possesses significant robustness and implicative potential which can contribute to the evolution of the strategic management field by providing a valid and reliable measure of CSP in developing countries applicable under any major theoretical perspective in strategic management serving the needs of not only the empirical research but also of the management practices intended to attain CA.

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems.

Olfaction plays a pivotal role in a mosquito’s lifecycle, influencing vital functions such as finding food, mates, identifying hosts, and locating sites for laying eggs. However, a detailed catalog of the olfactory genes in mosquitoes has remained elusive—until now. In this study, we compiled the olfactory genes catalog for four key malaria vectors: two major Indian species, Anopheles stephensi and Anopheles culicifacies , along with two African species, Anopheles gambiae and Anopheles funestus . Using an extensive genome-wide approach, we uncovered crucial carrier proteins like odorant binding proteins ( OBPs ), chemosensory proteins ( CSPs ), and several receptors, including odorant receptors ( ORs ), ionotropic receptors ( IRs ), and gustatory receptors ( GusRs ). A particularly striking discovery was the significantly higher number of OBP , OR , and IR genes in African malaria vectors compared to their Indian counterparts, hinting at the gene gain and functional diversification in these species. The invasive A. stephensi —which has spread from Asia to Africa—showed closer genetic ties to A. minimus and A. gambiae than to A. culicifacies . Furthermore, when examining the expression of CSPs and SAPs in the larval stage of A. stephensi , we found that pyrethroid-resistant mosquito larvae exhibited elevated expression of SAP2 and SAP3 , providing new evidence of their potential role in insecticide resistance. This study not only sheds light on the genetic basis of mosquito olfaction but also offers crucial insights into how these genes are linked to different physiological functions, paving the way for improved malaria control strategies.

To deal with potential conflicts between the triple-bottom-line expectations of investors and the performance of executives, firms can use incentives by integrating corporate social performance (CSP) targets into executive compensation. No evidence yet exists that CSP targets in executive compensation actually lead to an improvement of CSP results. Using a panel data set of 400 firms for the years 2008-2012 leading to 1846 firm-year observations, the relationships between CSP targets and CSP results and CSP improvements are analyzed. The results show that (a) the level of CSP has no effect on the use of CSP targets, (b) the use of CSP targets in general does not automatically lead to better CSP results, and (c) the use of quantitative, hard CSP targets is an effective way to improve CSP results, especially to lower CSP weaknesses.

This study complements previous empirical research on the business case for corporate social responsibility (CSR) by employing hitherto unused data on corporate social performance (CSP) and proposing statistical analyses to account for bi-directional causality between social and financial performance. By allowing for differences in the importance of single components of CSP between industries, the data in this study overcome certain limitations of the databases used in earlier studies.The econometrics employed offer a rigorous way of addressing the problem of endogeneity due to simultaneous causality. Although the study's results provide no evidence that there is a generic or universal business case for CSR, they indicate that there is a strong link between single stakeholder-related issues of CSR and financial performance. However, the analysis does not establish causality within these relationships.

This paper presents the system analysis and the techno-economic assessment of selected solar hydrogen production paths based on thermochemical cycles. The analyzed solar technology is Concentrated Solar Power (CSP). Solar energy is used in order to run a two-step thermochemical cycle based on two different red-ox materials, namely nickel-ferrite and cerium dioxide (ceria). Firstly, a flexible mathematical model has been implemented to design and to operate the system. The tool is able to perform annual yield calculations based on hourly meteorological data. Secondly, a sensitivity analysis over key-design and operational techno-economic parameters has been carried out. The main outcomes are presented and critically discussed. The technical comparison of nickel-ferrite and ceria cycles showed that the integration of a large number of reactors can be optimized by considering a suitable time displacement among the activation of the single reactors working in parallel. In addition the comparison demonstrated that ceria achieves higher efficiency than nickel-ferrite (13.4% instead 6.4%), mainly because of the different kinetics. This difference leads to a lower LCOH for ceria (13.06 €/kg and 6.68 €/kg in the base case and in the best case scenario, respectively).

Electroencephalography (EEG), magnetoencephalography (MEG) and related techniques are prone to glitches, slow drift, steps, etc., that contaminate the data and interfere with the analysis and interpretation. These artifacts are usually addressed in a preprocessing phase that attempts to remove them or minimize their impact. This paper offers a set of useful techniques for this purpose: robust detrending, robust rereferencing, outlier detection, data interpolation (inpainting), step removal, and filter ringing artifact removal. These techniques provide a less wasteful alternative to discarding corrupted trials or channels, and they are relatively immune to artifacts that disrupt alternative approaches such as filtering. Robust detrending allows slow drifts and common mode signals to be factored out while avoiding the deleterious effects of glitches. Robust rereferencing reduces the impact of artifacts on the reference. Inpainting allows corrupt data to be interpolated from intact parts based on the correlation structure estimated over the intact parts. Outlier detection allows the corrupt parts to be identified. Step removal fixes the high-amplitude flux jump artifacts that are common with some MEG systems. Ringing removal allows the ringing response of the antialiasing filter to glitches (steps, pulses) to be suppressed. The performance of the methods is illustrated and evaluated using synthetic data and data from real EEG and MEG systems. These methods, which are mainly automatic and require little tuning, can greatly improve the quality of the data. •Preprocessing is essential for EEG and MEG data analysis.•Robust methods for data preprocessing are not affected by glitches and artifacts.•Methods include robust detrending, rereferencing, inpainting and step removal.•These methods are effective and complementary with standard techniques such as ICA.

A protocol for terminal alkyne methylthiolation has been developed, utilizing an electrochemical strategy with dimethyl sulfoxide as both the solvent and the methylthiolating reagent. This organic electrochemical reaction, which employs commercially available reagents, proceeds efficiently at room temperature under transition‐metal‐free, external oxidant/reductant‐free, and base‐free conditions. The method affords a diverse array of alkyne sulfides in satisfactory yields and exhibits broad functional group compatibility. Potassium iodide serves as the electron transfer reagent, dimethyl sulfoxide as both the solvent and sulfur source, a carbon rod as the anode, and a magnesium sheet as the cathode. The mild thiomethylation of the terminal alkyne is successfully achieved under electrocatalytic conditions. Deuterated products can also be obtained by using dimethyl sulfoxide‐d6 while maintaining the yield.