Fiduciary duty or loyalty? Evidence from co-opted boards and corporate misconduct

We examine the effect of co-opted boards on corporate misconduct and document a significant positive relationship. Utilising a large sample of public U.S. companies from the period 2001 to 2015, we find that a one standard deviation increase in the proportion of co-opted directors on a board leads to a 4.3% rise in corporate misconduct. This outcome is robust to a series of sensitivity tests and continues to hold after accounting for potential endogeneity concerns. Further analyses indicate that co-opted directors propose fewer board agenda items, exhibit lower attendance at board meetings, and receive compensation packages in excess of industry norms, which exacerbate stakeholder-agency conflicts. Cross-sectional analysis demonstrates that the documented relationship is most pronounced among firms with weak external monitoring, greater CEO-board social ties, boards whose members have high career concerns, and where CEO power is low. Additional tests reveal that co-opted directors engage in more environmental- and workplace-related violations than other types of stakeholder violations. Overall, our investigation generates original evidence that the presence of co-opted directors aggravates the incidence of corporate wrongdoing. Our study contributes to the continuing debate on the role of boards of directors and has policy implications for those responsible for devising and monitoring effective systems of corporate governance

Application of matlab-based interface for the control of microbioreactor operation

This work presents the use of Arduino-based embedded system interfaced to MATLAB software packages as an alternative cost-effective solution for the control of the microbioreactor operation. In the presented work, a microbioreactor platform with a working volume of approximately 1.5 mL have been fabricated using a low-cost poly (methylmethacrylate) (PMMA) and poly(dimethylsiloxane) (PDMS) polymers. The reactor have been integrated with stirring control, fuzzy logic temperature control, and aeration feature via a miniature air compressor. Control program of the microbioreactor system was established using Simulink, MATLAB software were executed by interfacing the program with Arduino Mega 2560 microcontroller for input and output of signals. Numbers of experimentation were performed to validate and demonstrate the potential of the proposed method. Satisfactorily degree of control and supervision was achieved (± 1-3% of the set-point values). The entire microbioreactor system can be operated stably for a least 48 hours. The work demonstrated the usefulness of MATLAB software in establishing a microbioreactor operating interface that consisted merely few Simulink program block sets and executed on a low-cost Arduino board

Review on the effect of ceramic reinforcements with different weight percentage and process parameters on the mechanical properties of aluminium metal matrix composite

In current modern engineering applications, aluminium metal matrix composites (AMMC) have been gaining more popularity in automobiles and aerospace industry due to their unique material properties offered such as low density and high strength to weight ratio. Fabrication of AMMCs can be achieved by embedding the reinforcement into metal matrix. Hybrid-AMMCs are getting more attention as they reinforced by a combination of two or more different type particulates in order to achieve the combined advantages of both of them. It was reported in many works that the hybrid composites of exhibited better properties than those reinforced only with single particulate (Awasthi et al., 2018; Mahmoud et al., 2010)

Overexpression of endogenous multi‐copper oxidases mcoA and mcoC in Rhodococcus jostii RHA1 enhances lignin bioconversion to 2,4‐pyridine‐dicarboxylic acid

To improve the titre of lignin-derived pyridine-dicarboxylic acid (PDCA) products in engineered Rhodococcus jostii RHA1 strains, plasmid-based overexpression of seven endogenous and exogenous lignin-degrading genes was tested. Overexpression of endogenous multi-copper oxidases mcoA, mcoB, and mcoC was found to enhance 2,4-PDCA production by 2.5-, 1.4-, and 3.5-fold, respectively, while overexpression of dye-decolorizing peroxidase dypB was found to enhance titre by 1.4-fold, and overexpression of Streptomyces viridosporus laccase enhanced titre by 1.3-fold. The genomic context of the R. jostii mcoA gene suggests involvement in 4-hydroxybenzoate utilization, which was consistent with enhanced whole cell biotransformation of 4-hydroxybenzoate by R. jostii pTipQC2-mcoA. These data support the role of multi-copper oxidases in bacterial lignin degradation, and provide an opportunity to enhance titres of lignin-derived bioproducts

Preface : “Practical Research on Gamification in Education (2)”

<p>(A) Areas under the curves (AUC) of receiver operating characteristics (ROC) in 746 patients for pentosidine in relation to all-cause mortality.(B) Areas under the curves (AUC) of receiver operating characteristics (ROC) in 746 patients for pentosidine in relation to CVD-mortality.</p

Cutis Verticis Gyrata secondary to cerebriform intradermal naevus: a rare entity and its management

Cutis verticis gyrata (CVG) is a rare skin disorder characterised by thick skin folds and grooves, resembling the gyri and sulci of the cerebral cortex. It can be classified as primary essential, primary non-essential and secondary CVG. The rarity of the condition poses a challenge in the diagnosis and ultimately requires good communication between the clinician and the dermatopathologist for diagnosis. Many names have been given to the condition, including Robert-Unna syndrome, cutis verticis plicata, paquidemia verticis gyrata, cutis sulcata, cutis striata or “bulldog” scalp syndrome. To date, the treatment is still limited to plastic surgeons with excision of the folds, scalp reduction and reconstruction. In this article, we are sharing a rare case and the surgical management of a middle age gentleman with cutis verticis gyrata secondary to cerebral intradermal nevus (CIN) over his occipital scalp

The higher mortality associated with low serum albumin is dependent on systemic inflammation in end-stage kidney disease

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesBACKGROUND: The correlation of low serum albumin with mortality in patients with chronic kidney disease (CKD) is partly linked to its association with systemic inflammation. However, it is not clear to what extent albumin's correlation with mortality depends on concomitant systemic inflammation. Here we addressed this question in patients with CKD stage 5. METHODS: Serum albumin (S-Alb), systemic inflammation (high-sensitive C-reactive protein, hsCRP), cardiovascular disease (CVD) and nutritional status (subjective global assessment, SGA) were assessed at baseline in 822 patients: 523 incident dialysis patients, 212 prevalent hemodialysis (HD) and 87 prevalent peritoneal dialysis (PD) patients. Patients were divided into four groups according to hsCRP and S-Alb in each cohort: Group 1 -normal S-Alb and normal hsCRP (reference); Group 2 -low S-Alb and normal hsCRP; Group 3-normal S-Alb and high hsCRP; Group 4-low S-Alb and high hsCRP. Survival over 60 months was analyzed. RESULTS: In Cox analysis, Group 4 had an increased mortality risk (adjusted Hazard ratio (95% confidence interval): 1.62 (1.06-2.47); p = 0.02) whereas the augmented mortality risks for Groups 2 and 3 in univariate analyses were not significant after adjustments for age, gender, blood pressure, diabetes mellitus, smoking, SGA, renal function and renal replacement technique. CONCLUSIONS: Whereas mortality risk was increased in CKD stage 5 patients with low S-Alb and high CRP, it was not increased in patients with low S-Alb and normal CRP. Our observation suggests that inflammatory status should be taken into account when using S-albumin for risk assessment in CKD stage 5 patients.Baxter Healthcare Corporation Amgen Inc Baxter Novum, Department of Clinical Science, Intervention and Technology, Karolinska Institutet Swedish Medical Research Council Heart and Lung Foundation Martin Rind Foundation Njurfonden Westmans Foundatio

Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis

Genome-Wide Association Studies for Pasmo Resistance in Flax (Linum usitatissimum L.)

Pasmo is one of the most widespread diseases threatening flax production. To identify genetic regions associated with pasmo resistance (PR), a genome-wide association study was performed on 370 accessions from the flax core collection. Evaluation of pasmo severity was performed in the field from 2012 to 2016 in Morden, MB, Canada. Genotyping-by-sequencing has identified 258,873 single nucleotide polymorphisms (SNPs) distributed on all 15 flax chromosomes. Marker-trait associations were identified using ten different statistical models. A total of 692 unique quantitative trait nucleotides (QTNs) associated with 500 putative quantitative trait loci (QTL) were detected from six phenotypic PR datasets (five individual years and average across years). Different QTNs were identified with various statistical models and from individual PR datasets, indicative of the complementation between analytical methods and/or genotype × environment interactions of the QTL effects. The single-locus models tended to identify large-effect QTNs while the multi-loci models were able to detect QTNs with smaller effects. Among the putative QTL, 67 had large effects (3–23%), were stable across all datasets and explained 32–64% of the total variation for PR in the various datasets. Forty-five of these QTL spanned 85 resistance gene analogs including a large toll interleukin receptor, nucleotide-binding site, leucine-rich repeat (TNL) type gene cluster on chromosome 8. The number of QTL with positive-effect or favorite alleles (NPQTL) in accessions was significantly correlated with PR (R2 = 0.55), suggesting that these QTL effects are mainly additive. NPQTL was also significantly associated with morphotype (R2 = 0.52) and major QTL with positive effect alleles were present in the fiber type accessions. The 67 large effect QTL are suited for marker-assisted selection and the 500 QTL for effective genomic prediction in PR molecular breeding

The classification of electrooculography signals: A significant feature identification via mutual information

Stroke is currently known as the third most frequent reason for disability worldwide where the quality of life of its survivors in terms of their daily functioning is seriously affected. Brain-Computer Interface (BCI) is a system that can acquire and transform brain activity into readable outputs. This system is particularly beneficial to the people who encounter physical challenges in carrying out their daily life as the BCI outputs can be applied to BCI-based assistive devices. One of the BCI inputs that are frequently used is the Electrooculography (EOG) signal. EOG signal is the electrical voltage emitted from the movement of our eyeballs. This study aims to extract and identify significant statistical-based time-domain features based on the EOG signals acquired that would facilitate the classification of EOG movements via Support Vector Machine (SVM). The EOG signals were obtained via BioRadio. Five healthy subjects that ranged between 22 and 30 years old were involved in the EOG data acquisition. A total of 7 statistical time-domain features, namely, mean, standard deviation, variance, median, minimum, maximum, and standard error mean were extracted from all four BioRadio channels. The Mutual Information (MI) feature selection technique was employed to identify significant features. The 70:30 hold-out cross-validation technique was used in the study. It was demonstrated from the present investigation that an excellent comparable classification on both train and test dataset is attainable even by utilising the identified features. The findings further suggest the possible application of neurorehabilitation owing to the reduced computational expense resulting from the reduced feature set