Search results

Recent developments in the psychometric assessment of youth psychopathic traits suggest that the inclusion of a conduct disorder (CD) factor to the traditional three factors of the psychopathy construct may improve the incremental validity of these measures. The purpose of the current study is to examine whether the addition of a CD factor incrementally improves the ability of the Youth Psychopathic Traits Inventory Short version (YPI-S) to predict criminal recidivism.

A longitudinal quantitative research design was used with a sample detained male youth (N = 214, M_age = 16.4 years, SD_age = 1.3 years).

Results using the area under the curve analysis suggest that the inclusion of a CD factor slightly improves the capacity of the YPI-S to predict one-year general criminal recidivism, but it does not significantly increase its capacity to predict violent criminal recidivism. Results also indicate that a CD scale outperforms the YPI-S, even with an additional CD factor included, in terms of predicting one-year general and violent recidivism.

Self-reported youth psychopathic trait measures, even those that include a CD factor as a fourth factor, should be used with caution when the aim is to predict youth criminal recidivism.

To the best of the authors’ knowledge, this is the first study using a self-reported youth psychopathic traits measure with a CD factor to examine youth criminal recidivism.

This study aims to investigate value co-creation, its antecedents (i.e. customer delight and place identity) and the consequences (i.e. satisfaction and revisit intention) at eco-hotels concerning sustainable practices.

Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the data collected from 562 guests surveyed online who had stayed and contributed to sustainable practices through interaction and collaboration with eco-hotels.

The results revealed that customer delight and place identity are critical antecedents of value co-creation, significantly influencing guests' intention to revisit. Further, value co-creation significantly influences satisfaction and revisits intention. Satisfaction significantly influenced revisit intentions. Moreover, customer delight was the most critical factor affecting value co-creation, followed by the path between value co-creation and satisfaction. Finally, the results confirmed the mediating role of value co-creation and satisfaction.

This research can support hotel managers in comprehending the motivating factors and outcomes of value co-creation among guests, allowing efficient hotel strategies to be planned and implemented. Managers should prioritize customer delight and place identity to maintain guests' involvement in value co-creation, resulting in satisfaction and a willingness to return.

This study contributes to the literature by tackling the scarcity of research on the significance of value co-creation, its drivers and outcomes at eco-hotels concerning sustainable practices within an emerging market context.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

The novelty addressed here is undertaken by using tailor-made and fully characterized starch nanoparticles (SNPs) having a particle size ranging from 80 to 100 nm with a larger surface area, biodegradability and high reactivity as a starting substrate for cadmium ions and basic dye removal from wastewater effluent. This was done via carboxylation of SNPs with citric acid via esterification reaction using the dry preparation technique, in which a simple, energy-safe and sustainable process concerning a small amount of water, energy and toxic chemicals was used. The obtained adsorbent is designated as cross-linked esterified starch nanoparticles (CESNPs).

The batch technique was used to determine the CESNPs adsorption capacity, whereas atomic adsorption spectrometry was used to determine the residual cadmium ions concentration in the filtrate before and after adsorption. Different factors affecting adsorption were examined concerning pH, contact time, adsorbent dose and degree of carboxylation. Besides, to validate the esterification reaction and existence of carboxylic groups in the adsorbent, CESNPs were characterized metrologically via analytical tools for carboxyl content estimation and instrumental tools using Fourier-transform infrared spectroscopy (FTIR) spectra and scanning electron microscopy (SEM) morphological analysis.

The overall adsorption potential of CESNPs was found to be 136 mg/g when a 0.1 g adsorbent dose having 190.8 meq/100 g sample carboxyl content at pH 5 for 60 min contact time was used. Besides, increasing the degree of carboxylation of the CESNPs expressed as carboxyl content would lead to the higher adsorption capacity of cadmium ions. FTIR spectroscopy analysis elucidates the esterification reaction with the appearance of a new intense peak C=O ester at 1,700 cm⁻¹, whereas SEM observations reveal some atomic/molecules disorder after esterification.

The innovation addressed here is undertaken by studying the consequence of altering the extent of carboxylation reaction expressed as carboxyl contents on the prepared CESNPs via a simple dry technique with a small amount of water, energy and toxic chemicals that were used as a sustainable bio nano polymer for cadmium ions and basic dye removal from wastewater effluent in comparison with other counterparts published in the literature.

This study examines the effect of external debt and domestic capital formation on economic development in Sub-Saharan African (SSA) economies.

Using the Dynamic Common Correlation Effects (DCCE) technique and the Driscoll and Kraay fixed-effect technique, this paper conducts a multidimensional assessment of external debt and domestic investment on economic development across a panel of 35 SSA countries from 1995 to 2018. The data utilized are sourced from the World Development Indicators (2021) and the United Nations Development Program (UNDP) database (2021).

The results reveal that domestic investment has a positive impact on economic development in SSA countries, consistent across all three dimensions of the human development index (income, education and life expectancy). However, external debt exhibits an adverse effect on economic development, consistently yielding negative outcomes for life expectancy, education and income.

Based on these findings, the authors recommend that SSA economies implement appropriate policies, such as reducing bureaucratic requirements and addressing corruption, to enhance domestic capital investment. Additionally, efforts should be directed toward channeling contracted debt into productive sectors like road construction and electricity provision.

This study is among the first to assess the impact of domestic investment and external debt on the three dimensions of human development outlined by the UNDP. Furthermore, it employs a robust econometric method that considers cross-sectional dependence (CD).

Brazil uses the dollar as a vehicle currency to invoice its exports. This fact produces a tendency toward equalizing the prices of products in dollars in the international market and reducing the ability of firms to practice pricing-to-market (PTM). This study aims to evaluate the hypothesis by estimating error correction models in panel data, obtaining estimates of PTM for 25 manufacturing products exported by Brazil between 2010 and 2020.

This study uses the correlated common effect estimator proposed by Pesaran (2006) and Chudik and Pesaran (2015b) to estimate the PTM coefficients.

Results of this study indicate that exporters practice local-currency pricing stability for dollar prices. This study obtains that Brazilian exporters tend to stabilize their dollar price for exports, reducing heterogeneity between destination markets. The results are in agreement with the hypothesis of the prevalence of the coalescing effect of Goldberg and Tille (2008) and lower sensitivity of the markup adjustment to the specific market, as pointed out by Corsetti et al. (2018). The pricing of Brazilian exports in dollars reflects a profit maximization strategy that considers an international price system based on global demand for products.

In addition to analyzing the dollar role in the pricing of Brazilian exports through the triangular decomposition, this study also shows the importance of examining the cross-section dependence of errors, considering the heterogeneous cointegration in export pricing models and producing PTM estimates for short-term and long-term.

The present study aims to investigate the non-linear relationship between trade and income inequality to address goal 10 of sustainable development goals (SDGs) using the Kuznets Curve (KC) framework for major emerging countries during 1991–2020.

For this purpose, recent econometric techniques, such as Common Correlated Effect (CCE) and Dynamic Common Correlated Effect (DCCE) estimators have been employed to deal with the cross-section dependence (CD) that arises in panel data, while the robustness of the study is checked through Driscoll–Kraay standard errors method.

The empirical results of the study confirm the existence of inverted “U-shaped” relationship between trade and income inequality suggesting evidence for the trade-led KC in the panel of emerging countries. Along with the non-linear model, the threshold value is estimated to be between 3.5 and 4% of gross domestic product (GDP).

The authors' findings support that trade contributes significantly toward reducing income inequality and helps in achieving goal 10 of SDGs. Hence, trade policies appear to be more egalitarian. The results widen the scope for further research and provide insights for regulators and policymakers in modeling trade policies and changing the status quo trade policy framework accordingly.

The present study is a pioneering attempt to examine the non-linear relationship between trade and income inequality under the KC framework in light of the Agenda 2030 for sustainable development. The study also considers other explanatory factors that have an impact on income inequality. Furthermore, the study considers other explanatory factors that have an impact on income inequality, and the attempt to estimate the threshold value for the trade-led KC is novel and interesting.

This paper investigates the effect of financial development on bank profitability. The authors examine whether financial development is an important determinant of bank profitability.

The ordinary least square and the generalized method of moments regression methods were used to analyze the impact of financial development on the profitability of the Nigerian banking sector.

The authors find a significant negative relationship between the financial system deposits to GDP ratio and the non-interest income of Nigerian banks. This indicates that higher financial system deposits to GDP depresses the non-interest income of Nigerian banks. The result implies that the larger the size of the Nigerian financial system, the lower the profitability of banks in Nigeria. Also, the authors observe that bank concentration, nonperforming loans, cost efficiency and the level of inflation are significant determinants of the profitability of Nigerian banks.

It is recommended that regulators should establish market-enabling policies that encourage new banks to emerge in the banking industry. The entry of new banks can lead to increase in financial system deposits and credit supply for economic growth. Regulators also need to understand the role of Nigerian banks in promoting financial development and find ways to collaborate with banks towards financial sector development. Another implication of the findings for asset managers is that asset managers will need to take into account the prevailing level of financial development, particularly the size of the financial system, in their asset pricing and investment decisions. This will ensure that investors get value for their investments in Nigeria. The financial implication of the study is that the level of financial development in Nigeria can improve the finance-growth linkages in Nigeria through the efficient allocation of credit and capital to crucial sectors of the Nigerian economy to spur growth in those sectors.

Evidence dealing with how financial development affects the profitability of the banking sector in African countries is scarce in the literature, and is completely absent for Nigeria. This paper addresses this research gap.

The Ottoman cultural legacy in Algeria is made up of a diverse range of architectural structures. The Algerian government strategy in connection with the Ottoman old buildings is to restore them into museums. This study will attempt to present a contrastive analysis between two old palaces being under restoration (refurbishment), and the goal is to propose a museum route by calculating the ambiance aimed at circulating the rooms by visitors.

The authors analyzed the architectural components of the various entities by observing in situ and taking measurements for a single case study to get a sense of the results for the mid-season (spring and fall). The configuration was next evaluated by modeling the space syntax and combining it with a simulation of daylight luminance, for the period when the authors could not make measurements on-site. The ultimate goal is to combine these findings to suggest the ideal in-route for the future museum.

This research allowed the authors to propose a museum itinerary adapted to the new vocation of the palaces, which considers the daylight as an element of composition in the spaces of circulation.

The paper proposes solutions to a flow management problem encountered in several similar palaces converted into museums.

The study aims to raise questions on the museum, and to preserve such heritage from neglect by giving it a new life more adapted to the needs of the Algerian society.

The authors believe that this contribution will be a creative solution for issues related to the operation of palaces that have been converted into museums.

This paper makes a conceptual contribution by intersecting two strands of literature (return to work following health issues and industrial relations) to facilitate our understanding of the potential role of social dialogue in supporting return to work (RTW) following the diagnosis of a chronic illness. It conceptualises the levels and channels through which various actors and their interactions may play a role in RTW facilitation within the actor-centred institutional framework.

The paper uses an exploratory design based mainly on desk research but is also informed by roundtable discussions done in six countries as part of a larger project.

The conceptual and analytical framework (CAF) is developed to explain how various actors interact together in ways shaped by the RTW policy framework and the industrial relations systems, resulting in a continuum of RTW facilitation situations.

There is limited research on return-to-work policies following diagnosis of chronic illness from a comprehensive actor-oriented perspective. The existing literature usually focusses on just one stakeholder, overlooking the role of social dialogue actors. By bridging the two streams of literature and incorporating all potential actors and their interactions in a unitary model, the proposed framework provides a valuable tool to further discuss how successful RTW after a diagnosis of chronic illness can be facilitated.