This research aimed to investigate the role of humanizing leadership in enhancing the effectiveness of change management strategies within organizations. Specifically, it focused on how humanizing leadership influences change outcomes and the extent to which organizational culture moderates this relationship. The study addressed critical questions regarding the impact of leadership behaviors, such as model vulnerability, emotional intelligence, open communication, and psychological safety on effective change management and employee performance. A quantitative approach was employed to provide a comprehensive analysis of the phenomena. Quantitative data were collected from a sample of 325 employees through surveys that measured perceptions of Humanizing leadership behaviors, organizational culture, and change outcomes. Data was analyzed by IBM SPSS 26.0. The findings revealed that humanizing leadership behaviors significantly enhances the success of change initiatives, primarily through improved employee engagement and reduced resistance. Organizational culture was found to play a moderating role, amplifying the positive effects of empathetic and inclusive leadership practices. The study provides actionable recommendations for organizational leaders and managers to foster a culture that supports humanizing leadership. By adopting leadership strategies that emphasize vulnerability, empathy, and inclusivity, organizations can enhance their adaptability and resilience against the backdrop of continuous change. These findings are particularly valuable for enhancing managerial practices and informing policy within corporate settings.

Bagasse fiber from sugarcane waste is used with epoxy resin to make natural composites. The raw fibers are treated chemically to improve compatibility and adherence with the epoxy polymer. It’s anticipated that epoxy resin matrix composites reinforced with bagasse particles would work as a trustworthy replacement for conventional materials utilized in the building and automobile sectors. The amount and distribution of reinforcing particles inside the matrix are two factors that impact the composite’s strength. Furthermore, the precise proportion of reinforcing elements—roughly 20–30 weight percent—into the matrix plays a critical role in providing a noticeable boost in improving the properties of the composites. This research investigates the impact of reinforcing alkali-treated bagasse and untreated bagasse powder into an epoxy matrix on aspects of mechanical and morphological characteristics. The hand layup technique is used to create alkali-treated bagasse and untreated bagasse powder-reinforced epoxy composites. Composites are designed with six levels of reinforcement weight percentages (5%, 10%, 15%, 20%, 25%, and 30%). Microstructural analysis was performed using SEM and optical microscopes to assess the cohesion and dispersion of the reinforcing particles throughout the hybrid composites’ matrix phase. With reinforcement loading up to 20 wt%, the tensile strength, impact strength, and toughness of epoxy-alkali-treated bagasse and untreated bagasse powder-reinforced composites increased. In contrast, treated bagasse epoxy composites were superior to untreated epoxy composites in terms of efficacy. The results indicate that 20 wt% alkali bagasse powder provides better mechanical properties than other combinations.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

Surface-enhanced Raman scattering (SERS) spectrum has the characteristics of fast-detection, high-sensitivity and low-requirements for sample pretreatment. It plays a more and more important role in the detection of organic pollutants. In this study, MIL-101 and Au nanoparticles were prepared by hydrothermal method and aqueous solution reduction method respectively, and MIL-101/Au composite nanoparticles were prepared by electrostatic interaction. The SERS properties of the composite substrate were optimized by adjusting the size of Au nanoparticles and the surface distribution density of MIL-101 nanoparticles. The detection limit of Rhodamine 6G (R6G) for the composite substrate with the optimal ratio was investigated, which was as low as 10^–11 M. It is proved that MIL-101/Au composite nanoparticles have high sensitivity to probe molecules. When they are applied to the detection of persistent organic pollutants, the detection limit for fluoranthene can reach 10^–9 M and for 3,3’,4,4’-tetrachlorobiphenyl (PCB-77) can reach 10^–5 M.

The mining industry significantly impacts the three pillars of sustainable development: the economy, the environment, and society. Therefore, it is essential to incorporate sustainability principles into operational practices. Organizations can accomplish this through knowledge management activities and diverse knowledge resources. A study of 300 employees from two of the largest mining corporations in South Kalimantan, Indonesia, found that four out of five elements of knowledge management—green knowledge acquisition, green knowledge storage, green knowledge application, and green knowledge creation—have a direct impact on the sustainability of businesses. The calculation was determined using Structural Equation Modelling (SEM). However, the study also found that the influence of collectivist cultural norms inhibits the direct effect of green knowledge sharing on corporate sustainable development. The finding suggests that companies operating in collectivist cultures may need to take additional measures to encourage knowledge sharing, such as rewarding employees for sharing their expertise on green initiatives, supportive organizational culture, clear expectations, and opportunities for social interaction.

The use of autonomous weapons systems (AWS) has led to several opposing legal opinions regarding their violations of international law. The responsibility of the state, individuals, and corporations as producers, designers, and programmers is all being taken into consideration. If the decision to kill humans without “meaningful human control” is transferred to computers, it would be hard to attribute accountability for the actions of AWS to their corporations. Consequently, this means that corporate actors will enjoy impunity in all cases. The present paper indicates that the most significant problem arising from the use of AWS is the attribution of responsibility for its violation. Corporations are not subject to liability for the legitimate use of weapons under international law. The main problem with corporate responsibility, according to article 25 (4) of the Rome Statute, is that the provision only relates to individual criminal responsibility and that the ICC shall only have jurisdiction over natural persons. Nevertheless, corporations may be held accountable under aspects of international law. The paper proposes a more positive view on artificial intelligence, raising corporations’ accountability in international law by historically linking the judging of business leaders. The article identifies aiding and abetting as well as co-perpetration as the two modes of accountability under international law potentially linked to AWS. The study also explores the main ambiguity in international law relating to corporate aiding and abetting of human rights violations by presenting the confusion on determining the standards of these 2 modes of liability before the ICC and International ad doc Tribunal. Moreover, with the new age of war heavily dependent on AI and AWS, one cannot easily and precisely ascertain who must be held accountable for war crimes because of the unanticipated facts in decision-making combined with the aiding or abetting of violations of international law. International law prioritizes the goal of ending impunity for the individual and largely neglects the need to achieve the same goal for corporate complicity. In sum, progress to regulate the use of AWS by corporate actors could be enormously helpful to the cause of ending impunity.