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WASH-friendly schools for whom? Integrating the perception of different school stakeholders in Brazil
Get PDFEnsuring that schools are water, sanitation and hygiene (WASH) friendly requires WASH to be visibly implemented, inclusive and sustainable, engaging the entire school community, including direct and indirect stakeholders (school staff, students and their families). However, students, staff, and parents are often overlooked in the design of WASH solutions. This study aims to contrast different school stakeholders' perspectives on WASH and evaluate the extent to which the Human Right to Water and Sanitation (HRtWS) and its normative contents are being realized in schools within the basic education system, covering students aged 0 to 18. For that purpose, a mixed-method study was conducted involving seven schools located in a small municipality in the southeast region of Brazil. Data collection methods included on-site observations in schools, in-depth interviews with school staff and parents/legal guardians of students, and a one-day art-based research workshop with students around drinking water, sanitation, solid waste management and COVID-19. Several inconsistencies were identified when comparing primary findings with the schools' information previously provided by the Brazilian Government, including differences in the number of schools and levels of education offered, water sources, absence of bathrooms, and waste burning instead of solid waste collection. A clear violation of all the normative contents of the HRtWS was observed, which, in turn, were triggering and exposing students to incidents of violence such as bullying and verbal abuse. There was a misalignment among stakeholders about which interventions implemented amidst the COVID-19 pandemic are still in place. Moreover, discrepancies among school stakeholders' perceptions of WASH suggest insufficient communication among actors. As a result of this research, a list of recommendations formulated by stakeholders and the involvement and alignment of all stakeholders are the first steps in successfully co-creating solutions
A comprehensive study on the effect of the pyrolysis temperature on the products of the flash pyrolysis of waste tires
Get PDFThis study investigates the pyrolysis of tire granulates in an entrained-flow reactor at temperatures ranging from 500 °C to 750 °C, using two different feedstocks: passenger car tires (PCT) and truck tires (TT). The objective of the experiments is to determine the influence of temperature on the yield and properties of the pyrolysis products, namely pyrolytic carbon black, oil, and gas. The results show that as the temperature increases, the yield of the solid product decreases due to the increased conversion of rubber particles into volatiles. Approximately 90 % conversion is achieved at a temperature of 600 °C, after which no significant improvement in volatile conversion is observed. The solid carbon product of the pyrolysis process is compared with commercial N660-grade carbon black in terms of its physicochemical properties, and the results are discussed. An increase in temperature leads to a decrease in oil yield, accompanied by a corresponding increase in gas yield. This increase in gas yield is attributed to the thermal cracking of pyrolysis oil vapors into permanent gases, such as H2, CH4 and C1-C4 hydrocarbons, at higher temperatures.</p
A comparison of reinforcement learning policies for dynamic vehicle routing problems with stochastic customer requests
Get PDFThis paper presents directions for using reinforcement learning with neural networks for dynamic vehicle routing problems (DVRPs). DVRPs involve sequential decision-making under uncertainty where the expected future consequences are ideally included in current decision-making. A frequently used framework for these problems is approximate dynamic programming (ADP) or reinforcement learning (RL), often in conjunction with a parametric value function approximation (VFA). A straightforward way to use VFA in DVRP is linear regression (LVFA), but more complex, non-linear predictors, e.g., neural network VFAs (NNVFA), are also widely used. Alternatively, we may represent the policy directly, using a linear policy function approximation (LPFA) or neural network PFA (NNPFA). The abundance of policies and design choices complicate the use of neural networks for DVRPs in research and practice. We provide a structured overview of the similarities and differences between the policy classes. Furthermore, we present an empirical comparison of LVFA, LPFA, NNVFA, and NNPFA policies. The comparison is conducted on several problem variants of the DVRP with stochastic customer requests. To validate our findings, we study realistic extensions of the stylized problem on (i) a same-day parcel pickup and delivery case in the city of Amsterdam, the Netherlands, and (ii) the routing of robots in an automated storage and retrieval system (AS/RS). Based on our empirical evaluation, we provide insights into the advantages and disadvantages of neural network policies compared to linear policies, and value-based approaches compared to policy-based approaches.</p
Population balance modelling and reconstruction by quadrature method of moments for wet granulation
Get PDFPopulation balance methods utilised in multiphase flow simulations mark a significant advancement in computational fluid dynamics. However, existing approaches exhibit shortcomings, such as being prone to inaccuracies or being computationally prohibitive. Addressing these challenges, a recent innovation in closure for the method of moments is the introduction of quadrature based moments methods (QBMM). Discretising a distribution by a number of discrete elements, QBMM facilitate efficient and accurate tracking of density distributions, particularly for particle size distributions (PSD). However, obtaining the full particle size distribution information using these methods requires reconstructing the distribution from a finite set of moments, which is not a trivial step.This study introduces a novel combination of the maximum entropy reconstruction (MER) and QBMM, establishing a robust and rapid framework for the time evolution and reconstruction of PSDs. As proof of concept for this framework, we focus on the direct quadrature method of moments (DQMOM) with spatially homogeneous and monovariate distributions. We show that coupling of MER with DQMOM has numerous advantages. To verify the framework, special cases of constant growth, aggregation, and breakage are considered for which analytical solutions can be found. Furthermore, we show the advantage of using DQMOM with volume-based over length-based distributions, and address numerical as well as theoretical issues.Application of the framework is successfully conducted on the evolution of the PSD from a twin-screw wet granulation dataset, considering all active primary physical mechanisms in a wet granulation process, namely growth, aggregation, and breakage. This showcases the consistency of the proposed framework and underscores its applicability to real-world scenarios
Moving beyond us-versus-them polarization towards constructive conversations
Get PDFThe global surge of political polarization poses a significant threat to liberal democracies. The prevailing “us-versus-them” mentality prevents leaders from effectively addressing societal issues. While intergroup dialogue shows promise in bridging divisions among diverse identity groups, the dynamics of real-life conversations between individuals with opposing political identities remain underexplored. This study investigates a unique case study of constructive face-to-face interactions among American political elites on a contentious issue. By examining how opposing leaders collaborate towards shared goals, the research identifies communicative actions that can reduce polarization. This study delves into the complex dynamics of polarization, as both an issue-based and identity-based conflict, focusing on interactional framing strategies leaders use to navigate their differences. The findings reveal that political leaders bridge ideological divides by embracing multiple frames, reconnecting conflicting frames, and developing neutral non-political frames. They also foster positive relations, use superordinate identities, and decrease social distance, thereby bridging their identity gap. By fostering convergence rather than accentuating differences, they effectively counteract polarization. Studying rare examples of constructive bipartisan collaboration offers valuable insights into reducing polarization, restoring political trust among the general public, and mitigating broader societal impacts, ultimately strengthening the foundations of a healthy democracy.</p
Leaf carbon-based constituents of temperate forest species retrieved using PROSPECT-PRO
Get PDFThe retrieval of leaf carbon-based constituents of vegetation species and their separation from the overall leaf mass per area using radiative transfer models was historically challenging, until the recent re-calibration of the PROSPECT-PRO model. Nevertheless, it remains unexplored for temperate tree species. This study evaluated the retrieval of carbon-based constituents of fresh leaf samples from four European temperate tree species using the PROSPECT-PRO model. We collected a comprehensive dataset of 249 fresh leaf samples obtained from the top canopy of temperate forest species in Germany and the Netherlands. Measurements of the carbon-based constituents were conducted in the lab using a novel customisation of the traditional method, and the spectral measurements of leaf samples were obtained with the ASD FieldSpec-3 and integrating sphere. Employing a look-up table approach, the PROSPECT-PRO model was inverted across the 800 to 2400 nm wavelength range, and the model was also applied with optimal bands selected for leaf carbon-based constituents. The retrieval of carbon-based constituents yielded reasonable accuracy for the four species: European beech (R2 = 0.53, NRMSE = 0.36), English oak (R2 = 0.45, NRMSE = 0.34), Scots pine (R2 = 0.63, NRMSE = 0.36) and Norway spruce (R2 = 0.62, NRMSE = 0.26). Slight improvements were observed in the retrieval accuracies with the identified optimal spectral bands. As such, the NRMSE values decreased by 0.03 and 0.05 for European beech and English oak; however, they slightly worsened for Norway spruce and Scots pine by 0.05 and 0.01, respectively. This study highlights the effect of considering individual constituents in laboratory measurements and during the calibration of absorption coefficients within the model. This could have a more substantial influence on the retrieval accuracy of carbon-based constituents than the influence of water interference or solely applying optimal spectral bands
The state-of-the-art of N-of-1 therapies and the IRDiRC N-of-1 development roadmap
No full textIn recent years, a small number of people with rare diseases caused by unique genetic variants have been treated with therapies developed specifically for them. This pioneering field of genetic N-of-1 therapies is evolving rapidly, giving hope for the individualized treatment of people living with very rare diseases. In this Review, we outline the concept of N-of-1 individualized therapies, focusing on genetic therapies, and illustrate advances and challenges in the field using cases for which therapies have been successfully developed. We discuss why the traditional drug development and reimbursement pathway is not fit for purpose in this field, and outline the pragmatic, regulatory and ethical challenges this poses for future access to N-of-1 therapies. Finally, we provide a roadmap for N-of-1 individualized therapy development
Predicting retail customers' distress in the finance industry:An early warning system approach
Get PDFPredicting credit defaults is crucial for financial institutions to assess risk and make informed lending decisions. One of the most recent strategies banks and financial institutions have been testing to minimize losses that arise from credit default is the deployment of Early Warning Systems (EWS). By nature, this technique was primarily proposed and explored for commercial customers. However, this study proposes a comprehensive data-driven approach to model Early Warning Systems (EWS) for retail customers in the financial industry while using different Machine Learning (ML) models. We use Logistic Regression (LR), Gradient Boosting (GB), and Random Forest (RF) to classify customers' status, indicating the need to include potential default in a “watch list”. Additionally, we implement a fourth model (i.e., meta-model), whose predictions are based on the output of the other algorithms used (LR, GB, RF). Results indicate that the meta-model achieves higher accuracy than GB or any other individual model tested. From the management perspective, the findings indicate that a higher threshold for warning signals results in alerts closer to the overdue date, indicating increased sensitivity to emerging client deterioration. Conversely, lower thresholds focus more on the client's overall status. Furthermore, using the top ten features for training yields satisfactory overall results, but incorporating features beyond the top ten provides valuable supplementary information to be used in the decision-making process.</p
Smart Tech is all Around us – Bridging Employee Vulnerability with Organizational Active Trust-Building
Get PDFPublic and academic opinion remains divided regarding the benefits and pitfalls of datafication technology in organizations, particularly regarding their impact on employees. Taking a dual-process perspective on trust, we propose that datafication technology can create small, erratic surprises in the workplace that highlight employee vulnerability and increase employees’ reliance on the systematic processing of trust. We argue that these surprises precipitate a phase in the employment relationship in which employees more actively weigh trust-related cues, and the employer should therefore engage in active trust management to protect and strengthen the relationship. Our paper develops a framework of symbolic and substantive strategies to guide organizations’ active trust management efforts to (re-)create situational normality, root goodwill intentions, and enable a more balanced interdependence between the organization and its employees. We discuss the implications of our paper for reconciling competing narratives about the future of work and for developing an understanding of trust processes.</p
Cultural differences in microblogging:How Western IT Companies adapt Twitter (X) activities to the Chinese Weibo context
Get PDFCompanies worldwide use microblogs to communicate with stakeholders, but there may be cultural differences in how they do it. A content analysis was conducted comparing Twitter (currently known as X) and Weibo accounts of four Western IT companies; Weibo accounts of four similar Chinese companies served as benchmarks. Results show that Western microblog activities differed in many respects from Chinese practices. Specifically, they focused more on technology and less on marketing and community-building. In their localization strategies, Western companies chose to adapt (e.g., paying attention to community building), not adapt (e.g., keeping their technological profile), or cautiously adapt (e.g., using somewhat more emojis or reluctantly experimenting with sweepstakes). Cultural differences on microblogging platforms are comprehensive and multifaceted and cannot be easily reduced to established cultural dimensions. Processes of cultural adaptation, therefore, depend on profound knowledge of the business environment and cultural differences.</p