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The core attributes of conscientious brands: A stakeholder perspective
No full textConscientious brands go beyond corporate social responsibility initiatives, which are often disconnected from brand strategy, by embedding a moral belief system that drives strategic decisions and actions, and spurs positive transformative change. Recent research has highlighted several key attributes of conscientious brands, but most studies have only been based on the views of managers. To better understand the construct from a diverse stakeholder perspective, we conducted 68 in-depth qualitative interviews with senior managers of global brands, senior managers of marketing agencies, and consumers. The findings show that conscientious brands are driven by a transformative purpose, and a belief in stakeholder fairness, temporal responsibility and organizational openness. Additionally, the actions that result from beliefs are rooted in moral integrity and measured through key performance indicators. The findings also provide valuable nuances on the aligned, complementary, and conflictual perspectives of each stakeholder group
Machine Learning-Optimized Compact Wearable Frequency Reconfigurable Antenna for Sub-6 GHz/mm-Wave 5G Integration
No full textFuture 5G wireless systems will have substantial challenges in integrating the sub-6 GHz and millimeter-wave (mm-wave) bands due to their massive frequency ratios. This paper proposes a machine learning-optimized compact wearable frequency-reconfigurable antenna for sub-6 GHz/mm-wave 5G integration. Fabricated on a flexible Rogers Duroid substrate (27.8 × 14 × 0.508 mm³), the antenna initially employs a circular structure resonating at 28 GHz. Dual-band operation (3.5 GHz and 28 GHz) is achieved by etching an H-shaped slot into the rectangular patch. A PIN diode is employed to reconfigure the proposed antenna in the ON and OFF states. In the ON state, the antenna operates at 3.5 GHz and 28 GHz, achieving measured bandwidths of 25.4% and 73.2%, gains of 3.63 dBi and 5.25 dBi, and radiation efficiencies of 90.5% and 88%, respectively. In the OFF state, the antenna operates at 28 GHz, achieving a measured bandwidth of 72.9%, gain of 6.2 dBi, and a radiation efficiency of 89%. Bidirectional E-plane and omnidirectional H-plane radiation patterns are maintained across both bands. At 3.5 GHz, the specific absorption rate (SAR) value for 1 g and 10 g of human tissue is 0.438 W/kg and 0.0147 W/kg, while at 28 GHz, the SAR value is 0.801 W/kg and 1.09 W/kg, which comply with the FCC and ICNIRP standards. Bending tests (lap, chest, arm) demonstrate stable on-body performance. The antenna’s S11 was predicted using a supervised ML regression framework. Among tested algorithms, the decision tree achieved state-of-the-art accuracy (R²: 97.80%) with minimal errors (MAE: 0.72, MSE: 0.28, MSLE: 0.56, RMSLE: 0.81, RMSE: 0.66). The proposed antenna system is suitable for future 5G devices
A systematic review of research on just, equitable, responsible, and inclusive smart cities
Get PDFDigital technologies and infrastructure are essential to the development of smart cities. Yet, vulnerable populations often lack equitable access to such resources. In this context, integrating justice into smart city development serves as a crucial foundation for developing just and equitable cities. To explore this issue, we examined 3,067 articles and synthesized findings from 67 studies on justice in smart cities. Using deductive content analysis, we categorize justice issues into two distinct groups: types and dimensions. Among the various types of justice, infrastructural justice emerges as the most frequently discussed, appearing in 23 studies and highlighting significant disparities in access to basic urban infrastructure for marginalized communities. In terms of justice dimensions, procedural justice is the most prominent. Discussed in 27 studies, it emphasizes the importance of inclusive decision-making and the challenges posed by limited public awareness and tokenistic participation. The findings reveal that marginalized communities, particularly low-income groups, women, and individuals with disabilities, bear the brunt of exclusion, inequity, and marginalization in smart city developments. These communities are particularly vulnerable to gentrification, displacement, and reduced economic opportunities, further deepening existing inequalities. By positioning justice as a central element in smart city development, this study calls for a fundamental shift in the mindset of practitioners, advocating for policies and governance approaches that promote a just, equitable, responsible, and inclusive smart city ecosystem
Just The Way I am Wired: CEO Generation and Corporate Carbon Emissions
Get PDFIn this study, we analyse the link between CEO generational experience and corporate carbon emission pollution. When differentiating CEOs by their generational cohorts, we find that firms led by CEOs from the millennial generation and Generation X emit less carbon. Alternatively, firms led by CEOs from the Boomer generation tend to have a higher carbon footprint. One of the primary channels that explains the result is media coverage of climate concerns. With recent evidence suggesting that millennials engage more with modern media than other generational cohorts, the results of the analysis suggest that this exposure translates to reduced corporate carbon emissions when climate change concern is high. The findings are robust to alternative specifications, such as difference-in-differences regression, strict sample selection criteria and propensity score matching
Application of CEEMDAN algorithm in roundness error evaluation
No full textRoundness error is an important evaluation criterion for evaluating machining errors. Some advanced algorithms have been applied to roundness assessment, such as the empirical mode decomposition (EMD) algorithm. However, due to the issue of mode mixing in the EMD algorithm, this paper uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to evaluate roundness error. First, the same set of simulated circular hole radius measurement data was decomposed by EMD and CEEMDAN, respectively. Then, the direct component (DC) of each intrinsic mode function (IMF) generated after decomposition was used to calculate the cutoff wavenumbers of each IMF. The IMF was screened according to JB/T9924-2014, and the remaining IMF components were reconstructed (summed) after interfering signals were eliminated. The results of the roundness error calculated by the EMD and CEEMDAN algorithms were obtained and compared. The center of the circle fitted by the least square method was obtained by using the data points of the actual roundness contour coordinates, and the radius value of each sampling point was calculated. By comparing the experimental results, it was found that the accuracy of the CEEMDAN algorithm is higher than that of EMD, demonstrating the effectiveness of the CEEMDAN algorithm in roundness error evaluation
Six years strong: the transformative journey of the European Society of Cardiology Patient Forum
No full textAbstract unavailable
Leveraging LLMs for Non-Security Experts in Threat Hunting: Detecting Living off the Land Techniques
Get PDFThis paper explores the potential use of Large Language Models (LLMs), such as ChatGPT, Google Gemini, and Microsoft Copilot, in threat hunting, specifically focusing on Living off the Land (LotL) techniques. LotL methods allow threat actors to blend into regular network activity, which makes detection by automated security systems challenging. The study seeks to determine whether LLMs can reliably generate effective queries for security tools, enabling organisations with limited budgets and expertise to conduct threat hunting. A testing environment was created to simulate LotL techniques, and LLM-generated queries were used to identify malicious activity. The results demonstrate that LLMs do not consistently produce accurate or reliable queries for detecting these techniques, particularly for users with varying skill levels. However, while LLMs may not be suitable as standalone tools for threat hunting, they can still serve as supportive resources within a broader security strategy. These findings suggest that, although LLMs offer potential, they should not be relied upon for accurate results in threat detection and require further refinement to be effectively integrated into cybersecurity workflows
Optimized Resource Allocation for Cloud-Native 6G Networks: Zero-Touch ML Models in Microservices-based VNF Deployments
No full text6G, the next generation of mobile networks, is set to offer even higher data rates, ultra-reliability, and lower latency than 5G. New 6G services will increase the load and dynamism of the network. Network Function Virtualization (NFV) aids with this increased load and dynamism by eliminating hardware dependency. It aims to boost the flexibility and scalability of network deployment services by separating network functions from their specific proprietary forms so that they can run as virtual network functions (VNFs) on commodity hardware. It is essential to design an NFV orchestration and management framework to support these services. However, deploying bulky monolithic VNFs on the network is difficult, especially when underlying resources are scarce, resulting in ineffective resource management. To address this, microservices-based NFV approaches are proposed. In this approach, monolithic VNFs are decomposed into ‘micro’ VNFs, increasing the likelihood of their successful placement and resulting in more efficient resource management. This article discusses the proposed framework for resource allocation for microservices-based services to provide end-to-end Quality of Service (QoS) using the Double Deep Q Learning (DDQL) approach. Furthermore, to enhance this resource allocation approach, we discussed and addressed two crucial sub-problems: the need for a dynamic priority technique and the presence of the low-priority starvation problem. Using the Deep Deterministic Policy Gradient (DDPG) model, an Adaptive Scheduling model is developed that effectively mitigates the starvation problem. Additionally, the impact of incorporating traffic load considerations into deployment and scheduling is thoroughly investigated
A hybrid machine learning modelling for optimization of flood susceptibility mapping in the eastern Mediterranean
No full textFloods are considered one of the most destructive natural disasters due to the human and economic losses caused. The Eastern Mediterranean region is subject to devastating annual flood events due to the complex geographical characteristics of this region. Precise and reliable flood susceptibility prediction represents a complex and critical gap in the Eastern Mediterranean that provides a solid basis for developing flood risk management measures. Integrating machine learning (ML) algorithms and geospatial techniques represents a unique tool for reliable flood susceptibility prediction. This evaluation aims to improve flood susceptibility prediction in the Eastern Mediterranean by comparing the performance of four ML algorithms. This evaluation aims to optimize flood susceptibility prediction in the Eastern Mediterranean by comparing the performance of four ML algorithms, i.e. extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM) and artificial neural network (ANN), and hybridizing the strongest-performing algorithm with the other algorithms. In the Hrysoon river basin in western Syria, 2100 flood events with twenty driving factors were precisely identified to achieve the aim of this investigation. The performance of each algorithm was assessed using various error indicators, including the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). The results showed that the XGB (AUC = 0.995) algorithm achieved the strongest performance compared to the RF (AUC = 0.991), ANN (AUC = 0.983) and SVM (AUC = 0.979). Regarding the hybridization process, the results revealed that the XGB-SVM performance was the strongest (AUC = 0.995), followed by XGB-ANN and XGB-RF with an AUC value of 0.994. The current assessment illustrated that the distance to river is the most influential among conditioning factors followed by aspect, elevation, slope and rainfall. Overall, this study provided objective and constructive outputs that improved the accuracy of improving flood vulnerability in this region. These outputs enable the establishment of sustainable land management procedures in the Eastern Mediterranean and in Syria, especially in the post-war phase
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