Search Results (36)

Climate change today is considered one of the most severe global problems. Additionally, it is inherently linked to sustainable development, particularly considering that it is a widespread problem affecting virtually all regions, countries, and continents. Therefore, it is essential to monitor its perception and behaviours towards it. Hence, there is the need for a diagnosis of climate-change attitudes found in various parts of the world. This premise is the foundation for the original study reported in the article. It offers the results of a survey of 300 adult rural residents from southeastern Poland. The article aims to diagnose the respondents’ attitudes towards climate change in the context of sustainable development premises. Respondents’ opinions on the potential activities their local communities could take to be more sustainable are a substantial part of the results. The most common suggestions were low-emission public transport (in the Wadowice District, it was 55% of responses) and road and street redesign to promote safe cycling and walking environments (in the Kraków and Tarnów Districts, over 50% of responses). The respondents found the promotion of sustainable development among youth and older people to be the least important (in three out of five districts, this percentage did not exceed 20%). The other thematic block was specific environmental protection and sustainable development activities that society should prioritise over a five-year horizon. The participants considered the development of new ways to eliminate waste in total production (in the Tarnów District, over 73% of responses), new technologies to curb the carbon footprint and GHG emissions (over 63% in the Wadowice District), and new technologies to scrape and monitor air pollutants (approx. 70% in the Kraków District). Full article

The increasing adoption of electric vehicles (EVs) needs efficient and cost-effective charging solutions. This study presents a smart on-board charging system using low-cost materials while ensuring safe and optimized battery management. The proposed system is controlled by an Arduino MEGA 2560 microcontroller, integrating Pulse-Width Modulation (PWM) for precise voltage regulation and real-time monitoring of charging parameters, including voltage, current, and state of charge (SoC). The charging process is structured into three states (connection, standby, and charging) and follows a multi-stage strategy to prevent overcharging and prolong battery lifespan. A relay system and safety mechanisms detect disconnections and voltage mismatches, automatically halting charging when unsafe conditions arise. Experimental validation with a 12 V lead-acid battery verifies that the system follows standard charging profiles, ensuring optimal energy management and charging efficiency. The proposed charger demonstrates significant cost savings (~94.82 €) compared to commercial alternatives (1200 €–2000 €), making it a viable low-power solution for EV charging research and a valuable learning tool in academic environments. Future improvements include a printed circuit board (PCB) redesign to enhance system reliability and expand compatibility with higher voltage batteries. This work proves that affordable smart charging solutions can be effectively implemented using embedded control and modulation techniques. Full article

This study analyses pedestrian behaviour patterns at unsignalized crossings using multiple data-mining approaches, aiming to improve pedestrian safety by understanding the relationship between movement patterns, location, and infrastructure. Utilising the STATS19 dataset from the UK Department for Transport, applied data analysis techniques, including heatmap visualisation, association rule learning, and Principal Component Analysis (PCA) with clustering, to identify high-risk behaviours and provide targeted interventions. Heatmap visualisation identifies spatial patterns and high-risk areas, while association rule learning reveals the relationships between pedestrian behaviours and infrastructure elements, highlighting the importance of facility placement and accessibility in encouraging safe crossing. PCA combined with clustering effectively reduces data complexity, revealing key factors that influence pedestrian safety. The findings emphasise the need for appropriate infrastructure, such as strategically placed zebra crossings and central refuges, to guide pedestrian behaviour and reduce accident risks. Underutilised facilities like footbridges and subways require redesign to align with pedestrian preferences. By analysing the relationship between pedestrian behaviour and infrastructure, this study aligns with the United Nations’ sustainability goals, supporting evidence-based interventions to achieve safer and more sustainable urban development. The results of this study offer insights for urban planners to prioritise safety measures and infrastructure improvements that enhance pedestrian safety at unsignalized crossings. Full article

In the last few years, the materials research community has shown increased interest in Advanced Materials (AdMas) that are specifically designed to substitute the traditionally used materials, not only with a view to their sustainability, sourcing criticality, or scarcity, but also to maintaining or even enhancing their functionality and performance. The use of AdMas is particularly researched in sectors where the environmental impact of the traditional materials is substantial, in terms of waste production or resource consumption. Due to their novelty and potentially unpredictable impacts, and to add further value to their application, there is an increasing interest in the safety and sustainability of AdMas. In this context, a new 5-step Safe-and-Sustainable-by-Design (SSbD) framework was developed by the European Union, to support the (re-)design and development of novel materials. A guideline is presented for enforcing the (re-)design phase of the framework with paradigms to guide stakeholders and practically add value to the materials’ industry. The present manuscript analyzes the advances and challenges of the SSbD framework, showcasing its applicability and limitations and the added value compared to traditionally used assessment methodologies, to provide a comprehensive evaluation of the methodology and add value to the materials’ industry concerning safety and sustainability. Full article

Acute care provided in the hospital’s emergency department (ED) is a key component of the healthcare system that serves as an essential bridge between outpatient and inpatient care. However, due to the emergency-driven nature of presenting problems and the urgency of care required, the ED is more prone to unintended medication regimen changes than other departments. Ensuring quality use of medicines (QUM), defined as “choosing suitable medicines and using them safely and effectively”, remains a challenge in the ED and hence requires special attention. The role of pharmacists in the ED has evolved considerably, transitioning from traditional inventory management to delivering comprehensive clinical pharmacy services, such as medication reconciliation and review. Emerging roles for ED pharmacists now include medication charting and prescribing and active participation in resuscitation efforts. Additionally, ED pharmacists are involved in research and educational initiatives. However, the ED setting is still facing heightened service demands in terms of the number of patients presenting to EDs and longer ED stays. Addressing these challenges necessitates innovation and reform in ED care to effectively manage the complex, rising demand for ED care and to meet government-imposed service quality indicators. An example is redesigning the medication use process, which could necessitate a shift in skill mix or an expansion of the roles of ED pharmacists, particularly in areas such as medication charting and prescribing. Collaborative efforts between pharmacists and physicians have demonstrated positive outcomes and should thus be adopted as the standard practice in improving the quality use of medicines in the ED. Full article

Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset. Full article

There is limited research on robotic systems designed for Endoscopic Retrograde Cholangiopancreatography (ERCP) procedures using a side-view duodenoscope. The unique structure of the duodenoscope presents challenges to safely and precisely control the distal end pose. Control methods applied can reduce potential medical risks. We have redesigned the control section of the duodenoscope to facilitate its manipulation by a robotic system. An orthogonal compensator is employed to rectify the motion planes to standard planes. A hysteresis compensator based on the Prandtl-Ishlinskii model enables precise control of the distal pose of the duodenoscope. Furthermore, we utilize a contact force prediction model to prevent excessive contact force at the distal end. The performance of the modified duodenoscope is comparable to that of the standard duodenoscope. Following orthogonal compensation, the deviation angles of the motion planes is reduced by 32% to 98%. Post-hysteresis compensation, the root mean square error (RMSE) of the output angle of the distal end is decreased from 8.347° to 4.826°. The accuracy of distal end contact force prediction was approximately ±25% under conditions of high contact force. In conclusion, the modification and control strategy we proposed can achieve relatively safe and precise control of bending section, laying the foundation for the subsequent roboticization of duodenoscope systems for ERCP procedures. Full article

This study examined how socio-economic driving forces influence households’ choice of water, ranging from a piped water supply provided by Veolia to untreated sources contaminated with high levels of arsenic and pathogens. Households fall into three cluster groups based on variations in socio-economic status and physical, infrastructure, and institutional elements. About 64% of the variations are related to differences in awareness, willingness, and ability to pay for safe water sources. Families with higher monthly income showed interest in accepting Veolia’s house connection options, resulting in the shutdown of six community tap points and ultimately affecting the low-income households’ accessibility to Veolia water. A causal loop diagram showed five feedback loops influencing the choice of drinking contaminated water. Bayesian Network models were sensitive to the ability, accessibility, and willingness to pay for safe water, deep tube well distributions, installation and maintenance costs, ownership of tube wells, household income level, and the level of awareness. Results suggest that the risks of drinking contaminated water can be minimized by raising awareness; accepting arsenic removal techniques; sharing expenses; training for deep tube well installations and maintenance; increasing Veolia pipeline coverage; and redesigning the tap point distributions. These results help identify the relative importance of such interventions to improve water security in safe water-poor areas. Full article

As a crucial element in coal transportation, conveyor belts play a vital role, and monitoring their health is essential for the coal mine transportation system’s safe and efficient operation. This paper introduces a new ‘hardware-friendly’ method for monitoring belt conveyor damage, aiming to address the issue of large parameters and computational requirements in existing deep learning-based foreign object detection methods and their challenges in deploying on edge devices with limited computing power. This method is tailored towards edge computing and aims to reduce the parameters and computational load of foreign object recognition networks deployed on edge computing devices. This method improves the YOLOv8 object detection network and redesigns a novel lightweight ShuffleNetV2 network as the backbone network, making the network more delicate in recognizing foreign object features while reducing redundant parameters. Additionally, a simple parameter-free attention mechanism called SimAM is introduced to further enhance recognition efficiency without imposing additional computational burden. Experimental results demonstrate that the improved foreign object recognition method achieves a detection accuracy of 95.6% with only 1.6 M parameters and 4.7 G model computational load (FLOPs). Compared to the baseline YOLOv8n, the detection accuracy has improved by 3.3 percentage points, while the number of parameters and model computational load have been reduced by 48.4% and 42.0%, respectively. These works are more friendly to edge computing devices that tend to “hardware friendly” algorithms. The improved algorithm can reduce latency in the data transmission process, enabling the accurate and timely detection of non-coal foreign objects on the conveyor belt. This provides assurance for the subsequent host computer system to promptly identify and address foreign objects, thereby ensuring the safety and efficiency of the belt conveyor. Full article

Launched during the pandemic, the EU-funded JANUS project aimed to ensure the continuity of student workshops at universities using a virtual reality (VR) robotics laboratory. With the return to normality, the project has been redesigned to capitalise on the positive outcomes of the experience. The VR lab provides safe and unrestricted access to the labs and experiments with the machines, reducing the consequences of student mistakes and improving the user experience by allowing the experiment to be repeated from different angles, some of which are impossible to access in the real lab. In addition, integration with an interactive learning platform called “ViLLE” allows for continuous assessment of the learning experience. Self-evaluation of the material taught and learned can be integrated with the execution of the exercises that pave the way for Kaizen. Two VR workshops for the blended learning of robotics were developed during the JANUS project. Their evaluation reported favourable responses from the students whose learning performance was indirectly measured. Full article

The blood–brain barrier (BBB) is part of a neurovascular structure located in the brain’s micro vessels, that is essential to maintain brain homeostasis, but prevents the brain uptake of most drugs. Because of its importance in neuro-pharmacotherapy, the BBB has been the subject of extensive research since its discovery over 100 years ago. Major advances in understanding the structure and function of the barrier have been made. Drugs are re-designed to cross the BBB. However, despite these efforts, overcoming the BBB efficiently to treat brain diseases safely remains challenging. The majority of BBB research studies focus on the BBB as a homogenous structure throughout the different brain regions. However, this simplification may lead to an inadequate understanding of the BBB function with significant therapeutic consequences. From this perspective, we analyzed the gene and protein expression profiles of the BBB in the micro vessels from the brains of mice that were isolated from two different brain regions, namely the cortex and the hippocampus. The expression profile of the inter-endothelial junctional protein (claudin-5), three ABC transporters (P-glycoprotein, Bcrp and Mrp-1), and three BBB receptors (lrp-1, TRF and GLUT-1) were analyzed. Our gene and protein analysis showed that the brain endothelium in the hippocampus exhibits different expression profiles compared to the brain cortex. Specifically, brain endothelial cells (BECs) of the hippocampus express higher gene levels of abcb1, abcg2, lrp1, and slc2a1 compared to the BECs of the cortex regions with a trend of increase for claudin-5, while BECs of the cortex express higher gene levels of abcc1 and trf compared to the hippocampus. At the protein levels, the P-gp expression was found to be significantly higher in the hippocampus compared to the cortex, while TRF was found to be up-regulated in the cortex. These data suggest that the structure and function of the BBB are not homogeneous, and imply that drugs are not delivered similarly among the different brain regions. Appreciation of the BBB heterogeneity by future research programs is thus critical for efficient drug delivery and the treatment of brain diseases. Full article

Nanotechnology-based strategies have played a pivotal role in innovative products in different technological fields, including medicine, agriculture, and engineering. The redesign of the nanometric scale has improved drug targeting and delivery, diagnosis, water treatment, and analytical methods. Although efficiency brings benefits, toxicity in organisms and the environment is a concern, particularly in light of global climate change and plastic disposal in the environment. Therefore, to measure such effects, alternative models enable the assessment of impacts on both functional properties and toxicity. Caenorhabditis elegans is a nematode model that poses valuable advantages such as transparency, sensibility in responding to exogenous compounds, fast response to perturbations besides the possibility to replicate human disease through transgenics. Herein, we discuss the applications of C. elegans to nanomaterial safety and efficacy evaluations from one health perspective. We also highlight the directions for developing appropriate techniques to safely adopt magnetic and organic nanoparticles, and carbon nanosystems. A description was given of the specifics of targeting and treatment, especially for health purposes. Finally, we discuss C. elegans potential for studying the impacts caused by nanopesticides and nanoplastics as emerging contaminants, pointing out gaps in environmental studies related to toxicity, analytical methods, and future directions. Full article

Societies are getting more diverse, with social issues increasing, necessitating the need to intensify efforts to promote tolerance and respect for diversity. In this study, we report the approach employed to redesign and evaluate a general education course to enhance students’ knowledge of tolerance and respect for diversity, drawing on Islamic values in the United Arab Emirates. In collecting and analyzing data for the study, we adopted a qualitative approach to explore students’ nuanced and reflective understanding and application of the key concepts taught in the course. We retrieved and thematically analyzed 40 transcripts from sixty-nine students’ reflective assignments. Our results indicate that students see the education process based on Islamic principles as a socialization means for shaping human life, caring for others, demonstrating compassion, and sharing knowledge, as essential to fostering tolerance. Drawing on our findings, we recommend carefully redesigning courses to strengthen students’ knowledge of tolerance and respect for diversity using the authentic learning approach. This includes engaging them in activities to link class tasks to real-life contexts, providing them with safe learning spaces, and encouraging them to learn with peers from different backgrounds. Full article

Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet * that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model. Full article

Managing indoor environmental quality (IEQ) is a challenge in educational buildings in the wake of the COVID-19 pandemic. Adequate indoor air quality is essential to ensure that indoor spaces are safe for students and teachers. In fact, poor IEQ can affect academic performance and student comfort. This study proposes a framework for integrating occupants’ feedback into the building information modelling (BIM) methodology to assess indoor environmental conditions (thermal, acoustic and lighting) and the individual airborne virus transmission risk during teaching activities. The information contained in the parametric 3D BIM model and the algorithmic environment of Dynamo were used to develop the framework. The IEQ evaluation is based on sensor monitoring and a daily schedule, so the results show real problems of occupants’ dissatisfaction. The output of the framework shows in which range the indoor environmental variables were (optimal, acceptable and unacceptable) and the probability of infection during each lecture class (whether or not 1% is exceeded). A case study was proposed to illustrate its application and validate it. The outcomes provide key information to support the decision-making process for managing IEQ and controlling individual airborne virus transmission risks. Long-term application could provide data that support the management of ventilation strategies and protocol redesign. Full article