Andrzej Raszkowski, Bartosz Bartniczak, Amit Kumar
The aim of this article is to assess the quality of life (QoL) in Central and Eastern Europe through a comparative analysis that identifies key determinants of well-being and evaluates their relative importance across countries. The study situates the assessment within the unique historical and socio-economic transformation of the region, from centrally planned economies to market-oriented systems and subsequent integration into the European Union. Using 42 indicators grouped into thematic domains, material living conditions, health, education, labour activity, safety, governance, and the environment, the research applies descriptive statistics and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to construct a composite QoL index and rank eleven countries according to their performance. The findings reveal substantial disparities, with Estonia and Lithuania achieving the highest QoL scores, while Romania and Bulgaria lag behind. The results highlight the decisive influence of income levels, healthcare accessibility, educational attainment, and environmental quality on overall well-being. The paper concludes with evidence-based policy implications, emphasising investment in education, healthcare systems, sustainable infrastructure, and environmental protection as essential pathways to more balanced and inclusive development across the region.
This paper presents a theoretical discussion for environmentally-conscious job deployment and migration in cloud environments, aiming to minimize the environmental impact of resource provisioning while incorporating sustainability requirements. As the demand for sustainable cloud services grows, it is crucial for cloud customers to select data center operators based on sustainability metrics and to accurately report the ecological footprint of their services. To this end, we analyze sustainability reports and define comprehensive environmental impact profiles for data centers, incorporating key sustainability indicators. We formalize the problem as an optimization model, balancing multiple environmental factors while respecting user preferences. A simulative case study demonstrates the {potential} of our approach compared to baseline strategies that optimize for single sustainability factors.
In todays increasingly digital world, data has become one of the most valuable assets for organizations. With the rise in cyberattacks, data breaches, and the stringent regulatory environment, it is imperative to adopt robust data protection strategies. One such approach is the use of governance frameworks, which provide structured guidelines, policies, and processes to ensure data protection, compliance, and ethical usage. This paper explores the role of data governance frameworks in protecting sensitive information and maintaining organizational data security. It delves into the principles, strategies, and best practices that constitute an effective governance framework, including risk management, access controls, data quality assurance, and compliance with regulations like GDPR, HIPAA, and CCPA. By analyzing case studies from various sectors, the paper highlights the practicalchallenges, limitations, and advantages of implementing data governance frameworks. Additionally, the paper examines how data governance frameworks contribute to transparency, accountability, and operational efficiency, while also identifying emerging trends and technologies that enhance data protection. Ultimately, the paper aims to provide a comprehensive understanding of how governance frameworks can be leveraged to safeguard organizational data and ensure its responsible use.
As memory technologies continue to shrink and memory error rates increase, the demand for stronger reliability becomes increasingly critical. Fine-grain memory replication has emerged as an appealing approach to improving memory fault tolerance by augmenting conventional memory protection based on error-correcting codes with an additional layer of redundancy that replicates data across independent failure domains, such as replicating memory pages across different NUMA sockets. This method can tolerate a broad spectrum of memory errors, from individual memory cell failures to more complex memory controller failures. However, applying memory replication without a holistic consideration of the interaction between error-correcting codes and replication can result in redundant duplication and unnecessary storage overhead. We propose Replication-Aware Memory-error Protection (RAMP), a model that helps explore error protection strategies to improve the storage efficiency of memory protection in memory systems that utilize memory replication for performance and availability. We use RAMP to determine a protection strategy that can lower the storage cost of individual replicas while still ensuring robust protection through the collective protection conferred by multiple replicas. Our evaluation shows that a solution derived with RAMP enhances the storage efficiency of a state-of-the-art memory protection mechanism when paired with rack-level replication for disaggregated memory. Specifically, we can reduce the storage cost of memory protection from 27% down to 17.7% with minimal performance overhead.
<p>During the Quaternary, ice sheets experienced several retreat–advance cycles, strongly influencing climate patterns. In order to properly simulate these phenomena, it is preferable to use physics-based models instead of parameterizations to estimate the surface mass balance (SMB), which strongly influences the evolution of the ice sheet. To further investigate the potential of these SMB models, this work evaluates the BErgen Snow SImulator (BESSI), a multi-layer snow model with high computational efficiency, as an alternative to providing the SMB for the Earth system model iLOVECLIM for multi-millennial simulations as in paleo-studies. We compare the behaviors of BESSI and insolation temperature melt (ITM), an existing SMB scheme of iLOVECLIM during the Last Interglacial (LIG). Firstly, we validate the two SMB models using the regional climate model Modèle Atmosphérique Régional (MAR) as forcing and reference for the present-day climate over the Greenland and Antarctic ice sheets. The evolution of the SMB over the LIG (130–116 ka) is computed by forcing BESSI and ITM with transient climate forcing obtained from iLOVECLIM for both ice sheets. For present-day climate conditions, both BESSI and ITM exhibit good performance compared to MAR despite a much simpler model setup. While BESSI performs well for both Antarctica and Greenland for the same set of parameters, the ITM parameters need to be adapted specifically for each ice sheet. This suggests that the physics embedded in BESSI allows better capture of SMB changes across varying climate conditions, while ITM displays a much stronger sensitivity to its tunable parameters. The findings suggest that BESSI can provide more reliable SMB estimations for the iLOVECLIM framework to improve the model simulations of the ice sheet evolution and interactions with climate for multi-millennial simulations.</p>
Nicola Trozzi, Wiktoria Wodniok, Robert Kelly-Bellow
et al.
Abstract Background Plant growth and morphogenesis is a mechanical process controlled by genetic and molecular networks. Measuring mechanical properties at various scales is necessary to understand how these processes interact. However, obtaining a device to perform the measurements on plant samples of choice poses technical challenges and is often limited by high cost and availability of specialized components, the adequacy of which needs to be verified. Developing software to control and integrate the different pieces of equipment can be a complex task. Results To overcome these challenges, we have developed a computer automated micro-extensometer combined with low-cost optical tracking (Camelot) that facilitates measurements of elasticity, creep, and yield stress. It consists of three primary components: a force sensor with a sample attachment point, an actuator with a second attachment point, and a camera. To monitor force, we use a parallel beam sensor, commonly used in digital weighing scales. To stretch the sample, we use a stepper motor with a screw mechanism moving a stage along linear rail. To monitor sample deformation, a compact digital microscope or a microscope camera is used. The system is controlled by MorphoRobotX, an integrated open-source software environment for mechanical experimentation. We first tested the basic Camelot setup, equipped with a digital microscope to track landmarks on the sample surface. We demonstrate that the system has sufficient accuracy to measure the stiffness in delicate plant samples, the etiolated hypocotyls of Arabidopsis, and were able to measure stiffness differences between wild type and a xyloglucan-deficient mutant. Next, we placed Camelot on an inverted microscope and used a C-mount microscope camera to track displacement of cell junctions. We stretched onion epidermal peels in longitudinal and transverse directions and obtained results similar to those previously published. Finally, we used the setup coupled with an upright confocal microscope and measured anisotropic deformation of individual epidermal cells during stretching of an Arabidopsis leaf. Conclusions The portability and suitability of Camelot for high-resolution optical tracking under a microscope make it an ideal tool for researchers in resource-limited settings or those pursuing exploratory biomechanics work.
Efficient removal and recovery of uranium from mining wastewater are essential for environmental protection and resource sustainability. Microbial reduction of soluble U(VI) to insoluble U(IV) is a promising strategy, but the role of biostimulation via tailored carbon sources and electrochemical inputs remains underexplored. This study investigated how carbon sources and electrode stimulation affect U(VI) reduction efficiency, product formation, microbial communities, and metabolic functions. U(VI) removal followed the order of carbon source: glucose > lactic acid > sodium acetate. Electro-stimulation markedly enhanced U(VI) reduction, especially under sodium acetate conditions with E24h increased from 65.0% to 90.7% at 0.7 V, by promoting carbon sources utilization and accelerating the removal of competitive anions. Glucose and lactic acid promoted the formation of UO2, while sodium acetate favored U3O8. Electro-stimulation facilitated the formation of compact uranium precipitates, enhancing recovery potential and minimizing reoxidation risk. Electrochemical analyses revealed that glucose and lactic acid exhibited superior electrochemical behavior compared to sodium acetate. Combined biostimulation enriched redox-active, electroactive, and EPS-secreting microbial taxa, along with functional genes related to U(VI) reduction, electron transfer, and carbon metabolism. Glucose and lactic acid imposed stronger selection on microbial and genetic structures than sodium acetate. Electro-stimulation promoted metabolic diversification, enhancing microbial resilience and functional redundancy. This study offers valuable insights into electrochemical enhancement of the biological treatment of uranium-bearing wastewater.
Accommodating pedestrians crossing midblock has been shown to have harmful environmental consequences because of increased fuel consumption and CO2 emissions. Somewhat surprisingly, no studies were devoted to mitigating the environmental impact of midblock crossing. Our main contribution is to propose schemes that mitigate the increased fuel consumption and CO2 emissions due to pedestrian midblock crossing by leveraging information about the location and expected duration of the crossing. This information is shared in a timely manner with approaching cars. We evaluated the impact of car decisions on fuel consumption and emissions by exploring potential trajectories that cars may take as a result of messages received. Our extensive simulations showed that timely dissemination of pedestrian crossing information to approaching vehicles can reduce fuel consumption and emissions by up to 16.7%.
The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI, possibly shifting impacts from the use phase to the manufacturing phase in the life-cycle of hardware. This paper investigates the evolution of individual graphics cards production impacts and of the environmental impacts associated with training Machine Learning (ML) models over time. We collect information on graphics cards used to train ML models and released between 2013 and 2023. We assess the environmental impacts associated with the production of each card to visualize the trends on the same period. Then, using information on notable AI systems from the Epoch AI dataset we assess the environmental impacts associated with training each system. The environmental impacts of graphics cards production have increased continuously. The energy consumption and environmental impacts associated with training models have increased exponentially, even when considering reduction strategies such as location shifting to places with less carbon intensive electricity mixes. These results suggest that current impact reduction strategies cannot curb the growth in the environmental impacts of AI. This is consistent with rebound effect, where the efficiency increases fuel the creation of even larger models thereby cancelling the potential impact reduction. Furthermore, these results highlight the importance of considering the impacts of hardware over the entire life-cycle rather than the sole usage phase in order to avoid impact shifting. The environmental impact of AI cannot be reduced without reducing AI activities as well as increasing efficiency.
为推动黄曲霉毒素B1(AFB1)降解技术的应用，寻求高效、快捷、安全的AFB1降解技术，促进食品中黄曲霉毒素的防治工作，对AFB1降解技术（物理降解技术、化学降解技术、生物降解技术）产生的降解产物以及降解后食品安全性评价研究的现状进行了论述，概括了降解技术的不足之处，并对降解技术的发展趋势进行展望。物理降解技术较适合大规模应用，但微波、脉冲电场、低温等离子体等技术仍处于研发阶段，无法确保该技术的安全性与可靠性。化学降解技术的研究比较常见，但存在食品感官品质变差，营养成分损失或破坏，易引入新的化学残留等不足。生物降解技术具有性质温和，不造成食品中营养成分大量损失且绿色环保等优点，但仍处于实验室研发阶段。在今后的研究中，应加强寻找新型纳米材料发展光降解技术、或各种技术联合使用、或利用基因工程联合酶法脱毒等新型技术，同时应更深入地研究降解机制、降解产物、降解路径以及降解产物的安全性。In order to promote the application of aflatoxin B1(AFB1) degradation technology, seek efficient, fast and safe AFB1 degradation technology, and promote the prevention and control of aflatoxin in food, the research status of degradation products produced by AFB1 degradation technology (physical degradation technology, chemical degradation technology, biological degradation technology) and food safety evaluation after degradation were discussed. The shortcomings of degradation technology were summarized, and the development trend of degradation technology was prospected. Physical degradation technology is more suitable for large-scale application, but microwave, pulsed electric field, low temperature plasma and other technologies are still in the research and development stage, which cannot ensure the safety and reliability of the technology. The study of chemical degradation technology is relatively common, but there are some shortcomings, such as poor sensory quality of food, loss or destruction of nutrients, and easy introduction of new chemical residues. Biological degradation technology has the advantages of mild nature, no loss of nutrients in food and green environmental protection, but it is still in the laboratory research and development stage. In the future research, the search for new nanomaterials to develop photodegradation technology, the combination of various technologies, and the use of genetic engineering combined with enzyme detoxification method and other new technologies should be strengthened. At the same time, the degradation mechanism, degradation products, degradation pathways and the safety of degradation products should be further studied.
Maruf Misaal, Lai Fatt Chuah, Mokhtar Kasypi
et al.
In an interconnected world dominated by global trade and intricate supply chain management, the transportation and management of dangerous cargo such as flammable liquids, toxic chemicals and radioactive materials, present multifaceted challenges. These hazardous substances pose significant environmental and health risks, necessitating rigorous safety measures and regulatory oversight. This comprehensive overview examines the various types of dangerous cargo, their environmental implications and notable case studies, highlighting the critical importance of international cooperation and stringent regulations. It delves into the regulatory frameworks governing the transport of hazardous materials by rail, sea, air and land, emphasizing the pivotal role of institutions like the International Maritime Dangerous Goods Code and the Environmental Protection Agency. Analysis indicates a need for improved response times in monitoring programs, necessitating adaptability to diverse environments and specific circumstances. Monitoring and impact assessment programs within emergency response frameworks differ from those aimed at detecting long-term trends in physical, biological and chemical variables.
Chemical engineering, Computer engineering. Computer hardware
Generative adversarial networks (GANs) have shown remarkable success in image synthesis, making GAN models themselves commercially valuable to legitimate model owners. Therefore, it is critical to technically protect the intellectual property of GANs. Prior works need to tamper with the training set or training process, and they are not robust to emerging model extraction attacks. In this paper, we propose a new ownership protection method based on the common characteristics of a target model and its stolen models. Our method can be directly applicable to all well-trained GANs as it does not require retraining target models. Extensive experimental results show that our new method can achieve the best protection performance, compared to the state-of-the-art methods. Finally, we demonstrate the effectiveness of our method with respect to the number of generations of model extraction attacks, the number of generated samples, different datasets, as well as adaptive attacks.
Yoonchang Sung, Zhiang Chen, Jnaneshwar Das
et al.
Robotics has dramatically increased our ability to gather data about our environments, creating an opportunity for the robotics and algorithms communities to collaborate on novel solutions to environmental monitoring problems. To understand a taxonomy of problems and methods in this realm, we present the first comprehensive survey of decision-theoretic approaches that enable efficient sampling of various environmental processes. We investigate representations for different environments, followed by a discussion of using these presentations to solve tasks of interest, such as learning, localization, and monitoring. To efficiently implement the tasks, decision-theoretic optimization algorithms consider: (1) where to take measurements from, (2) which tasks to be assigned, (3) what samples to collect, (4) when to collect samples, (5) how to learn environment; and (6) who to communicate. Finally, we summarize our study and present the challenges and opportunities in robotic environmental monitoring.
Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training a NeRF has never been an easy task, the protection of its model copyright should be a priority. In this paper, by analyzing the pros and cons of possible copyright protection solutions, we propose to protect the copyright of NeRF models by replacing the original color representation in NeRF with a watermarked color representation. Then, a distortion-resistant rendering scheme is designed to guarantee robust message extraction in 2D renderings of NeRF. Our proposed method can directly protect the copyright of NeRF models while maintaining high rendering quality and bit accuracy when compared among optional solutions.
Introduction The burden of Mycobacterium avium complex (MAC) lung disease is increasing globally and treatment outcome is in general poor. Therapeutic drug monitoring has the potential to improve treatment outcome by ensuring adequate drug exposure. However, very limited population-based studies exist for MAC lung disease. This study aims to describe the distribution of drug exposure for key antimycobacterial drugs at population level, and to analyse them in relationship to treatment outcome in patients with MAC lung disease.Methods and analysis A prospective cohort aiming to include 100 adult patients diagnosed with and treated for MAC lung disease will be conducted in Shanghai Pulmonary Hospital, China. Blood samples will be collected after 1 month MAC treatment for measurement of macrolides, rifamycin, ethambutol, amikacin and/or fluoroquinolones, using a validated liquid-chromatography tandem mass spectrometry method. Respiratory samples will be collected at inclusion and once every 3 months for mycobacterial culture until treatment completion. Minimum inhibitory concentration (MIC) determination will be performed using a commercial broth microdilution plate. In addition to mycobacterial culture, disease severity and clinical improvement will be assessed from the perspective of lung function, radiological presentation and self-reported quality of life. Whole genome sequencing will be performed for any longitudinal isolates with significant change of MIC to explore the emergence of drug resistance-conferring mutations. The relationship between drug exposure and treatment outcome will be analysed and potential confounders will be considered for adjustment in multivariable models. Meanwhile, the associations between drug exposure in relation to MIC and markers of treatment response will be explored using Cox proportional hazards or binary logistic regression models, as appropriate.Ethics and dissemination This study has been approved by the ethics committee of Shanghai Pulmonary Hospital (No. K22-149Z). Written and oral informed consent will be obtained from all participants. The study results will be submitted to a peer-reviewed journal.Trial registeration number NCT05824988.