• Energy Research
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Simple indicators are often used to summarize the complexity of systems or products, commonly through color-coded labels paired with letters. These labels, like those indicating energy efficiency or nutritional ratings, help users quickly understand essential characteristics. Building on this approach, the Quality Urban Label (QUL) has been developed to assess public space adaptation to urban climate change. The QUL utilizes four key indicators, called quality components: air quality (pollutants), noise pollution, thermal comfort, and visual comfort. It ranges from 0 to 25 and is represented by a color and letter code (green, A, ranging from 0 to 2; blue, B, ranging from 6 to 11; orange, C, ranging from 15 to 19; and red, D, ranging from 22 to 25), with green representing better quality and red poorer quality. The QUL aims to evaluate public spaces based on energy consumption reduction, greenhouse gas emissions reduction, and progress toward carbon neutrality. This article explores some ecological and climate benefits of the QUL, especially in warm Mediterranean cities. An objective label that quantifies the alignment of urban public space with climate neutrality has numerous advantages, which are discussed in the article. In addition, it is a key tool for urban project planning, focused on reducing urban social inequalities and promoting a just energy transition of urban public space.

This paper addresses some questions that climate change raises for international law. It focuses in particular on the request for an advisory opinion submitted by the United Nations General Assembly to the International Court of Justice (ICJ) on 29 March 2023. This request is analysed as a further manifestation of the international community’s concern to clarify the international responsibility of states to prevent, mitigate and remedy the damage caused by climate change. The study argues that the ICJ could clarify obligations under existing treaties. There may also be a particular opportunity for the Court to expand its jurisprudence on other sources of international law. In particular, it asks whether the ICJ could confirm the emergence of a specific customary rule on the issue.

Even with the benefits of sustainability and climate change reporting, there is limited information on how municipalities are reporting on performance for external stakeholders in comparison with private sector organizations. The purpose of this research was to gain an understanding of the current state of sustainability and climate change reporting at the local level and to investigate the extent to which municipalities across Ontario, Canada, report. We used content analysis to identify the presence or non-presence of information on the websites of 38 municipalities and analyzed the results using descriptive statistics. Our analysis showed that the sample municipalities were not widely reporting on sustainability or climate change performance. Also, we identified a gap between the number of plans and reports produced by sample municipalities, with the latter being less common, indicating a need for an improved evaluation of plan implementation. Further, we found that a provincial regulation that required municipalities to make their energy conservation and demand management plans public did not guarantee publication of the plan on a municipality’s website. This study contributes to the growing field of sustainability and climate change planning and reporting by local governments and offers empirical evidence specific to Ontario, Canada.

Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.

The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the system, the facilities, and their personnel. This paper presents an innovative and reliable methodology for developing such applications, ensuring safety and efficiency throughout the process. It involves a staged transition from simulation to reality, wherein various components are validated at each stage. This iterative approach facilitates error identification and resolution, enabling subsequent real flights to be conducted with enhanced safety after validating the remainder of the system. Furthermore, this article showcases two use cases: wind turbine inspection and photovoltaic plant inspection. By implementing the suggested methodology, these applications were successfully developed in an efficient and secure manner.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

A quantitative study was carried out with the aim of analyzing the perspective of the students in relation to the factors that have influenced quality teaching during the confinement period resulting from the SARS-CoV-2 pandemic. To do this, an ad hoc questionnaire was designed and conducted in the months of June–July 2020. The sample consisted of 893 people who were studying at various Spanish universities. The results show little diversity in teaching methodologies; virtual teaching was carried out with the same parameters as face-to-face teaching. The role of the student body was one of passivity, consisting of little interaction with the teachers. Although it is true that there were no difficulties in following the course, there was limited attention paid to the emotional well-being of the students. Amongst the conclusions garnered significant, we point out the need to develop the adaptability of university teaching staff to unforeseen situations, as well as a continuing reflection on the model used in the teaching process mediated by digital technologies and the importance of promoting greater autonomy and self-regulation during learning.