bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms
Authors: Zangrando Niccolò , Fraternali Piero , Petri Marco , Pinciroli Vago Nicolò Oreste , Herrera González Sergio Luis 
Published: December 2022

AbstractThe diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training.
Survey data on university students’ experience of energy control, indoor comfort, and energy flexibility in campus buildings
Authors: Ma Zheng 
Published: December 2022

AbstractDue to large energy consumption and equipped building control systems, the majority of campus buildings have the potential to increase energy efficiency and provide energy flexibility. Among the three main types of occupants in the campus buildings (researchers/teachers, students, administration/management), students spend most of their time on learning activities in the campus buildings, and the energy performance can influence their learning performance. Therefore, this paper conducts a questionnaire targeting student occupants at a large engineering faculty in a Danish university to investigate occupants’ experience of energy control, indoor comfort, and options of energy flexibility in campus buildings. In total, 267 fully completed and usable questionnaires were received. The dataset is available in.xlsx format, and the questionnaire that was used to collect the data is also provided together.
Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances
Authors: Castangia Marco , Urbanelli Angelica , Abraha Girmay Awet , Camarda Christian , Macii Enrico , Patti Edoardo 
Published: December 2022

AbstractNowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world.
Simulation of a Cellular Energy System including hierarchies and neighborhoods
Authors: Dengler Gabriel , Bazan Peter , German Reinhard 
Published: December 2022

AbstractThe massive use of small energy resources and storage units causes a transition from a traditionally centralized to a decentralized energy system. To structure and coordinate the emerging changes in the energy system, the concept of Energy Cells (ECs) was developed. Several ECs can be combined to form a hierarchically superordinate EC. These hierarchically superordinate ECs can in turn be combined and thus form a complex hierarchy. An EC encapsulates coherent parts of an energy system and can communicate as well as exchange energy with other ECs at a different or the same level. It follows the idea of local balance of energy provision and demand. A network of ECs forms a Cellular Energy System (CES). In this paper, we develop a concept for modeling and simulating a CES. We accomplish this by beginning with atomic components like consumers, producers, and storage units and aggregating them with Hierarchical Controllers (HCs). Such a hierarchically structured energy system is part of various proposals. However, we are able to add neighborhood relations by introducing Local Controllers (LCs). This is more realistic and also opens many degrees of freedom for control strategies in such a system. Following the recursive structure of the CES itself, we define recursive functions for visiting the CES architecture and realizing various control strategies. We evaluate our approach in a series of partially randomized scenarios, showing notable differences in the performance of the CES regarding different control strategies in a larger example. We also provide a theoretical analysis of the computational complexity of the suggested approach.
A hierarchical and modular agent-oriented framework for power systems co-simulations
Authors: De Vizia Claudia , Macii Alberto , Patti Edoardo , Bottaccioli Lorenzo 
Published: December 2022

AbstractDuring the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid “smart”. Often, these domain-specific simulators have been then coupled with co-simulation platforms to test new scenarios. In parallel, agent-oriented approaches have been introduced to test distributed control strategies and include social and behavioural aspects typical of the consumer side. Rarely, simulators of the physical systems have been coupled with these innovative techniques, especially when social and psychological aspects have been considered. In order to ease the re-usability of these simulators, avoiding re-coding everything from scratch, we propose a hierarchical and modular agent-oriented framework to test new residential strategies in the energy context. If needed, the presented work enables the user to select the desired level of details of the agent-based framework to match the corresponding physical system without effort to test very different scenarios. Moreover, it allows adding on top of the physical data, behavioural aspects. To this end, the characteristics of the framework are first introduced and then different scenarios are described to demonstrate the flexibility of the proposed work: (i) a first stand-alone scenario with two hierarchy levels, (ii) a second co-simulation scenario with a photovoltaic panel simulator and (iii) a third stand-alone scenario with three hierarchy levels. Results demonstrate the flexibility and ease of use of the framework, allowing us to compare several scenarios and couple new simulators to build a more and more complex environment. The framework is in the early stages of its development. However, thanks to its properties in the future it could be extended to include new actors, such as industries, to get the full picture.
Application of energy informatics in Danish research projects
Authors: Howard Daniel Anthony , Ma Zheng , Jørgensen Bo Nørregaard 
Published: December 2022

AbstractTo enable sound scientific research in future energy informatics projects, it is necessary to obtain an overview of the current state of the research field to identify unaddressed gaps and challenges. Therefore, this paper aims to investigate the research trends and achievements within energy informatics in a Danish context within the last three decades. This paper reviews 207 energy informatics projects collected until the second quarter of 2022. Quantitative analysis results show that most projects have focused on applying energy informatics through energy-aware control of end-user consumption. The qualitative review finds an emphasis on data usage and end-users which aligns with the quantitative review. Furthermore, it tends to focus on specific end-users, e.g., buildings and heat pumps. Four overall recommendations are established: (I) Increased emphasis on research for sector coupling to aid in unlocking energy system flexibility, (II) project data value chain output, focusing on structuring and managing the data to make it applicable for future re-use, (III) utilizing industrial loads and incorporating an end-user perspective, (IV) inclusion of research institutions for the improved overall output of the projects through interdisciplinary solutions.
Agent-based modeling (ABM) for urban neighborhood energy systems: literature review and proposal for an all integrative ABM approach
Authors: Vuthi Petrit , Peters Irene , Sudeikat Jan 
Published: December 2022

AbstractAdvancing the energy transition in real-world urban settings is attracting interest within interdisciplinary research communities. New challenges for local energy balancing arise particularly in urban neighborhoods where densely populated buildings are facing the needs of the heat transition, an increasing use of battery-electric vehicles and an expansion of renewable energies. Agent-based modeling (ABM) is a suitable approach for addressing various interlinked aspects like market mechanisms and processes, technology adoption, different stakeholder roles and the formulation of policy measures. In this work, we analyze peer-reviewed, open-access literature on ABM for energy neighborhoods and discuss key modeling aspects like model purpose and outcome, the logic of agents and decision-making, the treatment of space and time, and empirical grounding. These ABM allow the study of local market mechanisms, local renewable energy generation, microgrids, the unfolding of heat transition, neighborhood mobility and the evaluation of policies like regulation and financial incentives. We find a lack of integrated neighborhood energy assessments that simultaneously look at the different energy forms and applications: heating, electricity, and mobility. We present a consolidated ABM concept that integrates these sectors. Thus, our work contributes to the advancement of ABM and to the understanding of how to promote the transition to a decarbonized society in urban settings.
Improving anomaly detection in SCADA network communication with attribute extension
Authors: Anwar Mahwish , Lundberg Lars , Borg Anton 
Published: December 2022

AbstractNetwork anomaly detection for critical infrastructure supervisory control and data acquisition (SCADA) systems is the first line of defense against cyber-attacks. Often hybrid methods, such as machine learning with signature-based intrusion detection methods, are employed to improve the detection results. Here an attempt is made to enhance the support vector-based outlier detection method by leveraging behavioural attribute extension of the network nodes. The network nodes are modeled as graph vertices to construct related attributes that enhance network characterisation and potentially improve unsupervised anomaly detection ability for SCADA network. IEC 104 SCADA protocol communication data with good domain fidelity is utilised for empirical testing. The results demonstrate that the proposed approach achieves significant improvements over the baseline approach (average $$F_{1}$$

F
1

score increased from 0.6 to 0.9, and Matthews correlation coefficient (MCC) from 0.3 to 0.8). The achieved outcome also surpasses the unsupervised scores of related literature. For critical networks, the identification of attacks is indispensable. The result shows an insignificant missed-alert rate ($$0.3\%$$

0.3
%

on average), the lowest among related works. The gathered results show that the proposed approach can expose rouge SCADA nodes reasonably and assist in further pruning the identified unusual instances.
A framework for quantifying forest wildfire hazard and fuel treatment effectiveness from stands to landscapes
Authors: Hood Sharon M. , Varner J. Morgan , Jain Theresa B. , Kane Jeffrey M. 
Published: December 2022

Abstract
Background
Wildland fires are fundamentally landscape phenomena, making it imperative to evaluate wildland fire strategic goals and fuel treatment effectiveness at large spatial and temporal scales. Outside of simulation models, there is limited information on how stand-level fuel treatments collectively contribute to broader landscape-level fuel management goals. Our objective here is to present a framework designed to measure fuel treatment effectiveness from stands to landscapes to inform fuel treatment planning and improve ecological and social resilience to wildland fire.

Results
Our framework introduces the concept of a fuel management regime, an iterative and cumulative evaluation from the stand to the landscape of fire hazard, fuel treatments, and wildland fire behavior and effects. We argue that the successfulness of fuel treatments within this regime must be evaluated based on pre-treatment fire hazard and post-wildland fire fuel treatment outcomes over large spatial and temporal scales. Importantly, these outcomes can be evaluated from the stand level to across a landscape through time, based on preidentified management objectives that define condition-based criteria that account for social values and environmental and ecological indicators used to determine the effectiveness of fuel treatments within a fuel management regime.

Conclusions
Evaluating the cumulative ability of fuel treatments to change landscape patterns of fire behavior and effects is challenging. By quantifying fire hazard, followed by evaluating outcomes of wildfires on environmental and ecological indicators and social values, it becomes possible to assess how individual fuel treatments placed within the context of a fuel management regime are effective based on desired conditions that address management objectives. This conceptual framework offers a much-needed middle-ground planning, monitoring, and reporting approach between overly simplistic annual reporting summaries of the area treated, number of fires, and burned area and detailed fire simulation modeling outcomes by putting individual treatments and fires in the context of current and desired vegetative conditions and social values. Our fuel treatment effectiveness framework examines the state of fuels through the lens of fire hazard and connects fuels to subsequent fire behavior and effects over time and space. The framework provides a way to focus regional and national fuel management planning efforts toward creating fuel management regimes that increase social and ecological resilience from wildfire.
Chlorogenic acid modulates the ubiquitin–proteasome system in stroke animal model
Authors: Shah Murad-Ali , Kang Ju-Bin , Koh Phil-Ok 
Published: December 2022

Abstract
Background
Chlorogenic acid, a phenolic compound, has potent antioxidant and neuroprotective properties. The ubiquitin–proteasome system is an important regulators of neurodevelopment and modulators of neuronal function. This system is associated with neurodevelopment and neurotransmission through degradation and removal of damaged proteins. Activation of the ubiquitin–proteasome system is a critical factor in preventing cell death. We have previously reported a decrease in the activity of the ubiquitin–proteasome system during cerebral ischemia. This study investigated whether chlorogenic acid regulates the ubiquitin–proteasome system in an animal stroke model. In adult rats, middle cerebral artery occlusion (MCAO) surgery was performed to induce focal cerebral ischemia. Chlorogenic acid (30 mg/kg) or normal saline was injected into the abdominal cavity 2 h after MCAO surgery, and cerebral cortex tissues were collected 24 h after MCAO damage.

Results
Chlorogenic acid attenuated neurobehavioral disorders and histopathological changes caused by MCAO damage. We identified the decreases in ubiquitin C-terminal hydrolase L1, ubiquitin thioesterase OTUB1, proteasome subunit α type 1, proteasome subunit α type 3, and proteasome subunit β type 4 expression using a proteomics approach in MCAO animals. The decrease in these proteins was alleviated by chlorogenic acid. In addition, the results of reverse transcription-polymerase chain reaction confirmed these changes. The identified proteins were markedly reduced in MCAO damage, while chlorogenic acid prevented these reductions induced by MCAO. The decrease of ubiquitin–proteasome system proteins in ischemic damage was associated with neuronal apoptosis.

Conclusions
Our results showed that chlorogenic acid regulates ubiquitin–proteasome system proteins and protects cortical neurons from neuronal damage. These results provide evidence that chlorogenic acid has neuroprotective effects and maintains the ubiquitin–proteasome system in ischemic brain injury.
Assessment of diaphragmatic role in dyspneic patients with pleural effusion
Authors: Shalaby Heba Mohamed , Ezzelregal Hieba Gamal 
Published: December 2022

Abstract
Background
Dyspnea that is caused by pleural effusion and affects patients’ quality of life may not be resolved after lung expansion following pleural tapping. This study aims to investigate the role that changes in the diaphragmatic shape and movement play in the development of dyspnea in those patients.

Methods
Thirty patients with pleural effusions and dyspnea were evaluated before and at 24 h after therapeutic thoracentesis. The primary outcomes are to investigate changes in diaphragmatic shape and movement before and after thoracentesis by chest ultrasound to evaluate factors causing effusion-related dyspnoea, while the secondary outcomes are firstly to investigate the effect of pleural effusion on the cardiorespiratory, functional, and diaphragmatic variables in causing dyspnea and secondly to detect the percentages and demographics of patients who experience post-drainage dyspnea improvement.

Results
Thirty patients with age >18 years old who had moderate to massive pleural effusion (either of benign or malignant etiology), with breathlessness were recruited from the Chest Department, Ultrasonography Unit, Ain-Shams University Hospitals. Almost all (96.7%) of the studied patients experienced shortness of breath with a mean modified Borg Score of 5.13 ± 1.78, tapping of varying amounts of pleural fluid ranging from 1000 to 2000 ml.
There is a highly significant improvement in the diaphragmatic excursion, with non-significant improvement in diaphragmatic shape, although nonsignificant correlation between diaphragmatic excursion and functional parameters (M. Borg score, spirometry, and 6MWD: 6-min walk distance), but a significant negative correlation between diaphragmatic excursion and amount of drained effusion (P value 0.045 and 95% CI for OR 1.041–36.779). 63.3% of patients experienced dyspnea improvement after thoracocentesis, and they showed highly significant improvements in M. Borg dyspnea score, spirometry, pulse rate, respiratory rate, 6MWD, and blood oxygen saturation.

Conclusion
In this study, we conclude that the improvement of the diaphragmatic excursion was negatively correlated with the amount of drained effusion, but no significant correlation was detected with the functional parameters and effusion-related indices that may be caused by the small sample size of the study.
Sexual and gender identity and note-leaving among adult suicide decedents in the USA
Authors: O'Rourke Holly P. , Hilley Chanler D. , Lowell Emily , Sheehan Connor M. 
Published: December 2022

BackgroundSuicide is one of the leading causes of preventable death in the USA, representing a critical public health threat. Suicide risks differ for different populations. In particular, the sexual and gender minority (SGM) population remains at increased risk for suicide. One of the circumstances that may differ for SGM and non-SGM individuals is the propensity to leave a suicide note. Information regarding note-leaving may be helpful in informing suicide prevention and intervention.AimsThis study documents the differences in note-leaving in SGM individuals compared with non-SGM individuals, using recent data from the National Violent Death Reporting System (N= 98 515) and accounting for important covariates.MethodWe fit a logistic regression model with SGM status and covariates predicting note-leaving in suicide.ResultsSGM decedents were 1.508 times more likely to leave a note than their non-SGM counterparts, controlling for demographic, mental health and substance use covariates.ConclusionsThese findings highlight the importance of tailoring suicide prevention and intervention efforts to meet the needs of SGM populations.
Endocrine disease history and the risk of postpartum depression
Authors: Rasmussen Marie-Louise H. , Poulsen Gry J. , Videbech Poul , Wohlfahrt Jan , Melbye Mads 
Published: December 2022

BackgroundPrevious research has suggested that some women are at increased risk of postpartum depression (PPD) because of an extra sensitivity to fluctuating hormones before and after parturition. This may particularly apply to women with endocrine disease, characterised by a less than optimal capability to self-regulate the hormonal feedback system.AimsTo investigate if women with endocrine disease history are at increased risk of developing PPD.MethodBased on information from Danish national registers, this nationwide cohort study included 888 989 deliveries (1995–2018). Endocrine disease history was defined as thyroid disease, pre-pregnancy diabetes, polycystic ovary syndrome and/or previous gestational diabetes within 10 years before pregnancy start. PPD was defined as use of antidepressants and/or hospital contact for depression within 6 months after childbirth.ResultsAmong 888 989 deliveries, 4.1% had a history of endocrine disease and 0.5% had a PPD episode. Overall, women with an endocrine disease history had a 42% (risk ratio 1.42, 95% CI 1.24–1.62) higher risk of PPD when compared with women with no endocrine disease. However, we also found the reverse association, whereby women with a PPD history had a 50% (hazard ratio 1.5, 95% CI 1.4–1.6) higher risk of endocrine disease when compared with women with no PPD history.ConclusionsWomen with endocrine disease history had a 40% higher risk of PPD compared with women with no endocrine disease. More attention should be given to pregnant women with endocrine disease history to increase awareness of early signs of PPD. The bi-directionality of the association points to a common underlying factor.