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Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst. [ABSTRACT FROM AUTHOR]

The continued proliferation of information technology in all aspects of our lives fosters benefits but also generates risks to individuals' privacy. In emerging contexts, such as government surveillance technologies, there is a dearth of research investigating the positive and negative drivers of citizens' acceptance. This is an important gap given the importance of citizen acceptance to the success of these technologies and the need to balance potentially wide-reaching benefits with any dilution of citizen privacy. We conduct a longitudinal examination of the competing influences of positive beliefs and privacy concerns on citizens' acceptance of a COVID-19 national contact tracing mobile application among 405 Irish citizens. Combining privacy calculus theory with social exchange theory, we find that citizens' initial acceptance is shaped by their perceptions of health benefits and social influence, with reciprocity exhibiting a sustained influence on acceptance over time and privacy concerns demonstrating a negative, albeit weak influence on willingness to rely on the application. The study offers important empirical and theoretical implications for the privacy literature in the government surveillance, location-based services, and mobile health application contexts, as well as practical implications for governments and developers introducing applications that rely on mass acceptance and reciprocal information disclosure. • Contact tracing applications offer both benefits and risks to individuals' privacy. • Perceived health benefits influence willingness to download and rely on these apps. • Social influence is an important factor in acceptance of contact tracing apps. • Reciprocity shapes acceptance of contact tracing apps prior to and post launch. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic accelerated the need to understand citizen acceptance of health surveillance technologies such as contact tracing (CT) apps. Indeed, the success of these apps required widespread public acceptance and the alleviation of concerns about privacy, surveillance, and trust., Objective: This study aims to examine the factors that foster a sense of trust and a perception of privacy in CT apps. Our study also investigates how trust and perceived privacy influence citizens' willingness to adopt, disclose personal data, and continue to use these apps., Methods: Drawing on privacy calculus and procedural fairness theories, we developed a model of the antecedents and behavioral intentions related to trust and privacy perceptions. We used structural equation modeling to test our hypotheses on a data set collected at 2 time points (before and after the launch of a national CT app). The sample consisted of 405 Irish residents., Results: Trust in CT apps was positively influenced by propensity to trust technology (β=.074; P=.006), perceived need for surveillance (β=.119; P<.001), and perceptions of government motives (β=.671; P<.001) and negatively influenced by perceived invasion (β=-.224; P<.001). Perceived privacy was positively influenced by trust (β=.466; P<.001) and perceived control (β=.451; P<.001) and negatively influenced by perceived invasion (β=-.165; P<.001). Prelaunch intentions toward adoption were influenced by trust (β=.590; P<.001) and perceived privacy (β=.247; P<.001). Prelaunch intentions to disclose personal data to the app were also influenced by trust (β=.215; P<.001) and perceived privacy (β=.208; P<.001) as well as adoption intentions before the launch (β=.550; P<.001). However, postlaunch intentions to use the app were directly influenced by prelaunch intentions (β=.530; P<.001), but trust and perceived privacy only had an indirect influence. Finally, with regard to intentions to disclose after the launch, use intentions after the launch (β=.665; P<.001) and trust (β=.215; P<.001) had a direct influence, but perceived privacy only had an indirect influence. The proposed model explained 74.4% of variance in trust, 91% of variance in perceived privacy, 66.6% of variance in prelaunch adoption intentions, 45.9% of variance in postlaunch use intentions, and 83.9% and 79.4% of variance in willingness to disclose before the launch and after the launch, respectively., Conclusions: Positive perceptions of trust and privacy can be fostered through clear communication regarding the need and motives for CT apps, the level of control citizens maintain, and measures to limit invasive data practice. By engendering these positive beliefs before launch and reinforcing them after launch, citizens may be more likely to accept and use CT apps. These insights are important for the launch of future apps and technologies that require mass acceptance and information disclosure., (©Grace Fox, Lisa van der Werff, Pierangelo Rosati, Theo Lynn. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 18.01.2024.)

This study examines public policy advocacy by pediatricians and other health professionals in the hashtag community: #putkids1st. The study explores 4321 tweets that feature the hashtag, generated by 1231 unique users largely drawn from the American Association of Pediatricians and its members. The data are used to explore the structural dynamics of the hashtag community, the role of homophily, and to test a source-message framework to predict and recommendations to help improve engagement and retransmission of professional health advocacy messages.

Streets perform a number of important functions and have a wide range of activities performed in them. There is a small but growing focus on streets as a more generalisable, atomised, and therefore more manageable unit of development and analysis than cities. Despite the public realm being one of the largest physical spaces on streets, the impact and potential of digitalisation projects on this realm is rarely considered. In this article, the smartness of a street is derived from the cyber-physical social infrastructure in the public realm, including data obtained from sensors, the interconnection between different services, technologies and social actors, intelligence derived from analysis of the data, and optimisation of operations within a street. This article conceptualises smart streets as basic units of urban space that leverage cyber-physical social infrastructure to provide and enable enhanced services to and between stakeholders, and through stakeholders' use of the street, generate data to optimise its services, capabilities, and value to stakeholders. A proposed conceptual framework is used to identify and explore how streets can be augmented and create value through cyber-physical social infrastructure and digital enhancements. We conclude with a discussion of future avenues of research.

After COVID-19, tuberculosis (TB) is the leading cause of death by an infectious disease in the world. This work presents a data set based on data collected from the Brazilian Information System for Notifiable Diseases (SINAN) for the period from January 2001 to April 2020 relating to patients diagnosed with tuberculosis in Brazil. The data from SINAN was pre-processed to generate a new data set with two distinct treatment outcome classes: CURED and DIED. The data set comprises 37 categorical attributes (including socio-demographic, clinical, and laboratory data) as well as the target class. There are 927,909 records of patients classified as CURED and 36,190 classified as DIED, totaling 964,099 records., (© 2022. The Author(s).)

Background: Malaria is curable. Nonetheless, over 229 million cases of malaria were recorded in 2019, along with 409,000 deaths. Although over 42 million Brazilians are at risk of contracting malaria, 99% percent of all malaria cases in Brazil are located in or around the Amazon rainforest. Despite declining cases and deaths, malaria remains a major public health issue in Brazil. Accurate spatiotemporal prediction of malaria propagation may enable improved resource allocation to support efforts to eradicate the disease., Methods: In response to calls for novel research on malaria elimination strategies that suit local conditions, in this study, we propose machine learning (ML) and deep learning (DL) models to predict the probability of malaria cases in the state of Amazonas. Using a dataset of approximately 6 million records (January 2003 to December 2018), we applied k-means clustering to group cities based on their similarity of malaria incidence. We evaluated random forest, long-short term memory (LSTM) and dated recurrent unit (GRU) models and compared their performance., Results: The LSTM architecture achieved better performance in clusters with less variability in the number of cases, whereas the GRU presents better results in clusters with high variability. Although Diebold-Mariano testing suggested that both the LSTM and GRU performed comparably, GRU can be trained significantly faster, which could prove advantageous in practice., Conclusions: All models showed satisfactory accuracy and strong performance in predicting new cases of malaria, and each could serve as a supplemental tool to support regional policies and strategies.

One of the main categories of Neglected Tropical Diseases (NTDs) are arboviruses, of which Dengue and Chikungunya are the most common. Arboviruses mainly affect tropical countries. Brazil has the largest absolute number of cases in Latin America. This work presents a unified data set with clinical, sociodemographic, and laboratorial data on confirmed patients of Dengue and Chikungunya, as well as patients ruled out of infection from these diseases. The data is based on case notification data submitted to the Brazilian Information System for Notifiable Diseases, from Portuguese Sistema de Informação de Agravo de Notificação (SINAN), from 2013 to 2020. The original data set comprised 13,421,230 records and 118 attributes. Following a pre-processing process, a final data set of 7,632,542 records and 56 attributes was generated. The data presented in this work will assist researchers in investigating antecedents of arbovirus emergence and transmission more generally, and Dengue and Chikungunya in particular. Furthermore, it can be used to train and test machine learning models for differential diagnosis and multi-class classification., (© 2022. The Author(s).)