Hasil untuk "Office management"

H. Tiselius, D. Ackermann, P. Alken et al.

Deborah D. Ingram, S. J. Franco

Shiyu Yang, M. Wan, Wanyu Chen et al.

Abstract A model predictive control system with adaptive machine-learning-based building models for building automation and control applications is proposed. The system features an adaptive machine-learning-based building dynamics modelling scheme that updates the building model regularly using online building operation data through a dynamic artificial neural network with a nonlinear autoregressive exogenous structure. The system also employs a multi-objective function that could optimize both energy efficiency and indoor thermal comfort, two often contradicting demands. The proposed model predictive control system is implemented to control the air-conditioning and mechanical ventilation systems in two single-zone testbeds, an office and a lecture theatre, located in Singapore for experimental evaluation of its control performance. The model predictive control system is compared against the original reactive control system (thermostat in the office and building management system in the lecture theatre) in each testbed. The model predictive control system reduces 58.5% cooling thermal energy consumption in the office and 36.7% cooling electricity consumption in the lecture theatre, as compared to their respective original control. Meanwhile, the indoor thermal comfort in both testbeds is also greatly improved by the model predictive control system. Developing a model predictive control system using machine-learning-based building dynamics models could largely cut down the model construction time to days as compared to its counterpart using physics-based models, which usually take months to construct. However, the machine-learning-based modelling approach could be challenged by lack of building operational data necessary for model training in case of model predictive control development before the building has become operational.

Hafiz Suliman Munawar, A. Hammad, S. Waller

Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.

D. Shimbo, N. Artinian, J. Basile et al.

The diagnosis and management of hypertension, a common cardiovascular risk factor among the general population, have been based primarily on the measurement of blood pressure (BP) in the office. BP may differ considerably when measured in the office and when measured outside of the office setting, and higher out-of-office BP is associated with increased cardiovascular risk independent of office BP. Self-measured BP monitoring, the measurement of BP by an individual outside of the office at home, is a validated approach for out-of-office BP measurement. Several national and international hypertension guidelines endorse self-measured BP monitoring. Indications include the diagnosis of white-coat hypertension and masked hypertension and the identification of white-coat effect and masked uncontrolled hypertension. Other indications include confirming the diagnosis of resistant hypertension and detecting morning hypertension. Validated self-measured BP monitoring devices that use the oscillometric method are preferred, and a standardized BP measurement and monitoring protocol should be followed. Evidence from meta-analyses of randomized trials indicates that self-measured BP monitoring is associated with a reduction in BP and improved BP control, and the benefits of self-measured BP monitoring are greatest when done along with cointerventions. The addition of self-measured BP monitoring to office BP monitoring is cost-effective compared with office BP monitoring alone or usual care among individuals with high office BP. The use of self-measured BP monitoring is commonly reported by both individuals and providers. Therefore, self-measured BP monitoring has high potential for improving the diagnosis and management of hypertension in the United States. Randomized controlled trials examining the impact of self-measured BP monitoring on cardiovascular outcomes are needed. To adequately address barriers to the implementation of self-measured BP monitoring, financial investment is needed in the following areas: improving education and training of individuals and providers, building health information technology capacity, incorporating self-measured BP readings into clinical performance measures, supporting cointerventions, and enhancing reimbursement.

Jared D. Harris, P. Bromiley

D. Biber, Federica Barbieri

C. Moreno, G. Laje, C. Blanco et al.

Changqing Liu, Zhongxiang Sun, Hongyang Wang et al.

Enhancements in water quality and soil characteristics of wetlands can improve the ecological environment of the area and enrich its biodiversity. The present study examined the effects of five distinct crops (i.e.: colza oil, mustard, Chinese cabbage, wheat, barley) cultivated in remediation plots, and evaluated their impact on water quality and soil fertility. The water quality within the remediated farmland was categorized as Class III (moderately polluted). The soil exhibited low total nitrogen and organic matter levels (the mean values were respectively 0.032% and 10.84 g/kg), and high readily available phosphorus and potassium concentration (the mean values were respectively 75.71 and 247.64 mg/kg). The soil fertility was comprehensively categorized as Class III (moderately polluted). Subsequently, the different components of bird droppings in the soil were investigated for their potential effects on soil fertility. The present research demonstrated that the remediation of farmland had the potential to enhance the quality of water and soil fertility in wetland. This, in turn, might result in an increased number of migratory birds inhabiting the area.

Arnon Archankul, Jacco J. J. Thijssen

We consider singular control in inventory management under Knightian uncertainty, where decision makers have a smooth ambiguity preference over Gaussian-generated priors. We demonstrate that continuous-time smooth ambiguity is the infinitesimal limit of Kalman-Bucy filtering with recursive robust utility. Additionally, we prove that the cost function can be determined by solving forward-backward stochastic differential equations with quadratic growth. With a sufficient condition and utilising variational inequalities in a viscosity sense, we derive the value function and optimal control policy. By the change-of-coordinate technique, we transform the problem into two-dimensional singular control, offering insights into model learning and aligning with classical singular control free boundary problems. We numerically implement our theory using a Markov chain approximation, where inventory is modeled as cash management following an arithmetic Brownian motion. Our numerical results indicate that the continuation region can be divided into three key areas: (i) the target region; (ii) the region where it is optimal to learn and do nothing; and (iii) the region where control becomes predominant and learning should inactive. We demonstrate that ambiguity drives the decision maker to act earlier, leading to a smaller continuation region. This effect becomes more pronounced at the target region as the decision maker gains confidence from a longer learning period. However, these dynamics do not extend to the third region, where learning is excluded.

Probowo Erawan Sastroredjo, Marcel Ausloos, Polina Khrennikova

Our research investigates the relationship between eco-innovation and earnings management among 567 firms listed on the FTSE All-Share Index from 2014 to 2022. By examining how sustainability-driven innovation influences financial reporting practices, we explore the strategic motivations behind income smoothing in firms engaged in environmental initiatives. The findings reveal a positive association between eco-innovation and earnings management, suggesting that firms may leverage ecoinnovation not only for environmental signalling but also to project financial stability and meet stakeholder expectations. The analysis further uncovers that the propensity for earnings management is amplified in firms facing financial constraints, proxied by low Whited-Wu (WW) scores and weak sales performance, and in those characterised by high financial opacity. We employ a robust multi-method approach to address potential endogeneity and selection bias, including entropy balancing, propensity score matching (PSM), and the Heckman Test correction. Our research contributes to the literature by providing empirical evidence on the dual strategic role of ecoinnovation -balancing sustainability signalling with earnings management, under varying financial conditions. The findings offer actionable insights for regulators, investors, and policymakers navigating the intersection of corporate transparency, financial health, and environmental responsibility.

B. Riley, B. Long

Stella Caroline Schenidt Bispo da Silva, Mariana Millan Fachi, Marinei Campos Ricieri et al.

Summary: Background: Care bundles are a recognised strategy to improve treatment. When managed through an Antimicrobial Stewardship Program (ASP) based on the pharmacist-led program model, care bundles can be an effective tool to guide decision making in clinical practice and to improve patient outcomes. This study aimed to evaluate the results of a pharmacist-led ASP which included a care bundle based on clinical outcomes of Staphylococcus aureus bacteraemia (SAB) in a paediatric hospital. Methods: A retrospective cohort study with multivariate analysis was conducted in a paediatric hospital in Brazil. The study comprised 120 paediatric patients with a positive blood culture for S. aureus with occurred between 2014 and 2021 and clinical and laboratory results consistent with infection. The study was classified into two periods: pre-intervention (n=44) and intervention (n=76). A pharmacist-led ASP program with a care bundle was established during the intervention period 2017–2021. The primary outcome assessed was the impact on clinical outcomes, including infection-related mortality and 90-day reinfection rate, both being considered therapeutic failure. Results: The multivariate analysis demonstrated that the following variables had an impact on primary outcome: infant patients [Odds ratio (OR) 12.998, P=0.044]; use of more than three antimicrobial treatment regimens [OR 0.006, P=0.017]; intervention period [OR 0.060, P=0.034]; bundle item 1 – follow-up blood culture [OR 18.953, P=0.049]; bundle item 2 – early source control [OR 0.002, P=0.018]; bundle item 4 – de-escalation to oxacillin for methicillin-sensitive S. aureus [OR 0.041, P=0.046]. Conclusions: The pharmacist-led ASP model showed an increase in adherence to the care bundle between the two study periods, with reduced probability of a negative outcome. Furthermore, risk factors for S. aureus bacteraemia were identified that may inform management and contribute to better patient outcomes in the paediatric population.

Atsede Gebrekidan, Hansa Haftu, Berhane Yohannes Hailu et al.

Abstract The armed conflict in Tigray, which spanned from November 2020 to November 2022, along with the accompanying siege, led to the near-total collapse of Tigray’s healthcare system. Type 1 Diabetes Mellitus, the most common chronic condition in children, requires significant lifestyle adjustments, including daily insulin injections, regular glucose monitoring, and dietary modifications; all of which are severely impacted by war and siege. This study compared Type 1 diabetes care for children at the Ayder Comprehensive Specialized Hospital, Tigray, during the conflict and siege period with that of the pre-war period. We conducted a retrospective cross-sectional survey, analyzing data from September 2019 to August 2020 (pre-war period) and comparing it with data from September 2021 to August 2022 (war and siege period). Descriptive statistics, including frequencies and percentages, were employed, and Pearson’s or Spearman’s correlation analyses were used to evaluate correlations where appropriate. We identified 143 pediatric patients admitted (56 during the pre-war period and 87 during the war and siege period), with a mean age of 109 months in both periods. During the war and siege, a higher proportion of diabetes admissions were due to diabetic ketoacidosis (DKA) (90%) compared to the pre-war period (75%). In the pre-war period, the most common trigger for DKA was infections (35%), while in the war and siege period, it shifted to malnutrition (47%), infections (46%), lack of access to healthcare facilities (31%), and running out of medicines (24%). Complications such as death, renal failure, cerebral edema, and shock were more prevalent during the war and siege periods. The case fatality rate was significantly higher during the war and siege (9%) compared to the pre-war period (0%), correlating strongly with the severity of DKA, the degree of hypokalemia, the presence of complications, and admission during the war and siege. Our study showed the negative impact of war and siege on diabetes care in children demonstrating a high rate of DKA admissions with increased severity, complications, malnutrition, and case fatality rates. People with diabetes especially type 1 deserve great attention during such a crisis as the lack of insulin could lead to severe complications including death.

R. I. Jayasinghe, M. Rasmy, T. Koike et al.

<p>Water scarcity and disasters, particularly floods and droughts, are the most devastating disasters in Sri Lanka, causing severe damage to agriculture, lives and property. This condition has been worsen further during the Covid19, due to isolation and travel restrictions. Collaboration and effective watershed management among the stakeholders is one of the key solutions for effective operation, but watershed management is more complex and complicated due to its physical processes, and conflicts among stakeholders. This study attends to attract stakeholders and develop Decision Making System by integrating complex hydrological model with dam operation and diversion, based on topography, rainfall, evaporation with bias-corrected satellite data. Finally, we developed an Integrated System for decision makers for effective water resource management in River basins. The proposed approach was applied to the complex Mahaweli watershed in Sri Lanka, to address prevailing issues.</p>

Fabio Franchi, Fabio Graziosi, Eleonora Di Fina et al.

Technological innovation is functional in various areas, from the digitization of Public Administration to citizen services and welfare. Today the &#x201C;Science of Where&#x201D; applies data-driven approach that uses geography to unlock understanding of our world, through Geographical Information System (GIS). GIS is thus linked to the social context, and the adoption of GIS as a decision-support tool is becoming increasingly widespread. In fact, this technology can help explore and visualize large volumes of data - dubbed &#x201C;the new oil&#x201D; by Clive Humby in 2006 - combining different aspects (geographic and non-geographic) for better management of activities and resources on Earth to maintain economic viability, environmental quality and smart living. Accordingly, this study presents a novel cloud-edge GIS architecture designed to support the Special Office for the Reconstruction of the Municipalities of the Seismic Crater (USRC), operating in Central Italy for the 2009 post-earthquake reconstruction process, to decentralize power at the edge of the network and create an advanced distributed system that improves operational efficiency, increases performance and ensures data security. Proposed solution will be useful for further work, such as urban regeneration, sub-service planning for smart communities, and analysis of (cyber-)security scenarios.

Liwei Deng, Tianfu Wang, Yan Zhao et al.

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.

Jason Kayembe, Iness Ben Guirat, Jan Tobias Muehlberg

This paper explores the intersection of privacy, cybersecurity, and environmental impacts, specifically energy consumption and carbon emissions, in cloud-based office solutions. We hypothesise that solutions that emphasise privacy and security are typically "greener" than solutions that are financed through data collection and advertising. To test our hypothesis, we first investigate how the underlying architectures and business models of these services, e.g., monetisation through (personalised) advertising, contribute to the services' environmental impact. We then explore commonly used methodologies and identify tools that facilitate environmental assessments of software systems. By combining these tools, we develop an approach to systematically assess the environmental footprint of the user-side of online services, which we apply to investigate and compare the influence of service design and ad-blocking technology on the emissions of common web-mail services. Our measurements of a limited selection of such services does not yet conclusively support or falsify our hypothesis regarding primary impacts. However, we are already able to identify the greener web-mail services on the user-side and continue the investigation towards conclusive assessment strategies for online office solutions.

Alejandra de la Rica Escudero, Eduardo C. Garrido-Merchan, Maria Coronado-Vaca

Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set on assumptions that are not supported by data in high volatility markets. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, we developed a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management, integrating the Proximal Policy Optimization (PPO) with the model agnostic explainable techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent suggestions. To the best of our knowledge, our proposed approach is the first explainable post hoc portfolio management financial policy of a DRL agent. We empirically illustrate our methodology by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time.