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The turbulent penetrative convection into a stable convective boundary layer represents an important phenomenon in environmental engineering and atmospheric science. In the present study, we present a series of numerical simulations performed by two modeling approaches: the high-fidelity Large-Eddy Simulations (LES), and the less computationally demanding transient Reynolds-Averaged Approach (TRANS), but with an advanced sub-scale turbulent heat flux model. By simulating different localized heat sources over the ground, and by performing a direct comparative assessment of results obtained by LES and TRANS, we confirmed an overall good agreement in predicting the time evolution of the horizontally averaged temperature profiles. Similarly, the morphology of instantaneous thermal plumes and large convective structures predicted by TRANS were in reasonable agreement with the referent LES predictions.

The combination of NK1 receptor antagonist (NK1 RA), setron and dexamethasone is necessary for the optimal prevention of chemotherapy induced nausea and vomiting (CINV) after highly emetogenic chemotherapy (HEC: AC and non-AC) and carboplatin. Palonosetron is the preferable setron when NK1 RA is not available. To evaluate availability and accessibility of antiemetics (AE) recommended by the MASCC/ESMO 2016 antiemetic guidelines in hospitals of the Eastern European and Balkan region. Data were collected by countries participating at the First Regional Education Meeting on Supportive Care for Eastern European and Balkan region. Availability was evaluated by the formulary availability and marketing authorization (registered or not). Accessibility was assessed by the coverage of the National Health Insurance Fund: fully reimbursed, partially reimbursed and not accessible (out--of--pocket). Results There is unequality in the access to AE across Eastern European and Balkan region. Clinically most important is the lack of access to NKIRAs which coupled with the lack of access to palonosetron, creates an important barrier for the prevention of CINV after HEC and carboplatin. Off-label use of olanzapine, as an effective alternative, is another barrier in successful prevention and control of CINV.

Cancer pain is the most common symptom of cancer. The implementation of the cancer pain guidelines depends on availability of and accessibility to guideline-recommended medications. The purpose of this research was to investigate the availability and accessibility of strong opioids in Eastern Europe and Balkans. The survey was conducted among 15 countries of the region with data collected for morphine, hydromorphone, oxycodone, methadone, transdermal fentanyl (fentanyl TD) and fentanyl for the breakthrough pain (fentanyl BTP). Methadone for substitution therapy was not included in this survey. Availability was evaluated by the formulary availability and marketing authorization. Accessibility was evaluated by the National Health Insurance Fund coverage as fully reimbursed, partially reimbursed or not accessible (out-of-pocket). Slovenia is the only country with all investigated strong opioids available and fully reimbursed (Table 1). Morphine is available and fully reimbursed in all countries. Fentanyl TD is available and fully reimbursed in 14/15 countries except in Albania in which it is out-of-pocket medication. Morphine in the only available and fully reimbursed strong opioid in Albania. Hydromorphone, oxycodone, methadone and fentanyl BTP are not available in 8/15, 4/15, 6/15 and 4/15 countries, respectively. All countries have at least one first-line strong opioid available and accessible with major differences among countries in the region.

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human–computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management.

The paper analyses health consequences of forced civilian displacement that occurred during the war in Croatia 1991–1995 which accompanied the demise of Yugoslavia. During the Serbo-Croatian conflict a quarter of Croatian territory was ceded, 22 000 people were killed and more than 500 000 individuals were displaced. Using the Croatian Adult Health Survey 2003 we identify the causal effect of forced migration on various dimensions of measured and self-assessed health. To circumvent the self-selection into displacement, we adopt an instrumental variable approach where civilian casualties per county are used as an instrument for displacement. We find robust adverse effects on probability of suffering hypertension and tachycardia as well as on self-assessed health and ‘Short form health survey’ health dimensions. Comparing ordinary least squares with instrumental variable estimates yields a conclusion of a positive selection into displacement with respect to latent health. Given the likely violation of the exclusion restriction, we use a method which allows the instrument to affect health outcomes directly and conclude that, even with substantial departures from the exclusion restriction, displacement still adversely affects health.

PurposeThis research aims to contribute substantively and methodologically to our understanding of CSR communications in the pharmaceutical sector targeted to employees. Of specific concern is how companies can most effectively communicate their CSR activities to employees.Design/methodology/approachMulti-methods were used to identify CSR-related communication trends and relationships in the largest pharmaceutical companies by market cap. Replicatable data are ROA from Bloomberg, ESG scores, employee satisfaction from Indeed.com and content of CSR and similar reports.FindingsSignificant findings include the content of CSR reports related to employee behaviors and human resource processes. Both HR behaviors and HR processes are related to return on assets ROA. Multinational differences were found in Indeed satisfaction scores and in trends in communications from the CSR reports.Research limitations/implicationsOne of the limitations was that not all pharmaceutical companies consistently reported their data.Practical implicationsGiven that the pharmaceutical sector is comprised of a high proportion of uniquely qualified types of employees, this type of information can be used by prospective employees to consistently include more HR processes. Especially missing is recruitment and selection data that can reasonably be expected to facilitate identifying prospective employees who align with the CSR mission.Originality/valueThe methodology used in this study allows for replication in the pharmaceutical sector. Moreover, it encourages using similar disaggregated ESG data sources to study CSR in other sectors.

This paper presents a reanalysis of four axial‐flow rotor simulation datasets to study the relationship between thrust and axial induction factor. We concentrate on high‐thrust conditions and study variations in induction factor and loads across the span of the different rotor blades. The datasets consist of three different axial‐flow rotors operating at different tip‐speed ratios and, for one dataset, also at different blockage ratios. The reanalysis shows differences between the blade‐resolved CFD results and a widespread empirical turbulent wake model (TWM) used within blade element momentum (BEM) turbine models. These differences result in BEM models underestimating thrust and especially power for axial‐flow rotors operating in high‐thrust regimes. The accuracy of BEM model predictions are improved substantially by correcting this empirical TWM, producing better agreement with blade‐resolved CFD simulations for thrust and torque across most of the span of the blades of the three rotors. Additionally, the paper highlights deficiencies in tiploss modelling in common BEM implementations and highlights the impact of blockage on the relationship between thrust and axial induction factors.

The actuator line method (ALM) is a widely used tool for the modelling of horizontal axis turbines and wind and tidal farm flows. The method uses a virtual blade representation to simulate the dynamics of wind turbines without the computational expense associated with resolving the blade geometry. Within the ALM, the flow is first sampled at each blade section, allowing for the calculation of the sectional lift and drag forces that are in turn imposed on the flow domain using a smearing kernel. In this work, the effect of the flow sampling method on the robustness of the ALM is discussed. Implementations of two widely used types of methods for horizontal axis wind turbines are tested: point sampling, where the flow is sampled at or near the collocation point, and volume average sampling where flow field information from multiple points centred at the collocation point is averaged into a representative blade‐local velocity vector. A third method, line average sampling, that was initially proposed for sampling flows from blade‐resolved simulations is adapted for the ALM framework. This method samples the flow symmetrically around a control element in order to eliminate the interference of the bound circulation, thus allowing the inflow velocity at the aerofoil to be determined. When evaluating power and thrust coefficients and the flow field at the rotor plane predicted by the ALM, the line average sampling approach is demonstrated to be more robust and converges faster with time step for two different rotors: a small wind rotor and a high‐solidity tidal rotor. This suggests that carefully selecting the sampling method could be key in alleviating the very strict time‐step restriction imposed by the ALM, a widely acknowledged limitation of the method. Such an advancement could contribute towards improving the computational efficiency and tractability of the method.

This paper introduces a novel respiratory detection system based on diaphragm wall motion tracking using an embedded ultrasound sensory system. We assess the utility and accuracy of this method in evaluating the function of the diaphragm and its contribution to respiratory workload. The developed system is able to monitor the diaphragm wall activity when the sensor is placed in the zone of apposition (ZOA). This system allows for direct measurements with only one ultrasound PZT5 piezo transducer. The system generates pulsed ultrasound waves at 2.2 MHz and amplifies reflected echoes. An added benefit of this system is that due to its design, the respiratory signal is less subject to motion artefacts. Promising results were obtained from six subjects performing six tests per subject with an average respiration detection sensitivity and specificity of 84% and 93%, respectively. Measurements were compared to a gold standard commercial spirometer. In this study, we also compared our measurements to other conventional methods such as inertial and photoplethysmography (PPG) sensors.