Search Results (11)

Use of the upper limb to engage in everyday activities is a key indicator of functional recovery of stroke survivors. In addition to functional capacity, personal and environmental factors contribute to real-world upper limb use post-stroke. We aimed to combine data from the experience sampling method (ESM), a method used to capture real-time engagement in daily activities, with accelerometry, an objective measurement of arm use, to evaluate arm use behaviours of adult stroke survivors living in real-world environments. Thirty mild–moderately impaired stroke survivors and 30 age-standardized healthy individuals were monitored over 7 days, using accelerometers on both wrists and four ESM beeps per day to capture individual experiences in daily activities. Stroke survivors showed significantly lower use of the affected arm across all activity domains compared to the non-dominant arm of healthy participants and reported perceived lower skill and higher challenge levels. Physical context, motor capabilities and activity type were associated with affected arm use behaviour, with greater use observed during social settings and in physically demanding tasks. These findings demonstrate that combining ESM with accelerometry provides a novel, ecologically valid framework to capture and interpret the interplay between capacity, context, and behaviour in everyday life. This approach offers opportunities to design personalized, context-aware rehabilitation strategies that promote meaningful functional reintegration after stroke. Full article

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation. However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs. To address these issues, this paper presents the design and implementation of a Battery Management System (BMS) tailored for E-Bike applications, with a focus on enhancing safety, reliability, and performance. The proposed BMS includes core functionalities such as State of Charge (SOC) estimation, temperature monitoring, and under-voltage and overcharge protection. Different approaches, including open-circuit voltage (OCV), Coulomb counting (CC), and Kalman filter techniques are employed to improve SOC estimation accuracy. The circuit for CC-based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink is used to validate design assumptions before hardware implementation. An Arduino Uno microcontroller was used to control the system, interfacing with an LM35 temperature sensor, a voltage divider, and an ACS712 current sensor. The BMS controls battery charging based on SOC levels and activates a cooling fan when the battery temperature exceeds 45 °C. It disconnects the charger at 100% SOC and triggers a beep alarm when the SOC falls below 40%. An external charger and regenerative charging from four electrodynamometers on the bicycle chain recharge the battery when the SOC drops below 20%, provided the load is disconnected. Measurement results closely matched simulation data, with the MATLAB model showing 44% SOC after 3 h, compared to the actual real-time 45.85%. The system accurately tracked charging/discharging patterns, validating its effectiveness. This compact and cost-effective BMS design ensures safe operation, improves battery longevity, and supports broader adoption of E-Bikes as an eco-friendly transportation solution. Full article

This study examines the impact of corruption on business innovation from a comparative perspective and shows that this relationship is inherently heterogeneous across firms and countries. It addresses two main research questions: (i) Does corruption facilitate or hinder innovation in the countries studied? (ii) To what extent is the relationship between corruption and innovation mediated/shaped by countries’ institutional configurations and firm characteristics (foreign and domestic ownership)? We analyze data from the fifth and sixth waves (2012–2016 and 2018–2019) of the EBRD’s World Bank Business Environment and Enterprise Performance Survey (BEEPS), using a balanced panel of 3584 establishments in 22 Eastern European and Central Asian economies. The results provide two key insights into the relationship between corruption and innovation. First, the institutional setting plays a crucial role in shaping both the strength and the direction of this relationship, for example, when comparing EU and non-EU countries. Second, the impact of corruption at the firm level varies depending on the ownership structure: the ‘greasing’ effect is particularly relevant for foreign firms operating in weak institutional environments, but appears to be ineffective—if not ‘sanding’—for foreign firms in contexts with stronger anti-corruption controls. Full article

Background: Several studies have documented the detrimental impacts of secondhand smoke (SHS) exposure to a range of pediatric respiratory conditions, including asthma, bronchitis, and reduced lung function. The aim of the study was to investigate the influence of SHS exposure on lung function, physical fitness, and body mass index (BMI) in children aged 10 to 14 years. Methods: This cross-sectional study included children aged 10 to 14 years at the Elementary School “Trilj” in Trilj, Croatia. Data on SHS exposure were collected using a questionnaire. Antropometric and spirometry measurements were performed. Physical fitness was assessed using the shuttle run (BEEP) test. Results: This study included 157 children, 89 (56.69%) boys and 68 (43.31%) girls. Children exposed to every day SHS in households had significantly lower values of forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1/FVC, peak expiratory flow (PEF) (p < 0.001) and higher z-score BMI levels (p = 0.018) in comparison to unexposed children. Logistic regression showed that children unexposed to SHS had higher odds for better results in the BEEP test (OR 62.45, 95% CI 21.26–179.24, p < 0.001). Children with poorer physical fitness, expressed by lower BEEP score levels, had significantly lower FVC, FEV1, FEV1/FVC, and PEF (p < 0.001). Conclusions: Every day SHS exposure in children was associated with poorer lung function, higher BMI, and poorer physical fitness. Full article

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping. Full article

Collaborative robots (cobots) have largely replaced conventional industrial robots in today’s workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human–robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots’ irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker’s performance in a human–robot collaborative environment. In this study, factory workers’ mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters. Full article

The green transition and green economic growth are policy priorities in the European Union. In this context, this study estimates the effects of environmental management on firm performance, in particular labour productivity. There is currently a lack of empirical evidence on this topic, although it is of great importance due to the increasing need for environmental practices across the globe. Therefore, to address this gap, we explore the relationship between several environmental variables on labour productivity, through the use of cross-sectional firm-level data. These data were obtained using the sixth wave of the Business Environment and Enterprise Survey (BEEPS VI). This study focuses on ten EU countries. The results obtained from the empirical analysis reveal that firms who employ an environmental manager and firms that are subject to energy taxes or levies both have higher productivity than those who do not; thus, firms that have employed or are subject to certain environmental practices reap the benefits of higher labour productivity. Furthermore, firms that use renewable energy have higher labour productivity than those that do not. Therefore, the results obtained allowed us to draw implications for both policy makers and managers. Full article

The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications. Full article

The relationship between firms’ exports and increases in productivity is generally regarded as positive. While the causal effects of process innovation are straightforward and positive, the effect of product innovation on productivity is ambiguous. However, there is a lack of empirical evidence on a joint effect that innovation and exports have on firms’ productivity. In our attempt to fill this gap, we explore individual and joint effects of innovation and exports on productivity by employing cross-sectional firm-level data. We use the sixth wave of the Business Environment and Enterprise Performance Survey (BEEPS VI: 2018–2020) conducted by the EBRD and the World Bank. Using a stratified random sampling, the data was collected from interviews with representatives of randomly chosen firms from 32 countries. The overall results suggest that exporting firms are more productive than non-exporters, while the impact of innovation is more heterogeneous. Whereas EU and high-income countries reap the productivity benefits, this effect is absent in other regions and countries with medium and low-income levels. Finally, our results indicate the absence of a joint effect of innovation and exports on productivity, across different geographical regions and countries of different income levels. Full article

An integrated transport service fare system, supported by an agreement for ticket revenue sharing among service providers, is an essential component to improve the experience of the users who can find single tickets for the integrated transport services they look for. A challenge is to find a model to share the revenue which all providers agree on. A solution is to adopt data-driven approaches where user-generated data are collected to extract information on the extent each transport service was used. This is consistently used. However, it suffers from incomplete data, as not all users always validate their ticket when checking out or when switching lines. We studied all technologies available to support automatic ticket validation in order to record when the users access and exit each service line. The contributions of this work are the following: we give an in-depth description of the inner workings of this novel approach describing how we take advantage of each technology; we present the developed solution (Beep4Me), which adds new functionalities to an existing mobile ticketing platform; and we describe our testing framework, which includes most cases users might encounter during a trip. Our results demonstrate how it is possible to collect key data related to validations which can be used first for clearing purposes and then for network planning/fleet optimization. Full article

To understand the animal welfare impact of virtual fencing stimuli (audio cue ‘beep’ and electrical stimulus) on naïve sheep, it is necessary to assess stress responses during the animal’s first encounters with these stimuli. Eighty Merino ewes were exposed to one of the following treatments (n = 16 animals per treatment): Control (no stimuli), beep, dog bark, manual restraint, and electrical stimulus. Collars were used to apply the audio and electrical stimuli. The restraint treatment showed an elevated cortisol response compared with the control (p < 0.05), but there were no differences between the other treatments and the control. There were no differences between treatments in vaginal temperature (p > 0.05). For behaviors, the sheep receiving the bark and beep treatments were more vigilant compared to the control (p < 0.05), there were more aversive responses observed in the electrical stimulus treatment compared to the control. Together, the responses showed that the beep stimuli were largely benign, the bark stimuli was minimally aversive, the electrical stimuli was acutely aversive, and the restraint was moderately aversive. These data suggest that, for sheep, their first exposure to the virtual fencing stimuli should be perceived as less aversive than a commonly used restraint procedure. Full article