Ričardas Krikštolaitis

This study examines how strategic ignorance operates within organizations. Using survey data from 406 employees, we develop and test a multidimensional framework capturing strategic ignorance across five domains. Results demonstrate strong interrelations among dimensions, indicating that strategic ignorance operates as an integrated organizational configuration. Cluster analysis identifies three intensity-based profiles—low, moderate, and high—suggesting that organizations differ primarily in overall levels rather than structural composition. Regression results show that tenure and sector are more strongly associated with strategic ignorance than individual characteristics. The study provides an empirical basis for understanding strategic ignorance as a contextually embedded organizational phenomenon.

Reducing energy consumption and improving energy efficiency are particularly important for mitigating climate change. The main motives for saving energy and improving energy efficiency are economic and environmental. It is also necessary to implement the environmental policy. Thus, this paper aims to analyse attitudes towards ecological policy and the impact of economic and environmental factors on energy consumption and energy efficiency behaviours in European Union (EU) countries in 2022. The analysis showed that environmental factors motivated people to reduce energy consumption more in rich EU countries, while economic factors were stronger in less wealthy countries. However, the level of economic development insignificantly influenced energy consumption and efficiency behaviours. The main positive determinants of energy efficiency behaviour were environmental attitudes, confidence in personal energy reduction, willingness to pay more to speed up the green transition, and attitudes toward environmental promotion policy. Meanwhile, the seriousness of the energy price problem and attitudes towards the ban and fee policy negatively influenced this behaviour. All analysed factors, except the attitudes toward environmental promotion policy, significantly and positively determined the energy consumption behaviour. Thus, promoting energy consumption reduction is very difficult.

Complications are frequent in cancer patients and contribute to adverse outcomes and higher healthcare costs, underscoring the need for earlier identification and prediction. This study evaluated the feasibility of using passively generated smartphone sensor data to explore early-warning signals of complications and symptom worsening during cancer treatment. A total of 108 patients were continuously monitored using accelerometer, GPS, and screen on/off data collected through the LAIMA application, while symptoms of depression, fatigue, and nausea were assessed every two weeks and complications were confirmed during clinic visits or emergency presentations. Smartphone data streams were aggregated into variables describing activity and sociability patterns. Machine learning models, including Decision Tree, Extreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine, were used for complication prediction, and time-series models such as Autoregressive Integrated Moving Average, Holt–Winters, TBATS, Long Short-Term Memory neural network, and General Regression Neural Network were applied to identify early behavioral changes preceding symptom reports. In this exploratory analysis, the ensemble model demonstrated high sensitivity (89%) for identifying complication events. Smartphone-derived behavioral indicators enabled earlier detection of depression, fatigue, and vomiting by about nine days in a subset of patients. These findings demonstrate the feasibility of passive smartphone sensor data as exploratory early-warning signals, warranting validation in larger cohorts.

This study investigated the effects of macronutrient type and concentration on the biomass yield and biochemical composition of hydroponically grown wheat sprouts (HWS), with the aim of identifying fertilization strategies that optimize both productivity and feed quality. HWS were cultivated using a nutrient film technique over a 7-day period under controlled environmental conditions, with treatments including calcium nitrate (CN1–CN3), potassium phosphate (CP1–CP3), potassium sulfate (CK1–CK2), and a balanced NPK 20–20–20 fertilizer (NPK1–NPK3), each applied at three increasing concentrations. The quantitative parameters assessed included biomass yield per unit of dry seed (DP, kg kg⁻¹) and dry matter content (DM, %), while qualitative traits included crude protein (CP), ether extract (EE), crude fiber (CF), and ash content. Results indicated that balanced NPK fertilization significantly enhanced performance, with NPK3 achieving the highest biomass yield (6.39 kg kg⁻¹), CP (24.26%), CF (5.63%), and ash (16.0%) content. In contrast, CN3 treatments reduced yield (4.84 kg kg⁻¹) despite increasing CP (19.65%), indicating trade-offs between nitrogen enrichment and vegetative expansion. Phosphorus-based treatments (CP2–CP3) improved nutrient density without suppressing yield. Regression analyses revealed strong correlations between DM and both CF (R² = 0.81) and ash (R² = 0.71), supporting their utility as indirect indicators of feed quality. EE content remained stable (2.07–2.67%) across all treatments, suggesting its limited responsiveness to macronutrient manipulation. These findings highlight the importance of nutrient synergy in hydroponic systems and provide a practical framework for tailoring fertilization regimes to meet specific agronomic and nutritional objectives in precision livestock feeding and provide practical guidance for optimizing hydroponic livestock feed production.

To explain the evolution of student entrepreneurship, this paper investigates how student intended career paths interrelate with university entrepreneurial environment in three country groups: developed economies, economies in transition, and developing economies. Empirical analysis is built on data of the Global University Entrepreneurial Spirit Students’ Survey (GUESSS), collected in 2018. Selected sample comprises of 198426 students from 31 countries, from which 15 are developed economies, 2 – economies in transition, and 14 – developing economies. Business creation related career aspirations, ongoing entrepreneurial activities, and university entrepreneurial environment are examined using statistical analysis methods for evaluating the association between distinguished country groups and entrepreneurial characteristics of students. According to results obtained, statistically significant differences exist among students from countries of different economic development level. Students in developing economies and economies in transition have considerably greater aspirations towards entrepreneurial career and activities. They also evaluate the courses on entrepreneurship and university entrepreneurial environment significantly more favourably than students in developed countries do. Students in less developed regions are also those who more frequently choose entrepreneurship specific education, which suggests that they more actively leverage university offerings to realise their higher entrepreneurial aspirations.

Variations between fruit cultivars can significantly impact their biochemical composition. The present research examined the variability in the qualitative and quantitative content of phenolic compounds in berry extracts of Actinidia kolomikta and Actinidia arguta cultivars. Additionally, antioxidant activities of berry extracts were evaluated. The total phenolic, flavonoid, proanthocyanidin contents and hydroxycinnamic acid derivatives were determined using the appropriate methodologies. The average amount of phenolic compounds in A. kolomikta berries (177.80 mg/g) was three times higher than that of A. arguta (54.45 mg/g). Our findings revealed that berries of A. kolomikta and A. arguta accumulated, on average, 1.58 RE/g DW (rutin equivalent/g dry weight) and 0.615 mg RE/g DW of total flavonoids, 1439.31 mg EE/g DW (epicatechin equivalent/g dry weight) and 439.97 mg EE/g DW of proanthocyanidins, and 23.51 mg CAE/g DW (chlorogenic acid equivalent/g dry weight) and 5.65 mg CAE/g DW of hydroxycinnamic acid derivatives, respectively. The cultivars of both species were characterized by higher antioxidant activity of total phenolic compounds determined using CUPRAC and FRAP methods compared to the ABTS^•+ method. The variability in phenolic compounds’ qualitative and quantitative content in tested berry extracts was evaluated by applying ultra-high performance liquid chromatography (UHPLC) coupled to mass spectrometry in tandem with electrospray ionization. Significant intraspecific differences in the amounts of total phenolic compounds, total flavonoid compounds, proanthocyanidins, and hydroxycinnamic acid derivatives were determined among cultivars. Four phenolic acids, eight flavonols, two flavones, and five flavon-3-ols were identified in the berry extracts.

The article analyses the evolution of the social dimension in energy security transition in Lithuania. It contributes to an emerging attempt in the literature to broaden the horizons of our understanding of societal transformation and energy transitions. The analysis reveals the interdependence of changing Lithuanian society and its developing energy security concerns throughout the Independence period, from the importance of material threats (ecological, economic, and political) to post-material values (such as climate change concerns) in the context of energy security. Accordingly, the energy sector undergoes a vivid transition from a totally dependent “Energy Island” to a diversified, interconnected, and self-sustainable system. Such a journey inevitably resulted in the highlight of the social dimension in energy security and provoked certain socio-economic implications, like changing public awareness and participation, social equity and justice, and socio-economic resilience and vulnerability. The research is based on case study principles (applying sociological discourse analysis through scientific articles’ qualitative content analysis method as well as energy security policy analysis reviewing all national strategies throughout the Independence period) and is focused on a unique Lithuanian context.

In forestry, growth functions form the basis of research and are widely used for the mathematical modeling of stand variables, e.g., tree or stand basal area, stand height, stand volume, site index, and many more. In this study, to estimate five-dimensional dependencies between tree diameter at breast height, potentially available area, height, crown area and crown base height, we used a normal copula approach whereby the growths of individual variables are described using a stochastic differential equation with mixed-effect parameters. The normal copula combines the marginal distributions of tree diameter at breast height, potentially available area, height, crown area, and crown base height into a joint multivariate probability distribution. Copula models have the advantage of being able to use collected longitudinal, multivariate, and discrete data for which the number of measurements of individual variables does not match. This study introduced a normalized multivariate interaction information measure based on differential entropy to assess the causality between tree size variables. In order to accurately and quantitatively assess the stochastic processes of the tree size variables’ growth and to provide a scientific basis for the formalization of models, an analysis method of the synergetic theory of information entropy has been proposed. Theoretical findings are illustrated using an uneven-aged, mixed-species empirical dataset of permanent experimental plots in Lithuania.

Background: Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. Objective: We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. Methods: We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. Results: As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. Conclusions: This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. International Registered Report Identifier (IRRID): PRR1-10.2196/49096