Missing Knowledge Research Articles

World delirium awareness and quality survey in 2023-a worldwide point prevalence study.

Delirium, an acute brain dysfunction, is proposed to be highly prevalent in clinical care and shown to significantly increase the risk of mortality and dementia. To report on the global prevalence of clinically documented delirium and delirium-related clinical practices in wards caring for paediatric and adult patients in healthcare facilities. A prospective, cross-sectional, 39-question survey completed on World Delirium Awareness Day, 15 March 2023. Clinicians or researchers with access to clinical data. The primary outcome was the prevalence of clinically documented delirium at 8:00 a.m. (4h) and 8:00 p.m. (±4h). Secondary outcomes included delirium-related care practices and barriers to use. Descriptive statistics were calculated and multilevel modelling was completed. 1664 wards submitted surveys from 44 countries, reporting on delirium assessments at 8:00a.m. (n = 36048) and 8:00p.m. (n = 32867); 61% reported use of validated delirium assessment tools. At 8:00a.m., 18% (n = 2788/15458) and at 8:00p.m., 17.7% (n = 2454/13860) were delirium positive. Top prevention measures were pain management (86.7%), mobilisation (81.4%) and adequate fluids (80.4%). Frequently reported pharmacologic interventions were benzodiazepines (52.7%) and haloperidol (46.2%). Top barriers included the shortage of staff (54.3%), lack of time to educate staff (48.6%) and missing knowledge about delirium (38%). In this study, approximately one out of five patients were reported as delirious. The reported high use of benzodiazepines needs further evaluation as it is not aligned with best-practice recommendations. Findings provide a benchmark for future quality improvement projects and research.

A measles virus collective infectious unit that caused lethal human brain disease includes many locally restricted and few widespread copy-back defective genomes.

During virus replication in cultured cells, copy-back defective viral genomes (cbDVGs) can arise. CbDVGs are powerful inducers of innate immune responses in vitro, but their occurrence and impact on natural infections of human hosts remain poorly defined. We asked whether cbDVGs were generated in the brain of a patient who succumbed to subacute sclerosing panencephalitis (SSPE) about 20 years after acute measles virus (MeV) infection. Previous analyses of 13 brain specimens of this patient indicated that a collective infectious unit (CIU) drove lethal MeV spread. In this study, we identified 276 replication-competent cbDVG species, each present in over 100 copies in the brain. Six species were detected in multiple forebrain locations, implying that they travelled long-distance with the CIU. The cbDVG to full-length genomes ratio was often close to 1 (0.6-1.74). Most cbDVGs were 324-2,000 bases in length, corresponding to 2%-12% of the full-length genome; all are predicted to have complementary terminal sequences. If improperly encapsidated, these sequences have the potential to form double-stranded structures that can induce innate immune responses. To assess this, we examined the transcriptome of all brain specimens. Several interferon and inflammatory response genes were upregulated, but upregulation levels did not correlate with cbDVG levels in the specimens. Thus, the CIU that drove MeV pathogenesis in this brain includes, in addition to two complementary full-length genome populations, many locally restricted and few widespread cbDVG species. The widespread cbDVG species may have been positively selected but how they impacted pathogenesis remains to be determined.IMPORTANCECopy-back defective viral genomes (cbDVGs) can drive virus-host interactions. They can suppress virus replication directly, by competing with full-length genomes, or indirectly by stimulating antiviral immunity. In vitro, cbDVG can slow down infections and promote persistence, but there is limited documentation of their presence in human hosts or of their impact on disease. We had the unique opportunity to analyze the brain of a patient who succumbed to subacute sclerosing panencephalitis, a rare but lethal consequence of measles. We detected more than 270 distinct cbDVG species; most were restricted to one specimen, but several reached all lobes of the forebrain, suggesting positive selection. Our analyses provide the missing knowledge of the diversity of cbDVG in a natural infection of a human host. They also reveal that a collective infectious unit that caused lethal human brain disease includes few widespread cbDVG, in addition to two ubiquitous complementary full-length genome populations.

In vitro capsule or biofilm formation of Streptococcus uberis and bacteriological cure of bovine mastitis.

The relationship between the in vitro detected virulence factors biofilm and capsule formation of Streptococcus (S.) uberis isolates of clinical mastitis in dairy cows and the bacteriological cure rate after antibiotic therapy was investigated in order to better understand the importance of these virulence factors for the bacteriological cure rate. A total of 111 clinical mastitis (CM) cases were collected, in which S. uberis was bacteriologically detected. All mastitis cases were treated in accordance with the approval conditions of the antibiotic udder tubes used. Individual cow information including age, number of lactations, current lactation mastitis and antimicrobial treatment received was recorded. The microtiter plate test was used to detect biofilm formation and Anthony capsule staining was used to detect capsular capacity. Statistical analyses were performed to characterize the correlation between in vitro virulence factors and bacteriological cure (BC) rate. 30.5% (n=29) of the S. uberis isolates of bacteriologically cured cases and 34.5% (n=10) of the isolates of bacteriologically non-cured mastitis cases were found to have the ability to produce capsules in vitro. 70.7% (n=58) of the S. uberis isolates from bacteriologically cured mastitis cases had the ability to produce biofilm in vitro, whereas 58.6% (n=17) of the isolates of non-cured mastitis cases showed ability in producing biofilm. No correlation was found between the in vitro ability of S. uberis to form capsules and biofilms and the BC rate after antibiotic treatment of bovine mastitis. The present work has shown that the investigated in vitro virulence factors are not associated with the BC after antibiotic therapy. Further studies on the role of S. uberis virulence factors are needed to complete the missing knowledge on the difficulties in curing S. uberis mastitis. This study is of great clinical relevance since it enhances the understanding of the occurrence of BC in S. uberis mastitis cases. The investigated virulence factors are often addressed as possible reasons for therapy failure, although respective scientific studies are missing.

Community knowledge graph abstraction for enhanced link prediction: A study on PubMed knowledge graph

Objective:As new knowledge is produced at a rapid pace in the biomedical field, existing biomedical Knowledge Graphs (KGs) cannot be manually updated in a timely manner. Previous work in Natural Language Processing (NLP) has leveraged link prediction to infer the missing knowledge in general-purpose KGs. Inspired by this, we propose to apply link prediction to existing biomedical KGs to infer missing knowledge. Although Knowledge Graph Embedding (KGE) methods are effective in link prediction tasks, they are less capable of capturing relations between communities of entities with specific attributes (Fanourakis et al., 2023). Methods:To address this challenge, we proposed an entity distance-based method for abstracting a Community Knowledge Graph (CKG) from a simplified version of the pre-existing PubMed Knowledge Graph (PKG) (Xu et al., 2020). For link prediction on the abstracted CKG, we proposed an extension approach for the existing KGE models by linking the information in the PKG to the abstracted CKG. The applicability of this extension was proved by employing six well-known KGE models: TransE, TransH, DistMult, ComplEx, SimplE, and RotatE. Evaluation metrics including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@k were used to assess the link prediction performance. In addition, we presented a backtracking process that traces the results of CKG link prediction back to the PKG scale for further comparison. Results:Six different CKGs were abstracted from the PKG by using embeddings of the six KGE methods. The results of link prediction in these abstracted CKGs indicate that our proposed extension can improve the existing KGE methods, achieving a top-10 accuracy of 0.69 compared to 0.5 for TransE, 0.7 compared to 0.54 for TransH, 0.67 compared to 0.6 for DistMult, 0.73 compared to 0.57 for ComplEx, 0.73 compared to 0.63 for SimplE, and 0.85 compared to 0.76 for RotatE on their CKGs, respectively. These improved performances also highlight the wide applicability of the extension approach. Conclusion:This study proposed novel insights into abstracting CKGs from the PKG. The extension approach indicated enhanced performance of the existing KGE methods and has applicability. As an interesting future extension, we plan to conduct link prediction for entities that are newly introduced to the PKG.

Unpacking the Gap Box Against Data-Free Knowledge Distillation.

Data-free knowledge distillation (DFKD) improves the student model (S) by mimicking the class probability from a pre-trained teacher model (T) without training data. Under such setting, an ideal scenario is that T can help generate "good" samples from a generator (G) to maximally benefit S. However, existing arts suffer from the non-ideal generated samples under the disturbance of the gap (i.e., either too large or small) between the class probabilities of T and S; for example, the generated samples with too large gap may exhibit excessive information for S, while too small gap leads to the limited knowledge in the samples, resulting into the poor generalization. Meanwhile, they fail to judge the "goodness" of the generated samples for S since the fixed T is not necessarily ideal. In this paper, we aim to answer what is inside the gap box; together with how to yield "good" generated samples for DFKD? To this end, we propose a Gap-Sensitive Sample Generation (GapSSG) approach, by revisiting the empirical distilled risk from a data-free perspective, which confirms the existence of an ideal teacher (T *), while theoretically implying: (1) the gap disturbance originates from the mismatch between T and T *, hence the class probabilities of T enable the approximation to those of T *; and (2) "good" samples should maximally benefit S via T's class probabilities, owing to unknown T *. To this end, we unpack the gap box between T and S as two findings: inherent gap to perceive T and T *; derived gap to monitor S and T *. Benefiting from the derived gap that focuses on the adaptability of generated sample to S, we attempt to track student's training route (a series of training epochs) to capture the category distribution of S; upon which, a regulatory factor is further devised to approximate T * over inherent gap, so as to generate "good" samples to S. Furthermore, during the distillation process, a sample-balanced strategy comes up to tackle the overfitting and missing knowledge issues between the generated partial and critical samples by training G. The theoretical and empirical studies verify the advantages of GapSSG over the state-of-the-arts.

The role of training in the “school-to-work” transition in the Russian labour market

PurposeThe article aims to describe the role of training and examines the impact on the wages of university and college graduates.Design/methodology/approachWe use nationwide administrative data on university and college graduates in 2019. The population includes 1.3 million observations, of which 222,000 (∼16%) received training after graduation from an educational institution (from July 2019 to 2022). We used OLS and the “difference-in-differences” methods to estimate the returns to training. Estimates obtained using the DID method turned out to be several times smaller because they consider unobserved characteristics (abilities).FindingsWe obtained several key findings. First, the participation of graduates in training is high, despite their recent education. Second, undergoing training is conditional on the existence of wage returns. The results show a wage premium of 17.8% (OLS method) and 2.0% (DID method). Third, graduates from nonselective universities (with low state exam score) try to participate more actively in training to acquire missing knowledge and skills. The wage premium for graduates from nonselective universities is 19.1% (OLS method) and 5.1% (DID method). Fourth, there is a high return to training for graduates from socially relevant fields (education and healthcare), where training is regular and mandatory.Originality/valueThis paper is one of the first to estimate the involvement and returns to training for graduates using nationwide administrative data in Russia.

Greener view, safer drive: Using repeated field experiments and deep transfer learning technique to investigate impacts of urban road landscapes on driving performance

Driving in urban environments is an essential part of urban residents’ daily life. We still know little about impacts of a wide range of greenness on driving performance in real urban environments, after controlling for socioeconomic, demographic, driving record, and other environmental factors. This missing knowledge prevents policymakers and professionals from using appropriate planning and design of green landscapes to create safe driving environments for numerous urban residents. This study aimed to address this significant knowledge gap by using real-world driving experiments. Each of thirty-four residents performed seven driving tasks so 238 driving tasks were completed in total. Each task lasted one hour and followed a randomly assigned sequence. Road greenness and other environmental characteristics were analyzed using deep transfer learning semantic segmentation based on live videos (30 frames per second), recorded by a camera positioned to capture the driver’s eye view. A serial communication technology, known as Controller Area Network bus (CANbus), was employed to continuously measure driving performance using four parameters. A series of hierarchical regression analyses yielded three major findings: First, an increased mean of greenness was associated with improved driving performance, as demonstrated by all four parameters. Second, an increased variation of greenness was also associated with better driving performance in three parameters. Finally, the mean of greenness displayed a stronger positive relationship with driving performance than the variation of greenness in three parameters. The findings imply that both the quantity and quality of green landscapes are critical for promoting driving performance in urban areas.

A dose of nature to reduce sexual crimes in public outdoor spaces: Proposing the Landscape-Sexual Crime Model

Sexual crime is a critical global social problem. There remains a critical knowledge gap concerning whether and to what extent sexual crimes in public outdoor spaces can be influenced by landscape morphology of green spaces. This missing knowledge hinders the effective use of green spaces to reduce sexual crimes in these public settings. To address this issue, we collected a dataset comprising 5,155 cases of sexual crimes that occurred in public outdoor spaces in the United States from August 2021 to July 2022. A random forest model was employed to examine the statistical relationships between landscape morphology and sexual crimes. Additionally, we utilized the Shapley Additive Explanations (SHAP) model to quantify the interaction effects of landscape morphology with socioeconomic and demographic characteristics. This study yields three key findings: (1) Both the proportion and configuration factors of landscape morphology may significantly influence the sexual crime probability. (2) The relationships between landscape morphology and sexual crimes are nonlinear, and threshold values for the satisfactory dose and the preferred dose of green spaces can be identified. (3) There are significant interaction effects between landscape morphology with socioeconomic and demographic characteristics, emphasizing the importance of prioritizing green space interventions in socioeconomically disadvantaged areas. Lastly, through summarizing the findings of this study and previous research, we propose the Landscape-Sexual Crime Model (LSCM), which advocates for further research to explore effective strategies for using green spaces to reduce sexual crimes.

Knowledge Graph Completion Using a Pre-Trained Language Model Based on Categorical Information and Multi-Layer Residual Attention

Knowledge graph completion (KGC) utilizes known knowledge graph triples to infer and predict missing knowledge, making it one of the research hotspots in the field of knowledge graphs. There are still limitations in generating high-quality entity embeddings and fully understanding the contextual information of entities and relationships. To overcome these challenges, this paper introduces a novel pre-trained language model-based method for knowledge graph completion that significantly enhances the quality of entity embeddings by integrating entity categorical information with textual descriptions. Additionally, this method employs an innovative multi-layer residual attention network in combination with PLMs, deepening the understanding of the joint contextual information of entities and relationships. Experimental results on the FB15k-237 and WN18RR datasets demonstrate that our proposed model significantly outperforms existing baseline models in link prediction tasks.

Discretization and decoupled knowledge distillation for arbitrary oriented object detection

In recent years, lightweight object detection networks have been increasingly applied to remote sensing platforms due to their fast inference speed and flexible deployment advantages. Knowledge distillation methods have been widely used to reduce the performance gap between large and small models, and many studies have combined knowledge distillation with object detection. However, existing knowledge distillation methods often overlook the transfer of localization knowledge. Therefore, this paper proposes a method called Discretized Position Knowledge Distillation (DPKD) to improve the use of knowledge distillation in object detection. Specifically, the DPKD method incorporates a Discretization Algorithm Module (DAM), which leverages both general probability distribution and cross-Gaussian distribution to transfer high-quality bounding box position and pose information. Additionally, the Position Knowledge Distillation (PKD) method splits the target and non-target bounding boxes to form the distillation loss function, addressing the issue of missing background knowledge transfer during the distillation process. To further enhance the learning of high-quality bounding boxes, a Region Weighting Module (RWM) based on EIoU is introduced in DPKD, assigning weights to the various bounding boxes in the teacher's output. The effectiveness of DPKD in the field of remote sensing image object detection in multi-modal scenarios was verified through multi-modal training on the publicly available DOTA and HRSID datasets.

Supporting ANFIS interpolation for image super resolution with fuzzy rough feature selection

Image Super-Resolution (ISR) is utilised to generate a high-resolution image from a low-resolution one. However, most current techniques for ISR confront three main constraints: i) the assumption that there is sufficient data available for training, ii) the presumption that areas of the images concerned do not involve missing data, and iii) the development of a computationally efficient model that does not compromise performance. In addressing these issues, this study proposes a novel lightweight approach termed Fuzzy Rough Feature Selection-based ANFIS Interpolation (FRFS-ANFISI) for ISR. Popular feature extraction algorithms are employed to extract the potentially significant features from images, and population-based search mechanisms are utilised to implement effective FRFS methods that assist in selecting the most important features among them. Subsequently, the processed data is entered into the ANFIS interpolation model to execute the ISR operation. To tackle the sparse data challenge, two adjacent ANFIS models are trained with sufficient data where appropriate, intending to position the ANFIS model of sparse data in the middle. This enables the two neighbouring ANFIS models to be interpolated to produce the otherwise missing knowledge or rules for the model in between, thereby estimating the corresponding outcomes. Conducted on standard ISR benchmark datasets while considering both sufficient and sparse data scenarios, the experimental studies demonstrate the efficacy of the proposed approach in helping deal with the aforementioned challenges facing ISR.

Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification

Vision-language foundation models have been incredibly successful in a wide range of downstream computer vision tasks using adaptation methods. However, due to the high cost of obtaining pre-training datasets, pairs with weak image-text correlation in the data exist in large numbers. We call them weak-paired samples. Due to the limitations of these weak-paired samples, the pre-training model are unable to mine all the knowledge from pre-training data. The existing adaptation methods do not consider the missing knowledge, which may lead to crucial task-related knowledge for the downstream tasks being ignored. To address this issue, we propose a new adaptation framework called Data Adaptive Traceback (DAT). Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data to enable the downstream tasks. Furthermore, we adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning. We conduct extensive experiments that show our proposed DAT approach meaningfully improves various benchmark datasets’ performance over traditional adaptation methods by simply.

OntoFact: Unveiling Fantastic Fact-Skeleton of LLMs via Ontology-Driven Reinforcement Learning

Large language models (LLMs) have demonstrated impressive proficiency in information retrieval, while they are prone to generating incorrect responses that conflict with reality, a phenomenon known as intrinsic hallucination. The critical challenge lies in the unclear and unreliable fact distribution within LLMs trained on vast amounts of data. The prevalent approach frames the factual detection task as a question-answering paradigm, where the LLMs are asked about factual knowledge and examined for correctness. However, existing studies primarily focused on deriving test cases only from several specific domains, such as movies and sports, limiting the comprehensive observation of missing knowledge and the analysis of unexpected hallucinations. To address this issue, we propose OntoFact, an adaptive framework for detecting unknown facts of LLMs, devoted to mining the ontology-level skeleton of the missing knowledge. Specifically, we argue that LLMs could expose the ontology-based similarity among missing facts and introduce five representative knowledge graphs (KGs) as benchmarks. We further devise a sophisticated ontology-driven reinforcement learning (ORL) mechanism to produce error-prone test cases with specific entities and relations automatically. The ORL mechanism rewards the KGs for navigating toward a feasible direction for unveiling factual errors. Moreover, empirical efforts demonstrate that dominant LLMs are biased towards answering Yes rather than No, regardless of whether this knowledge is included. To mitigate the overconfidence of LLMs, we leverage a hallucination-free detection (HFD) strategy to tackle unfair comparisons between baselines, thereby boosting the result robustness. Experimental results on 5 datasets, using 32 representative LLMs, reveal a general lack of fact in current LLMs. Notably, ChatGPT exhibits fact error rates of 51.6% on DBpedia and 64.7% on YAGO, respectively. Additionally, the ORL mechanism demonstrates promising error prediction scores, with F1 scores ranging from 70% to 90% across most LLMs. Compared to the exhaustive testing, ORL achieves an average recall of 80% while reducing evaluation time by 35.29% to 63.12%.

ProMvSD: Towards unsupervised knowledge graph anomaly detection via prior knowledge integration and multi-view semantic-driven estimation

Knowledge graphs (KGs) have found extensive applications within intelligent systems, such as information retrieval. Much of the research has predominantly focused on completing missing knowledge, with little consideration given to examining errors. Unfortunately, during customizing KGs, diverse unpredictable errors are virtually unavoidable to be introduced, and these anomalies significantly impact the performance of applications. Detecting erroneous knowledge presents a formidable challenge due to the costly acquisition of ground-truth labels. In this work, we develop an unsupervised anomaly detection framework named ProMvSD, aiming to adapt KGs of varying scales via serialization components. To overcome the insufficient contextual information provided by the topological structure, we introduce the large language model as a reasoner to extract prior knowledge from extensive pre-trained textual data, thereby enhancing the understanding of KGs. Anomalous triple may result in a larger semantic gap between the head and tail neighborhoods. To uncover latent anomalies effectively, we propose a multi-view semantic-driven model (MvSD) based on the assumptions of self-consistency and information stability. MvSD jointly estimates the suspiciousness of triples from three hyperviews: node-view semantic contradiction, triple-view semantic gap, and pathway-view semantic gap. Extensive experiments on three English benchmark KGs and a Chinese medical KG demonstrate that, for the top 1% of the most suspicious triples, we can detect real anomalies with at most 99.9% accuracy. Furthermore, ProMvSD significantly outperforms state-of-the-art representation learning baselines, achieving a 29.2% improvement in detecting all anomalies.

‘A kindergarten teacher must have 100 eyes and 100 ears!’: Kindergarten teachers' experiences coping with child sexual abuse and problematic sexual behavior

BackgroundChild sexual abuse (CSA) and problematic sexual behavior (PSB) are worldwide phenomena that occur across all ages. Kindergarten teachers' proactive involvement can be crucial to the prevention, disclosure and intervention of CSA and PSB. However, research on their experiences of contending with CSA and PSB remains limited. ObjectiveThis study examines kindergarten teachers' experiences in Israel with the CSA and PSB of their students. Participants and settingSemi-structured interviews were conducted with 31 teachers: 11 secular Jewish, seven religious Jewish, nine Druze Arab, and four Muslim Arab. MethodsA qualitative analysis was conducted using the interview transcripts as data. ResultsThe analysis revealed three themes illustrating teachers' professional transformations regarding their knowledge of these phenomena: 1) initial shock, uncertainty and sense of responsibility when exposed to CSA and PSB due to missing knowledge, 2) implementation of prevention and intervention strategies regarding CSA and PSB, and 3) embracing a social role to disseminate CSA and PSB knowledge. The findings indicated that the majority of the teachers went from overwhelming shock and fear due to a lack of knowledge in coping with CSA and PSB to a sense of responsibility as a community leader. ConclusionsThe fragmentation of the Israeli education system isolates kindergartens, and the lack of training and education for the teachers left them alone when contending with the CSA and PSB of their students. Nevertheless, the participants exhibited remarkable agency and resourcefulness, gaining the necessary knowledge and acting as knowledge agents within their communities.

Hierarchical Perceptual Graph Attention Network for Knowledge Graph Completion

Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networks (GNNs) perform well in knowledge graph completion, but most existing graph neural network-based knowledge graph completion methods tend to aggregate neighborhood information directly and individually, ignoring the rich hierarchical semantic structure of KGs. As a result, how to effectively deal with multi-level complex relations is still not well resolved. In this study, we present a hierarchical knowledge graph completion technique that combines both relation-level and entity-level attention and incorporates a weight matrix to enhance the significance of the embedded information under different semantic conditions. Furthermore, it updates neighborhood information to the central entity using a hierarchical aggregation approach. The proposed model enhances the capacity to capture hierarchical semantic feature information and is adaptable to various scoring functions as decoders, thus yielding robust results. We conducted experiments on a public benchmark dataset and compared it with several state-of-the-art models, and the experimental results indicate that our proposed model outperforms existing models in several aspects, proving its superior performance and validating the effectiveness of the model.

Towards the Integration of Direct Contact Prelithiation into the Manufacturing Process for Large-Format Lithium-Ion Batteries

The increasing requirements for high-performance lithium-ion batteries from applications such as electric aviation and electric mobility require further improvement of this energy storage technology. Prelithiation is a method to improve the energy density and lifespan by integrating additional lithium during battery cell production to compensate for occurring irreversible losses of active lithium, which lead to a reduced energy density and capacity fade. Direct contact prelithation incorporates lithium metal in contact to the anode active material to generate a lithium reservoir, which can be consumed by parasitic reactions inside the cell.One factor limiting direct contact prelithiation to laboratory scale is the missing knowledge how to integrate direct contact prelithiation into the manufacturing system of battery cells and how subsequent process steps after lithium insertion are affected. The presented work investigates the effect of direct contact prelithiation on the cell assembly and cell finalization processes of the lithium-ion battery production chain. The occurring challenges to integrate the formed anode-lithium-composite in the cell assembly processes are likely to be similar to those for pure lithium electrodes. The electrolyte filling and formation processes show a high need for adaptation regarding direct contact prelithiation, since the first lithiation of the anode and the formation of the solid electrolyte interphase is already initiated with electrolyte filling. Successful industrialization of direct contact prelithiation requires attention to safety concerns due to exothermic chemical reactions, accurate determination of the solid electrolyte interphase to ensure high cell quality, and streamlining of the currently time-consuming formation process.

Implementation of fall sensors in long-term care : An interview study to identify promoting and inhibiting factors from the perspective of professional caregivers

Digital support systems are becoming increasingly more important in long-term inpatient care facilities. Welfare technologies have the potential to make avaluable contribution to maintaining independence in advanced age. At the same time the technologies can support professional caregivers. The aim of the study was to describe the expectations and experiences with anew technology, using the example of afall sensor system, from the perspective of professional caregivers. We used aqualitative design with semistructured interviews in two long-term inpatient care facilities. In one facility, three individual interviews took place, while in the other long-term care facility, agroup interview with three nursing professionals was conducted. Additionally, one individual interview was conducted with aperson in aleadership role in each facility. The time from implementation of the fall sensors to the interviews was between 1 and 3 months. Data were analyzed using qualitative content analysis in MAXQDA. The study demonstrated that there was acorrespondence between expectations of and retrospective experiences with the new technology among professional nurses. The main facilitating factors of the implementation that were identified were timely information about the risk of falling or afall that has occurred, maintenance of residents' autonomy and freedom of movement as well as the enhancement of the sense of security among nursing professionals and the associated psychological relief effect. The inhibiting factors of the implementation were compulsory presetting, false alarms and faulty handling of the technology due to missing knowledge.

E. N. Tsimbaeva. Women and men in the history and literature of the mid-18th — early 20th centuries: Behind the stage of a literary text

E. Tsimbaeva’s monograph analyzes the literature of the mid-18th — early 20th cc. in comparison with the realia of the everyday that were omitted from books by contemporary writers as too obvious and have become obscure and unknown to present-day readers. Notably, the book suggests that the commonly accepted rules and conventions of a period would often determine the plots of contemporary works of literature and shape the images and psychology of their characters, whereas new generations of readers may have trouble understanding their seemingly random and strange behaviour. The scholar argues that the bygone realia of the everyday and customs left behind the scenes of a literary text may not be deduced by the modern reader and therefore provide material for studies by present-day historians. Tsimbaeva’s study focuses on this missing knowledge and its cultural and literary significance.

FORMATION OF PROFESSIONAL COMPETENCE OF FUTURE BACHELOR OF ECONOMY IN HIGHER EDUCATION INSTITUTIONS

Based on the competence approach, such meta-educational constructs as competence and professional competence are revealed in the article. It is stated that the formation of professionally significant competencies of future Bachelor’s of Economics is focused on the humanistic style of interaction between students, lecturers and employers. The purpose of the article is the formation of professional competence of future Bachelor of Economics in higher educational institutions. It is aimed at developing self-and mutual evaluation of the most significant results for future professional activity, creative ‒ is associated with the purpose, content, activity of lecturers and students, developing ‒ focusing on the maximum development of students' reflexive and creative abilities. The factors that determine the professionally significant qualities of future Bachelor of Economics are determined. The necessity of forming professionally significant qualities in future Bachelor of Economics (ability to navigate information flows; learn, filling in the missing knowledge; have competitiveness and mobility, to solve non-standard professional tasks and strive for self-actualization in the chosen professional field) is proved. It is also aimed at developing students’ personal functions (motivation, orientation to the future profession, self-realization). The following prospects for further research have been identified: revealing of relationships between the level of professionally significant competences formation of future bachelor’s of Economics and their career growth; conceptual and methodological foundations of continuous education of bachelor’s of Economy in the context of competence approach; productive interaction between university and employers in improving the efficiency of professional training of future bachelors to work.