Introduction: Social support is not a one-way relationship but is based on the connections people have with other people, groups, and the wider community. This study aimed to assess the perception of social support by people in the third age and to investigate the correlation of social support with the sociodemographic characteristics of the respondents. Methods: A quantitative cross-sectional study was conducted with 147 elderly people who actively use the services of the Center for Health Promotion and Improvement “Generacija” in Sarajevo. The Multidimensional Scale of Perceived Social Support (MSPSS) was used to assess social perceptions. Results: The results show a weak negative relationship between age and the total scale (r = −0.199, p = 0.05), with older people having lower scores on the total scale. A significant relationship was found between the subscale other factors and age (r = −0.202, p = 0.05). The evaluation of the performance of daily activities correlates weakly with the evaluation of the friend’s subscale (r = 0.186, p = 0.05). The friend’s subscale correlates significantly with the quality of social life (r = 0.227, p = 0.05). The subjective assessment of the quality of social life after arriving at the center showed a correlation with the overall scale score (r = 0.182, p = 0.05) and especially with the friend subscale (r = 0.219, p = 0.05), with the increase in social life and the subscales examined in both cases. Conclusion: Users of the “Generacija” center rate social support on the MSPSS with high scores, with users receiving the most support from family. The sociodemographic characteristics of the respondents have an impact on the perception of social support by the users of the Center for Health Promotion and Improvement “Generacija,” more specifically; they were statistically significantly influenced by age, the way of performing daily activities, the quality of social life and the quality of social life after arrival at the Center.

Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4% - 90% in critical network conditions when compared to state-of-the-art.

Introduction: Previous studies have found that, in addition to the general factors for the occurrence of pain and reduced mobility of the cervical spine, the use of electronic devices promotes these, the excessive use of which can also lead to the occurrence of depressive symptoms in students. The aim of this study was to determine the mobility limitation of the cervical spine in students with reported neck pain, to determine the degree of disability and depression due to neck pain, to determine the correlation of mobility limitation of the cervical spine with the degree of disability and depression of students, and to determine the correlation of the degree of disability with the degree of depression. Methods: The research was conducted as a cross-sectional study from May to July 2021 at the University of Zenica in four faculties. The study used the General Questionnaire and two standardized questionnaires to assess disability due to neck pain (Index of Disability due to Neck Pain) and the degree of depression (patient health questionnaire). Results: A total of 147 students with reported neck pain participated in the study. A limitation of mobility was found in 30.6% of the students in flexion, 25.2% in rotation, 23.8% in lateral flexion, and 20.4% on extension. Mild disability due to neck pain was found in 58.5% of students, moderate in 23.8%, and severe in 2.7%. 45.6% of the students had mild depression, 18.4% had moderate depression, and 5.4% had severe depression.Conclusion: Restricted flexion and rotation are more common than restricted lateral flexion and extension of the cervical spine. About half of the students who reported neck pain had a mild degree of disability and mild depression. A strong positive correlation was found between the degree of disability and depression in students with neck pain.

Graphical abstract

With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study’s simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.

O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict handover events and understand the value of such predictions for all stakeholders that rely on the communication network to conduct their business. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. The models were trained on real network data collected from a commercial O-RAN setup deployed in our OpenIreland testbed. Our results show that the proposed approach can be optimized for either recall or precision, depending on the defined application level objective. We also link the performance of the Machine Learning (ML) algorithm to the network operation cost. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%, compared to long term resource purchases.

Detecting failures early in cloud-based software systems is highly significant as it can reduce operational costs, enhance service reliability, and improve user experience. Many existing approaches include anomaly detection in metrics or a blend of metric and log features. However, such approaches tend to be very complex and hardly explainable, and consequently non-trivial for implementation and evaluation in industrial contexts. In collaboration with a case company and their cloud-based system in the domain of PIM (Product Information Management), we propose and implement autonomous monitors for proactive monitoring across multiple services of distributed software architecture, fused with anomaly detection in performance metrics and log analysis using GPT-3. We demonstrated that operations engineers tend to be more efficient by having access to interpretable alert notifications based on detected anomalies that contain information about implications and potential root causes. Additionally, proposed autonomous monitors turned out to be beneficial for the timely identification and revision of potential issues before they propagate and cause severe consequences.

Clusters of neurons generate electrical signals which propagate in all directions through brain tissue, skull, and scalp of different conductivity. Measuring these signals with electroencephalography (EEG) sensors placed on the scalp results in noisy data. This can have severe impact on estimation, such as, source localization and temporal response functions (TRFs). We hypothesize that some of the noise is due to a Wiener-structured signal propagation with both linear and nonlinear components. We have developed a simple nonlinearity detection and compensation method for EEG data analysis and utilize a model for estimating source-level (SL) TRFs for evaluation. Our results indicate that the nonlinearity compensation method produce more precise and synchronized SL TRFs compared to the original EEG data.

Cilj ovog istraživanja bio je istražiti kvalitetu i sastav eteričnog ulja pet uzoraka kamilice od kojih je jedan uzorak bio uzorak ekološki uzgojene kamilice dok su ostali uzorci bili iz konvencionalnog uzgoja s područja Virovitičko-podravske županije. Uzorci kamilice uzeti su od rane i kasne žetve tijekom mjeseca svibnja 2022. godine. Za fizikalno-kemijske analize korištene su sušene cvjetne glavice kamilice. Određivao se apigenin-7-glukozid na HPLC-u i sastav eteričnog ulja koristeći GC-MS. Sadržaj apigenin-7-glukozida kretao se od 0,49% do 0,85%. U eteričnom ulju identificiran je ukupno 71 spoj. Glavni spojevi eteričnog ulja bili su (E,E)-α-farnezen u rasponu od 28,6% do 10,8%, bisabolol oksid B u rasponu od 28,1% do 11,7% i (E)-β-farnezen u rasponu od 14,4% do 10,9%. Osim toga, detektirani su i brojni drugi spojevi koji doprinose bogatom kemijskom profilu ovog eteričnog ulja i koji su važan pokazatelj kvalitete i vrijednosti same sirovine.

In recent years, notable advancements have been made in medical imaging technology, with Magnetic Resonance Imaging (MRI) assuming a pivotal role in the diagnosis of brain tumors. Despite these advancements, medical image segmentation continues to pose a formidable challenge, as highlighted by various factors documented in existing literature. This study delves into the cutting-edge developments in Deep Learning for semantic segmentation, specifically concentrating on the precise identification of brain tumor pixels in 2D images. Employing U-Net and DeepLabV3+architectures, the research provides experimental evidence that underscores the unparalleled performance of DeepLabV3+with the Binary Cross Entropy loss function, offering valuable insights for enhancing the accuracy of brain tumor segmentation in medical imaging.

This paper presents a robust exploration of the capabilities of conditional Generative Adversarial Networks (GANs) in harnessing labeled data to produce high-quality labels for unlabeled samples. By leveraging conditional information, our approach guides the network to generate contextually relevant labels for specific time series data, accelerating the labeling process. A comprehensive evaluation of our model's performance, incorporating diverse metrics, visual representations, and his-tograms, illuminates the effectiveness of conditional GANs for the Assistive Label Generation (ALG) of time series Arabidopsis thaliana images. The Structural Similarity Index (SSIM) high-lights an average similarity of 98.89 % between the generated and manually labeled images. This innovative methodology holds the promise of significantly reducing labeling efforts.

The precise detection of plant centres is important for growth monitoring, enabling the continuous tracking of plant development to discern the influence of diverse factors. It holds significance for automated systems like robotic harvesting, facilitating machines in locating and engaging with plants. In this paper, we explore the YOLOv4 (You Only Look Once) real-time neural network detector for plant centre detection. Our dataset, comprising over 12,000 images from 151 Arabidopsis thaliana accessions, is used to fine-tune the model. Evaluation of the dataset reveals the model's proficiency in centre detection across various accessions, boasting an mAP of 99.79% at a 50 % IoU threshold. The model demonstrates real-time processing capabilities, achieving a frame rate of approximately 50 FPS. This outcome underscores its rapid and efficient analysis of video or image data, showcasing practical utility in time-sensitive applications.

OBJECTIVE The primary objective of this investigation is to systematically scrutinize extant surgical studies delineating Four-Level Anterior Cervical Discectomy and Fusion (4L ACDF), with a specific emphasis on elucidating reported surgical indications, clinical and radiological outcomes, fusion rates, lordosis correction, and the spectrum of complication rates. METHODS The literature review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, employing the MEDLINE (PubMed), Embase, and Scopus databases. This analysis encompasses studies implementing the 4L ACDF procedure, with detailed extraction of pertinent data pertaining to surgical methodologies, types of employed interbody cages, clinical and radiological endpoints, rates of fusion, and the incidence of complications. RESULTS Among the 15 studies satisfying inclusion criteria, a marginal increment in the year 2022 (21.4%) was discerned, with a preponderance of study representation emanating from China (35.7%) and the United States (28.6%). 50% of the studies were single-surgeon studies. Concerning follow-up, studies exhibited variability, with 42.9% concentrating on periods of five years or less, and an equivalent proportion extending beyond this timeframe. Across the amalgamated cohort of 2457 patients, males constituted 51.6%, manifesting a mean age range of 52.2-61.3 years. Indications for surgery included radiculopathy (26.9%) and myelopathy (46.9%), with a predilection for involvement at C3-7 (24.9%). Meta-analysis yielded an overall complication rate of 16.258% (CI 95%: 14.823%-17.772%). Dysphagia (4.563%), haematoma (1.525%), hoarseness (0.205%), C5 palsy (0.176%) were the most prevalent complications of 4L ACDF. Fusion rates ranging from 41.3% to 94% were documented. CONCLUSION The 4L ACDF is commonly performed to address mylopathy and radiculopathy. While the surgery carries a complication rate of around 16%, its effectiveness in achieving bone fusion can vary considerably.

This study aimed to assess the impact of the war in Ukraine on the mental health of individuals who had previously experienced the war in Bosnia and Herzegovina. A total of 649 respondents aged 35 and above, who were either directly recruited or indirectly affected as civilians during the war in Bosnia and Herzegovina, participated in this cross-sectional survey. The World Health Organization's Impact Event Scale (IES) and Self-Reporting Questionnaire (SRQ) were used to measure the impact of war events in Bosnia and Herzegovina and Ukraine on the respondents and to assess their mental health, respectively. The findings demonstrate a significant association between war events in Ukraine and the reactivation of post-traumatic stress disorder (PTSD) symptoms in individuals previously exposed to the war in Bosnia and Herzegovina. The results highlight the significant influence of the war in Ukraine on the reactivation of PTSD symptoms in individuals with prior exposure to the war in Bosnia and Herzegovina. Additionally, considering the various risk factors associated with PTSD reactivation, this study provides insights into the broader impact of war activities, including factors beyond the specific conflict in Ukraine.