During the Covid-19 health emergency, telemedicine was an essential asset through which health systems strengthened their response during the critical phase of the pandemic. According to the post-pandemic economic reform plans of many countries, telemedicine will not be limited to a tool for responding to an emergency condition but it will become a structural resource that will contribute to the reorganization of Healthcare Systems and enable the transfer of part of health care from the hospital to the home-based care. However, scientific evidences have shown that health care delivered through telemedicine can be burdened by numerous ethical and legal issues. Although there is an emerging discussion on patient safety issues related to the use of telemedicine, there is a lack of reseraches specifically designed to investigate patient safety. On the contrary, it would be necessary to determine standards and specific application rules in order to ensure safety. This paper examines the telemedicine-risk profiles and proposes a position statement for clinical risk management to support continuous improvement in the safety of health care delivered through telemedicine.

The aim of this paper is to analyse the influence of foliar application of a biostimulative fertilizer on some of the elements of raspberry fruit quality of the Polka variety. The research was conducted in 2015, according to the system of controls and treatment. Slavol VVL, a foliar fertilizer with biostimulating effects was applied for treatment. A total of 12 quantitative and qualitative properties were analyzed depending on the influencing factor, namely: total sugar content, reducing sugars, invert sugars, sucrose, water content, dry matter, total acidity, vitamin C, total phenols, total flavonoids and antioxidant capacity, and fruit weight. After the completed analyzes, it can be concluded that raspberry plants treated with Slavol VVL were characterized by the highest values ​​of total acidity (2.07%), dry matter (14.86%), and vitamin C content (25.15 mg/100 g of fresh weight).

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

Background Pharmacogenomics (PGx) testing can reduce toxicities and improve efficacy of several drugs used to treat cancer and associated symptoms. PGx results can be determined from germline whole-exome sequencing (WES), but somatic mutations may cause discordance between tumor and germline DNA. Since clinical diagnostic sequencing in oncology frequently only includes tumor DNA, there would be clinical value in calling germline PGx genotypes from tumor DNA. Thus, the purpose of this study was to assess the feasibility of using somatic WES data to call germline PGx genotypes. Methods Germline and somatic WES data were obtained as part of the clinical workflow for 64 patients treated at the solid molecular tumor board clinic at Indiana University. Aldy v3.3 was implemented in LifeOmic’s Precision Health Cloud™ to call PGx genotypes from somatic WES. Somatic Aldy calls were compared with previously validated Aldy germline calls for 8 genes: CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, and TPMT. Somatic read depth was >100x, except for the intronic CYP3A4*22 variant, which was >30x. Results Somatic and germline Aldy calls were compared for a total of 512 genotypes and 56 (11%) calls were discordant. Discordant calls were most common for CYP2B6 (23.4%), followed by CYP2D6 (14.1%), CYP2C19 (10.9%), CYP2C8 (6.3%), and DPYD (6.3%). In contrast, all Aldy calls were concordant for CYP3A5 and TPMT. 38 out of 64 subjects (59%) had discordant calls for at least one gene. The most common first cancer diagnoses in our cohort were colorectal (9.3%), breast (7.8%), and pancreatic (7.8%), and the rates of discordant Aldy calls did not differ by cancer type (p>0.05 for all cancer types). Based on our analyses of discordant calls, we anticipate that adjusting Aldy’s thresholds for variant calling may allow Aldy to determine genotypes from somatic WES data. Conclusion In most cases, genotype calls of drug metabolism genes from tumor DNA reflected the germline genotypes; however, additional work needs to be done to determine if the remaining discordant calls can be corrected by modifying the informatics tools or if they are due to somatic mutations. Citation Format: Wilberforce A. Osei, Tyler Shugg, Reynold C. Ly, Steven M. Bray, Benjamin A. Salisbury, Ryan R. Ratcliff, Victoria M. Pratt, Ibrahim Numanagić, Todd Skaar. Pharmacogenomics genotyping from clinical somatic whole exome sequencing: Aldy, a computational tool [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1151.

The paper analyzes the normative-formative framework that denotes the connection between memory and identity as a crucial origin of conflicts. In addition to concerns about memory politics, historical revisionism and ethnonational identity collectivism, the paper dissolves the connection between phenomena highlighting outcomes of the peace process, transitional justice, and its ethical/moral connotations. The study argues that Western Balkan’s sociopolitical stability depends on declining conflicting and contradictory memory order within radical sociopolitical processes. The revisionist contention memorializes conflicts and wars as the fundamental concept of ethnicity/religion/nation. It overlaps with the neoliberal and neoconservative reduction of all competitive relations, in which only the stronger have the right to existence. Discarding dominant ethnopolitical narratives is essential for conflict transformation and transitional justice for all ethnoreligious communities. The Balkan historical events and conflicting memory (WW2, Yugoslav wars) caused sociopolitical dominion shaping the collective behavior of ethnic groups. The damaging ethnic/religious practice of genocide denial and honoring war crimes within people’s social lives can become a matrix for future conflicts. Placing memory politics with radical populism is a critical condition of collective identity politics in the former Yugoslavia. Scientific rationality can provide a solid path through the anomalies in the form of political ideologies.

The growing need for autonomous systems in offshore industries has contributed to the increased use of machine learning methods. These systems promise to improve safety in operations. However, the methods as enablers of autonomy are susceptible to various failures while interpreting data and making decisions. Several studies have highlighted the lack of research on the reliability and resilience of autonomous systems powered by these standard methods. Recent research provides sets of data interpretation methods. Despite the popularity of machine learning, there is a significant drop in knowledge when these methods result in failures. These failures further support autonomous systems in making wrong decisions. For autonomous systems, resilience and safety management should be an integrated functionality for recovery from risky situations and reporting of incidents. This research proposes an overview of machine learning methods for interpreting sensor data captured by drones operated manually and autonomously. We apply Isolation Forest for anomaly detection analysis and evaluate the Decision tree, Random forest, kNN, Logistic Regression, SVM, and, Naive Bayes for classification analysis. The methods are chosen based on their adequacy and comparative research prevalence. Comparison between the two drone operation modes contributes to understanding the reliability level for autonomously collected data. This research’s results provide an evaluation of machine learning methods’ performance across sensor data.

The growing penetration of renewable resources such as wind and solar into the electric power grid through power electronic inverters is challenging grid protection. Due to the advanced inverter control algorithms, the inverter-based resources present fault responses different from conventional generators, which can fundamentally affect the way that the power grid is protected. This paper studied solar inverter dynamics focused on negative-sequence quantities during the restoration period following a grid disturbance by using a real-time digital simulator. It was found that solar inverters can act as negative-sequence sources to inject negative-sequence currents into the grid during the restoration period. The negative-sequence current can be affected by different operating conditions such as the number of inverters in service, grid strength, and grid fault types. Such negative-sequence responses can adversely impact the performance of protection schemes based on negative-sequence components and potentially cause relay maloperations during the grid restoration period, thus making system protection less secure and reliable.

Background: In last 2 decades, there have been substantial changes in the utilization patterns of antihypertensive medicines following new clinical trials and the introduction of new treatment guidelines. The aim of this study was to analyze utilization and prescribing patterns regarding antihypertensive medicines in the Republic of Srpska, Bosnia and Herzegovina during an 11-years follow-up according to national and European treatment guidelines. Methods: In this retrospective, observational study, medicine utilization data were analyzed between 2009–2019 period using the ATC/DDD methodology and expressed as the number of DDD/1,000 inhabitants/day (DID/TID). The medicine utilization 90% (DU90%) method was used for determine the quality of prescribing. Results: During the observed period, the use of antihypertensive medicines increased more than 3-times (125.97 DDD/TID in 2009 vs 414.95 DDD/TID in 2019), corresponding to a rise in the prevalence of hypertensive patients from 91.7/1,000 to 186.3/1,000 in the same period. This was mainly driven by increased use of angiotensin converting enzyme inhibitors with 241.69%, beta blockers with 146.87%, calcium channel blockers with 251.55%, and diuretics with 178.95%. Angiotensin receptor blockers were the fastest growing group of antihypertensive medicines in this period and their utilization increased nearly 40 times. Conclusions: The overall antihypertensive medicines utilization was largely influenced by national and ESH/ESC guidelines and strongly corresponded to the positive medicine list of the national health insurance fund. Antihypertensive medicines utilization is comparable with medicine utilization trends in other countries.

A modernidade tem trazido mudanças efetivas na sociedade. Com o advento da globalização e o avanço tecnolígico na área de comunicação as profissões sofrerão fortes mudanças no modus operandi ao longo do tempo e as legislações não tiveram essa mudança brisca respeitando essa mesma velocidade. Dado a isso, na área do transporte de passageiros não se furtou a isso, além dos conhecidos taxis, surgiram várias empresas de transporte de pessoas por aplicativo, onde motoristas disposibilizam seus carros para o transporte terceirizando seu serviço para operadoas desses aplicativos, Dentro desse viés a legislação vigente fala de responsabilidade subsidiada, porém a ausência de uma lei específica, a falta de jurisprudência atua e atualizada deixam lacunas nas leis para a tomada de descisões sobre a relação emprego e prestação de serviços desses motoristas de aplicativos. Sobre essas grandes mudanças não coabitaram com as novas demandas surgidas no campo trabalhista gerando confusão nas decisões e até mesmo trazendo certas dificuldades aos juristas nas decisões da criação de jurisprudência.Portanto, é preciso uma revisão bibliográfica, de forma a percepção de pareceres, um novo olhar legislativo, observando as nuances das escritas a fim de traçar parâmetros interpretativos para estabelecer conceitos sobre essa relação de emprego, onde não está claro o posicionamento de ambos, nem se quer o posicionamento da ordenação jurídica a esse respeito.

The study empirically explores the relationship between OFDI and the home country’s institutional quality by employing a panel of 23 European emerging countries between 2000 and 2019. In doing so, the study employs the VECM estimation procedure. The key findings of this research indicate that the rate of adjustment to reach long-run equilibrium in European post-transition countries is lower than in European transition countries. In conclusion, there is evidence, for the period being investigated, of causality between the home country’s institutional quality and OFDI in both regions. Also, most of the transition countries are still in the process of building the institutional environment, with many institutional voids and different starting points of their internalization process. In fact, most of the countries, especially European transition countries, are in stage 2 or stage 3 of their investment development path (IDP) development, where IFDI stock still remains higher than OFDI stock.

PURPOSE OF THE STUDY The hip fracture is the leading cause of disability and deaths in elderly. If left untreated, the hip fracture results in inability to walk, and therefore the patient is dependent on the other's people help. The aim of this study was to determine the predictive values of the functional status, present comorbidities, and the types of treatment on the long-term functional status and mortality in patients with the hip fracture. MATERIAL AND METHODS We analyzed the data about the patients with the hip fractures treated in the period between 01.07.2006. and 01.07.2016., with the mean follow-up period between 34.3±4.9 months. The data included radiographs, age, comorbidities, functional status on admission, functional status on discharge, and follow-ups. RESULTS Female sex, increasing age, and poorer functional status upon admission, conservative treatment, trochanteric fracture, the presence of neurological or lung disease, diabetes, were all significant risk factors leading to poorer functional outcomes on the latest follow-up (p < 0.05). On the other hand, the patients who were mobile upon admission had a longer survival (38.1±3.51 months) as compared with the other patients (12.3±5.1 months; χ2=25.202, p = 0.001). Besides, the results of this study revealed that the direct, statistically significant relationship exists between the untreated internal and neurological diseases, and increased mortality and poorer functional outcomes on the latest follow-up. CONCLUSIONS These results are useful for the orthopedic and trauma surgeons, who are treating these patients, the physical medicine and rehabilitation specialists, who are performing the rehabilitation of these patients, but also the health policy makers, who may promote the programmes of healthy aging (treating the diseases, maintaining fitness, etc.) Key words: hip fracture; comorbidity; mortality; functional status.

Biological carbon sequestration is considered an important strategy to mitigate climate change. The energy crop Miscanthus × giganteus has great sequestration potential. The objective of this study was to determine: a) the dry matter yield and carbon content in aboveground and belowground biomass; b) the total carbon balance in the plant and soil pool. The study was conducted in continental Croatia (N 45°51′01.32″; E 16°10′35.85″) by the destructive harvesting of five-year-old Miscanthus × giganteus stands established by rhizomes (MxgR) and seedlings (MxgS) in the spring of 2021. The soil sampling was conducted in 2016 and 2022. The average amount of carbon in the aboveground biomass of MxgR and MxgS is 11.51 t/ha and 9.87 t/ha, respectively, and in the belowground biomass it is 13.18 t/ha and 14.90 t/ha, respectively. The carbon balance in the plant pool of MxgR is three times lower (1.67 t/ha) than that in the plant pool of MxgS (5.03 t/ha). The total soil carbon content increased by 8.7 t/ha under MxgR and by 14.8 t/ha under MxgS during 2016–2022. Therefore, under the studied agroecological conditions, seedlings should be preferred over rhizomes in the selection of planting material. The obtained data represent valuable input data for sequestration modeling.

Sažetak: Autori u radu analiziraju položaj stečajnog upravitelja prema novom Zakonu o stečaju u Federaciji Bosne i Hercegovine koji je stupio na snagu 15. srpnja 2021. godine. Stečajni upravitelj kao operativno tijelo u stečajnom postupku ima središnju ulogu i od njegovog postupanja ovisi i učinkovitost stečajnog postupka. Položaj stečajnog upravitelja je specifičan jer u sebi objedinjuje dvije funkcije. On je tijelo stečajnog postupka s javnim ovlastima i zastupnik je dužnika po zakonu. Zbog tog specifičnog položaja je iznimno bitno i na koji način je reguliran njegov rad i nadzor nad njegovim postupanjem. Radi jačanja povjerenja u pravosudni sustav izbor i imenovanje stečajnih upravitelja trebao bi biti transparentan. Iako su nove zakonske odredbe unaprijedile sveukupan položaj stečajnih upravitelja, autori daju kritički osvrt na pitanja koja su ostala neregulirana i koja bi stečajni postupak učinila transparentnijim i učinkovitijim. Ključne riječi: stečaj, stečajni postupak, privremeni stečajni upravitelj, stečajni upravitelj, povjerenik

Even though interest in Machine Learning Based Clinical Decision Support Systems (ML-CDSS) has been rapidly growing in recent years, most research and development is exclusively focused on secondary and tertiary care – even though effective diagnostic support in primary care could significantly improve both the circumstances, the process and the outcome of general practice. In this paper, we study the suitability of five supervised machine learning algorithms to the problem of multiclass classification with sparse Boolean features on a primary care data set, and we examine the robustness of the algorithms to incomplete data. We introduce our own classification algorithm, the Artificial Intelligence Based Diagnostic Assistant (AIDA), which is capable of incorporating both symptoms and contextual information into its diagnostic process, thus modeling the decision-making of physicians in a novel and accurate manner. Through our experimental results we show that AIDA is by far the most suitable classification algorithm for ML-CDSS applications in primary care, owing to its high accuracy and outstanding robustness to missing, sparse information.