Complicated Types Research Articles

An Intelligent Diagnostic Method for Multisource Coupling Faults of Complex Mechanical Systems

In the actual environment, there are difficult points such as complicated mechanical system fault types, random fault locations, and inconspicuous minor fault signals, which make it difficult to accurately diagnose faults. This paper proposes a new method for fault diagnosis of an adaptive multisensor bearing-gear system based on GAF/MTF (Gramian angular fields and Markov transition fields) and ResNet (deep residual network). First, we establish a multisensor signal acquisition system to monitor the running signals of the bearing-gearbox composite test bench in real time. Faulty parts include multiple types of composite faults of different sizes, different fault types, and different transmission stages. Second, based on GAF/MTFs, the multichannel timing signal collected by using the acquisition system is converted into multichannel pictures, and pictures are fused and compressed into three-channel pictures. Finally, we input these pictures into ResNet for fault diagnosis. The experimental results show that the GAF/MTF-ResNet model has a recognition accuracy of 72.14% for a total of 520 classification label test sets under different motor speeds, different sampling times, and different types of faults. Among them, the accuracy of the motor speed and sampling time is close to 100%, and the accuracy of gearbox failure and bearing failure is 75.25% and 88.97%, respectively. This shows that the method provides new ideas for the composite fault diagnosis of mechanical systems under different working conditions and different types of faults and has theoretical guiding significance.

Grammatical transformations in translation of political speeches

The paper is devoted to the problem of translation of political speeches, which is extremely relevant today, as political activity plays a special role in society, affects the country’s place in the international arena and its relations with other coun-tries. Politicians have the opportunity to appeal to both the international community and their compatriots, gain recognition, demonstrate their leadership and represent their country in the best possible way via speeches. Translation of political speeches is considered to be one of the most complicated types of translation, which is the area of increased linguistic responsibility. Translation transformations, which are defined as certain structural or semantic changes that are the result of lexical and grammatical differences in languages, are used to achieve translation equivalence (translation adequacy) despite the differences in the formal and semantic systems of two languages. The research is aimed at examining the peculiarities of political speeches and difficulties arising in the process of their rendering into Ukrainian. This paper consists of six main sections. The first is an introduction to the study. The second examines the definition and features of political speeches given by national and foreign scientists. The third section explains the methodology used in the study and the data selection process. In the course of analysis, the existing scientific literature concerning the topic at hand providing valid grounds for the practical part of the work has been analyzed and grammatical translation transformation examples have been selected and examined. The research data was taken from the website of the US Embassy in Ukraine. Thereafter the focus shifts to challenges the students face in translation of political speeches and highlights the grammatical transformations classification that constituted the theoretical basis of the research. The fifth section is devoted to analyzing the data and the last section summarizes the findings of the study.

Spatial-Temporal Tensor Ring Norm Regularization for Infrared Small Target Detection

Infrared small target detection (ISTD) technique is widely used in infrared searching and tracking (IRST) and military surveillance. Existing detection methods must sufficiently address the challenges of the heterogeneous background with high concealment targets. In this letter, we propose a novel spatial-temporal tensor ring norm regularization (STT-TRNR) to detect infrared small targets. First, to utilize the spatial and temporal context information in a sequence, nonrepetitive spatial-temporal patches are formed by sliding windows in the consecutive frames. The patches are stacked into a tensor structure with spatial and temporal information. Second, the tensor ring nuclear norm is introduced to approximate the rank of the background tensor. The tensor ring regularization improves the correlation between dimensions, protects the internal structure of the tensor, and avoids the dimension disaster caused by train decomposition. Third, the local contrastive feature is used as a priori information to suppress the false alarms caused by the corner edges and other noises and avoid the distortion caused by target movement. Finally, the alternating direction multiplier method (ADMM) is employed to reconstruct the sequence images and retrieve the targets. The experimental results reveal that the suggested model provides improved detection performance and higher robustness across various complicated scene types.

Bir Öksüz Kızıl Geyik Yavrusunda (Cervus elaphus) Distal Metakarpus Kırığı ve Bandajlı Tedavisi (Olgu Sunumu)

In this case report, a 1-week-old female red fawn (Cervus elaphus), one of the largest deer species in the world, constituted the study material. In the clinical examination, posture disorder, severe lameness and malnutrition in young deer were determined in the anterior left and right feet. Radiographic examination of the case revealed a distal diaphyseal transversal fracture in the left metacarpal bone, and a fracture in the lateral 1st and medial 2nd phalanx distal in the right anterior extremity. General anesthesia was not preferred due to the lack of complicated fracture types and malnutrition in the young deer, and the treatment of the extremities, which were placed in a plaster bandage with mild sedation, was kept under follow-up. After the first week of 21-day plaster application, open wound formation on the foot was noticed and wound treatment was performed by opening a window on the plaster. It was aimed to inform our colleagues and other readers about the red deer phenomenon, which has been taken under protection because its population has decreased in Turkey, and to raise awareness.

Power Reimagined: Advancing Women Into Emerging Leadership Positions.

Academic medicine is evolving from the traditional model of a medical school and teaching hospital owned by the same entity to one with complex academic medical centers and health systems. This increased complexity is evident not only in the funding streams and organizational priorities of these growing health systems but also in the evolution of leadership roles toward more matrixed positions and more individuals who hold both medical school and health system roles. Given this changing landscape, the authors of this commentary raise the following questions: Will the levers of power remain in the hands of those in traditional academic roles? Or are they moving toward those in roles that are more aligned with the clinical enterprise and health system? Then, if this shift is occurring, what is needed to prepare women to be competitive candidates for these new roles? Because of the long history of and current gender imbalance in academic leadership roles, professional development programs have traditionally focused on preparing women to advance through the faculty ranks and for department chair and decanal roles. With the shift to more complicated health systems, the definitions, responsibilities, and types of leadership roles in academic medicine are also evolving to include nontraditional academic positions in the health system, such as c-suite and other senior executive roles. In parallel to the gender inequities in traditional roles, women are also underrepresented in health system leadership roles. Therefore, it is critical to explicitly identify emerging roles in health care leadership, address systemic barriers, and actively train and prepare women with the knowledge, skills, and experience required for these positions. Only with consistent attention to outcomes and the implementation of intentional systems to engage, prepare, and advance women will the gender gap be closed.

Contact Overlap Calculation Algorithms and Benchmarks Based on Blocky Discrete-Element Method

The mechanical response of granular materials has been investigated widely using discontinuous modeling, such as the discrete-element method (DEM). Contact detection and contact resolution have been critical issues when modeling multiple body contacts, especially for arbitrary polyhedral blocks. In this study, the contact overlap calculation algorithms, including polyhedron–polyhedron and polyhedron–boundary contact, were developed to calculate the contact characteristics. For polyhedron–polyhedron contact, the contact overlap volume algorithm is developed based on the geometric dualization theory. The Gilbert–Johnson–Keerthi (GJK) and Quickhull algorithms are used to calculate the overlap polyhedron. The contact characteristics, such as normal direction (n), contact area (a), and penetration depth (un) could be extracted from the contact overlap volume. For polyhedron–boundary contact, a novel and effective algorithm is presented, where the polyhedron–boundary contact is transformed into polyhedron–triangle contact. Then, two types of benchmarks are used to verify the previously mentioned algorithms, which demonstrated that the algorithms could handle the complicated contact types and maintained contact continuity even from face to edge contact. As a complex benchmark, the failure process in a masonry structure is simulated and compared with the model test.

A Land Cover Background-Adaptive Framework for Large-Scale Road Extraction

Background: Road network data are crucial in various applications, such as emergency response, urban planning, and transportation management. The recent application of deep neural networks has significantly boosted the efficiency and accuracy of road network extraction based on remote sensing data. However, most existing methods for road extraction were designed at local or regional scales. Automatic extraction of large-scale road datasets from satellite images remains challenging due to the complex background around the roads, especially the complicated land cover types. To tackle this issue, this paper proposes a land cover background-adaptive framework for large-scale road extraction. Method: A large number of sample image blocks (6820) are selected from six different countries of a wide region as the dataset. OpenStreetMap (OSM) is automatically converted to the ground truth of networks, and Esri 2020 Land Cover Dataset is taken as the background land cover information. A fuzzy C-means clustering algorithm is first applied to cluster the sample images according to the proportion of certain land use types that obviously negatively affect road extraction performance. Then, the specific model is trained on the images clustered as abundant with that certain land use type, while a general model is trained based on the rest of the images. Finally, the road extraction results obtained by those general and specific modes are combined. Results: The dataset selection and algorithm implementation were conducted on the cloud-based geoinformation platform Google Earth Engine (GEE) and Google Colaboratory. Experimental results showed that the proposed framework achieved stronger adaptivity on large-scale road extraction in both visual and statistical analysis. The C-means clustering algorithm applied in this study outperformed other hard clustering algorithms. Significance: The promising potential of the proposed background-adaptive network was demonstrated in the automatic extraction of large-scale road networks from satellite images as well as other object detection tasks. This search demonstrated a new paradigm for the study of large-scale remote sensing applications based on deep neural networks.

DEM contact model for spherical and polyhedral particles based on energy conservation

The mechanical behaviors of granular materials are greatly influenced by the geometric shapes, which is widely investigated using the discrete element method (DEM). Accurate contact detection is a critical issue in modelling complicated contact types, especially for arbitrary shaped particles. In this study, the contact detection algorithms for spherical and polyhedral particles based on energy conservation are classified into polyhedron-polyhedron and sphere-polyhedron contacts. The polyhedron-polyhedron and sphere-polyhedron contact detection algorithms are developed under the DEM framework of Coupling Simulator (CoSim-DEM). Geometric dualization theory is used in the polyhedron-polyhedron contact detection algorithm to construct the overlapping volume of polyhedral particles. For the sphere-polyhedron contact detection, the algorithm can transfer into the contact between sphere and triangles composed of polyhedron, and the contact forces between arbitrary shaped particles can be calculated based on calculated contact characteristics. Particle collision tests with different particle shapes and contact types are used to validate the aforementioned algorithms. Experiments and simulation studies of deposition tests are conducted to validate the effectiveness and stability of algorithms. Furthermore, the rotating drum tests are used to analyze the influence of particle shapes on the calculation efficiency. Finally, a large-scale rotating drum test involving different kinds of particles with random sizes is performed, demonstrating the robustness of algorithms based on CoSim-DEM.

The Prognostic Value of Ultrasound Findings in Preoperatively Distinguishing between Uncomplicated and Complicated Types of Pediatric Acute Appendicitis Based on Correlation with Intraoperative and Histopathological Findings.

Objective: This study compares the preoperative ultrasound findings of all children with a clinical picture of acute appendicitis on the basis of intraoperative and histopathological findings to assess the feasibility of this approach in preoperatively distinguishing between uncomplicated and complicated cases. Methods: This retrospective study includes 224 pediatric patients who underwent ultrasound prior to appendectomy at our institution between January 2016 and February 2022. Logistic regression analysis was used to investigate the association between sonographic and intraoperative histopathological findings. Results: Of the 224 participants, 61.1% were intraoperatively diagnosed with uncomplicated appendicitis (59.8% male). Multivariate logistic regression analysis revealed that patients with a higher appendiceal diameter, presence of appendicolith, and peritonitis were more likely to suffer from complicated appendicitis. Finally, the common anatomical position of the appendix and an appendiceal diameter greater than 6 mm had the highest sensitivity (94.6% and 94.5%, respectively) for predicting complicated appendicitis, with the most specific (99.3%) sonographic finding being the existence of an abscess. Conclusions: Preoperative abdominal ultrasound in children with a clinical diagnosis of acute appendicitis can distinguish between uncomplicated and complicated appendicitis in most cases of pediatric appendicitis. A higher appendiceal diameter, the presence of appendicolith, and peritonitis are parameters noted by ultrasound that strongly predict complicated appendicitis.

Identification of Different Shallow Foundations Using 3D Electrical Resistivity Modeling

The electrical resistivity method has been successfully used to detect shallow buried foundations, almost, by using 2D electrode arrays. Basic geometry foundations (e.g., buried walls) have been investigated, although, more complicated foundation designs (e.g., stepped footing, pile group with a pile cap) are widely used in the construction industry. Investigation of these complicated foundation types, and engaging 3D surveys, therefore, are required. Multiple 3D electrical resistivity forward modeling was used to simulate different foundations (isolated simple footing, isolated stepped footing, combined simple footings, and pile group with a pile cap). The generated data sets then inverted using a robust inversion algorithm where RES3DINVx64 was used to perform the inversion process for the 3D models. The results from the 3D inverse modeling, suggest the pole-dipole and dipole-dipole arrays for all the investigated foundations.

Addressing Surgical Site Infections: Development and Implementation of an Apparent Cause Analyses Process for Hospital and System-Level Improvement

Background Surgical site infections (SSIs) are the most costly type of healthcare-associated infection and remain one of the most complicated infection types to reduce. The need for bundled interventions, agreement from multiple parties, and difficulty assessing proven reduction methods makes sustaining improvement processes difficult. To help drive improvement work, a standardized SSI review process centered around apparent case analysis (ACA) was implemented. This review process was paired with a data dashboard to assess overall compliance with the Centers for Disease Control and Prevention (CDC) category IA recommendations. Methods Surgical services, surgeons, and infection prevention agreed to use CDC category IA recommendations to reduce SSIs as standard benchmarks to identify gaps in best practice. A standardized ACA template specific to SSIs was created to review infections. Deviations in care were communicated to site leadership and system perioperative committees. Process improvement tactics were generated at the hospital level based on site needs. Needs were determined through a baseline assessment of compliance with CDC category IA recommendations. Deficiencies were tracked systematically across each hospital and analyzed for trends through the use of a data dashboard. Results Implementation of the ACA process has led to increased surgeon understanding of SSI reduction elements and the value of infection prevention case review and surveillance. All hospitals within the healthcare system have implemented the CDC SSI reduction guidelines and are working toward 90% compliance with category IA recommendations. Conclusions Apparent cause analysis has shown to be a reliable and engaging tool to bridge communication between infection prevention and surgical services at a multi-hospital healthcare system. Using a standardized ACA reporting tool in conjunction with real-time data has allowed surgical leadership to drive change and increase compliance with CDC category IA recommendations. Surgical site infections (SSIs) are the most costly type of healthcare-associated infection and remain one of the most complicated infection types to reduce. The need for bundled interventions, agreement from multiple parties, and difficulty assessing proven reduction methods makes sustaining improvement processes difficult. To help drive improvement work, a standardized SSI review process centered around apparent case analysis (ACA) was implemented. This review process was paired with a data dashboard to assess overall compliance with the Centers for Disease Control and Prevention (CDC) category IA recommendations. Surgical services, surgeons, and infection prevention agreed to use CDC category IA recommendations to reduce SSIs as standard benchmarks to identify gaps in best practice. A standardized ACA template specific to SSIs was created to review infections. Deviations in care were communicated to site leadership and system perioperative committees. Process improvement tactics were generated at the hospital level based on site needs. Needs were determined through a baseline assessment of compliance with CDC category IA recommendations. Deficiencies were tracked systematically across each hospital and analyzed for trends through the use of a data dashboard. Implementation of the ACA process has led to increased surgeon understanding of SSI reduction elements and the value of infection prevention case review and surveillance. All hospitals within the healthcare system have implemented the CDC SSI reduction guidelines and are working toward 90% compliance with category IA recommendations. Apparent cause analysis has shown to be a reliable and engaging tool to bridge communication between infection prevention and surgical services at a multi-hospital healthcare system. Using a standardized ACA reporting tool in conjunction with real-time data has allowed surgical leadership to drive change and increase compliance with CDC category IA recommendations.

Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications.

Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times due to their inherent sample-by-sample data processing, while traditional usage of both of these architectures assumes a fixed observation interval during both training and testing, the sample-by-sample processing capabilities of recurrent neural networks opens the door for alternative approaches. Rather than assuming that the testing and observation intervals are equivalent, the observation intervals can be “decoupled” or set independently. This can potentially reduce training times and will allow for trained networks to be adapted to different applications without retraining. This work illustrates the benefits and considerations needed when “decoupling” these observation intervals for spectrum sensing applications, using modulation classification as the example use case. The sample-by-sample processing of RNNs also allows for the relaxation of the typical requirement of a fixed time duration of the signals of interest. Allowing for variable observation intervals is important in real-time applications like cognitive radio where decisions need to be made as quickly and accurately as possible as well as in applications like electronic warfare in which the sequence length of the signal of interest may be unknown. This work examines a real-time post-processing method called “just enough” decision making that allows for variable observation intervals. In particular, this work shows that, intuitively, this method can be leveraged to process less data (i.e., shorter observation intervals) for simpler inputs (less complicated signal types or channel conditions). Less intuitively, this works shows that the “decoupling” is dependent on appropriate training to avoid bias and ensure generalization.

Enhanced Fuzzing System for Proactive Security Gap Exposure

Lots of safety vulnerabilities have been harming software and hardware. Grey box fuzzer is one of the most successful methods for automatic vulnerability detection. Most research focus on using fuzzer for SW quality check only. This paper introduced how to leverage fuzzer to exposure security gap as early as possible. Conventional Grey box Fuzzers like AFL can open perform fuzzing against the whole input and spend more time on smaller seeds with lower execution time, which significantly impact fuzzing efficiency for complicated input types. In this work, we introduce one intelligent grey box fuzzing for Intel Media driver, MediaFuzzer, which can perform effective fuzzing based on selective fields of complicated input. Also, with one novel calling depth-based power schedule biased toward seed corpus which can lead to deeper calling chain, it dramatically improves the vulnerability exposures (~6.6 times more issuesexposed) and fuzzing efficiency (~2.7 times more efficient) against the baseline AFL for Intel media driver with almost negligible overhead.

Comprehensive approach to carbonate rocks typing based on core laboratory experiments data

The paper presents results of the comprehensive approach to identification of complicated carbonate rocks types. The subject of research is the Mishrif formation core from one of the Mesopotamian oil-and-gas province field wells. At the first stage the 3 lithological petrophysical rocks types were identified based on core nuclear magnetic resonance experiments taking into account structural features, which are determined with description data of thin sections. At the second stage the 5 petrophysical rocks types were identified based on routine and special core analyses taking into account secondary changes influence, which is determined by thin sections description data. The identified petrophysical rocks types were described in detail, linear discriminant functions were deduced. These functions allow predicting the identified petrophysical types with the suggested parameters. Comparison of the lithological petrophysical and petrophysical types has shown that the pore space structure of the studied deposits depended on two factors: the first is structural features and the second is postdepositional alterations.

Oxymoron in language consciousness and communicative practice

The paper deals with a stylistic figure «oxymoron» which expresses a polar contradiction of concepts. Sematic and pragmatic properties of this stylistic device have been described. Semantically, simple and complicated (metaphoric) types of oxymoron may be singled out. Simple oxymorons include occasional units made by combinations of affixes or roots with contradictory meanings in compound words. Standard attributive nominal collocations make the majority of oxymorons. The simplest type of such collocations is a single-root word combination with a negative prefix for a part of the dyadic unit. Elaborated oxymorons are formed with constructions formed on the basis of the formula «Though A, but B» (e. g. They have never lost a chance to lose a chance). A special subgroup of oxymorons is made of words including a negative component which is not directly expressed in the affix. Oxymorons are often used as headlines because of their high energetic potential. Another subgroup of the figures in question may be defined as metaphoric oxymorons, both parts of their dyadic unit is semantically transformed. A pragmatic approach to a description of oxymorons makes it possible to characterize the main text types attracting such stylistic figures. Pragmatically, ironical, emblematic and symbolic varieties of this stylistic figure are described. I argue that in habitual and publicist communication oxymoron is used for a comical effect, whereas in aphorisms and poetry it aims to paradoxically express a new meaning.

Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features

Non-invasive and accurate method for continuous blood glucose monitoring, the self-testing of blood glucose is in quest for better diagnosis, control and the management of diabetes mellitus (DM). Therefore, this study reports a multiple photonic band near-infrared (mbNIR) sensor augmented with personalized medical features (PMF) in Shallow Dense Neural Networks (SDNN) for the precise, inexpensive and pain free blood glucose determination. Datasets collected from 401 blood samples were randomized and trained with ten-fold validation. Additionally, a cohort of 234 individuals not included in the model training set were investigated to evaluate the performance of the model. The model achieved the accuracy of 97.8% along with 96.0% precision, 94.8% sensitivity and 98.7% specificity for DM classification based on a diagnosis threshold of 126 mg/dL for diabetes in fasting blood glucose. For non-invasive real-time blood glucose monitoring, the model exhibited ± 15% error with 95% confidence interval and the detection limit of 60–400 mg/dL, as validated with the standard hexokinase enzymatic method for glucose estimation. In conclusion, this proposed mbNIR based SDNN model with PMF is highly accurate and computationally cheaper compared to similar previous works using complex neural network. Some groups proposed using complicated mixed types of sensors to improve noninvasive glucose prediction accuracy; however, the accuracy gain over the complexity and costs of the systems harvested is still in questioned (Geng et al. in Sci Rep 7:12650, 2017). None of previous works report on accuracy enhancement of NIR/NN using PMF. Therefore, the proposed SDNN over PMF/mbNIR is an extremely promising candidate for the non-invasive real-time blood glucose monitoring with less complexity and pain-free.

Instant catapult steam explosion pretreatment of wheat straw liquefied polyols to prolong the slow-release longevity of bio-based polyurethane-coated fertilizers

The direct liquefaction product of crop straws for bio-based polyurethane-coated fertilizers (BPCFs) has low hydroxyl values and complicated composition types, resulting in poor crosslinking degree, high hydrophilicity, and short release longevity. This study created an efficient physicochemical instant catapult steam explosion pretreatment wheat straw (SEWS) technology to remove small molecular hemicellulose and difficult-to-film-forming ions, improve straw liquefied polyol quality, and extend the release longevity of BPCFs. The results revealed that the steam explosion process (2.0 MPa for 5 min) broken the straw cell wall, hydrolyzed hemicellulose, and significantly decreased the difficult-to-film-forming ions (K+, Ca2+, and Mg2+). The SEWS liquefied residue rate dropped 38.1% to 5.5%, while the hydroxyl value increased 36.51% to 125.32 mg KOH g−1. The liquid polyol of SEWS and diphenyl-methane-diisocyanate formed a tightly cross-linked, homogenous bio-based membrane with no visible holes. Under the condition of 3% weight of the core fertilizer, the release longevity of SEWS increased from 14 to 32 days. The use of instant catapult steam explosion technology for high-efficiency pretreatment of biomass materials is conducive to improving the quality of bio-based polyols and the industrial production of bio-based controlled-release fertilizers. Furthermore, the pretreatment technology promotes the use of biomass for sustainable agriculture.

Deep learning for bone marrow cell detection and classification on whole-slide images.

Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, there are many challenges to perform automated BM NDC analysis on whole-slide images (WSIs), including large dimensions of data to process, complicated cell types with subtle differences. To the authors best knowledge, this is the first study on fully automatic BM NDC using WSIs with 40x objective magnification, which can replace traditional manual counting relying on light microscopy via oil-immersion 100x objective lens with a total 1000x magnification. In this study, we develop an efficient and fully automatic hierarchical deep learning framework for BM NDC WSI analysis in seconds. The proposed hierarchical framework consists of (1) a deep learning model for rapid localization of BM particles and cellular trails generating regions of interest (ROI) for further analysis, (2) a patch-based deep learning model for cell identification of 16 cell types, including megakaryocytes, mitotic cells, and four stages of erythroblasts which have not been demonstrated in previous studies before, and (3) a fast stitching model for integrating patch-based results and producing final outputs. In evaluation, the proposed method is firstly tested on a dataset with a total of 12,426 annotated cells using cross validation, achieving high recall and accuracy of 0.905 ± 0.078 and 0.989 ± 0.006, respectively, and taking only 44 seconds to perform BM NDC analysis for a WSI. To further examine the generalizability of our model, we conduct an evaluation on the second independent dataset with a total of 3005 cells, and the results show that the proposed method also obtains high recall and accuracy of 0.842 and 0.988, respectively. In comparison with the existing small-image-based benchmark methods, the proposed method demonstrates superior performance in recall, accuracy and computational time.

ТЕОРЕТИКО-МЕТОДОЛОГІЧНІ ЗАСАДИ СТРАХУВАННЯ ІНВЕСТИЦІЙНИХ РИЗИКІВ У СИСТЕМІ УПРАВЛІННЯ НИМИ

The article elaborates theoretical and methodological principles and practical recommendations for improving the investment risk management system through the use of insurance mechanisms. It was determined that the management of investment risks is an important prerequisite for ensuring the effective functioning of business entities and the economy in general. It has been established that investment risk management involves forecasting the possibility of the occurrence of potentially risky events. This makes it possible to take timely measures to prevent or reduce the degree of consequences from a risk that cannot be localized. It is noted that investment risk insurance is a fairly popular method of risk management in developed countries. This method is one of the most complicated and problematic types of insurance. The essence of investment risk insurance is to protect the property interests of business entities in the event of an insured event by special insurance companies at the expense of monetary funds, which they form by receiving insurance premium contributions from the insured. It has been established that an effective option for minimizing investment risks is the combination of two universal methods: self-insurance in the form of reserving and insurance. It is noted that the complex use or combination of the above methods allows to increase the economic security of the implementation of risky investment projects and to achieve and further maintain an acceptable level of risks of investment activities of economic entities. Monitoring the results of risk management through insurance involves monitoring compliance with the insurance program and promptly making changes to the risk strategy of the investment project in case of unsatisfactory results. The complex use or combination of the methods and tools presented in the work allows to increase the economic security of the implementation of risky investment projects, as well as to achieve and further maintain an acceptable level of risks in the financial and investment activities of the economic entity.

Severe pneumonia: Treatment outcome and its determinant factors among under-five patients, Jimma, Ethiopia.

Introduction:Over 2 million children die from pneumonia each year accounting for almost one in five children’s deaths worldwide which is estimated to be 18% of mortality cases. Therefore, this study is aimed to assess treatment outcome and its determinant factors among under-five patients, Jimma, Ethiopia.Methods:Study design was conducted on 522 under-five children with severe pneumonia from 1 January 2017 to 30 December 2020. Pretested chart review format was used to collect data. Data were entered into EpiData, version 3.1, and exported to Statistical Package for the Social Sciences, version 23, for analysis. Logistic regression analysis with 95% confidence interval was used to declare statistical significance at p value <0.05.Results:Among 522 under-five children with severe pneumonia, majority (83.91%) of them were improved, whereas 1 over 6 (16.09%) of them were died. This finding showed that children who have malnutrition (adjusted odds ratio = 7.23 (3.17–14.51), p = 0.000), positive serostatus for HIV (adjusted odds ratio = 5.01 (1.91–12.13), p = 0.001), history of upper respiratory tract infections (adjusted odds ratio = 3.27 (1.55–6.91), p = 0.002), unvaccinated (adjusted odds ratio = 4.35 (1.60–11.79), p = 0.004), having complicated types of pneumonia (adjusted odds ratio = 8.48 (4.22–16.65), p < 0.001), and comorbidity disease (adjusted odds ratio = 5.21 (2.03–13.3), p < 0.001) were statistically significant with mortality.Conclusion:This study showed that mortality secondary to severe pneumonia was high. Being malnourished, positive serostatus for HIV infection, history of upper respiratory tract infections, unvaccinated, having complicated type of pneumonia, and other comorbidity disease were identified as determinant factors of mortality. Committed, harmonized, and integrated intervention needs to be taken to reduce mortality from severe pneumonia by enhancing child’s nutrition status, early detection and treatment, effectively vaccinating children, and preventing other comorbidity diseases.