Optimal Resource Utilization Research Articles

Precision in Pulmonary Embolism Diagnosis: Leveraging D-dimer Levels With Computed Tomography Pulmonary Angiography (CTPA) Insights.

Introduction Pulmonary embolism (PE) remains a critical condition requiring timely diagnosis and treatment. The use of D-dimer, a fibrin degradation product, as a biomarker, combined with computed tomography pulmonary angiography (CTPA), is a common practice in diagnosing PE. Aim This study aims to increase diagnostic accuracy for PE by relating the D-dimer levels to the findings on CTPA. Specifically, it aimsto calculate the sensitivity and specificity of D-dimer levels against CTPA results and also establish the association of D-dimer levels with the location of the PE. Methods This retrospective analysis was conducted at a tertiary care hospital, including patients who underwent CTPA and had D-dimer levels recorded over a one-year period. The total sample size was 124. D-dimer levels were categorized into four groups based on CTPA findings: Category 0 (no PE), Category I (peripheral PE), Category II (PE in lobar arteries), and Category III (central embolisms in the pulmonary trunk or arteries). Statistical analyses were performed to evaluate the correlation between D-dimer levels and CTPA findings, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results The study found that Category 0 (no PE) had an average D-dimer of 3.6 mg/L, Category I (peripheral PE) had 4.3 mg/L, Category II (PE in lobar arteries) had 3.6 mg/L, and Category III (central embolisms) had 7.1 mg/L. The sensitivity of D-dimer in predicting PE was 1.0, and the specificity was 0.2. The PPVwas 0.3208, and the NPVwas 1.0. These findings indicate a significant correlation between elevated D-dimer levels and the presence of PE. Conclusion Integrating D-dimer levels with CTPA findings can improve diagnostic accuracy and efficiency for PE. Establishing reliable D-dimer cutoff values may help clinicians better stratify patient risk and make informed decisions about the need for imaging, thereby optimizing resource utilization and minimizing unnecessary CTPA scans. This study highlights the potential benefits of combining biomarker analysis with imaging results in the clinical management of PE.

PENGARUH KERJA SAMA TIM, KOMITMEN DAN MOTIVASI KERJA TERHADAP KINERJA KARYAWAN DENGAN KEPUASAN KERJA SEBAGAI VARIABEL INTERVENING DI PT. INKO ORGANIK JAYA KABUPATEN SITUBONDO

At PT. Inko Organik Jaya in Situbondo Regency really needs employees who are committed to improving optimal work and utilization of human resources. And contribute to employee performance. The aim of this research is to analyze and test the influence of teamwork, commitment and work motivation on performance through job satisfaction. This research is quantitative research. The population used in this research was 33 employees of PT. Inko Organik Jaya, Situbondo Regency. The sampling technique uses saturated sampling. Data analysis and hypothesis testing in this research used the Structural Equation Method - Partial Least Square (PLS-SEM). The results of the direct influence hypothesis test using the Smart PLS 3.0 application, show that Teamwork has a positive effect on Job Satisfaction, Teamwork has a significant positive effect on Performance, Commitment has a positive but not significant effect on Job Satisfaction, Commitment has a significant positive effect on Performance, Motivation has a significant positive effect on Job Satisfaction, Motivation has a significant positive effect on Performance. The results of the indirect effect hypothesis test show that teamwork on performance through job satisfaction has a positive but not significant effect, commitment to performance through job satisfaction has a significant positive effect, motivation on performance through job satisfaction has a significantly positive effect.

Enhanced Prairie Dog Optimization with Differential Evolution for solving engineering design problems and network intrusion detection system

This paper introduces a novel hybrid optimization algorithm, PDO-DE, which integrates the Prairie Dog Optimization (PDO) algorithm with the Differential Evolution (DE) strategy. This research aims to develop an algorithm that efficiently addresses complex optimization problems in engineering design and network intrusion detection systems. Our method enhances the PDO's search capabilities by incorporating the DE's principal mechanisms of mutation and crossover, facilitating improved solution exploration and exploitation. We evaluate the effectiveness of the PDO-DE algorithm through rigorous testing on 23 classical benchmark functions, five engineering design problems, and a network intrusion detection system (NIDS). The results indicate that PDO-DE outperforms several state-of-the-art optimization algorithms regarding convergence speed and accuracy, demonstrating its robustness and adaptability across different problem domains. The PDO-DE algorithm's potential applications extend to engineering challenges and cybersecurity issues, where efficient and reliable solutions are critical; for example, the NIDS results show significant results in detection rate, false alarm, and accuracy with 98.1%, 2.4%, and 96%, respectively. The innovative integration of PDO and DE contributes significantly to stochastic optimization and swarm intelligence, offering a promising new tool for tackling diverse optimization problems. In conclusion, the PDO-DE algorithm represents a significant scientific advancement in hybrid optimization techniques, providing a more effective approach for solving real-world problems that require high precision and optimal resource utilization.

Cost-Effective Scalability in Cloud Monitoring Systems: A Comparative Stud

This research article explores strategies for cost-effective scalability in cloud monitoring systems. As the complexity and scale of IT-based infrastructure are growing, an efficient monitoring system becomes vital for maintaining performance and optimizing resources while managing costs. The current study aims to shed light on various approaches to cost-effective scalability by considering factors such as data collection methods, storage optimization, and adaptive monitoring techniques. A comparison between cloud monitoring tools, Amazon Cloud Watch and Datadog, has been made to gain a better understanding of the monitoring tools. The research indicates that a multi-faceted approach is necessary for cost-effective scalability in cloud monitoring systems, and there must be a holistic approach in the selection of cloud monitoring tools, depending on the organization's requirements. In the later sections, strategies, like distributed data collection, hierarchical aggregation, adaptive sampling, and machine learning- based predictive scaling, can significantly improve monitoring system scalability while optimizing resource utilization.

Characteristics and Outcomes of Home-Ventilated Children Undergoing Noncardiac Surgery.

To determine the frequency of children with chronic respiratory failure (CRF) and home ventilator dependence undergoing surgery at a tertiary children's hospital, and to describe periprocedural characteristics and outcomes. We conducted a retrospective cohort study of patients with CRF and home ventilator dependence who underwent noncardiac surgery from January 1, 2013, to December 31, 2019. Descriptive statistics were used to report patient and procedural characteristics. Univariable and multivariable analyses were used to assess for factors associated with 30-day readmission. We identified 416 patients who underwent 1623 procedures. Fifty-one percent of patients used transtracheal mechanical ventilation (trach/vent) support at the time of surgery; this cohort was younger (median age 5.5 vs 10.8 years) and more complex according to American Society of Anesthesiologists status compared with bilevel positive airway pressure-dependent patients. Postoperatively, compared with bilevel positive airway pressure-dependent patients, trach/vent patients were more likely to be admitted to the ICU with longer ICU length of stay (median 5 vs 2 days). Overall 30-day readmission rate was 12% (n = 193). Presence of chronic lung disease (adjusted odds ratio 1.65, 95% confidence interval 1.01-1.69) and trach/vent dependence (adjusted odds ratio 1.65, 95% confidence interval 1.02-2.67) were independently associated with increased odds for readmission. Children with CRF use anesthetic and surgical services frequently and repeatedly. Those with trach/vent dependence have higher hospital and ICU resource utilization. Although overall mortality for these patients is quite low, underlying diagnoses, nuances of technology dependence, and other factors for frequent readmission require further study to optimize resource utilization and outcomes.

Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks

Internet of Things (IoT) has the potential to significantly impact various domains e.g. health, transportation, automation, and emergency response to both man-made and natural disasters, particularly in scenarios where human decision is challenging. In this research a Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks has been proposed. This framework ensures robust security and optimized data handling through granular access control mechanisms based on predefined roles. By leveraging RBAC, it mitigates unauthorized access risks, thereby safeguarding sensitive IoT data during transmission and storage. Our approach emphasizes efficiency by streamlining data processing workflows, reducing latency, and optimizing resource utilization. The framework is designed to scale with IoT network expansions and adapt to evolving security needs, promising enhanced reliability and trustworthiness in data operations for contemporary IoT environments. According to this research work current security effectiveness is 99 percent.Home area network (HAN) can be used for smart connectivity of different home appliances using IoT and automatic start and stop feature may be possible.Access control server (ACS) is used to control access and provide permission for different operations.

A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Buffer Stock Inventory Control Mechanism: An Approach of Minimizing the Buffer Stock Level Through Segmentation at a Tertiary Care Rural Hospital.

Background Efficient management of hospital inventories, particularly pharmaceuticals, is essential for ensuring timely patient care and optimizing resource allocation.Buffer stock is the backup stock, which is kept for providing a supply of drugs when the main stock is consumedand new stock is in the process of procurement.The buffer stock is sometimes kept in excess, which causes unnecessary overutilization of the financial resources. Therefore, the author's team aimed to optimize the inventory control of the buffer stock.This study addressed these challenges by integrating always better control (ABC) and vital, essential, and desirable (VED) analysis to categorize drugs based on consumption patterns and clinical importance to minimize buffer stock and ensure optimum resource allocation. To overcome these challenges, there is a need to integrate financial and clinical factors into inventory management decisions. Methods This study was done at the pharmacy of an apex hospital situated in a rural region of western India. The study aimed at the categorization of drugs that require strict inventory management control. The drugs and consumables used in the month of April 2024 in the hospital pharmacy were considered for the study& inventory control methods were applied. Results The study discovered that a subgroup of drugs accounted for a significant portion of the pharmacy's budget, with 12.18% categorized as high-value (Category A), 22.07% as moderate-value (Category B), and 65.74% as low-value (Category C) items. ABC analysis showed that Category A drugs consumed 70.1% of the average daily expenditure (ADE) of the pharmacy, while Category B and Category C drugs contributed 19.9% and 9.98% of the ADE, respectively. VED analysis classified drugs into vital (11.34%), essential (58.26%), and desirable (30.39%) categories. The ABC-VED matrix further categorized drugs into subgroups, with class I items constituting 20.62% of the inventory and accounting for 73.82% of the ADE. Class II items comprised 56.69% of the inventory, consuming 22.91% of the ADE, while class III items constituted 22.68% of the inventory and consumed 3.28% of the ADE. Conclusion ABC and VED analysis integration provides a comprehensive framework for optimizing drug inventory management in healthcare facilities. By considering both financial and clinical factors, this approach enables tailored management strategies, minimizing buffer stock, eliminating stockouts, and enhancing patient care. The rural hospital's location is responsible for maintaining the buffer stock level and the reorder level quantity. This study highlighted the role of inventory control techniques in healthcare facilities, particularly in pharmacies, to ensure the availability of essential medications while optimizing resource utilization. By integrating segmentation techniques such as ABC and VED analysis, this study provided valuable insights into categorizing drugs based on their consumption patterns, criticality, andimportance in the clinical area.

A comparative analysis of the composition of the primary bioactive molecules, antioxidant activities, and anti-inflammation abilities of the roots of Zanthoxylum nitidum harvested at different years of cultivation

Zanthoxylum nitidum is a traditional medicinal herb prevalent in China and Southeast Asia. However, despite being a widely cultivated perennial woody plant, an accurate reference and guide for determining the appropriate harvesting age in lacking. This study employed high-performance liquid chromatography (HPLC) and ultraviolet-visible (UV-Vis) spectrophotometry, along with in vitro antioxidant and in vivo anti-inflammatory assays, to comprehensively analyze and compare the roots of Z. nitidum at 1–9 and 14 years. The results indicated a positive correlation between the increase in age and the root biomass. The highest decoction rates were observed in the roots of the 5- (44.08 %) and 4-year-old plants (40.42 %). However, the total alkaloid content (TAC) and the levels of nitidine chloride (NC), chelerythrine, and sanguinarine chloride initially increased but then decreased; their highest contents were 1.49 % (3-year-old), 0.50 % (7-year-old), 0.50 % (5-year-old), and 0.061 % (6-year-old), respectively. The levels of magnoflorine exhibited a trend of 3-year peaks; the respective contents were 0.36 % (3-year-old), 0.30 % (5-year-old), and 0.33 % (14-year-old). The change in the in vitro antioxidant potency composite index correlated with the wave-like trends in the total flavonoid content (TFC) and total phenolic acid (TPA). The indices for 9-, 5-, and 3-year-olds ranked among the top three and were >90. Extracts of Z. nitidum roots demonstrated a therapeutic effect in dextran sulfate sodium-induced ulcerative colitis mice, with that from the roots of 5-year-old plants showing the highest impact. Overall, the Z. nitidum roots of 4–6-year-old plants can be harvested as high-quality raw materials that meet the Pharmacopoeia of the People's Republic of China standards. This study provides scientific references for aspects such as the rational harvesting of Z. nitidum during artificial cultivation, the optimal utilization of land resources, and clinical use.

Leveraging Hyperforce for Scalable and Secure Multi-Cloud Solutions: An Architectural and Implementation Guide

Purpose: In today’s fast-paced digital world, businesses are turning to multi-cloud strategies to boost their performance, flexibility, and resilience. This paper aims to provide a straightforward guide on leveraging Salesforce’s Hyperforce in a multi-cloud environment. Hyperforce is designed to work with major public cloud providers, offering unmatched scalability, data residency compliance, and efficiency. The primary objective is to help IT professionals and decision-makers fully leverage Hyperforce in a multi-cloud strategy to drive digital transformation and maintain competitiveness. Methodology: This paper explores the key aspects necessary for a successful Hyperforce and multi-cloud setup, including architectural design, implementation steps, data management, security, performance optimization, and cost management. Through a combination of clear technical insights and real-world examples, the paper illustrates the challenges and best practices associated with implementing Hyperforce in a multi-cloud environment. Findings: By spreading workloads across different cloud platforms, companies can enhance redundancy, avoid vendor lock-in, and optimize resource utilization. The paper identifies critical areas such as architectural design, security, and cost management that are essential for successful multi-cloud adoption using Hyperforce. Additionally, the paper delves into emerging trends and future innovations in the multi-cloud space, providing insights into the evolving landscape and what lies ahead for Hyperforce. Unique Contribution to Theory, Practice and Policy: The paper recommends that IT professionals and decision-makers focus on fully understanding and utilizing Hyperforce’s capabilities within a multi-cloud strategy. This involves following best practices in architectural design, implementation, data management, and security to maximize the benefits of Hyperforce. The insights and tools provided in this paper are intended to equip organizations with the knowledge necessary to effectively implement Hyperforce and drive their digital transformation efforts forward.

Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learning

With the widespread adoption of edge computing and the rollout of 5G technology, the edge network is experiencing rapid growth. Edge computing enables the execution of certain computational tasks on edge devices, fostering more efficient resource utilization. However, the reliability of the edge network is constrained by its network connections. Network instability can significantly compromise service quality. An effective service function chain (SFC) migration algorithm is essential to optimize resource utilization, enhance service quality. This paper begins by analyzing the current research landscape of edge networks and SFC migration algorithms. Subsequently, the challenges associated with edge network and SFC migration are formally articulated, leading to the proposal of a SFC migration algorithm based on deep reinforcement learning (DRL) with a focus on reliability assurance (RA-SFCM). The algorithm leverages multi-agent deep reinforcement learning to dynamically perceive changes in the edge network environment. It introduces an advantage function to evaluate the performance of each agent relative to the average level and incorporates a central attention mechanism with multiple attention heads to better capture the interdependencies and relationships among different agents. Additionally, this paper innovatively defines and quantifies the reliability of the migration process. By introducing a reliability penalty mechanism based on the migration target nodes and link capacity, it enhances the reliability of the migration schemes. The experimental results conclusively demonstrate the remarkable advantages of the RA-SFCM algorithm in terms of real-time performance, resource utilization efficiency, and reliability. Compared to algorithms such as Sa-VNFM, ROVM, and DLTSAC, RA-SFCM exhibits superior performance. For RA-SFCM, the optimized deployment migration strategy enhances real-time performance, precise resource management improves utilization efficiency, and advanced fault tolerance mechanisms strengthen reliability.

The Improvement of Faster-RCNN Crack Recognition Model and Parameters Based on Attention Mechanism

In recent years, computer vision technology has been extensively applied in the field of defect detection for transportation infrastructure, particularly in the detection of road surface cracks. Given the variations in performance and parameters across different models, this paper proposes an improved Faster R-CNN crack recognition model that incorporates attention mechanisms. The main content of this study includes the use of the residual network ResNet50 as the basic backbone network for feature extraction in Faster R-CNN, integrated with the Squeeze-and-Excitation Network (SENet) to enhance the model’s attention mechanisms. We thoroughly explored the effects of integrating SENet at different layers within each bottleneck of the Faster R-CNN and its specific impact on model performance. Particularly, SENet was added to the third convolutional layer, and its performance enhancement was investigated through 20 iterations. Experimental results demonstrate that the inclusion of SENet in the third convolutional layer significantly improves the model’s accuracy in detecting road surface cracks and optimizes resource utilization after 20 iterations, thereby proving that the addition of SENet substantially enhances the model’s performance.

Optimized k-Nearest neighbors search implementation on resource-constrained FPGA platforms

The k-Nearest Neighbors (kNN) algorithm is a fundamental machine learning classification technique with wide-ranging applications. Among various kNN implementation choices, FPGA-based heterogeneous systems have gained popularity due to FPGA's inherent parallelism, energy efficiency, and reconfigurability. However, implementing the kNN algorithm on resource-constrained embedded FPGA platforms, typically characterized by constrained programmable resources shared among various application-specific hardware units, necessitates a kNN accelerator architecture that balances high performance, hardware efficiency, and flexibility. To address this challenge, in this paper, we present a kNN hardware accelerator unit designed to optimize resource utilization by utilizing sequential, i.e. accumulation-based, instead of pipelined/parallel distance computations. The proposed architecture incorporates two key algorithmic optimizations to reduce the iteration count of the sequential distance computation loop: a dynamic lower bound enabling early termination of the distance computation and an online element selection that maximizes partial distance growth per iteration. We further enhance the accelerator's performance by incorporating multiple optimized sequential distance computation units, each dedicated to processing a segment of the training dataset. Our experiments demonstrate that the proposed approach is scalable, making it applicable to various hardware platforms and resource constraints. In particular, when implemented on an AMD Zynq device, the proposed single-core kNN accelerator occupies a mere 5 % of the FPGA's resources while delivering a speedup of 3 – 5 times compared to the kNN software implementation running on the accompanying ARM A9 processor. For the 8-core kNN accelerator, the resource utilization stands at 30 %, while the speedup factor ranges between 25 and 35.

Interference management and power scheduling in femtocell networks with the optimized power scheduling BiLSTM

With the proliferation of femtocell networks, seamless hand-off, and efficient power management are crucial for ensuring undisturbed connectivity and the optimal utilization of resources. Certain limitations of the existing methods include limited adaptability to dynamic network conditions, suboptimal hand-off decision-making, and inefficient power allocation. To tackle the addressed drawbacks of the existing interference management, the Vocal Hunt Optimization-based Power Scheduling BiLSTM (VHO-based PS-BiLSTM) is introduced in the research. This model aims at achieving a seamless transition during the hand-off events while optimizing the power consumption, ultimately enhancing the energy efficiency of the network and maximizing the end-user quality of service. The optimized Bidirectional Long Short-term Memory (BiLSTM) achieves superior performance by acquiring the sequential dependencies in energy consumption data, effectively capturing underlying patterns, and enabling the storage and utilization of data from both past and future time steps. The BiLSTM classifier is optimized with the Vocal Hunt Optimization (VHO) to improve the efficiency of the detection process and in turn, the power scheduling is carried out by the VHO individually based on the detected hand-off. The efficiency of the research is measured in terms of accuracy, sensitivity, specificity, delay, energy, Quality of service (QoS), and throughput which achieve 96.8 %, 95.76 %, 96.25 %, 109.38 ms, 0.999 J, 0.513, and 0.487 respectively.

Improving the pozzolanic reactivity of recycled powders from retired wind turbine blades by removing the polymer phase through thermal treatment

By 2050, the world will accumulate a substantial 43.4 million tons of waste wind turbine blades. Utilizing the powder from grinding these waste blades as a new supplementary cementitious material (SCM) presents an optimal recycling solution and resource utilization. The powder primarily consists of both organic (polymer resin) and inorganic (glass fiber) phases. It is essential to remove the polymer phase to achieve improved pozzolanic reactivity of the glass phase. This removal was achieved through thermal treatment in this study. Based on thermogravimetric (TG) analysis, polymer removal temperatures were set at 550 °C, 600 °C, 700 °C, and 800 °C. The optimal temperature was identified as 550 °C for the highest activity in the alkaline solution. The impact of polymer removal on pozzolanic reactivity was analyzed through various tests including paste flowability, exothermic heat flow, compressive and flexural strength, mercury intrusion porosimeter (MIP), TG, and scanning electron microscope (SEM). For paste sample with 30 % replacement ratio, the experimental results show that, the removal of polymer phase is beneficial in achieving a 0.46-h shorter hydration induction period, 9.4 % higher flowability, 10 % less pore volume at 90 days, and 20 % higher compressive strength. This study confirms that the pozzolanic reactivity of recycled powders from retired wind turbine blades can be improved by removing the polymer phase, making the recycled powder a potential SCM. This research is crucial for developing a new recycling route for waste wind turbine blades.

Agricultural sector circular economy development: Agroecological approach

This study aimed to investigate the feasibility of using agroecological concepts to establish a circular economy in the agricultural industry. The paper examined circular economy as a novel economic development paradigm that seeks to optimise resource utilisation and reduce waste, particularly in the agriculture industry. The main principle of the circular economy is resource conservation, which includes the rational use of land, water and energy, as well as the preservation of biodiversity and the improvement of soil fertility. An analysis was conducted on the economic advantages of adopting a circular economy in the agriculture industry, which include the reduction of production costs, enhancement of productivity, and generation of new employment opportunities. The study also addressed the importance of digitalisation, which can significantly increase the efficiency of agricultural production using modern digital technologies for monitoring soil conditions, managing water resources and optimising processes. This paper examined the effects of the war in Ukraine on the agricultural industry, specifically highlighting a substantial decrease in the output of key crops and livestock products. A comprehensive examination of the destruction of infrastructure, soil, and water contamination exposed severe environmental issues resulting from the attacks. The successful circular concepts implemented by European countries such as the Netherlands, Sweden, Denmark, and France can be applied to the post-war revival of the Ukrainian agriculture industry. The study also addressed the experience of the Ukrainian company Kernel, which successfully implements circular practices such as recycling of production waste, use of biomass and introduction of precision farming, which reduces environmental impact and increases production efficiency. Based on the analysis, recommendations for the post-war reconstruction of Ukraine’s agricultural sector were proposed, including the identification of priority regions for recovery, stimulation of innovation and modern technologies, support for small and medium-sized farms, development of irrigation systems, environmental restoration and protection, international cooperation and support, and education and training

Factors Influencing the Sustainability of SMEs in Marginalized Areas: A Study of Dullu Municipality, Dailekh

This research scrutinizes the underlying factors influencing the sustainability of SMEs in marginalized areas, with a particular focus on dimensions such as creativity and innovation, resource availability, regulatory compliance, networking and collaboration, and market competitiveness. It is conducted in Dullu Municipality, Dailekh, and employs an explanatory research design complemented by quantitative methods. Convenience sampling methodology is employed to gather data from a sample of 225 SME proprietors, who are selected as respondents for the study, utilizing survey questionnaires. Inferential statistics, including correlation and regression analysis, reveal the significant impact of creativity and innovation, efficient resource availability, regulatory compliance, networking and collaboration, and market competition on sustainable SMEs. The findings of the study recommend that owners of SMEs give more priority to innovation, optimal utilization of resources, and fostering networks and collaboration among SMEs. Furthermore, policymakers are urged to design initiatives supporting regulatory compliance and incentivizing innovation. Future research is recommended to explore market dynamics and compliance barriers, informing strategies for sustainable SMEs in marginalized locales.

Exploring strategies promoting interprofessional collaborative practice in spinal cord injury rehabilitation at a private South African hospital group

Background. Implementing an interprofessional collaborative practice (IPCP) in hospitals improves health outcomes, enhances patient safety and reduces length of stay by optimising resource utilisation. Although this approach was desired at a private hospital group in South Africa (SA), the specific strategies for clinical guidelines and capacity management pertaining to spinal cord injury (SCI) patients and the implementation of IPCP were unknown. Objective. To explore strategies for implementing clinical guidelines and capacity management for SCI rehabilitation to promote IPCP in a private hospital group in SA. Method. This exploratory descriptive qualitative study included participants who were selected through purposive sampling. The participants comprised 11 staff from various disciplines and management of the private hospital group, one local and one international expert in IPCP. Three online focus groups (n=13) were conducted, involving three to five participants. The transcriptions were analysed thematically using Braun and Clarke’s framework. Results. Interprofessional communication and tools as well as capacity development were three emergent themes from the data. Strategies pertaining to communication methods, digitisation, visual displays, patient care information, educational information and education of healthcare workers were suggested to promote IPCP. Conclusion. These findings from the emergent themes could assist in implementing and integrating an IPCP approach into the rehabilitation service. Further research assessing the efficacy of implementing the IPCP strategies and digital platform would be beneficial.

Approaches to determining the cost of laboratory tests

Background. Determining the cost of laboratory tests is a significant factor in the context of economic analysis and management of clinical diagnostic laboratories. Information about costs allows medical organizations to plan operational activities more efficiently, optimize resource utilization, and exercise control over expenditures. These measures are aimed at improving the financial position and enhancing the operational efficiency of the laboratories.Objective: to review various methods for calculating the cost of laboratory tests.Material and methods. The study included the collection and analysis of publications on calculating cost of laboratory tests, determining their prime cost in PubMed/MEDLINE and Google Scholar databases, as well as in Google and Yandex search engines for similar “cost laboratory test” query. The regulatory documents of the Russian Federation in this area were considered.Results. For calculating the prime cost of laboratory tests such methods as direct costing, regulatory prime costing, total prime costing, activity-based costing (ABC), standard costing, costing for base service, department costing, cost-volume analysis are used. The ABC and standard cost methods provide the most accurate assessment of the impact of various factors.Conclusion. The approaches established by regulatory documents do not contradict each other and represent the implementation of total costing method. At the same time, a medical organization may use other methods for calculating the cost of laboratory tests, depending on the objectives of the analysis, the specifics of the laboratory and the cost accounting method adopted in accounting policy, the management accounting system.

Rapid identification of back-flow in three distributaries of Jingjiang Reach, middle Yangtze River, China

Unusual back-flow events occurred in 2015 and 2017 at the near-mainstream ends of the three distributaries of the Jingjiang Reach, middle Yangtze River, China. It is presently challenging to precisely quantify back-flow discharges at river distributary ends where normal-flow discharges are measured chronically. This problem is more complicated at the ends of the three Jingjiang distributaries, each of which is more than 100 km long, and thus favors the generation of different back-flow forms. We use empirical formulae based on the Saint-Venant Equation and the Fall Index Method to quantify impacts on flow discharges at the two ends of each distributary of three key hydrodynamic variables: (1) water discharge at Zhicheng hydrological station, (2) total discharge from the four southwest tributary rivers, and (3) water levels at the two ends of the distributaries. We use the variable control approach to identify the contribution ratios of natural and human driving factors to flow discharges at the two ends of the distributaries during five historical stages. The empirical formulae provide reasonable results, with high values of correlation coefficients (R2 = 0.84 ∼ 0.98) obtained, demonstrating that the total water discharge from the four southwest tributary rivers is negligible. These formulae perform well in reproducing observed flow discharges at the two ends of the distributaries, especially the maximum discharges and cumulative volumes for normal-flow and back-flow events. Three charts are devised for different flow conditions at the two ends, from which back-flow events can be rapidly identified. Before impoundment of the Three Gorges Dam (TGD), riverbed adjustment caused by the lower Jingjiang Cutoff Projects and the Gezhou Dam contributed the most to flow discharge changes (≥50 %) at the near-mainstream ends of the distributaries. Afterwards, precipitation dominated (>60 %). In the future, back-flow events are expected only to occur at Mituosi (i.e., Taipingkou) and Nanxian hydrological stations, for which optimal reservoir operation and water resource utilization could remedy the back-flow crisis. Use of climate models and more advanced technologies such as remote sensing and GIS are recommended to enhance the reliability of predictions.