Management Approach Research Articles

Sustainable humification of food waste slurry through thermally activated persulfate oxidation

This study introduces a thermoanalytical approach utilizing sodium persulfate (PS) to expedite the humification of food waste (FW) through advanced oxidation processes (AOPs). The method harnesses heat-induced free radicals to break down complex organic compounds into simpler forms, leading to the formation of humic substances. We conducted an analysis of changes in soluble chemical oxygen demand (SCOD), dissolved organic carbon (DOC), ammonia nitrogen, soluble proteins, and sugars, employing advanced techniques such as three-dimensional fluorescence spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, and scanning electron microscopy (SEM). The findings indicate that the activation of PS at 70°C effectively increases SCOD and DOC levels, converts proteins into soluble forms, and enhances NH4+-N content. This study, which focuses on the utilization of thermal methods, affirms that the thermal activation of persulfate (PS) significantly boosts the humification of food waste (FW), presenting a sustainable approach for waste management and resource recovery.

Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data.

Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.

Strategies for a successful collaboration between a rural hospital and an academic medical centre

Rural hospitals are facing unprecedented financial challenges in the United States. The many factors contributing to the economic headwinds for rural hospitals result in reduced clinical services offered in local communities. Affiliations, mergers and acquisitions are a path for rural facilities to keep their doors open. Maintaining independence as a rural hospital by employing an affiliation approach with a larger health system can be a sustainable path under the right circumstances. This paper outlines a successful 12-year affiliation between a critical access hospital (CAH) and an academic medical centre (AMC) in Oregon. This paper outlines the underlying structure and governance of the collaboration, important management approaches, lessons learned and the results over the life of the affiliation. The results for the CAH include the growth of annual ambulatory visits from 15,000 to 131,000, adding 430 new employees, and increasing the total annual operating revenue from US$51m to US$193m. The results for the AMC include the development of an innovative joint venture rural cancer centre, the growth of rural graduate medical education rotations, and expanding clinical department faculty who work in a rural community setting. Enhancing health care in underserved areas aligns with the AMC’s statewide mission to serve all Oregonians as the AMC. There have been significant benefits to patients by increasing access to speciality services locally, including reducing miles travelled for cancer care. Our case study demonstrates that an affiliation structure between a CAH and an AMC can be a sustainable and beneficial approach to improving access to quality health care in rural communities and can strengthen the financial performance of both organisations.

How to Distinguish Non-Inflammatory from Inflammatory Pain in RA?

Managing non-inflammatory pain in rheumatoid arthritis (RA) can be a huge burden for the rheumatologist. Pain that persists despite optimal RA treatment is extremely challenging for patient and physician alike. Here, we outline the latest research relevant to distinguishing non-inflammatory from inflammatory RA pain and review the current understanding of its neurobiology and management. Nociplastic pain is a recently introduced term by the international pain community. Its definition encompasses the non-inflammatory pain of RA and describes pain that is not driven by inflamed joints or compromised nerves, but that is instead driven by a functional reorganisation of the central nervous system (CNS). Insights from all areas of nociplastic pain research, including fibromyalgia, support a personalised pain management approach for non-inflammatory pain of RA, with evidence-based guidelines favouring use of non-pharmacological interventions. Future developments include novel CNS targeting pharmacotherapeutic approaches to treat nociplastic pain.

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.

Integrity-centered framework for determining protected areas boundary: An application in the China's national park

National parks are critical components of protected areas and are drawing increased global attention. Clear and rational boundaries may provide a scientific foundation for the sustainable protection and management of these areas. However, criteria for feasible national park delimitation and techniques for resolving conflicts between socioeconomic development and environmental protection are still poorly defined, particularly regarding integrity during park establishment. Consequently, a systematic analytical framework is essential for advancing research and practice. This study developed a systematic framework to determine the spatial boundaries, using the Kunlun Mountain region in China as a case for empirical analysis. Key issues in national park development—ecology and management—were systematically integrated into this framework. Most importantly, spatial connectivity, a critical concern in national park planning, was thoroughly considered for improving integrity optimization, an aspect often overlooked in previous delimitation studies. The results demonstrated that our framework effectively identified potential national park boundaries, achieving a balance between development and conservation. Furthermore, core protected and general control areas were delineated to support differentiated management approaches. This framework offers a scientific foundation for policymakers and managers to pursue sustainable protection and management of national parks.

Integrating AI in financial risk management: Evaluating the effects of machine learning algorithms on predictive accuracy and regulatory compliance

This research focuses on adopting ML models in risk management and how such factors influence predictive abilities and compliance with relevant rules. With more financial institutions using some of these advanced AI technologies in their decision-making capacities, a clear understanding of their effectiveness and what legal compliance would mean for their growth becomes vital. This research presents a comprehensive literature review of traditional risk management methods compared to the newer, AI-based methodologies by meticulously evaluating difficult standard measurements, including accuracy, precision, and recall. Further, the research analyses the compliance risks that arise with AI, especially concerning significant regulations such as Basel III and GDPR, which are essential in preserving financial stability and customer confidence. The study shows that applying AI approaches enhances predictive efficiency to a very high degree and the pressing and major legal concerns that institutions face. Moreover, the studies reveal the beneficial sectors for applying machine learning for operational risk management and provide guidelines for employing AI. To improve and strengthen risk management approaches and guarantee strict compliance with current and future implementing regulations, this study offers pertinent information to current discourses regarding the future of finance within the rising context of technological advancements.

Management Approaches to Intraluminal Thrombi in Acutely Symptomatic Carotid Stenosis.

Management Approaches to Intraluminal Thrombi in Acutely Symptomatic Carotid Stenosis.

Interdisciplinary Approach to Managing Complex Traumatic Dental Injuries in the Young Permanent Dentition.

Traumatic dental injuries (TDIs) are a frequent challenge in pediatric dentistry, requiring a coordinated interdisciplinary approach for successful management. These injuries can affect various dental tissues and pose significant diagnostic and therapeutic challenges, particularly in young patients with developing dentition. This review aims to explore the complexities of managing TDIs in young permanent dentition, focusing on the role of orthodontics, endodontics, and other specialties in providing comprehensive care. We highlight the importance of interdisciplinary collaboration in achieving optimal functional and aesthetic outcomes, emphasizing the need for updated diagnostic tools and treatment protocols tailored to the growing child. Specific recommendations are provided for clinicians on integrating orthodontic and endodontic management in trauma cases, focusing on minimizing complications and ensuring long-term prognosis.

A multi-agent deep reinforcement learning approach for optimal resource management in serverless computing

A multi-agent deep reinforcement learning approach for optimal resource management in serverless computing

Pediatric hypoparathyroidism: etiological and clinical evaluation in a tertiary center.

This study aims to evaluate the etiology, clinical presentation, and management of pediatric hypoparathyroidism in a tertiary center. A retrospective review was conducted on pediatric patients diagnosed with hypoparathyroidism at the Pediatric Endocrinology Clinic from March 2021 to June 2023. Data on demographic characteristics, presenting symptoms, laboratory findings, genetic analyses, and treatment outcomes were collected. A total of 56 patients (31 females) were included. The median age at diagnosis of the patients was 5.5 years (range 0.04-17 years), and the median age of symptom onset was 5 years (range 0.04-16.5 years). The etiology was genetic and idiopathic in 39 patients (70.9%), with syndromic forms, familial isolated hypoparathyroidism, and hypomagnesemia identified. DiGeorge syndrome was present in 14 patients, making it the most common syndromic form. The syndromes associated with hypomagnesemia were those with mutations in the TRMP6 and CLDN16 genes. Sixteen patients (29.1%) had acquired causes, primarily post-thyroid surgery and autoimmune conditions. Common symptoms included muscle spasms (32.7%) and seizures (21.8%). Laboratory findings revealed a median serum calcium level of 6.7 mg/dL (3.8-8.5) and median serum phosphorus level of 7.7 (4.9-12.5) mg/dL. Treatment primarily involved calcitriol [The median dose of calcitriol is 25 ng/kg/day (range: 25-50 ng/kg/day)] and calcium [The median dose of calcium gluconate is 0.7 mL/kg (range: 0.5-1 mL/kg) and oral calcium is 1000 mg (range: 700-1300 mg)] supplementation. Intravenous calcium gluconate treatment was administered to 39 (70.6%) patients, oral calcium carbonate therapy was given to 16 (29.1%) patients, and calcitriol treatment was initiated for 51 (91.1%) patients. Complications such as nephrocalcinosis (7, 13.7%) and hypercalciuria (7, 13.7%) were observed in some patients. This study emphasizes the significant genetic component, particularly syndromic, in pediatric hypoparathyroidism, highlighting the need for comprehensive genetic evaluation and a multidisciplinary approach for effective management, especially concerning complications. In this way, early and accurate diagnosis will reduce unnecessary tests, treatment approaches, and repeated hospital visits. Regular monitoring is essential to mitigate potential complications associated with long-term treatment.

Clinical and genetic diversity in Iranian individuals with RAPSN-related congenital myasthenic syndrome.

Congenital myasthenic syndromes (CMSs) are genetic disorders affecting motor function with variable symptoms. RAPSN-related CMS, caused by mutations in the RAPSN gene, leads to muscle weakness. Accurate diagnosis is essential for proper management. This study aims to analyze six Iranian families affected by RAPSN-CMS, focusing on clinical manifestations, genetic variants, treatment response, and outcomes. Clinical assessments, genetic analysis, and whole-exome sequencing were performed on the six families to identify RAPSN gene mutations. The study examined symptoms, disease severity, age of onset, treatment response, and outcomes. Treatment with pyridostigmine and salbutamol was given to assess its effectiveness. Three homozygous known variants in RAPSN gene were identified: c.491G > A in three families, c.264C > A in two families, and c.-210A > G in one family. Clinical assessments showed diversity in symptoms and treatment responses. Pyridostigmine and salbutamol treatment improved symptoms and quality of life. This study highlights the significance of molecular diagnosis for RAPSN-related congenital myasthenic syndromes (CMS) in Iran, marking the first comprehensive genetic analysis in the region. The identification of specific pathogenic variants underscores the unique genetic landscape of local patients. Furthermore, our long-term follow-up revealed variable treatment responses, emphasizing the need for personalized care strategies. The clinical variability among patients with identical mutations necessitates a multidisciplinary approach for effective management. By enhancing genetic awareness and refining follow-up methods, we aim to improve diagnosis accuracy and interventions, fostering better outcomes for affected families in the Iranian population.

Foreign body lodged in the subglottic area: a case report in a child

Subglottic foreign bodies are rare and represent an extreme emergency that can threaten life. Our aim is to share our experience in the management and multidisciplinary approach to a subglottic foreign body in a child. We report the case of a 4-year-old child from a rural area, who was admitted as an emergency. He was referred by the pediatric team after presenting with symptoms of foreign body aspiration. The general examination revealed laryngeal dyspnea (stage IV) with unstable hemodynamic parameters. ENT examination showed pharyngeal granulations; near-obstructive hypertrophy of the inferior turbinates, muco-purulent rhinorrhea with pale nasal mucosa; cerumen plugs, and intact tympanic membranes. Pulmonary auscultation noted crackles in both lung fields. We diagnosed a laryngo-tracheal foreign body complicated by stage IV laryngeal dyspnea. The child underwent an emergency tracheostomy followed by endoscopy and removal of the foreign body. The postoperative course was uncomplicated. Although rare, subglottic foreign bodies present a challenge for early management. Therefore, we emphasize to our colleagues the severity of subglottic foreign bodies. Prevention is the best therapeutic alternative.

Total Quality Management, Market Orientation, and Performance: Empirical Insights from the Moroccan Hospitality Industry

The intensity of competition in the hospitality industry has long since highlighted the importance of addressing the intellectual aspect through management practices that focus on the environment, which have become essential in responding to changing market demands. Market Orientation (MO) is widely recognized as a critical growth strategy. It enables organizations to use real-time market intelligence to capitalize on business opportunities and make strategic and operational decisions that align with changing market dynamics. This study takes MO as the research object and investigates the factors influencing its implementation focusing on Total Quality Management (TQM) practices. The research also examines the impact of MO on hotel performance (HP) and assesses how interfunctional coordination (IC) moderates the MO/HP relationship. The study formulates a comprehensive theoretical model, presenting 3 hypotheses. Subsequently, the questionnaire was distributed to hotels based in Morocco, and a total of 93 usable responses were collected. PLS-SEM via Smart PLS 4.0 is used to test the model. Results showed that TQM practices are internal drivers of MO and that MO fosters HP. Moreover, IC moderates the MO/HP relationship, reducing the direct effect. This research offers an empirical contribution by examining how hotels embrace two managerial approaches to enhance performance. Furthermore, it provides a deeper explanation of the current challenges within the Moroccan hospitality industry that justify the focus of the study.

Immune Checkpoint Inhibitor Associated Rheumatoid Arthritis.

Immune checkpoint inhibitors (ICI) have revolutionized cancer therapy over the past decade. Unfortunately, immune related adverse events (irAEs) are common, including rheumatologic adverse events. These rheumatologic irAEs include de novo rheumatoid arthritis-like presentations or flares of pre-existing rheumatoid arthritis, collectively called ICI-associated rheumatoid arthritis. In this article we review the different mechanisms of disease activity and management approaches including use of conventional (cs) DMARDs and biologic (b) DMARDs in this patient population. Other forms of ICI-induced inflammatory arthritis e.g., PMR-like or Spondylarthritis-type IA, are beyond the scope of this review. The heterogeneous presentations of inflammatory arthritis in patients receiving immune checkpoint inhibitors has made this a challenging area to study. Nonetheless, recent studies are providing better understanding on the mechanisms of de novo disease and flares in patients with rheumatoid arthritis. About half of patients with pre-existing rheumatoid arthritis flare after receiving checkpoint inhibitors. Persistent arthritis is often encountered in patients receiving combination immune checkpoint inhibitors. Outcomes on overall survival do not differ in rheumatoid arthritis patients receiving checkpoint inhibitors compared to their non-arthritis counterparts. Rheumatologist play a critical role in the management of active rheumatoid arthritis induced by checkpoint inhibitors. Collaboration with oncology colleagues will continue to be a crucial component in providing quality care to these patients. While the use of glucocorticoids is often the first line therapy for active inflammatory arthritic disease, we recommend earlier consideration of DMARDs just as we inverted the treatment pyramid several decades ago, for rheumatoid arthritis.

Blue Economy and the Impact of Industrialisation on Sustainable Livelihoods: A Case Study of Fisheries in the North Coastal Region of Java

This research explores the impacts of the blue economy and industrialisation on sustainable livelihoods in the coastal areas of the North Coast of Java, with a particular focus on the fisheries sector.The main objective of this research is to analyse how changes in blue economy practices, which include sustainable marine resource management approaches, interact with industrialisation processes and their impact on the survival of local fishing communities.The research method used was qualitative with a case study approach, where data was collected through in-depth interviews with fishermen, government officials, and relevant stakeholders, as well as direct observation in the field. Data analysis was conducted using thematic analysis techniques to identify patterns and significant relationships between the factors involved.The results of this study show that industrialisation in the coastal areas of Java's North Coast has vital and complex impacts on the sustainable livelihoods of local fishing communities. On the one hand, industrialisation provides new economic opportunities, such as jobs in the fishing and manufacturing sectors, which can increase community income. However, on the other hand, negative impacts on the aquatic environment due to industrial activities and overexploitation of natural resources threaten the sustainability of the marine ecosystem, which is the main source of livelihood for fishermen.The managerial implications of these findings emphasise the need to integrate blue economy policies with the maturity of sustainable industrial development planning. Policy development that accommodates the needs of local ecosystems and fishing communities, with a pattern of regulating marine protected zones and supporting the shift to environmentally friendly fisheries practices, is key to ensuring the sustainability of fishers' livelihoods and healthy coastal ecosystems. This research provides important insights for policy makers and practitioners involved in marine fisheries resource management and industry development in coastal areas.

Conocimiento del manejo de las lesiones precancerosas de cérvix de los profesionales de salud, en una empresa social del estado de baja complejidad del departamento del Valle del Cauca, Colombia

Introduction: Cervical cancer, according to estimates from the Global Cancer Observatory (GLOBOCAN), was the third cause of cancer and the fourth cause of mortality associated with cancer in Colombia during 2020. Despite multiple screening and detection interventions in the country, The expected results have not been obtained in terms of a decrease in cases of cervical cancer and an increase in the early diagnosis of premalignant lesions. Objective: To determine the knowledge of health personnel about precancerous lesions of the cervix, according to the Colombian clinical practice guide, in a low-complexity State Social Enterprise in the department of Valle del Cauca, Colombia. Materials and methods: Descriptive cross-sectional study in 42 health professionals, made up of doctors, professional nurses and nursing assistants from a low-complexity hospital entity in the department of Valle del Cauca, through a self-administered virtual survey. With the results obtained, univariate analyzes of the included variables were carried out, and using frequency tables, bar graphs and cakes, the behavior of the analysis variables was described. Results: 42 participants were included, with an average age of 34 years; 57% were nursing assistants and professional nurses. The average work experience in years was 9. 54.8% of participants had received training to perform cervical cancer screening. Knowledge of risk factors is significantly affected by the occupation of health personnel. When evaluating the screening and diagnosis of precancerous lesions of the cervix, it was found that most respondents know that screening is related to their detection. In relation to treatment, between 79 and 100% of pro- fessionals know the therapeutic alternatives. Regarding the behavior of health personnel regarding the results of the screening and the subsequent management approach, the doctors had a higher percentage of overall correctness. Conclusion: The study shows that there were misconceptions and inadequate approaches among the health professionals surveyed. It is necessary for low-complexity health entities to develop and disseminate cervical cancer prevention guidelines and health education materials to ensure standardization of the quality of services provided by health workers.

High-resolution banana row maps for the characterization of spatial variability in the field

ABSTRACT Precision agriculture (PA) practices in banana production chains have received limited attention. Based on the literature, the investigation of spatial and temporal variability in banana orchards should be customized according to the characteristics of the crop. This study aimed to develop and evaluate methods for mapping the spatial variability in soil properties at row- and clump-resolutions in a banana orchard, and to generate row and clump maps with high-spatial-resolution soil property information. A banana orchard was investigated, and georeferenced soil sampling was conducted with calibration and validation points. Methods for reconstructing banana rows and clumps were proposed, called Methods 1 and 2 and Alternative Methods 1 and 2. Surface and line maps at row- and clump-resolutions for soil chemical and physical properties were generated using ordinary kriging and Voronoi polygons. Subsequently, the discrepancies between the data obtained from the validation points and the predictions devised from the surfaces generated by the proposed approaches were calculated, and the RMSE was used as a performance parameter. Methods 1 and 2 were appropriate and reliable approaches for site-specific management and allow for specific and optimized crop management in banana cultivation, offering greater accuracy in cultivation operations such as fertilization.

Plant Diversity, Productivity, and Soil Nutrient Responses to Different Grassland Degradation Levels in Hulunbuir, China

Grassland degradation could affect the composition, structure, and ecological function of plant communities and threaten the stability of their ecosystems. It is essential to accurately evaluate grassland degradation and elucidate its impacts on the vegetation–soil relationship. In this study, remote sensing data based on vegetation coverage were used to assess the degradation status of Hulunbuir grassland, and five different grassland degradation degrees were classified. Vegetation community composition, diversity, biomass, soil nutrient status, and their relationships in different degraded grasslands were investigated using field survey data. The results showed that grassland degradation significantly affected the species composition of the vegetation community. As degradation intensified, species richness declined, with the proportion of Gramineae and Legume species decreasing and Asteraceae species increasing. Additionally, the proportion of annual species initially increased and then decreased. Degradation also markedly reduced aboveground, belowground, and litter biomass within the communities. Soil moisture, electrical conductivity, organic carbon, total carbon, total potassium, and hydrolyzable nitrogen contents in non-degraded areas were higher than those in severely degraded areas. Conversely, soil total phosphorus content and bulk density gradually increased with degradation. Nitrate nitrogen and ammonium nitrogen levels in severely degraded soils were significantly higher than those in non-degraded soils. Plant diversity in the study area was significantly positively correlated with aboveground biomass and belowground biomass, and it positively correlated with soil nutrient total carbon and available carbon but negatively correlated with soil bulk density. Results of the partial least squares path model showed that grassland degradation had significant negative effects on plant diversity, soil nutrients, and biomass. Soil nutrients were the main factors affecting ecosystem productivity. The direct effect of plant diversity on biomass was not significant, suggesting that soil nutrients may play a more important role than plant diversity in determining biomass during grassland degradation. The results illustrated the relationships among soil nutrients, plant diversity, and biomass in degraded grasslands and emphasized the importance of an integrated approach in the effective management and restoration of degraded grasslands.

Exploring spatial reasoning performances of CNN on linear layout dataset

Abstract Spatial reasoning, a fundamental aspect of human intelligence, is essential for machine learning models to understand and interpret object relationships. It is crucial for numerous real-world applications, ranging from autonomous navigation to urban planning. The lack of comprehensive datasets limits the development and evaluation of models that can effectively handle spatial reasoning tasks. Existing datasets often contain complex spatial reasoning problems with overlapping spatial relationships, making it challenging to diagnose specific aspects that a model struggles with. We address this gap by introducing a new dataset of linear layouts. This dataset is systematically designed to exhibit a range of spatial relations and complexity levels. Analyzing spatial reasoning through linear layout generation offers a more structured and manageable approach to understanding how models learn and interpret spatial relationships. Linear layout generation has broad applicability and is of fundamental importance in design and optimization. To benchmark dataset, we develop LinLayCNN, a generic data-driven method that applies shallow, one-dimensional convolutional neural network (CNN), to generate linear layouts in an iterative process. Experimental results reveal that LinLayCNN can effectively solve fundamental spatial challenges even with the relatively small size of the training set. It is capable of precise object placement, making it a robust tool for linear layout generation. Current layout generation methods focus on domain-specific solutions, often fail to maintain the precision needed for technical domains, such as accurate sizing, and object counting. They also require a substantial amount of data to function effectively. LinLayCNN overcame these issues. This study also enhances our understanding of CNNs' capabilities in spatial reasoning, highlight their potential to advance the field of layout generation. As a result, our approach establishes a clear benchmark for evaluating spatial reasoning and aids in development of models that can more effectively understand and reason about space.