Effective Management Control Research Articles

Indonesia’s real estate value potential: the role of green innovation and effective management control systems

Indonesia’s real estate value potential: the role of green innovation and effective management control systems

Sensitive dual-channel lateral flow immunoassay tagged with high brightness latex microsphere for simultaneous detection of respiratory viral antigens

Respiratory infectious diseases pose a serious threat to human health worldwide. Timely diagnosis is crucial for effective clinical management and infection control. Herein, we developed a dual-channel lateral flow immunoassay (DC-LFIA) for simultaneous sensitive detection of influenza A virus (IAV) and respiratory syncytial virus (RSV) nucleoprotein (NP) antigens. The high brightness latex microspheres (HB-LMs) exhibiting typical absorption peak at 540 nm with uniform size and excellent optical stability, were prepared by a simple surfactant-free emulsion polymerization by mixing Sudan red, which overcame the complexity and color instability met for traditional synthesis methods. After affinity evaluation, two antibody pairs have good affinity for IAV NP and RSV NP were separately identified. Then, combining antibody pairs as probes and HB-LMs as based signal tag, a DC-LFIA was constructed, by which the visual limit of detection is 25 pg/mL, 100 pg/mL for IAV NP and RSV NP, respectively, 10-fold more sensitive than gold nanoparticles based LFIA. The calculated limit of detection (S/N=3) of IAV NP and RSV NP was improved 22.8- and19.5-fold, respectively, in comparison with gold nanoparticles based LFIA. Additionally, the developed sensor shows excellent specificity and reproducibility. Furthermore, the as-proposed method was validated through the analysis of clinical samples, in good agreement with commercial colloidal gold kits. Thus, the developed DC-LFIA has great potential for clinical application in the multiplex detection of respiratory viral antigens.

Tracing sulfate sources in a tropical agricultural catchment with a stable isotope Bayesian mixing model

Sulfate (SO42−) is an essential anion in drinking water and a vital macronutrient for plant growth. However, elevated sulfate levels can impact ecosystem or human health and could be an important indicator of acid rock drainage or pollution. Therefore, monitoring SO42− sources and transport is important for water quality assessments. This study focused on exploring the sources and transformations of SO42− as well as estimating the proportional contribution of the potential SO42− pollutant sources to groundwater and surface water in a tropical river basin, the Densu River Basin. The study used major ions combined with stable sulfur and oxygen isotope compositions and a Bayesian isotope mixing model, MixSIAR. The major ion characteristics indicate that SO42− concentrations remain stable throughout the rainy and dry seasons but originate from diverse sources. The multi-isotope model (δ34SSO4, δ18OSO4) identified four potential SO42− sources: detergent, precipitation, sewage, and sulfate fertilizer. However, the δ34SSO4 and δ18OSO4 values of the fertilizer source signatures overlapped with those of precipitation and sewage. Nevertheless, the contributions from each source were disentangled using the MixSIAR model, which revealed sewage as the most dominant SO42− pollutant in the Densu Basin, accounting for ~47 % of sulfate in groundwater and ~ 56 % of sulfate in surface water. Sulfate fertilizer (~33 %) was the second most important source after sewage for groundwater, while detergent (~23 %) was the second most important source for surface water. The redox processes of bacterial sulfate reduction and sulfide oxidation were determined to have a minimal impact on the sulfur isotope fractionation within the basin. This study highlights the benefits of combining major ions, sulfur isotopes and the MixSIAR model for identifying sources of sulfate. This approach accounts for uncertainties in source contributions which allows for more robust and reliable apportionment of sulfate sources. The study emphasizes the need for effective waste management and pollution control measures to protect water quality and provides vital guidelines on how to partition sulfate sources on a large catchment scale and evidence for making pollution management decisions on water resources.

Water Management in Anion Exchange Membrane Fuel Cells Via Distribution of Relaxation Times Analysis

In response to growing concerns regarding air pollution and energy scarcity, anion exchange membrane fuel cells (AEMFCs) have emerged as a promising technology for power generation due to their high efficiency, zero emissions, and the abundance of hydrogen resources. Within AEMFCs, water serves as a reactant, being consumed at the cathode, and generated at the anode, which can lead to an imbalance in the water content within the fuel cell. Maintaining an appropriate water distribution is essential for efficient charge transport and electrochemical reaction kinetics. Therefore, the development of a comprehensive water management strategy is crucial to ensure the stable operation and durability of AEMFCs.Characterizing the water condition of the anode and cathode in different operation conditions (normal, drying, or flooding) is a critical step that provides data foundation for devising effective water management control strategies. Electrochemical impedance spectroscopy (EIS) offers a means to investigate different resistances (kinetics, mass transport, and ohmic losses) over varying time scales during operation to assess water transport. The conventional interpretation of EIS data typically relies on equivalent circuit models, necessitating prior assumptions and initial component selections. In this study, we employed the distribution of relaxation times (DRT) methodology, known for its powerful ability to separate components, to conduct a more precise analysis of polarization processes. Initially, electrochemical impedance dynamics related to hydroxide transport, hydrogen oxidation reactions, and oxygen reduction reactions were effectively extracted. Subsequently, DRT was employed to investigate the loss and variation trends of each polarization process under conditions of anode and cathode water flooding and drying conditions, respectively. Furthermore, to gain a deeper understanding of the effects of water content on mass transport and electrochemical reaction kinetics, a three-dimensional multi-physics model for AEMFCs was developed. This model incorporated water phase transition, heat and mass transfer, liquid water and dissolver water transport, and electrochemical reactions. These endeavors represent a comprehensive and systematic approach to guide the utilization of the distribution of relaxation times in fuel cells.

Current state, sources, and potential risk of heavy metals in sediments of typical inner lakes in the Dongting Lake Area

The region has been increasingly experiencing environmental issues related to heavy metals (HMs) due to human activities. However, the contamination levels of HMs (Cr, Ni, Cu, Zn, As, Cd, and Pb) in various inner lakes in the Dongting Lake area remain unclear. In this study, surface sediment samples from 9 inner lakes and sediment core samples from 3 typical inner lakes were collected to evaluate the spatial distribution, contamination status, environmental and ecological risks, and possible sources of these HMs. The results showed that all HMs, except Zn, exceeded background values, with particularly high levels observed in aquaculture lakes compared to other lakes. Vertical profiles of HMs from 3 lakes show that the various types of lakes in the study area have different pollution histories. Additionally, results from Sediment quality guidelines (SQGs), Contamination factor (CF), Geo-accumulation index method (Igeo), and Hakanson potential ecological risk index (PERI) indicated that As, Cd, and Pb are the primary pollutants. The multivariate analysis concluded that Ni, Cu, Zn, As, Cd, and Pb had anthropogenic sources, primarily from agricultural, fisheries, industrial, and transportation activities, while Cr was influenced by natural factors. The results of this study enhance our understanding of sedimentary metal content in inner lakes and emphasize the importance of controlling HMs content in these lakes. This information is crucial for developing effective lake management and pollution control strategies.

Land use and urbanization indirectly control riverine CH4 and CO2 emissions by altering nutrient input

Urban rivers are recognized as significant sources of methane (CH4) and carbon dioxide (CO2) emissions. Despite this, the influence of land use and urbanization on carbon emissions across rural-urban rivers at the watershed scale has been insufficiently explored. This study utilized in-situ surveys of the Liao River in northern China to investigate the spatial and temporal variations of CH4 and CO2 emissions and their relationship with urbanization and its potential controlling factors. The findings revealed that CH4 emissions peaked in fall, whereas CO2 emissions were highest in summer. The average fluxes of CH4 and CO2 at the water-gas interface were 1387.22 ± 2474.98 µmol·m−2·d−1 and 52.78 ± 54.44 mmol·m−2·d−1, respectively. Water quality parameters accounted for 80.49 % of the total variation in CH4 and CO2 concentrations and fluxes. Structural equation modeling indicated that TN, TP, DTC, and conductivity had direct effects on riverine CH4 and CO2 emissions, with standardized direct effects of 0.50 and 0.49, respectively. Nutrient input emerged as the primary driver, increasing CH4 and CO2 concentrations and fluxes, particularly in urban-adjacent river sections likely receiving higher nutrient loads. This study underscores that land use and urbanization indirectly influence riverine CH4 and CO2 emissions by modifying nutrient inputs. Effective land use management and nutrient input control are recommended strategies to mitigate riverine CH4 and CO2 emissions.

Spatio-Temporal Analysis of Wildfire Regimes in Miombo of the LevasFlor Forest Concession, Central Mozambique

Wildfires are an intrinsic and vital driving factor in the Miombo ecosystem. Understanding fire regimes in Miombo is crucial for its ecological sustainability. Miombo is dominant in Central Mozambique, having one of the highest fire incidences in the country. This study evaluated the spatio-temporal patterns of fire regimes (intensity, seasonality, frequency and fire return interval) in the LevasFlor Forest Concession (LFC), Central Mozambique using remotely sensed data from 2001 to 2022. We conducted hotspot spatial statistics using the Getis-Ord Gi* method to assess fire distribution and patterns. The results revealed that 88% of the study area was burnt at least once from 2001 to 2022, with an average burned area of 9733 ha/year (21% of LFC’s total area). Fires were more likely to occur (74.4%) in open and deciduous Miombo types. A total of 84% of the studied area, burned in a range of 4 to 22 years of fire return interval (FRI) over the 21 assessed. Only 16% of the area was affected by high to very high FRI (1 to 4 years), with an average FRI of 4.43 years. Generally, fires are more frequent and intense in September and October. These results highlight the usefulness of remote sensing in evaluating long-term spatiotemporal fire trends for effective fire management strategies and control measures in African savanna ecosystems.

A review of control strategies for optimized microgrid operations

AbstractMicrogrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.

FACE RECOGNITION BASED OFFICE RESOURCE CONTROLLING

In contemporary office environments, effective resource management and access control are essential for maintaining security and optimizing productivity. Traditional methods of resource management and access control, such as manual logging and key- based systems, are often cumbersome, prone to errors, and lack real-time monitoring capabilities. In this paper, we propose a Face Recognition-Based Office Resource Controlling (FROC) system that leverages the capabilities of facial recognition technology to automate resource management and access control processes. The FROC system utilizes advanced deep learning algorithms to accurately identify individuals based on facial biometrics, granting or restricting access to office resources accordingly. By integrating facial recognition technology with centralized resource management software, the FROC system enables seamless and efficient control over office facilities, equipment, and confidential information. Additionally, the system offers real-time monitoring and logging of resource usage, enhancing security and accountability within the office environment. Through a combination of hardware components, including surveillance cameras and access control devices, and software modules for facial recognition and resource tracking, the FROC system provides a comprehensive solution for modern office resource management. We evaluate the performance and effectiveness of the FROC system through extensive testing in simulated office environments, demonstrating its potential to streamline operations, improve security, and optimize resource utilizationin office settings. Keywords: Convolutional Neural Networks, image recognition, Tensor Flow, Deep Learning, Teachable Machine,

Impact of Pharmacist-Led Intervention on Adherence to Inhalers, Inhalation Technique, and Disease Control Among Asthma/COPD Patients in a Resource Limited Center: An Interventional Study.

Asthma and Chronic obstructive pulmonary disease (COPD) are chronic respiratory conditions characterized by airflow obstruction and respiratory symptoms. Adherence to prescribed inhaler therapy and correct inhalation technique are essential for effective disease management and optimal disease control. However, non-adherence and incorrect inhalation technique are common challenges faced by patients with asthma and COPD, leading to suboptimal treatment outcomes and increased healthcare burden. To study the impact of a pharmacist-led intervention on inhaler adherence, inhalation technique, and disease control among patients with asthma and COPD. A pre-post interventional design assessed the effects of pharmacist-led intervention on inhaler adherence, inhalation techniques, and disease control in asthma and COPD patients at Dhulikhel Hospital in Nepal. Inclusion criteria: adult patient clinically diagnosed with asthma or COPD patients of all genders. The intervention comprised counseling patients with aids like videos, and informational leaflets. Impact was measured using checklist method for inhalation technique, the Test of Adherence to Inhaler (TAI) questionnaire for adherence to inhaler, and "Asthma Control Test (ACT)" or "COPD Assessment Test (CAT)" for disease control. The pharmacist-led intervention significantly increased adherence to inhalers, evidenced by a notable rise in the proportion of patients with good adherence (P<0.001). Sporadic, deliberate, and unwitting noncompliance pattern also improved significantly after the intervention (P<0.001, P<0.001 and P=0.001). Inhalation technique exhibited substantial improvement after intervention (P<0.001). The analysis indicated significant moderate negative correlations between "TIA" and "CAT" [ρ=-0.31; P=0.01], and between "inhalation technique score" and "CAT score" [ρ=-0.31; P=0.01] suggesting that as adherence to inhaler usage and inhalation technique improve, CAT scores tend to decrease, indicating reduced disease impact on the patient. This study shows the potential efficacy of pharmacist-led intervention in enhancing adherence to inhaler, inhalation technique, and disease control in respiratory conditions such as asthma and COPD.

A novel hydrodynamic-water quality coupling model for high-efficiency and high-resolution simulations of urban NSPs

Nonpoint source pollution (NSP) is one of the main factors leading to black odor in urban rivers. A better understanding and simulation of the formation and transport mechanism of NSP is critical to the development of effective urban rainwater management and pollution control strategies. This work presents a novel hydrodynamic-water quality coupling model to quantitatively simulate the entire physical process of the urban NSP by integrating two-dimensional (2D) NSP build-up, wash-off and transport modules into the source code of the GPU-accelerated surface water and transport (GAST) model. The advantages of this model are that high-resolution terrain is considered, such as key microtopography, such as roads and houses, which will have a diversion effect on the transport of hydrodynamic water quality and affect, and detailed hydrodynamic processes based on physical processes are fully considered. Additionally, a graphical processing unit (GPU)-based acceleration method is introduced to increase the parallel performance of the computing process. Three scales of cases were used to verify the model. First, indoor experimental data from the model roof and measured data from the campus roof under two short-duration rainstorms were used for nonpoint source model verification. The Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), Bayesian information criterion (BIC) and Akaike information criterion (AIC) were utilized to assess the applicability of the different NSP wash-off equations added to this model. Based on this model, the NSP mechanism processes of residential buildings were examined. The results indicate that this model is suitable for various pollutant wash-off formulas and is capable of high-efficiency and robust predictions of the sources, transmission paths, and spatiotemporal distribution characteristics of urban nonpoint sources. We quantitatively simulated the generation and transport of NSP from a single roof and proposed an NSP traceability method based on detailed hydrodynamic processes. This model can provide strong technical support for the precise treatment of urban NSP and the construction of sponge facilities.

Optimizing the detection of emerging infections using mobility-based spatial sampling

Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals’ movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.

Assess The Workload Management Model and Enhanced Quality Control in The Central Sterile Supply Department: A Study Protocol.

Introduction The Central Sterile Supply Department (CSSD) plays a crucial role in ensuring the safety and efficacy of medical instruments and equipment used in healthcare settings. Effective workload management and quality control are essential for maintaining high standards of patient care and minimizing the risk of healthcare-associated infections. This study protocol aims to assess the implementation and impact of a new workload management model and enhanced quality control measures in a CSSD setting. Methods The study will employ a mixed-methods approach, including quantitative analysis of workload data, qualitative interviews with CSSD staff, and observational assessments of workflow and quality control procedures. The workload management model under investigation involves the allocation of resources based on demand patterns and the implementation of scheduling algorithms to optimize staff efficiency. Enhanced quality control measures include the implementation of advanced sterilization technologies, rigorous inspection protocols, and continuous staff training initiatives. Results The study expects to provide insights into the effectiveness of the workload management model in optimizing resource allocation and improving staff productivity. Additionally, it aims to evaluate the impact of enhanced quality control measures on the reliability and safety of instrument sterilization processes. Findings from this study will inform recommendations for further improvements in CSSD operations and contribute to the advancement of best practices in sterile supply management. Conclusion The successful implementation of an effective workload management model and enhanced quality control measures in the CSSD is crucial for maintaining patient safety and ensuring the efficient functioning of healthcare facilities. By systematically evaluating the implementation and impact of these interventions, this study protocol seeks to contribute to the ongoing improvement of CSSD operations and the enhancement of healthcare quality.

Characterization and pathogenicity of Colletotrichum camelliae causing brown blight disease of tea (Camellia sinensis) in Malaysia

Tea (Camellia sinensis (L.) Kuntze) is a dicotyledonous woody shrub species widely cultivated to produce a beverage made from its leaves. Brown blight is a destructive foliar fungal disease that reduces tea production and degrades quality, resulting in lower market value. Typical brown blight symptoms observed in three commercial tea plantations in Malaysia were characterized as brownish to black lesions on young leaves. The lesions expanded with age, becoming darker and developing into necrotic cells. A total of 45 fungal isolates were isolated from brown blighted tea leaves and identified as Colletotrichum camelliae. Morphological characteristics coupled with universal spacer region and multigene phylogenetic relationships using the internal transcribed spacer (ITS), β-tubulin (tub2) and glyceraldehyde-3-phosphate dehydrogenase (gapdh) were used to accurately identify fungal isolates. The results of the pathogenicity test revealed that C. camelliae was responsible for causing brown blight disease of tea. This study highlights the occurrence of C. camelliae which causes tea brown blight in Malaysia. These findings may assist in disease monitoring, strict quarantine and effective control management of diseased tea plants.

The role of neuromodulation in the management of drug-resistant epilepsy.

Drug-resistant epilepsy (DRE) poses significant challenges in terms of effective management and seizure control. Neuromodulation techniques have emerged as promising solutions for individuals who are unresponsive to pharmacological treatments, especially for those who are not good surgical candidates for surgical resection or laser interstitial therapy (LiTT). Currently, there are three neuromodulation techniques that are FDA-approved for the management of DRE. These include vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS). Device selection, optimal time, and DBS and RNS target selection can also be challenging. In general, the number and localizability of the epileptic foci, alongside the comorbidities manifested by the patients, substantially influence the selection process. In the past, the general axiom was that DBS and VNS can be used for generalized and localized focal seizures, while RNS is typically reserved for patients with one or two highly localized epileptic foci, especially if they are in eloquent areas of the brain. Nowadays, with the advance in our understanding of thalamic involvement in DRE, RNS is also very effective for general non-focal epilepsy. In this review, we will discuss the underlying mechanisms of action, patient selection criteria, and the evidence supporting the use of each technique. Additionally, we explore emerging technologies and novel approaches in neuromodulation, such as closed-loop systems. Moreover, we examine the challenges and limitations associated with neuromodulation therapies, including adverse effects, complications, and the need for further long-term studies.This comprehensive review aims to provide valuable insights on present and future use of neuromodulation.

Conceptual Model of The Role of Territorial Management Controller and Good Governance

Purpose: This literature review aims to explore the intersection of management control and good governance at the local level, specifically within territorial authorities. The central purpose is to understand how management control can act as a strategic tool to enhance transparency, efficiency, and accountability in local governance. Method/design/approach: Methodologically, the review involves a comprehensive exploration of academic databases to identify relevant articles, theses, and books on the subject. The synthesis of findings encompasses historical perspectives, theoretical frameworks, and empirical studies, providing a holistic overview of the complex relationship between management control and local governance. Results and conclusion: The review synthesizes findings that reveal the multifaceted nature of good governance, the historical evolution of governance concepts, and the intricate link between management control and organizational strategy. It highlights the various responsibilities of territorial management controllers, encompassing decision support, education, and mediation functions. Research implications: The review suggests future research directions and underscores the practical importance of adapting private sector tools to the public sector while considering the specificities of territorial governance. Socially, effective management control practices are seen as potential enhancers of citizen satisfaction, transparency, and accountability in local governance. Originality/value: The originality and value of the literature review lie in its comprehensive exploration of historical governance evolution, the conceptualization of good governance, and the integration of management control into the public sector. It provides a rich framework for studying their interaction in territorial communities, offering insights that can inform both research and practical applications in local governance.

Occurrence and abundance of microplastics and plasticizers in landfill leachate from open dumpsites in Sri Lanka

This is the first attempt that investigate the abundance of plasticizers in leachate sediment in the scientific literature, alongside the debut effort to explore the abundance of microplastics and plasticizers in landfill leachate and sediment in Sri Lanka. Microplastics in sizes ranging from ≥2.0–5.0, ≥1.0–2.0, and ≥ 0.5–1.0 mm were extracted from the leachate draining from ten municipal solid waste open dump sites and sediment samples covering seven districts. Microplastics were extracted by density separation (Saturated ZnCl2) followed by wet peroxide digestion and the chemical identification was conducted by Fourier Transform Infrared spectroscopy. Plasticizers were extracted to hexane and analyzed by high-performance liquid chromatography. The total mean microplastic abundance in leachate was 2.06 ± 0.62 mg/L whereas it was 363 ± 111 mg/kg for leachate sediments. The most frequently found polymer type was polyethylene (>50%), and white color was dominant. The average concentration of bisphenol A (BPA), benzophenone (BP) and diethyl-hydrogen phthalate (DHEP) in leachate was 158 ± 84.4, 0.75 ± 0.16 and 170 ± 85.8 μg/L respectively. Furthermore, BP and DHEP in leachate sediment was 100 ± 68.3 and 1034 ± 455 μg/kg respectively. As landfill leachate is directly discharged into nearby surface and groundwater bodies that serve as sources of drinking water, the study highlights the potential concerns of microplastic and plasticizer exposure to the surrounding Sri Lankan community through consumption of contaminated drinking water. Therefore, there is a timely need of develop the effective waste management and pollution control measures to minimize the possible threats to both the environment and human health.

A Comparison of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) in River Water Quality Prediction

River water is a crucial natural resource utilized for various purposes, including agriculture and drinking. Human activities such as mining, industrial discharge, and improper waste management contribute to river water pollution, affecting its quality and posing risks to human health. Monitoring and predicting river water quality are essential for effective management and pollution control. The research focuses on Dissolved Oxygen (DO), and comparing of Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) to developed prediction models. Evaluation of the models’ performance shows that the ANN model outperforms LSTM in predicting Dissolved Oxygen (DO) concentrations, achieving lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Although LSTM exhibits lower Mean Squared Error (MSE), the ANN model demonstrates better accuracy in minimizing the average distance between predicted and actual values. The findings suggest that ANN-based models offer good performance in river water quality prediction, with potential for further enhancement through additional variables or model architecture adjustments.

Peran Sistem Pengendalian Manajemen dalam Mendukung Circular Economy Sebagai Bentuk Kontribusi Perusahaan dalam Pelestarian Lingkungan pada Cita Entertainment

This research aims to determine the role of the management control system in supporting Circular Economy standards at Event Organizer CV Cita Entertainment. This research method uses a qualitative method with a descriptive analysis approach. Results show that the scope of this research includes budget planning, logistics and evaluation processes. And the items used are items that are not disposable and can be reused for further activities, in accordance with the 3R concept (Reduce, Reuse, Recycle). Planning and budgeting of goods is carried out carefully so that the circular economy concept can be realized and aims to save the budget. Waste processing procedures that focus on the reuse of durable goods. This is achieved by recording and updating data regarding items that can be used repeatedly. This way, they don't end up as trash, contributing to overall waste minimization. This research aims to provide an understanding of the application of Circular Economy principles in industrial management activities through an effective management control system and targeted waste processing procedures

Diagnosis of human brucellosis: Systematic review and meta-analysis.

Brucellosis, a widely spread zoonotic disease, poses significant diagnostic challenges due to its non-specific symptoms and underreporting. Timely and accurate diagnosis is crucial for effective patient management and public health control. However, a comprehensive comparative review of available diagnostic tests is lacking. This systematic review addressed the following question: 'What is the accuracy of the available tests to confirm human brucellosis?' Two independent reviewers examined articles published up to January 2023. The review included original studies reporting symptomatic patients with brucellosis suspicion, through any index test, with sensitivity and/or specificity as outcomes. As exclusion criteria were considered: sample size smaller than 10 patients, studies focusing on complicated brucellosis, and those lacking essential information about index or comparator tests. Sensitivity and specificity were assessed, with consideration for the index test, and 'culture' and 'culture and standard tube agglutination test (SAT)' were used as reference standards. Bias assessment and certainty of evidence were carried out using the QUADAS-2 and GRADE tools, respectively. A total of 38 studies reporting diagnostic test performance for human brucellosis were included. However, the evidence available is limited, and significant variability was observed among studies. Regarding the reference test, culture and/or SAT are deemed more appropriate than culture alone. Rose Bengal, IgG/IgM ELISA, and PCR exhibited equally high performances, indicating superior overall diagnostic accuracy, with very low certainty of the evidence. This systematic review underscores the potential of the Rose Bengal test, IgG/IgM ELISA, and PCR as promising diagnostic tools for brucellosis. However, the successful implementation and recommendations for their use should consider the local context and available resources. The findings highlight the pressing need for standardization, improved reporting, and ongoing advancements in test development to enhance the accuracy and accessibility of brucellosis diagnosis.