Successful Method Research Articles

Pests, Diseases, Growth and Yield of Tomato as Influenced by Variety and Cultivation Technology

Tomato (<i>Solanum lycopersicum</i> L.) is one of the most important vegetables in the world. However, dearth of knowledge exists on cultivation technology that contributes to increased production of the crop. Meanwhile, low yielding varieties, high pests and diseases attacks, climate variability and poor soil fertility are among key production constraints that limit the increased production and productivity of tomato in Sierra Leone. A two-year field experiment was conducted at the School of Agriculture and Food Sciences experimental site during 2022 and 2023 to evaluate the effects of variety and cultivation technology (CT) on pests, diseases, growth, yield and productivity of tomato. The experiment was laid in a 2 × 4 factorial (i.e. two varieties of tomato, and four treatments: CT 1, CT 2, CT 3 and CT 4 known as control) arranged in a randomized complete block design (RCBD) with three replications. Results showed that organic (CT 1 and CT 2) and inorganic (CT 3) treatments had a positive impact on growth parameters of tomato. The CT 1 (chicken dung, mulching, and neem extract biopesticide) was most effective in promoting vegetative growth and higher fruit yield, while CT 2 (NPK 15:15:15, urea, promethrin herbicide, and chlorpyrifos pesticide) exhibited highest potency in reducing population and damage caused by diseases and pests. Findings demonstrate that improved variety and cultivation technology boost tomato tolerance to pests and diseases, as well as its growth and yield performances that could be exploited for increased production and fruit quality of the crop. The CT1 was the most effective, followed by CT 2, while CT 4 or control plots had the lowest performance. The outperformance of the organic treatments relative to the inorganic and control is suggested to be attributable to its nitrogen-rich components. Weed control was also established to be effective in both inorganic and CT 2 treatments. The findings suggest that the CT 1 should be promoted for sustainable tomato cultivation, prioritizing environmentally friendly methods for long-term success.

The jailed coiling technique: An endovascular solution for saccular aneurysms with suboptimal fixation sites.

To evaluate the midterm results of patients with saccular aneurysms treated with the jailed coiling technique. A retrospective review of 17 patients treated with the jailed coiling technique over a 7 year period, between 2018 and 2024 at our institution. Treatment entails placing an endograft across the neck of the saccular aneurysm followed by coiling of the aneurysm through an extraluminal catheter. Debranching procedures of the aortic arch were performed when necessary in order to create a sealing zone of >5mm. Data collected included demographics, anatomical features of the lesions and sealing zones, indication for intervention, method of treatment and technical success, sac expansion, and reinterventions. 17 patients (median age 69, 14 males) were treated for saccular aneurysms with short, suboptimal sealing zones (aortic arch 13, distal thoracic aorta 2, celiac trunk 1, brachiocephalic artery 1). Among the aortic arch aneurysms, six required preoperative debranching procedures of the aortic arch in order to create a short landing zone of 5mm, making them eligible for the procedure. Technical success was obtained in all patients. One perioperative mortality occurred. Mean follow-up among those treated for arch aneurysms was 32 months (range 1-71 months). One patient who was treated for an aortic arch aneurysm had a persistent endoleak. No sac enlargement was observed. None of the patients required interventions and none experienced aneurysm related mortality. The jailed coiling technique is a safe and effective method to treat saccular arterial aneurysms with suboptimal, short sealing zones. It can be utilized for saccular aneurysms located within the aortic arch and for aneurysms located in other locations where coiling or stent grafting is not an option. The procedure can be performed with minimal morbidity with a high percentage of success. Long term durability of the repair needs to be determined.

IMPACT OF IMPLEMENTATION OF METHODS OF COACHING FOR SUCCESS, PARTICIPATIVE, AFFILIATIVE, VISIONARY AND SITUATIONAL (COPAVISIT) AT OPERATIONAL MANAGEMENT OF CELLULAR TELECOMMUNICATIONS COMPANIES IN DIGITAL ERA

The development of the telecommunications industry in Indonesia is determined by the company's ability to manage operational with using methods of coaching for success, participative, affiliative, visionary and situational. The meaning of coaching for success are with treating people as they want to be treated, that is by indirectly giving punishment, participative by taking part in the problems faced by company management, affiliative by maintaining good relationships with employees and company partners, visionary support progress company, situational is done by looking at the condition of the company that is required to be able to implement the marketing industry 4.0. This research method uses a quantitative approach, the type of quantitative descriptive research with the nature of research that is explanatory. The data used are secondary data, namely employees of cellular telecommunication in Area of Regional I of Medan City as many as 800 people. The sampling technique used in this study using the Slovin formula so that the number of samples used in this study was 89 people. Sampling uses a simple random sampling approach. The results obtained in this study are coaching for success methods have a negative and significant influence, participative and affiliative, have a positive and significant influence, while visionary and situational have no influence on the operational management of cellular telecommunications companies in Medan City.

Cross-Modality Medical Image Segmentation via Enhanced Feature Alignment and Cross Pseudo Supervision Learning.

Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods have emerged as a pivotal strategy for cross modality analysis. These methods typically utilize generative adversarial networks (GANs) for both image-level and feature-level domain adaptation through the transformation and reconstruction of images, assuming the features between domains are well-aligned. However, this assumption falters with significant gaps between different medical image modalities, such as MRI and CT. These gaps hinder the effective training of segmentation networks with cross-modality images and can lead to misleading training guidance and instability. To address these challenges, this paper introduces a novel approach comprising a cross-modality feature alignment sub-network and a cross pseudo supervised dual-stream segmentation sub-network. These components work together to bridge domain discrepancies more effectively and ensure a stable training environment. The feature alignment sub-network is designed for the bidirectional alignment of features between the source and target domains, incorporating a self-attention module to aid in learning structurally consistent and relevant information. The segmentation sub-network leverages an enhanced cross-pseudo-supervised loss to harmonize the output of the two segmentation networks, assessing pseudo-distances between domains to improve the pseudo-label quality and thus enhancing the overall learning efficiency of the framework. This method's success is demonstrated by notable advancements in segmentation precision across target domains for abdomen and brain tasks.

Optimizing robotic arm control using deep Q-learning and artificial neural networks through demonstration-based methodologies: A case study of dynamic and static conditions

This paper uses robot programming techniques, such as Deep Q Network, Artificial Neural Network, and Artificial Deep Q Network, to address challenges related to controlling robotic arms through demonstration learning. Static and dynamic states of the subjects were the subjects of experiments. Each method's classification accuracy process success values and experimental condition combination were evaluated. The DQN method demonstrated favourable classification accuracy outcomes, achieving an Accuracy value of 0.64 for the fixed dice and 0.52 for the moving dice. The Response value was 0.51 for the fixed dice and 0.41 for the moving dice, indicating a moderate level. The ANN method demonstrated lower accuracy, with Accuracy values of 0.59 and 0.56 and Response values of 0.61 and 0.58, respectively. The ADQN method demonstrated superior outcomes, with Accuracy values of 0.66 and 0.59 and Response values of 0.67 and 0.61. During the initial learning iterations, ADQN demonstrated the highest success rate at 33.67 %, whereas DQN and ANN achieved 28.39 % and 20.13 % success rates, respectively. As the number of iterations increased, all methods demonstrated improvement in their results. ADQN maintained a high success rate of 97.59 %, while DQN and ANN attained 82.16 % and 88.66 %, respectively. As the number of iterations increases, the results of all methods improve, but the success rate of the Artificial Deep Q Network remains high. As the number of iterations increases, both Deep Q Network and Artificial Neural Network demonstrate the potential to achieve good results. Overall, the findings support the efficacy of robot programming techniques that incorporate demonstration learning. The Artificial Deep Q Network is the most successful and fast-converging method suitable for various robot control tasks. These findings provide a foundation for future research and large-scale, comprehensive learning applications for complex rot control.

Big Data Analytics and its Approaches: The Digital Transformation of the Future

This study is all about the importance- of methods in big data study, and what they might mean for future- digital growth projects. The rese-arch explores current patte-rns, issues, and smart plans. This gives valuable information for groups figuring out the- tricky mixture of data study and digital growth. The focus of the pape-r is on important methods like batch handling, stream handling, and machine- learning. It looks at their strengths, limits, and use-s. We see the-ir effects on growing and improving. We fore-cast future patterns, tackle issue-s and suggest inventive solutions for succe-ss. This research acts like a guide- for groups that want to line up their digital changes with the- best methods for continuous success.

A literature review on construction project success evaluation criteria and methods

A literature review on construction project success evaluation criteria and methods

Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom(DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.

Role of social networks and entrepreneurial success: Understanding the dynamics of knowledge acquisition and green entrepreneurial orientation

Small and medium organizations (SMEs) are the backbone of any country, especially emerging economies like Pakistan. Yet, they hesitate to make long-term investments due to financial and market turbulence, inflation, limited economic opportunities, and political instability, especially in developing countries. In such a context, relying on social networks could benefit entrepreneurs, but its relationship with entrepreneurial success remains unclear. Using network theory as a theoretical standpoint, this study aims to understand the underlying mechanisms of the relationship between social networks and entrepreneurial success in SME industries of Pakistan. The indirect effect of knowledge acquisition and the moderating role of green entrepreneurial orientation are also tested. The data was collected from 311 Pakistani SMEs through a self-administered questionnaire and analyzed using structural equation modeling. The results revealed that social networks strongly influenced knowledge acquisition and entrepreneurial success and confirmed the positive moderating role of green entrepreneurial orientation in all relationships. This study significantly underwrites academic literature by identifying a unique theoretical framework that tests the underlying methods for entrepreneurial success for SMEs in an emerging economy. For practitioners, this study empirically proves the significance of social networks that SME managers should consider during strategic planning to achieve business success.

Role of Phytoconstituents in Cancer Treatment: A Review.

Over the years, natural compounds have become a significant advancement in cancer treatment, primarily due to their effectiveness, safety, bio-functionality, and wide range of molecular structures. They are now increasingly preferred in drug discovery due to these attributes. These compounds, whether occurring naturally or with synthetic modifications, find applications in various fields like biology, medicine, and engineering. While chemotherapy has been a successful method for treating cancer, it comes with systemic toxicity. To address this issue, researchers and medical practitioners are exploring the concept of combinational chemotherapy. This approach aims to reduce toxicity by using a mix of natural substances and their derivatives in clinical trials and prescription medications. Among the most extensively studied natural anticancer compounds are quercetin, curcumin, vincristine, and vinblastine. These compounds play crucial roles as immunotherapeutics and chemosensitizers, both as standalone treatments and in combination therapies with specific mechanisms. This review article provides a concise overview of the functions, potentials, and combinations of natural anticancer compounds in cancer treatment, along with their mechanisms of action and clinical applications.

Unraveling the Mechanism of the Brust-Schiffrin Formation of Au25(SR)18 through Mass Spectrometry.

The Brust-Schiffrin (BS) method for gold nanoparticle (Au NP) synthesis is celebrated for its ability to produce highly monodisperse NPs from toluene-water solutions, in contrast to aqueous methods, such as the Turkevich method. Despite the method's success, the actual formation mechanisms remain largely unknown due to difficulty in studying the intermediates with species-differentiating techniques such as mass spectrometry (MS) or nuclear magnetic resonance (NMR). The issue lies in the use of solvents poorly compatible with these techniques and the difficulty in differentiating useful intermediate species from side products and impurities in such one-pot reactions. Herein, we use our recently formulated fully aqueous BS reaction to study the formation mechanisms. MS is chiefly employed to capture the intermediate species, and the Au25(SR)18 nanocluster is used as a thermodynamically reliable end-point. We find that the BS method may comprise a unilateral complex-shedding stage in addition to the known thiol-etching stage.

Novel Discounted Adaptive Critic Control Designs With Accelerated Learning Formulation.

Inspired by the successive relaxation method, a novel discounted iterative adaptive dynamic programming framework is developed, in which the iterative value function sequence possesses an adjustable convergence rate. The different convergence properties of the value function sequence and the stability of the closed-loop systems under the new discounted value iteration (VI) are investigated. Based on the properties of the given VI scheme, an accelerated learning algorithm with convergence guarantee is presented. Moreover, the implementations of the new VI scheme and its accelerated learning design are elaborated, which involve value function approximation and policy improvement. A nonlinear fourth-order ball-and-beam balancing plant is used to verify the performance of the developed approaches. Compared with the traditional VI, the present discounted iterative adaptive critic designs greatly accelerate the convergence rate of the value function and reduce the computational cost simultaneously.

Immobilisation of ZnO nanoparticles on carbon and on glass fibres for visible light photocatalytic applications

Crystalline zinc oxide (ZnO) nanoparticles (NP) have been used for the evaluation of the effect of manganese doping on the photocatalytic performance towards the degradation of pharmaceutical products and for the preparation of photoactive composite materials based on fibres and ZnO.The presence of manganese effected on the particles size, as obtained by TEM analysis, and the impact on the crystallographic and optical properties was shown by XRD and DRS analysis. The multiple oxidation state of the doping manganese and its impact on the oxygen vacancies of the material, together with possibility of introducing energy levels in the forbidden zone and changes in the bandgap energy could contribute to improve the catalyst properties. However, and contrasting with some reported works, no advantage using the doped particles was observed on the photocatalytic response under UV or visible light irradiation.Later, the ZnO NP were immobilised by in situ hydrothermal approach on carbon and glass fibres and the composites show good nanoparticles coverage on the SEM images and the EDS, FTIR and DRS data are consistent with the presence of NP on the surfaces. Those composites have been successfully used for the degradation of the Diclofenac (DCF) and Carbamazepine (CMZ) pollutants. The complexity of the photodegradation under UV and visible light, due to the production of absorbing degradation products, was unveiled by UV–vis spectroscopy and HPLC analysis, and showed that distinct products may be detected during degradation, depending on the radiation used. The photostability of ZnO nanoparticles and the CF/ZnO composite maintaining 83 % of its initial efficiency after three cycles of reuse highlight the method's success in supporting nanoparticles on substrates and demonstrates promising applicability in fields such as environmental remediation.

طريقة القياس الطيفي المتزامن لتقدير كل من سيبروفلوكساسين وسيفاليكسين باستخدام طريقة H - point للاضافة المعيارية

طريقة سهلة وبسيطة ودقيقة لتقدير السبروفلوكساسين في وجود السيفاليكسين او العكس بالعكس في خليط منهما. طبقت الطريقة المقترحة بطريقة الاضافة القياسية لنقطة بنجاح في تقدير السبروفلوكساسين بوجود السيفاليكسين كمتداخل عند الاطوال الموجية 240-272.3 نانوميتر وبتراكيز مختلفة من السبروفلوكساسين 4-18 مايكروغرام . مل-1 وكذلك تقدير السيفاليكسين بوجود السبروفلوكساسين الذي يتداخل باطوال موجية 262-285.7 نانوميتر وبتراكيز مختلفة من السيفاليكسين 6-18 مايكروغرام . مل-1 في مزيج لهما. اظهرت النتائج عدم وجود اي تداخلات من قبل المواد المضافة التي تحتويها الادوية على هذه المركبات وضمن حدود كشف السبروفلوكساسين يساوي 0.1732 مايكروغرام . مل-1 وعقارالسيفاليكسين يساوي 0.4620 مايكروغرام . مل-1 . الانحراف القياسي النسبي المئوي اقل من 2% . تم تطبيق الطريقة بنجاح لتقدير العقارين في بعض المستحضرات الصيدلانية. تعتبر الطريقة المقترحة من الطرق القليلة التكلفة وعدم حاجتها الى ادخال الادوية في سلسلة من التفاعلات وتثبيت لظروف التفاعل لغرض تقديرها وانما تتم عن طريق تقدير الدواء بعد اذابته مباشرة في الماء المقطر بوجود العقار الاخر معه في مزيج وتعتبر من الطرق الناجحة في التقدير خاصة للادوية المتقاربة في طيف الامتصاص لها والتي من غير الممكن ايجاد طرق لفصل الدوائين وتقديرهما بصورة ادق من هذه الطريقة المقترحة دون التداخل وتاثير احدهما على الاخر. الطريقة المقترحة في هذا البحث كانت ناجحة في تقدير كل من السبروفلوكساسين والسيفاليكسين في مزيج لهما دون تداخل دواء مع الاخر.

Fractional periodic boundary value and Cauchy problems with Hilfer–Prabhakar operator

We introduce a successive approximations method to study one fractional periodic boundary value problem of the Hilfer-Prabhakar type. The problem is associated to the corresponding Cauchy problem, whose solution depends on an unknown initial value. To find this value we numerically solve the so-called ’determining system’ of algebraic or transcendental equations. As a result, we determine an approximate solution of the studied problem, written in a closed form. Finally, we evaluate efficiency of our method on a nonlinear numerical example.

Image Processing Techniques for Crack Detection in MPI of Springs

This study investigates image processing techniques for detecting surface cracks in spring steel components, with a focus on applications like Magnetic Particle Inspection (MPI) in industries such as railways and automotive. The research details a comprehensive methodology that covers data collection, software tools, and image processing methods. Various techniques, including Canny edge detection, Hough Transform, Gabor Filters, and Convolutional Neural Networks (CNNs), are evaluated for their effectiveness in crack detection. The study identifies the most successful methods, providing valuable insights into their performance. The paper also introduces a novel batch processing approach for efficient and automated crack detection across multiple images. The trade-offs between detection accuracy and processing speed are analyzed for the Morphological Top-hat filter and Canny edge filter methods. The Top-hat method, with thresholding after filtering, excelled in crack detection, with no false positives in tested images. The Canny edge filter, while efficient with adjusted parameters, needs further optimization for reducing false positives. In conclusion, the Top-hat method offers an efficient approach for crack detection during MPI. This research offers a foundation for developing advanced automated crack detection system, not only to spring sector but also extends to various industrial processes such as casting and forging tools and products, thereby widening the scope of applicability.

Correlation between pulp sensibility and magnetic resonance signal intensity following regenerative endodontic procedures in mature necrotic teeth- a retrospective cohort study

BackgroundWith increasing studies being published on regenerative endodontic procedures (REPs) as a treatment modality for mature necrotic teeth, the assessment of outcomes following regenerative endodontic procedures has become more challenging and the demand for a better understanding of the regenerated tissues following this treatment is rising. The study aimed to correlate cold, electric pulp testing (EPT), and magnetic resonance imaging (MRI) signal intensity (SI) in mature necrotic teeth treated with regenerative endodontic procedures.MethodologyThis retrospective cohort study included eighteen adult patients who experienced tooth necrosis in mature maxillary anterior teeth recruited from the outpatient clinic, Conservative Dentistry Department, Faculty of Dentistry, Alexandria University, Alexandria, Egypt from July 2017 until December 2018 with 12 months of follow-up. regenerative endodontic procedures via blood clot were performed. The canals were instrumented by ProTaper Next (PTN) files until final sizes X3 or X5. Biodentine was used as cervical plug material. Pre and post-operative clinical follow-up was done where the patients’ responses to cold and electric pulp testing were given a scoring system and were compared to the normal contralateral tooth. Pre and post-operative magnetic resonance imaging signal intensity of both the involved tooth and its contralateral at the middle and the apical thirds of the root canals were assessed after 3, 6, and 12 months. Data was analyzed using the ANOVA, Friedman and Bonferroni tests. Significance was set at a p-value < 0.05.ResultsAll 18 teeth scored a baseline score of “2” for cold and electric pulp testing. There was a significant difference between scores of the cold test at baseline and 12-month follow-up (p < 0.001). There was a significant difference between scores of the electric pulp testing of baseline and 12-month follow-up (p < 0.001). There was a moderately significant indirect (inverse) correlation between magnetic resonance imaging signal intensity and cold test in both the middle and apical thirds at 12 months. No significant correlations were detected between magnetic resonance imaging signal intensity and electric pulp testingat any of the time intervals (p > 0.05).ConclusionMagnetic resonance imaging is a successful non-invasive method to assess outcomes of regenerative endodontic procedures and correlating it with another reliable method of assessing pulpal responses, cold test, could validate these outcomes.Clinical trial registrationThe study was registered with ClinicalTrials.gov (ID: NCT03804450).

Synergizing Geophysical Analysis and Geotechnical for Artificial Recharge Well and Aquifer Studies: A Comprehensive Review

The harvesting of surface runoff stands as a pivotal process in artificially recharging groundwater, playing a crucial role in augmenting groundwater within aquifers. Groundwater recharge via recharge wells is one of the successful direct sub-surface methods. To pinpoint the most suitable locations for recharge wells, a combination of geophysical surveys, including Electrical Resistivity, Magnetic, and Electrologging surveys, was used. These geophysical surveys serve as essential tools for comprehending the sub-surface lithology and played a pivotal role in identifying distinct aquifers and determining their respective dimensions, thereby aiding in the precision of recharge well placement. Our objective is to review the various methods to assess the underground water storage capacity of the aquifer based on tests viz. Sieve Analysis, Soil Water Retention Capacity, and Lateral Permeability tests. These combination of theoretical and practical evaluations aim to determine the aquifer's potential for storing groundwater. Geophysical surveys are invaluable for deriving various relationships through diverse analyses. Moreover, exploring the correlation between geophysical methods, such as electrical resistivity, and shear strength parameters is essential for comprehending the strength characteristics of subsurface layers.

Nanocomposites consisting of titanium dioxide nanoparticles, antisense oligonucleotides, and photoactive groups as agents for effective action on nucleic acids

Relevance. Studies on model systems have confirmed the effectiveness of antisense oligonucleotides, including those that contain photoactive groups, for the modification of nucleic acids. However, this strategy has not yet found wide application due to the lack of successful methods for the cellular delivery. The development of effective preparations capable of acting on target nucleic acids in cells is an urgent task.&#x0D; The objective of the work is to create nanocomposites consisting of TiO2 nanoparticles, antisense oligonucleotides, and photoactive groups and to study their effect on target nucleic acids by the example of inhibition of influenza A virus replication in the cellular system.&#x0D; Materials and methods. Influenza virus A/Aichi/2/68 (A/H3N2), N-succinimide ether of p-azidotetrafluorobenzoic acid, TiO2 nanoparticles, and oligodeoxyribonucleotides have been used in the work. The antiviral activity of the proposed nanocomposites has been studied on the MDCK cells infected with the A/H3N2 virus.&#x0D; Results and discussion. Unique nanocomposites have been created, which consist of three functional components, i.e., titanium dioxide nanoparticles, antisense oligonucleotides, and the photoactive tetrafluoroarylazide group, respectively, providing penetration into cells, selective interaction with target nucleic acids, and photomodification of the targets. A significant antiviral site-specific action of the nanocomposites has been demonstrated against the influenza A virus in the cellular system, which exceeds the effect of the analogous samples that contain no photoactive groups.&#x0D; Conclusion. The biological activity of the created nanocomposites has been demonstrated by the example of highly effective suppression of influenza A virus replication in the cellular system. The results indicate the prospects of using the proposed drugs to affect target nucleic acids inside cells.

A Hybrid Simulation Platform for quality-aware evaluation of complex events in an IoT environment

Complex Event Processing (CEP) is a successful method to transform simple IoT events created by sensors into meaningful complex business events. To enhance availability, an event fabrication mechanism is integrated within the CEP model, generating synthetic events to offset missing data, resulting in a quality-aware CEP model. In this model, generated complex events are characterized by quality properties, namely completeness and timeliness. To empirically assess the quality of complex events through experimentation, we have developed a hybrid simulation platform. The platform’s dual nature stems from its distinctive approach of simulating sensor behaviors while concurrently running the quality-aware CEP IoT platform. Users can conduct experiments that closely mimic actual operational scenarios and have, in real-time, full visibility and control over all involved aspects, including composite transformations, quality assessment, event fabrication and its effectiveness, and aggregated reports. A representative experiment in an IoT-enabled greenhouse with missing events is presented to demonstrate the usefulness of the platform. The contribution of the hybrid simulation platform is twofold: provide (a) quality assessment of complex events, using two established quality properties for IoT environments with specific computation formulas and (b) a comprehensive testbed covering all aspects of a typical IoT setup for realistic experimentation. Together, these elements provide significant cost–benefit advantages by enabling researchers and practitioners to pre-optimize operational efficiency and decision-making in IoT systems.