Decision-making Process Research Articles

“You’re fired”: the influence of heuristic-systematic processing in communication and termination decisions in organizational environments

PurposeDecisions regarding the termination of organizational members are not only common but also represent difficult actions for managers. Despite their importance and frequency, managers often make or avoid them based on incomplete or faulty decision-making criteria. Previous research suggests that decision-makers are subject to internal influences that play significant roles in their decision-making.Design/methodology/approachUsing a controlled between-groups experimental design, this study, guided by the heuristic-systematic model, tested to what extent decision-makers rely on heuristic cues versus the systematic processing of more relevant information when making termination decisions.FindingsFindings suggest the order in which information is presented influences participants’ decision-making and influences the information-processing structure.Practical implicationsFindings suggest practical considerations for managers, management trainees and others who engage in termination decisions as to potential influencing factors.Originality/valueThis research adds to the understanding of the decision-making process in organizational contexts.

Vaccine decision-making among pregnant women: a protocol for a cross-sectional mixed-method study in Brazil, Ghana, Kenya and Pakistan

Maternal immunization is a critical strategy to prevent both maternal and infant morbidity and mortality from several infectious diseases. When the first COVID-19 vaccines became available during the pandemic, there was mixed messaging and confusion amongst the broader public and among those associated with health care systems about the recommendations for COVID-19 vaccinations in pregnancy in many countries. A multi-country, mixed-methods study is being undertaken to describe how vaccine decision-making occurs amongst pregnant and postpartum women, with a focus on COVID-19 vaccines. The study is being conducted in Brazil, Ghana, Kenya, and Pakistan. In each country, participants are being recruited from either 2 or 3 maternity hospitals and/or clinics that represent a diverse population in terms of socio-economic and urban/rural status. Data collection includes cross-sectional surveys in pregnant women and semi-structured in-depth interviews with both pregnant and postpartum women. The instruments were designed to identify attitudinal, behavioral, and social correlates of vaccine uptake during and after pregnancy, including the decision-making process related to COVID-19 vaccines, and constructs such as risk perception, self-efficacy, vaccine intentions, and social norms. The aim is to recruit 400 participants for the survey and 50 for the interviews in each country. Qualitative data will be analyzed using a grounded theory approach. Quantitative data will be analyzed using descriptive statistics, latent variable analysis, and prediction modelling. Both the quantitative and qualitative data will be used to explore differences in attitudes and behaviors around maternal immunization across pregnancy trimesters and the postpartum period among and within countries. Each country has planned dissemination activities to share the study findings with relevant stakeholders in the communities from which the data is collected and to conduct country-specific secondary analyses.

SỬ DỤNG CÂY QUYẾT ĐỊNH ĐỂ XÂY DỰNG CÔNG CỤ ĐÁNH GIÁ KẾ HOẠCH BÀI DẠY NHẰM PHÁT TRIỂN NĂNG LỰC HỌC SINH

This study explores the application of decision trees - a machine learning technique in the development of a tool for evaluating lesson plans. This is a new approach to conducting educational evaluation tools. The goal is to enhance the lesson design skills of student teachers and instructors through self-assessment and adjustment of their lesson plans. Decision trees are used to simulate the decision-making process, providing accurate and consistent evaluations of the descriptions of appropriate teaching aids and materials in line with educational objectives. The paper describes the construction of a teacher's lesson plan evaluation tool with six criteria, and the initial use of this tool to improve the quality of lesson plans for teachers and student teachers. Initial results indicate that the ability of student teachers and instructors to construct application activities and articulate objectives has been enhanced.

Pedestrian Path Choice in Crowded Indoor Environments

Abstract. Indoor crowds can be regarded as dynamic obstacles and they could impede the movement of pedestrians. For instance, the scope of crowds could significantly impact accurate path choices. There are few discussions from an individual’s perspective to reveal the decision-making process of reasonable path choices. In this paper, we aim to address how an indoor pedestrian would walk in a crowded indoor environment. We propose a method to simulate a pedestrian’s path choices during wayfinding process. It is wrapped in a workflow that can capture near-real-time crowd uncertainty. First, the measurement errors of crowds are alleviated with a Bayesian filter. According to crowd locations at each moment, the method of Kernel Density Estimation (KDE) is applied to a grid model of the building to obtain the uncertainty map of the crowds. Based on a two-level spatial model, the logical path represented by a room sequence can be derived from a ’room-door’ network, while detailed path choices on the ’operational’ level are refined in each room. By considering both crowd uncertainty and time cost, path choices for a user are determined by using the A* algorithm at each time slot. By iterating the optimal path to the next door/the target in the current room, the user location of the next moment can be inferred. Our test initially validated the feasibility of the method on the path choices of a pedestrian. In the future, we plan to further conduct field tests with different users in crowded indoor environments.

A Real-Time Key-Finding Algorithm Based on the Signature of Fifths

The signature of fifths is a special kind of music representation technique enabling acquisition of musical knowledge. The technique is based on geometrical relationships existing between twelve polar vectors inscribed in the circle of fifths, which represent individual pitch-classes detected in a given composition. In this paper we introduce a real-time key-detection algorithm which utilizes the concept of the signature of fifths. We explain how to create the signature of fifths and how to derive its descriptors required by the algorithm, i.e., the main directed axis of the signature of fifths, the major/minor mode axis, the characteristic vector of the signature of fifths, the characteristic angle of the signature of fifths, and the angle of the major/minor mode. We performed a series of experiments to test the algorithm’s effectiveness. The results were compared with those obtained using key-detection approaches based on key-profiles. All experiments were conducted using works composed by J.S. Bach, F. Chopin, and D. Shostakovich. The distinctive features of the presented algorithm, with respect to the considered key-detection approaches, are computational simplicity and stability of the decision-making process.

GOVERNANÇA CORPORATIVA PARA A GESTÃO FINANCEIRA

The objective of this work is to highlight the importance of corporate governance for the financial management of business organizations. In this sense, a bibliographic research methodology was chosen. Corporate governance fosters the credibility of organizations by providing financial information that determines the decision-making processes of internal and external stakeholders. In this case, the financial manager who practices governance must demonstrate characteristics and competencies focused on ethics and transparency so that the organization's financial performance is achieved through a systemic vision correlated with the internal and external sectors of the companies, permeated by both good governance practices and the increase of investment capital. Thus, corporate governance plays an important role in contributing to organizations by minimizing risks, reducing indebtedness, and increasing the market value of companies that implement it, as it is seen as a factor of respect for those who invest their capital in a business aiming for the profitability of their investment.

Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Achieving stable and reliable autonomous driving in complex traffic environments while ensuring safety under unpredictable conditions is a critical challenge in autonomous driving technology. To address this issue, this study proposes the Safedrive Dreamer navigation framework, which aims to reduce the reliance on trial-and-error learning in real-world scenarios, thereby mitigating the risks associated with dynamic driving conditions and enhancing vehicle foresight. This framework integrates the predictive capabilities of world models with the constrained Markov decision process (CMDP) and safety reinforcement learning to accurately anticipate future environmental changes. This ensures the reliability of autonomous driving routes, thereby improving both safety and efficiency. Furthermore, to reduce trial-and-error costs in real-world applications, this study employs PAC-Bayesian methods to derive generalization error bounds between simulations and reality, enabling a more effective transfer of knowledge and experience from simulations to real-world scenarios. Validation experiments in simulated and real environments showed that Safedrive Dreamer significantly outperformed existing autonomous driving solutions by 3.8% in key safety metrics, excelling in collision avoidance and risk reduction. This study provides new insights into the integration of world models into decision-making processes to enhance decision-making capabilities in safety–critical applications, thereby contributing significantly to the improvement of autonomous driving safety and reliability.

GOVERNABILIDADE EMPRESARIAL PELA PERSPECTIVA FINANCEIRA ESTRATÉGICA

every company is made up of several sectors, all of unique importance. There is, however, one that stands out for reflecting a more specific nucleus, whose collection of information has an impact on the business decision-making process, that is, the financial one. The movement and administration of the measured capital are the most challenging tasks for the financial manager, so that it goes beyond mere mathematical calculations. It involves the organizational structure in its systemic view, the mapping of the market with its needs and expectations, the determination of objective criteria, regardless of the unpredictable risk factors. These are able to change the decision chain to the point of transforming business strategic planning and its corresponding way of managing. Then the administrator or the financial manager emerges as the protagonist of the corporation's leverage or setback, insofar as the manipulation of the information obtained, some of which are privileged, may assist in the exact allocation of the funds raised, the results of which may give the company notoriety. Corporate governance thus takes a macro perspective, so that financial strategies will be fundamental to mitigate eventual failures in the business endeavor.

The Shared Experience Actor–Critic (SEAC) Approach for Allocating Radio Resources and Mitigating Resource Collisions in 5G-NR-V2X Mode 2 Under Aperiodic Traffic Conditions

5G New Radio (NR)-V2X, standardized by 3GPP Release 16, includes a distributed resource allocation Mode, known as Mode 2, that allows vehicles to autonomously select transmission resources using either sensing-based semi-persistent scheduling (SB-SPS) or dynamic scheduling (DS). In unmanaged 5G-NR-V2X scenarios, SB-SPS loses effectiveness with aperiodic and variable data. DS, while better for aperiodic traffic, faces challenges due to random selection, particularly in high traffic density scenarios, leading to increased collisions. To address these limitations, this study models the Cellular V2X network as a decentralized multi-agent networked Markov decision process (MDP), where each vehicle agent uses the Shared Experience Actor–Critic (SEAC) technique to optimize performance. The superiority of SEAC over SB-SPS and DS is demonstrated through simulations, showing that the SEAC with an N-step approach achieves an average improvement of approximately 18–20% in enhancing reliability, reducing collisions, and improving resource utilization under high vehicular density scenarios with aperiodic traffic patterns.

The development of a roadmap for Health Technology Assessment implementation in Moldova.

Moldova strives for universal health coverage (UHC) and considers health technology assessment (HTA) an important policy instrument to inform the choice of services that should be covered to progressively realize UHC. It plays a key role in determining which technologies are coved, considering various dimensions often including cost-effectiveness, budget impact, and feasibility. This paper reports on work undertaken to develop a roadmap for HTA implementation, using evidence-informed deliberative processes (EDPs) as the guiding framework. Between 2020 and 2022, we undertook several activities that informed the roadmap. We conducted a needs assessment and an assessment of European HTA best practices using a combination of desk research, interviews and surveys. We then conducted a document review of six selected HTA systems, complimented by expert interviews from three neighbouring countries. The roadmap provided a suggested HTA process, which included instructions on how to execute the different steps of the EDP framework to enhance the legitimacy of HTA-informed decision-making. The roadmap encompasses several aspects such as how to organize stakeholder involvement, how to identify and operationalize decision criteria, and how to make the decision process transparent. Guidance was given to the country on establishing a legal framework for HTA; a communication strategy; strengthening capacity and leveraging HTA expertise. The country was also given tailored advice on the positioning of the HTA-agency, first as an entity within the Ministry of Health or the National Agency of Public Health but with the long-term vision for it to be independent of any state institution. Despite several challenges, including a partial disruption because of the Covid-19 pandemic, the development of the roadmap for HTA implementation was completed and agreed upon by the Ministry of Health in Moldova. This work has helped motivate and support the country in HTA institutionalization.

Family satisfaction with care provided to patients deceased in intensive care unit

Background & Objective: Patient’s family satisfaction is a crucial but often overlooked indicator of the quality of the intensive care unit (ICU). The goal was to assess how satisfied patients’ families were with the treatment they received for terminally ill patients, where we worked with them to create a care plan. Methods: The study was conducted in ICU at Pakistan Kidney and Liver Institute and Research Center (PKLI & RC), Lahore, Pakistan which is a quaternary care center between December 1st 2022 and April 30th 2023. We used the Family Satisfaction in the Intensive Care Unit 24 (FS-ICU 24) questionnaire to interview the next of kin of patients who passed away in our ICU. Results: Of the 330 patients admitted to the intensive care unit (ICU) between December 1, 2022, and April 30, 2023, 42 patients (12.7%) passed away. Mean age was 46.8 ± 14.5 years, with 28 men (66.7%) and 14 females (33.3%). Mean length of ICU stay was 5.57 ± 5.84 day. On a scale of 1-5 (1 = very Dissatisfied; 5 = fully satisfied) the overall family’s satisfaction score was 4.21 ± 0.52 with ICU consultants received a high satisfaction rating for informing families about their patients’ conditions and involving them in the decision-making process. Conclusion: The study showed involving family members in the care plan and helping them prepare for death not only helps them deal better but also makes them feel satisfied with the treatment they received in the intensive care unit, It makes them appreciate that they were able to participate in the decision-making process. doi: https://doi.org/10.12669/pjms.40.10.8697 How to cite this: Sharieff S, Sajjal A, Idrees A, Rafai WA. Family satisfaction with care provided to patients deceased in intensive care unit. Pak J Med Sci. 2024;40(10):2441-2446. doi: https://doi.org/10.12669/pjms.40.10.8697 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wind Power Plant Location Selection with Fuzzy Logic and Multi-Criteria Decision-Making Methods

This study aims to examine the effectiveness of the fuzzy multi-criteria decision-making methods, especially Fuzzy Weight by Envelope and Slope (F-WENSLO) and Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) for evaluating the potential locations for wind power plants. The study's objective is to provide a solid framework for deciding which locations are most suitable for wind energy projects, taking into account various criteria and expert opinion. The study includes the evaluation of five potential places including Gürün, Kangal, Divriği, Ulaş and Zara in Sivas province of Turkey. The evaluation criteria include wind speed and direction, altitude, land use, environmental impacts, infrastructure proximity, social acceptance, economic costs, security and risk factors, climatic conditions and legal and permit requirements. Scores from experts from various fields and weights of criteria were determined. The analysis revealed that Ulaş and Kangal got the highest point for the wind power plant installation. As Ulaş gets the highest point due to favorable wind conditions, favorable altitude and advantageous land use; Kangal stood out as a strong candidate because of its acceptable wind speed, positive social acceptance and low economic costs. The study highlights the importance of integrating multiple criteria and expert assessments in the decision-making process. The findings suggest that fuzzy multi-criteria decision-making methods can be effectively supportive of wind power plant site selection. The study provides valuable information for project managers and policy makers, emphasizing the importance of criteria such as security in the choice of location, legal requirements and social acceptance. Future research may expand these findings to examine the integration of additional criteria, alternative locations, and other multi-criteria decision-making techniques.

Contractor-oriented optimization for wind farm maintenance decision-making considering partial monitoring information and degraded working efficiency

Maintenance strategies play a crucial role in the development and success of wind farm management, as they have a significant impact on the profitability of wind generation. The current maintenance decision-making process focuses mostly on the wind farm owner, while ignoring the benefits from the perspective of maintenance contractor. To bridge this gap, a novel contractor-oriented maintenance strategy is developed for both onshore and offshore wind farms to maximize profits for the maintenance contractor. Compared to the reported works, two main contributions are made. First, it is the first time that the maintenance strategy of a wind farm is designed from the perspective of the maintenance contractor, with emphasis on an accurate profit assessment. For this purpose, by utilizing remaining useful life (RUL) information from monitorable and non-monitorable components, a degradation-related efficiency model is constructed to quantify the loss of turbine efficiency due to degraded components and to incorporate it into the maintenance decision-making process. Secondly, various identifiable failure modes of the turbine components are investigated and simulated, as the contractor is more concerned about the maintenance costs under different failure modes. A case study using the dataset collected from real wind farms is provided to demonstrate and validate the proposed maintenance optimization method. The results indicate the proposed method can contribute to the maximum profits of the contractor while satisfying the requirements of the wind farm owner.

Novel Multi-Criteria Decision Analysis Based on Performance Indicators for Urban Energy System Planning

Urban energy systems planning presents significant challenges, requiring the integration of multiple objectives such as economic feasibility, technical reliability, and environmental sustainability. Although previous studies have focused on optimizing renewable energy systems, many lack comprehensive decision frameworks that address the complex trade-offs between these objectives in urban settings. Addressing these challenges, this study introduces a novel Multi-Criteria Decision Analysis (MCDA) framework tailored for the evaluation and prioritization of energy scenarios in urban contexts, with a specific application to the city of Bozen-Bolzano. The proposed framework integrates various performance indicators to provide a comprehensive assessment tool, enabling urban planners to make informed decisions that balance different strategic priorities. At the core of this framework is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is employed to systematically rank energy scenarios based on their proximity to an ideal solution. This method allows for a clear, quantifiable comparison of diverse energy strategies, facilitating the identification of scenarios that best align with the city’s overall objectives. The flexibility of the MCDA framework, particularly through the adjustable criteria weights in TOPSIS, allows it to accommodate the shifting priorities of urban planners, whether they emphasize economic, environmental, or technical outcomes. The study’s findings underscore the importance of a holistic approach to energy planning, where trade-offs are inevitable but can be managed effectively through a structured decision-making process. Finally, the study addresses key gaps in the literature by providing a flexible and adaptable tool that can be replicated in different urban contexts to support the transition toward 100% renewable energy systems.

Analyzing missingness patterns in real-world data using the SMDI toolkit: application to a linked EHR-claims pharmacoepidemiology study

BackgroundMissing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example.MethodsWe utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase‐4 inhibitors (DPP‐4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015–2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications.ResultsHigh levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies.ConclusionsWe were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data.

Agricultural trade liberalization, governance quality, and technical efficiency in the agricultural sector of Southeast Asia

International trade has been regarded as an essential factor in enhancing a country's productivity and efficiency. Nevertheless, developing countries squander money and resources as a result of their deficient institutional quality, impeding their ability to profit from specialization and trade. Therefore, this study intends to investigate the impact of agricultural trade liberalization and governance quality on technical efficiency in Southeast Asia's agricultural sector using balanced panel data spanning from 2002 to 2021. The research utilizes translog stochastic frontier analysis (SFA) with a single-stage maximum likelihood estimation (MLE) to simultaneously calculate the time-varying technical efficiency scores and explore the core factors influencing agricultural inefficiency. The findings reveal that the average output-oriented technical efficiency for ASEAN-8 countries was 94 %. This suggests that there is significant potential to enhance technical efficiency in agricultural production by up to 6 % by addressing the adverse impacts of technical inefficiency. The research findings further point out that Malaysia is the most technically efficient country, having a technical efficiency score of 99.82 %, followed by Vietnam (99.75 %), Thailand (99.70 %), Lao PDR (98.90 %), Myanmar (95.42 %), Indonesia (91.64 %), the Philippines (90.65 %), and Cambodia (76.11 %). The results of disaggregated agricultural trade liberalization demonstrate a significant reduction in agricultural inefficiency in Southeast Asia through agricultural exports and imports. The findings also emphasize that improvements in the rule of law positively contribute to agricultural efficiency, whereas enhancements in terms of voice and accountability and regulatory quality appear to reduce it. Based on these findings, the government should consider enlarging open and liberalized trade policies in order to facilitate the exchange of technology and knowledge within the agriculture sector. Additionally, the government should involve farmers, agricultural cooperatives, local communities, and other relevant stakeholders in the decision-making process to ensure that policies address the specific needs and constraints faced by the sector.

Adaptive and transparent decision-making in autonomous robots through graph-structured world models

ABSTRACT The growing ubiquity of autonomous robots across different fields necessitates that agents adapt to diverse tasks and ensure transparency and intelligibility in their decision-making processes. This study presents a novel framework that combines a graph-structured world model with large language models (LLMs) to address these requirements. First, a latent space is created to capture the reachability between distinct states. Next, a graph is constructed within this latent space by clustering an offline dataset that effectively captures the complex dynamics of the environment. Subsequently, LLMs are employed to redefine the reward function and relabel the dataset, thereby establishing a well-defined Markov decision process based on the previously learned graph. This relabeling process ensures that the agent's decision space aligns with user intentions. Consequently, a predictive world model is obtained, offering insights into potential future states and facilitating graph-based planning. Moreover, by inputting the planned path of the agent in the graph-structured world model into LLMs, natural language explanations can be generated to provide transparency in the decision-making process. Experiments on the D4RL benchmark validated the effectiveness of our approach in long-horizon planning, its adaptability to different user tasks, and its inherent explainability.

Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to Support Empirical Antibiotic Therapy Decisions in Infected Ischemic Leg Ulcers-A Feasibility Study.

Objective: Patients with peripheral artery occlusive disease (PAD) are at risk of developing foot ulcers, which can subsequently lead to foot infections and an increased risk of amputation. In cases of severe ischemic foot infections (IFIs), the empirical use of antibiotics can be limb-saving. However, there is currently no evidence-based guidance on the choice of empirical antibiotic therapy for IFI. Methods and Design: This retrospective single-center cohort study included 216 hospitalized patients with severe IFI undergoing endovascular revascularization. Weighted-Incidence Syndromic Combination Antibiograms (WISCAs) were calculated to guide empirical antibiotic choice. Results: The two most common causative pathogens for IFI were S. aureus and P. aeruginosa, with frequencies of 19.8% and 6.1%, respectively. The calculation of WISCAs revealed a low empirical coverage of amoxicillin (AMX) or clindamycin (CLN) with 21.6% and 27.7%, respectively. The empirical coverage of amoxicillin/clavulanic-acid (AMC), trimethoprim/sulfmethoxazole (SXT), and ciprofloxacin (CIP) was 50.6%, 53.1%, and 55.4%, respectively. Piperacillin/tazobactam (PT) exhibited the highest empirical coverage, with 82.5% as calculated by WISCAs. The calculated WISCAs did not significantly alter when stratified by the clinical characteristics of the patients. Conclusions: The empirical antibiotic coverage of CLN and AMX was low. SXT represents a promising empirical alternative in the case of IFI, irrespective of comorbidities and the WIfI score. WISCAs can assist in the decision-making process regarding empirical antibiotic therapy choices in cases of IFI.

Interpretable Mixture of Experts for Decomposition Network on Server Performance Metrics Forecasting

The accurate forecasting of server performance metrics, such as CPU utilization, memory usage, and network bandwidth, is critical for optimizing resource allocation and ensuring system reliability in large-scale computing environments. In this paper, we introduce the Mixture of Experts for Decomposition Kolmogorov–Arnold Network (MOE-KAN), a novel approach designed to improve both the accuracy and interpretability of server performance prediction. The MOE-KAN framework employs a decomposition strategy that breaks down complex, nonlinear server performance patterns into simpler, more interpretable components, facilitating a clearer understanding of how predictions are made. By leveraging a Mixture of Experts (MOE) model, trend and residual components are learned by specialized experts, whose outputs are transparently combined to form the final prediction. The Kolmogorov–Arnold Network further enhances the model’s ability to capture intricate input–output relationships while maintaining transparency in its decision-making process. Experimental results on real-world server performance datasets demonstrate that MOE-KAN not only outperforms traditional models in terms of accuracy but also provides a more trustworthy and interpretable forecasting framework. This makes it particularly suitable for real-time server management and capacity planning, offering both reliability and interpretability in predictive models.

Spider plant (Cleome gynandra) breeding priorities and preferences among landraces in Zimbabwe

Background: Participatory approaches such as participatory rural appraisal (PRA) and participatory variety selection (PVS) methods empower farmers to play a greater role in the decision-making process and in shaping the future improvement of crops. However, few studies have established breeding priorities and assessed variety preferences for spider plant, a nutrient-rich and medicinal crop mainly grown as landraces by farmers.Aim: This study aims to set spider plant breeding priorities and select preferred varieties.Setting: This study was conducted at seven farmer field schools (FFSs) located in various districts of Zimbabwe during the 2021–2022 summer season.Methods: Landrace genotypes were planted in a variety of demonstration plots where farmers met, learned and discussed important breeding traits, ultimately ranking the landrace genotypes. Important traits of spider plant were identified and prioritised to establish breeding priorities. The final ranking was conducted at the end of the season, and the data were analysed for significance.Results: The top six most important breeding priorities across the FFS can be ranked as follows: size of leaves number of branches taste late maturity germination early leaf growth. Preferences for different genotypes varied among FFSs. CGGUR, CGMRGP-Marondera and CGUZG1 were preferred for higher branching habits. Landraces with larger leaf sizes, were CGNPGRC356, CGMRGP-Marondera.Conclusion: The preferred genotypes were as follows: Tsholotsho = CGGUR, Rushinga = CGKENYA, Chiredzi = CGMRG1, Mudzi = CGMRGP-Marondera, Horticulture Research Institution – Research community (HRI-R) = CGMRG1 and CGKENYA, and M-Chiota = CGZIM and CGUZG1. Across FFSs, significantly (p 0.001) higher preference ranks were in the following order: CGMRGP-Marondera CGMRG1 CGNPGRC355 CGKENYA.Contribution: These genotypes can be utilised for future genetic improvement, seed production and variety release.