Hasil untuk "Management information systems"

G. Schreiber, H. Akkermans, A. Anjewierden et al.

Deniz Appelbaum, A. Kogan, M. Vasarhelyi et al.

Luay Jum’a, Ahmed Adnan Zaid, Mohammed Othman

<i>Background</i>: This study conceptualizes supply chain ambidexterity through two capabilities, supply chain adaptability and agility. Accordingly, it investigates the impact of supply chain adaptability and agility on green product innovation (GPI) and supply chain sustainability in Jordanian manufacturing firms. It also examines the mediating role of GPI in these relationships. The study is based on dynamic capabilities theory (DCT) as the theoretical foundation. <i>Methods</i>: A quantitative research approach was employed, with data collected from 346 supply chain managers using a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used for analysis. <i>Results</i>: The findings reveal that supply chain adaptability does not directly influence sustainability but significantly enhances GPI, which positively impacts sustainability. Supply chain agility, however, directly and significantly improves both GPI and sustainability, highlighting its importance in achieving sustainable supply chain performance. Additionally, GPI mediates the relationship between supply chain ambidexterity and sustainability, reinforcing its role as a key enabler of eco-friendly supply chain management. These findings provide theoretical and managerial implications. <i>Conclusions</i>: The study extends DCT by confirming the role of GPI in linking supply chain ambidexterity to sustainability. Managers should prioritize agility, invest in sustainable products, and adopt green practices to enhance competitiveness.

Chavdar Z. Vezirov, Atanas Z. Atanasov, Plamena D. Nikolova et al.

This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and time constraints. Various technological and technical solutions were evaluated through simulations and manual data processing. The proposed methodology was applied to a real-world case in Kalipetrovo, Bulgaria. The results include a 3.5-fold reduction in required tractors and a 50% decrease in tractor driver needs, achieved through extended working hours and shift scheduling. Additional benefits were identified from replacing conventional tillage with deep tillage, resulting in higher fuel consumption but improved soil preparation. Detailed resource schedules were created for machinery, labor, and fuel, highlighting seasonal peaks and optimization opportunities. The approach relies on spreadsheets and free AI-assisted platforms, proving to be a low-cost, accessible solution for mid-sized farms lacking advanced digital infrastructure. The findings demonstrate that structured information integration can support the effective renewal and utilization of tractor and machinery fleets while offering a scalable basis for decision support systems in agricultural engineering.

Sachiko Lim, Paul Johannesson

BackgroundNovel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology. ObjectiveThis study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance. MethodsThe ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals. ResultsWe designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information. ConclusionsThe development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

Janet Page‐Reeves, Cristina Murray‐Krezan, Mark R. Burge et al.

Abstract This project compared the effectiveness of two evidence‐based models of culturally competent diabetes health promotion: the diabetes self‐management support empowerment model (DSMS) and the chronic care model (CCM). Our primary outcome was improvement in patient capacity for diabetes self‐management as measured by the diabetes knowledge questionnaire (DKQ) and the patient activation measure (PAM). Our secondary outcome was patient success at diabetes self‐management as measured by improvement in A1c, depression scores using the PHQ‐9, and body mass index (BMI). We also gathered data on the cultural competence of the program using the Consumer Assessment of Healthcare Providers and Systems Cultural Competence Set. We compared patient outcomes in two existing sites in Albuquerque, New Mexico that serve a large population of Latinx diabetes patients from low‐income households. Participants were enrolled as dyads—a patient participant (n = 226) and a social support participant (n = 226). Outcomes over time and by program were analyzed using longitudinal linear mixed modeling, adjusted for patient participant demographic characteristics and other potential confounding covariates. Secondary outcomes were also adjusted for potential confounders. Interactions with both time and program helped to assess outcomes. This study did not find a difference between the two sites with respect to the primary outcome measures and only one of the three secondary outcomes showed differential results. The main difference between programs was that depression decreased more for CCM than for DSMS. An exploratory, subgroup analysis revealed that at CCM, patient participants with a very high A1c (>10) demonstrated a clinically meaningful decrease. However, given the higher cultural competence rating for the CCM, statistically significant improvement in depression, and the importance of social support to the patients, results suggest that a culturally and contextually situated diabetes self‐management and education program design may deliver benefit for patients, especially for patients with higher A1c levels. Key Points The team was interdisciplinary. The information presented describes results of both qualitative and quantitative data and involves interpretations that are interdisciplinary in nature. The study design used methods from multiple disciplines.

Yiqi Wang, Yang Li, Ruijie Li et al.

Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane’s spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles’ ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers.

Emília Malcata Rebelo

Any planning decision to develop and implement a territorial plan or to make a public investment will engender different kinds of externalities, such as increased land values. The research reported in this article aims to lay the foundations for the development of a market-based land tool based on the capture of betterments that stem from public investments or planning decisions, which will provide municipalities with funds to meet, at least, part of the costs with environmental services.

Ravi Samikannu, Abid Yahya, Muhammad Usman Tariq et al.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Marilia C.P. Borges, Sérgio B. Abreu, Carlos H.R. Lima et al.

Basic sanitation services are essential for human development, promoting health and inhibiting the spread of waterborne diseases. The availability of information on water and sanitation services at the local level supports the formulation, implementation and improvement of public policies aimed at advancing the provision of basic sanitation services to the population. In Brazil, the National Water and Sanitation Data System (SNIS), administered by the Ministry of Regional Development (MDR), is the largest information system for water and sanitation services in the country. Here we present the significant aspects of SNIS and offer the most recent results of water and sanitation services in the country, which reveals that water supply is the sanitation service closest to achieve the universalization preconized by the United Nations with almost 93% of the population served. The situation of sanitary sewer services reveals that only 61.9% of the Brazilian population have sewer collection systems, while only 78.5% of the collected volume is actually treated. The remaining 22.5% of the raw sewer is directly disposed in the environment. With respect to the generated sewer, only 49.1% of the volume is treated. The solid waste data show that a large part of the urban population is served by home collection services. The major challenge of this component is to ensure that the final destination is environmentally appropriate, since there are still many dumps that receive waste from different municipalities. The urban drainage data show that most Brazilian municipalities still have deficiencies in the planning of drainage services.

Alessia Maria Rosaria Tortora, Valentina Di Pasquale, Raffaele Iannone

Maintenance management is assuming an increasingly important role and garnering increased attention in Small and Medium Enterprises (SMEs). However, the difficulty of collecting data and processing information is evident in such contexts. In the current literature, few maintenance maturity models focus on the maintenance information management practices field. Moreover, though the existing models allow for assessing the maturity level, they do not indicate or assist in identifying and defining actions to reach the highest level. Furthermore, these models are not suitable for any type of organisation, as the assessment areas defined are quite generic (high level). For this reason, this paper proposes an innovative model for assessing the maturity level of maintenance management information practices in Small and Medium Enterprises (SMEs). The model provides the organisation with the strengths and weaknesses of their maintenance information management practices. The proposed model allows a clear measure of the maturity of the maintenance information management practices in smaller industrial contexts and provides a customised improvement programme. The model proposed supports small and medium companies to improve the effectiveness and efficiency of their maintenance management information infrastructure. The maturity model developed, in addition to being an assessment tool, provides and supports knowledge on the behaviours and practices for achieving world-class results.

Huiren Tian, Pengxin Wang, Kevin Tansey et al.

The rapid and effective acquisition of crop yield information is critical to the stability of food markets and development and implementation of related policies. It is an important baseline observation that is used for ensuring regional and global food security. In this study, a novel deep learning framework was developed for winter wheat yield estimation using meteorological data and two remotely sensed indices, Vegetation Temperature Condition Index (VTCI) and Leaf Area Index (LAI) at the main growth stages of winter wheat in the Guanzhong Plain. The proposed deep learning model was based on Long Short-Term Memory (LSTM) neural network with an attention mechanism (ALSTM), which the main idea is to assign attention to the key parts of the input sequence that affect the target vectors so that the specific features can be accurately extracted. The ALSTM model provided an improved estimation accuracy (R2 = 0.63, MAPE = 8.20%, RMSE = 502.71 kg/ha, NRMSE = 11.15%) as compared with the LSTM (R2 = 0.55, MAPE = 13.46%, RMSE = 699.92 kg/ha, NRMSE = 15.52%). A validation based on leave-one-year-out-validation further substantiated the robustness of ALSTM with smaller values of NRMSE and MAPE (13.63% and 11.54%). We demonstrated that the ALSTM model provided good generalization ability for sampling sites under different farming systems, including irrigation and rain-fed sampling sites. In addition, we evaluated the relative importance of each input variable in determining yields based on stepwise sensitivity analysis. It was found that LAI at the heading-filling stage and the milk stage as well as VTCI at the jointing stage contributed more than other input feature variables towards the corresponding yield. In conclusion, our findings highlighted that the attention mechanism helped to improve the interpretability of neural networks and the ALSTM model along with remotely sensed biophysical indices can provide a reliable and robust estimation of crop yield. An accurate estimation of wheat yield is not only helping towards informed crop management decisions but it will improve efficiency and sustainability of farming operations.

Petrenko Vyacheslav, Gurchinskiy Mikhail

High complexity of mobile cyber physical systems (MCPS) dynamics makes it difficult to apply classical methods to optimize the MCPS agent management policy. In this regard, the use of intelligent control methods, in particular, with the help of artificial neural networks (ANN) and multi-agent deep reinforcement learning (MDRL), is gaining relevance. In practice, the application of MDRL in MCPS faces the following problems: 1) existing MDRL methods have low scalability; 2) the inference of the used ANNs has high computational complexity; 3) MCPS trained using existing methods have low functional safety. To solve these problems, we propose the concept of a new MDRL method based on the existing MADDPG method. Within the framework of the concept, it is proposed: 1) to increase the scalability of MDRL by using information not about all other MCPS agents, but only about n nearest neighbors; 2) reduce the computational complexity of ANN inference by using a sparse ANN structure; 3) to increase the functional safety of trained MCPS by using a training set with uneven distribution of states. The proposed concept is expected to help address the challenges of applying MDRL to MCPS. To confirm this, it is planned to conduct experimental studies.

Weidong Mao, Xiang Zou, Zhongquan Guo et al.

Coal mine waters often have high salinity, hardness and alkalinity. The treatment of coal mine water requires careful management of multi-stage reverse osmosis (RO) systems to achieve effective recovery of water for domestic reuse, as well as zero liquid discharge to minimise the impact to the local environment. Design of RO systems for coal mine water treatment has been limited to the use of commercial design packages provided by membrane manufacturers, which do not provide insights into the impact of operating parameters such as feedwater salinity, concentrations of sparingly soluble salts, feed pressure and their interactions with different RO modules on the fouling/scaling potential of RO membranes. This also restricts the use of novel RO products and the delivery of an optimum design based on real needs. In this work, a mathematical model was developed to simulate a standard brackish water RO pressure vessel consisting six full-size RO membrane elements, using computational fluid dynamics (CFD). The model can be used to predict the permeate flowrate, water recovery levels, as well as the spatial information of the accumulation and scaling potential of sparingly soluble salts on the membrane surface. The results obtained from the model showed good agreement with the results obtained from the commercial RO design software WAVE. The CFD model was then used to predict the scaling threshold on various positions of a full-scale RO element, at different operating conditions, using parametric simulations based on Central Composite Designs. Outputs from this work not only provide insights into the microscopic flow characteristics of multiple full-scale elements in the RO pressure vessel, but also predicts the position where scaling would occur, at different feed conditions, for any RO products.

Mirmojtaba Gharibi, Zahra Gharibi, Raouf Boutaba et al.

In this work, we introduce a microscopic traffic flow model called Scalar Capacity Model (SCM) which can be used to study the formation of traffic on an airway link for autonomous Unmanned Aerial Vehicles (UAVs) as well as for the ground vehicles on the road. Given the 3D trajectory of UAV flights (as opposed to the 2D trajectory of ground vehicles), the main novelty in our model is to eliminate the commonly used notion of lanes and replace it with a notion of density and capacity of flow, but in such a way that individual vehicle motions can still be modeled. We name this a Density/Capacity View (DCV) of the link capacity and how vehicles utilize it versus the traditional One/Multi-Lane View (OMV). An interesting feature of this model is exhibiting both passing and blocking regimes (analogous to multi-lane or single-lane) depending on the set scalar parameter for capacity. We show the model has linear local (platoon) and asymptotic linear stability. Additionally, we perform numerical simulations and show evidence for non-linear stability. Our traffic flow model is represented by a nonlinear differential equation which we transform into a linear form. This makes our model analytically solvable in the blocking regime and piece-wise analytically solvable in the passing regime. Finally, a key advantage of using our model over an OMV model for representing UAV’s flights is the removal of the artificial restriction on passing via only adjacent lanes. This will result in an improved and more realistic traffic flow for UAVs.

Muhammad Mazhar Bukhari, Bader Fahad Alkhamees, Saddam Hussain et al.

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP-SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes.

Adela J. Chen, Marie-Claude Boudreau, R. Watson

J. Laitinen, E. Korkiakangas, J. P. Mäkiniemi et al.

María Amelia Linari, Alejandro Daín, María Lidia Ruíz et al.

Innovative technologies bring great possibilities of increasing human well-being. However, technological progress does not guarantee equitable health outcomes. While technological advances define the way in which people, systems and information interact, communities with fewer resources tend to be left excluded, and that will subsequently have an impact on quality. Publications explain that in communities where technological solutions have been imposed, there has later been abandoned equipment, software that is incompatible and frustrated management policies. Nevertheless, there are some cases of general technological implementations that undermine equity, justice and human rights. For example, the use of high technology in medical interventions as preventive measures or diagnosis, the use of genes which prevent the reuse of crop seeds allowed for consuming, and many more. To obtain equitable results, the design and planning of the technology must respect the ethical principles, local values and their folklore, among other points. Decisions require compromise in the medium and long term and local leadership.

Y. Yusuf, A. Gunasekaran, M. S. Abthorpe