Operative Management Research Articles

Use of Transverse Abdominis block for Post Operative pain management in Patients undergoing Emergency Cesarean Sections : a prospective study

INTRODUCTION Postoperative pain management is an essential aspect of perioperative care for patients undergoing emergency Cesarean sections. Adequate pain control not only improves patient comfort and satisfaction but also facilitates early mobilization, reduces the risk of complications, and shortens hospital stays. Transverse abdominis plane (TAP) block is a well-established technique for providing analgesia to the anterior abdominal wall, which can significantly reduce postoperative pain. In recent years, ultrasound guidance has been increasingly used to improve the accuracy and safety of TAP block. This research article aims to review the use of ultrasound-guided TAP block for postoperative analgesia in patients undergoing emergency Cesarean Sections. Methodology : A total of 60 patients who underwent Emergency Cesarean Sections were included in the study. All received bilateral US-guided TAP blocks with either ropivacaine 0.5% 20 ml on each side i. e. 40 ml total or saline. All participants received a spinal anaesthetic with bupivacaine, followed by postoperative acetaminophen, non-steroidal anti-inflammatory drugs, and patient-controlled i.v. tramadol . Each patient was assessed 24 h after delivery for PCA Tramadol usage, average pain score, nausea, vomiting, itch and duration of hospital stay. Results and Conclusion Out of the total 60 patients , 30 were in the study group and 30 in placebo group. Total PCA Tramadol use in 24 h was reduced in the study group compared with the placebo group ( P<0.05). The active group reported improved satisfaction with their pain relief measured by visual analogue scale as compared with the placebo group ( P=0.008). There were no local complications attributable to the TAP block. Transverse abdominis plane block was effective in providing analgesia with a substantial reduction in tramadol use during 24h after cesarean section when used as adjunctive to standard analgesia.

Analysing the Influence of Augmented Reality on Organization Performance via Supply and Logistics Value Chain Functions: A Hybrid ANN-PLS Model Assessment in the Gulf Cooperation Council Region

Background: Despite the resurgence of interest in augmented reality (AR) due to Industry 4.0 and its ability to resolve several challenges faced by current business models, comprehensive research examining the capabilities of AR in supply chain management (SCM) and logistics remains limited. This article aims to investigate the potential effects of AR technology on organizational performance through the mediation role of SCM and logistics value chain functions to address the existing knowledge gap. Methods: This research employed a cross-sectional design and an explanatory survey as a deductive approach for hypothesis development. The primary data collection method involved the self-administration of a questionnaire to furniture suppliers located in the Gulf Cooperation Council (GCC), including six countries. Of the 656 questionnaires submitted to suppliers, 483 were considered usable, yielding a response rate of 73.6%. The research utilized partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) techniques to evaluate the gathered data. Results: The current paper’s statistical evidence demonstrates that AR implementation has a positive impact on the supply and logistics value chain activities and organizational performance of furniture suppliers in the GCC region. Moreover, it illustrates that the design and planning variable of supply chain value dominates as the primary predictor of organization performance. The results indicated that the ANN strategy provided a more comprehensive explanation of internally generated constructs compared to the PLS-SEM technique. Conclusions: This study demonstrates its usefulness by advising furniture industry decision-makers on what to avoid and what aspects to consider when creating plans and regulations. The report also suggests operations managers apply machine learning (ANN) for prediction and decision-making in supply and operations value chains. This essay looks at how the AR and resource-based supply value chain view may affect company performance across countries, firm sizes, and ages.

Optimization of breeding program design through stochastic simulation with evolutionary algorithms.

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

Harnessing Collaborative Innovation: How Knowledge Sharing Drives Performance in Philippine Hardware MSMEs

The study examines the mediating effect of knowledge sharing on the relationship between collaborative innovation capabilities and innovation performance within hardware companies in the Philippines. It seeks to fill the gap in local research by exploring how collaborative efforts and knowledge-sharing practices influence the innovation outcomes of micro, small, and medium enterprises (MSMEs) in the hardware sector. A quantitative approach was employed, surveying operational managers from 80 hardware companies in Metro Manila. The study used Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the relationships between collaborative innovation capabilities, knowledge sharing, and innovation performance. The study found that collaborative innovation capabilities have a significant positive effect on innovation performance. Additionally, knowledge sharing acts as an important mediator, enhancing the effectiveness of collaborative efforts in driving innovation outcomes. This study provides valuable insights into the hardware industry in the Philippines, where there is a lack of local research on the dynamics of collaboration, knowledge sharing, and innovation. It offers practical implications for hardware MSMEs, highlighting the importance of fostering collaboration and implementing effective knowledge-sharing systems to drive innovation and maintain competitiveness in a fast-paced market.

A review of early small bowel obstructions in staged IPAA procedures.

Small bowel obstructions (SBOs) are a common complication following staged IPAA. Our goal was to compare early post-operative SBO outcomes between different staged ileal pouch-anal anastomosis (IPAA) and to further analyze the type of procedures required in patients who needed operative management of SBO. In this retrospective cohort study, we selected all patients who presented to our tertiary care center between 2008 and 2017, with ulcerative colitis or IBD-Unspecified colitis and who underwent a primary total proctocolectomy with IPAA for medically refractory disease or dysplasia (n = 623). Patients were divided into 4 groups based on staged IPAA received (1 stage vs 2 stage vs modified 2 stage vs 3 stage). Primary outcomes were compared between both groups using univariate analysis and included SBO within 6months post-operatively of one of the IPAA stages or 18months since the first surgery for patients who underwent 1 or 2 stage IPAAs, and need for readmission and reoperation. Risk factors for SBO, reoperation and readmission due to SBO were evaluated using multivariate logistic regression. Stage 2 and 3 patients had the highest rates of SBO post-operatively. There was a significant difference in the rate of readmission between groups, with 2 and 3 stage groups having the highest rates (p < 0.05) but the rate of SBO and reoperations was not significantly different between groups. Of all patients with an SBO, 34% required operative intervention. Of these, 65% underwent diagnostic laparoscopy with lysis of adhesions and 27% required an open procedure. Small bowel obstructions are a common complication of staged IPAA procedures. Patients who underwent 2 and 3 stage IPAA had the highest rates of SBO, although not statistically significant. A third of patients with an SBO required surgical intervention, with diagnostic laparoscopy and lysis of adhesions being the most common procedure.

On-the-go table grape ripeness estimation via proximal snapshot hyperspectral imaging

The monitoring of grapes for ripeness estimation is a practice that enables fruit harvesting at the optimal time. Hyperspectral Imaging (HSI) represents a non-destructive and high-throughput alternative to traditional laboratory analyses. Current literature approaches perform hyperspectral measurements using line scan sensors or low-resolution static snapshot cameras, which hinder a fast per-bunch ripeness characterization. We propose a framework for on-the-go collection and processing of proximal snapshot hyperspectral images to estimate single bunch ripeness parameters. Focusing on table grapes (Vitis vinifera L. cv. Red Globe), we collected images under natural illumination with a hyperspectral camera (500–900 nm) mounted on a moving vehicle in an experimental block sited in Piacenza, Italy. We investigated images collected in August and September 2021 representing two ripening stages. The composition of the imaged grape bunches was determined through laboratory chemical analyses to predict Total Soluble Solids (TSS) and anthocyanin concentration. The images were pre-processed via multimodal image registration to correct the unalignment of bands due to the vehicle motion, and the single bunches were automatically identified on false RGB images through a Mask Region-Convolutional Neural Network (Mask R-CNN) instance segmentation network. The mean spectra of the bunches were used as input features of a Partial Least Squares Regression (PLSR) model to predict the chemical parameters at single bunch and whole vine scales. The regression model of TSS had an R2 (10-fold nested cross-validation) of 0.75 and 0.85 on a per-bunch and per-vine basis, respectively. The regression model of anthocyanin had an R2 of 0.68 and 0.49 on a per-bunch and per-vine basis, respectively. The results suggest the potential of using snapshot hyperspectral images for high-throughput analysis of a per-bunch grape ripeness estimation. The method described in this study could give valuable information to improve grape ripening monitoring and management of harvest operations and even allow for precise and automated robotic harvesting.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

Cerebrospinal fluid drain placement and comprehensive strategies for spinal cord protection in open thoracoabdominal aortic aneurysm repair.

Ischaemic spinal cord injury remains a significant challenge in thoracoabdominal aortic repairs. Modern techniques have reduced spinal cord injury rates yet managing patients during and after thoracoabdominal aortic repairs remains complex. This article outlines our comprehensive approach to the prevention of spinal cord injuries in open thoracoabdominal aortic repair operations, focusing on the placement of cerebrospinal fluid drain and intraoperative strategies to enhance spinal cord protection. Preoperative planning involves thorough patient assessment, prehabilitation and nutritional support, detailed imaging review, thorough operative planning and patient blood management. Intraoperative measures include the use of neuromonitoring techniques like near-infrared spectroscopy and motor evoked potentials, as well as cerebrospinal fluid drainage together with blood pressure management to optimize spinal cord perfusion. Postoperative management focuses on maintaining haemodynamic stability with high mean arterial pressure, along with close monitoring and management of the cerebrospinal fluid drain to improve spinal cord perfusion. Additionally, thromboelastography-guided strategies are crucial for optimizing coagulation and addressing postoperative bleeding complications. The goal of this multifaceted approach is to minimize the risk of spinal cord injury, thereby improving patient outcomes and reducing the incidence of postoperative paraplegia. Our video tutorial shows some of our preoperative and intraoperative techniques for spinal cord protection in thoracoabdominal aortic repairs.

Heat transfer study on a stator-permanent magnet electric motor: A hybrid estimation model for real-time temperature monitoring and predictive maintenance

When electrical machines operate without a specific cooling system, the surrounding environment plays a crucial role in the rise of temperature and the duty cycle of operation. More clearly, a natural convection cooling system with a low value of heat transfer coefficient carries the risk of thermal breakdown, insufficient safety, and reliability. This paper studies the heat transfer aspects of a low-power flux switching permanent magnet (FSPM) motor under natural convection cooling to implement a novel real-time sensor-less temperature monitoring system. Thermal and electromagnetic experiments are carried out to create foundations for transient and steady-state numerical models. A data-driven, deep learning algorithm estimates the core and permanent magnet (PM) eddy current losses in real-time, besides the already available copper and friction losses. Subsequently, a two-node thermal equivalent circuit in a hybrid model with a feed-forward neural network estimates the dynamic temperature profile of windings and PMs. It is indicated that the worst-case estimation error is below 7.5%, and the configuration is applicable under a wide range of operation states and environmental conditions. Lastly, the system, including the power source, FSPM motor, and hybrid temperature estimation unit, will be implemented in MATLAB/Simulink to investigate the fault prediction and operation management capabilities.

Cooperative Object Transport Via Non-Contact Prehensile Pushing by Magnetic Forces

Abstract Cooperative robot systems are an essential candidate for object transportation solutions. They offer cost-efficient and flexible operation for various types of robotic tasks. The benefits of cooperative robot systems have triggered the improvement of the object transportation field. In this study, a new way of transporting objects by cooperative robots is presented. The proposed method is performed by the pushing action of the magnetic forces of the robots. The permanent magnets mounted on the mobile robots and the cart create this repelling force. The rectangular object carrier cart equipped with passive caster wheels can be manipulated on flat terrains easily and be assigned to carry different shapes of objects. Using a carrier cart has the advantage of eliminating the vertical loads on the robots. Controlling a non-contact pushing method offers a low computational burden since simple velocity and position updates are adequate for operation management. Compared with the other methods of object transportation systems, the non-contact pushing method provides a faster operation with less sensitivity to control errors. Both simulations and real-world experiments are conducted and the performances are given comparatively with a generalized frictional contact object-pushing method. The results show that the proposed method provides 10.48% faster and 20.03% more accurate object transportation compared to the frictional contact method. It is envisioned that the presented method can be a promising candidate for object transportation tasks in the industry.

Symptom Resolution After Operative Management of Complications From Transvaginal Mesh: Notice of Update.

Symptom Resolution After Operative Management of Complications From Transvaginal Mesh: Notice of Update.

INtelligent toolkit for reconnaissance, assessments and prehospital support in Perilous InciDents: a realistic experiment in prehospital environment

BackgroundFirst responders, when arriving at a disaster, need a rapid analysis of the environment in which they are going to operate, as they have to assess the conditions surrounding potential victims and neutralize any risks that may exist.The EU-funded INTREPID develops a new technology platform to assist first responders when arriving on the scene of a disaster. The project INTREPID aims to support safer operations in the form of more efficient, fast, and safe disaster site assessments. The objective of the study is to implement new technologies into rescue operations to facilitate and improve situational awareness and operation management capabilities to save lives.The focus of the study is relevant to the field of mass casualty incident management and disaster, as proper communication is extremely relevant in the management of catastrophes.MethodThe first phase of the project started with a qualitative methodology SCRUM, for catching the end user’s feedback and requirements to design the interface platform. It was developed a platform to support first responders in disasters areas improving the 3D scanning and analysis of disaster areas. This platform is based on the concepts of intelligence amplification and eXtended Reality, with hololens, drones and robots. The project continued with a β phase in which the platform with all tools integrated were tested in simulated mass casualty disasters.ResultsThese technologies are tested in different disaster scenarios: A flooded subway stop in Stockholm, an accident in the chemical industry in Marseille, and a man-made explosion in a hospital in Madrid. Through this platform, first responders can immediately initiate operations without exposing personnel to potential harmful risks without specialized equipment, with all important information shared and coordinated, among all responders, whether they are security, firefighters, or emergency health professionals.ConclusionsThe performance pilots and the questionnaire results validated the effectiveness and usability of the final version of the INTREPID platform and tools.

Digital Transformation in Operations Management: Leveraging Technology to Improve Business Efficiency

Digital transformation in operations management has become an urgent need for companies to improve efficiency, productivity, and competitiveness in the face of an increasingly complex and competitive business environment. Digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data provide opportunities for companies to optimize various aspects of operations. This article aims to explore how digital transformation can be applied in operations management to support these goals. The research method used is a literature review, where various previous studies and industry reports are analyzed to identify the benefits, challenges, and impacts of digital technology implementation on business operations. The results show that the implementation of digital technology enables companies to speed up workflows, reduce costs, improve process accuracy, and prepare the foundation for continuous innovation. In the discussion, it is also explained that although there are challenges in terms of investment costs, training, and data security, companies that successfully overcome these barriers will gain a significant competitive advantage. The implications of these findings are the importance of continued investment in technology and human capital development, as well as the adoption of sustainability strategies to support long-term growth.

Blockchain Solutions for Logistic Management

Blockchain technologies have the potential to fundamentally change logistics and supply chain management. By leveraging the capabilities of blockchain technology, businesses can increase efficiency, reduce costs, and improve security and trust in operations. However, there are still difficulties to overcome in terms of uptake and implementation. This article examines the various blockchain technologies applicable in the field of logistics, presents the benefits and limitations of blockchain technologies in this aspect, and offers a summary of the existing technologies used in the logistics sector. According to this, blockchain-based models applicable both to a specific stage of the logistics process (e.g., transportation of goods, materials, and feedstocks; management of warehouse operations; cargo tracking; etc.) and related insurance services have been proposed. The proposed models have been tested in a lab environment on the HyperLedger Fabric platform, and the results show that they are fully functional.

Pediatric Cervical Spine Trauma: A Narrative Review.

Narrative review. To provide an updated overview of pediatric cervical spine trauma. Pediatric cervical spine trauma can cause debilitating morbidity and mortality and neurological impairment. The unique anatomic features of the developing cervical spine can predispose children to injuries. We reviewed the pediatric cervical spine trauma literature in PubMed and EMBASE. Pediatric cervical spine injury occurs in 1%-2% of pediatric trauma. The most frequent cause is motor vehicle collisions, with sports-related injuries being more common in older children. Larger head-to-body ratios and tissue elasticity can predispose young children to a greater risk of injury higher in the craniocervical junction and cervical spine. Standardized protocols and classification systems, such as the Pediatric Cervical Spine Clearance Working Group protocol and the AO Spine Injury and Subaxial Cervical Spine Injury Classifications, are valuable in triage as well as for assessing the need for operative versus nonoperative management. In general, operative approaches and principles are similar to those in adults, with modern instrumentation and fusion techniques achieving high rates of successful arthrodesis. Effective management and treatment of pediatric cervical spine injuries depends on early and accurate diagnosis, a thorough understanding of pediatric spinal anatomy, and a versatile surgical armamentarium.

Developing a cross-functional team coordination framework: A model for optimizing business operations

In today's complex business environment, effective cross-functional team coordination is crucial for optimizing operations and achieving organizational goals. This study introduces a comprehensive framework designed to enhance cross-functional team coordination through structured operational management strategies. By integrating key principles of collaboration, communication, and accountability, the framework aims to bridge departmental silos and promote a culture of teamwork. The proposed model is structured around three core components: communication channels, collaborative tools, and performance metrics. First, it emphasizes the establishment of robust communication channels that facilitate real-time information sharing and feedback among team members from diverse functions. This fosters transparency and aligns objectives, ensuring that all team members are on the same page. Second, the framework advocates for the implementation of collaborative tools that streamline project management and task allocation. These tools enable team members to track progress, manage workloads, and share resources effectively, thereby enhancing overall productivity. By utilizing technology that supports remote collaboration, organizations can also adapt to the increasing trend of virtual teams, ensuring that geographical barriers do not hinder coordination. Finally, the model incorporates performance metrics that allow teams to evaluate their effectiveness and make data-driven decisions. By establishing clear key performance indicators (KPIs) related to team outcomes, organizations can continuously monitor progress and identify areas for improvement. This iterative approach not only optimizes current operations but also fosters a culture of continuous improvement within cross-functional teams. The framework is designed for applicability across various industries, demonstrating its versatility in addressing the unique challenges faced by organizations. By adopting this structured approach to cross-functional coordination, businesses can enhance their operational efficiency, reduce project delays, and improve overall team morale. This study contributes to the existing literature on operational management by providing a practical model that organizations can implement to optimize their business operations through improved cross-functional teamwork.

A data-driven operations management model: Implementing MIS for strategic decision making in tech businesses

In today’s rapidly evolving technological landscape, data-driven decision-making is critical for business growth and competitiveness. This paper presents a comprehensive operations management model that integrates Management Information Systems (MIS) to facilitate strategic decision-making in technology firms. The proposed model emphasizes the transformation of raw data into actionable insights, enabling organizations to optimize their operations and respond effectively to market dynamics. By systematically incorporating MIS into operational frameworks, the model provides a structured approach to data collection, analysis, and dissemination. It identifies key performance indicators (KPIs) that are essential for monitoring business performance and aligning operational activities with strategic objectives. Through case studies of leading tech companies, the model illustrates how data-driven methodologies can lead to improved forecasting, resource allocation, and overall operational efficiency. The framework is designed to be adaptable, allowing technology firms to customize the MIS components based on their unique business needs and market conditions. Key features of the model include real-time data analytics, predictive modeling, and user-friendly dashboards that enhance visibility across departments. These components empower decision-makers with timely and relevant information, fostering a culture of informed decision-making at all organizational levels. Moreover, the model addresses common challenges faced by tech businesses, such as data silos and integration issues. By promoting a centralized data repository and standardized reporting mechanisms, it enhances collaboration among teams and ensures consistency in data interpretation. The implications of this model extend beyond operational efficiency, influencing strategic initiatives such as product development, market expansion, and customer engagement. In conclusion, this data-driven operations management model not only supports strategic decision-making in technology firms but also positions them for sustainable growth in a competitive marketplace. By leveraging MIS effectively, organizations can harness the power of data to drive innovation and enhance overall business performance.

Designing circular supply chains with digital technologies for competitive sustainability: An operation management perspective

The objective of this research is to investigate how digital technologies, specifically blockchain, artificial intelligence (AI), and the Internet of Things (IoT), can be integrated into circular supply chains to enhance sustainability and preserve a competitive advantage. The goals of circular supply chains, which are founded on the ideas of the circular economy, are to reduce waste, promote resource regeneration, and lessen their negative effects on the environment. The study highlights the critical role that digital technologies play in guaranteeing transparency, promoting real-time monitoring, and optimizing resource distribution. Blockchain offers traceability and trust, IoT boosts visibility and operational efficiency within supply chains, and AI improves demand forecasting and resource efficiency. Though the benefits are obvious, there are significant obstacles to overcome, including high implementation costs, complex technological issues, scalability issues, and regulatory barriers. The research demonstrates how these digital technologies can effectively promote sustainability while enhancing operational efficiency through case studies of well-known companies like HP, Walmart, and Philips. The report also highlights important future trends and makes suggestions for companies looking to develop their workforces, invest in digital technologies, and work together on circular economy platforms. In the end, digital tool adoption is essential for creating supply chains that are more sustainable and competitive in the modern global economy.

Measurement Service Productivity: The Case Study of the Wedding Photography Businesses in Khon Kaen Province

There is an attempt of measuring service productivity in order to analyze performance of the service business. Since wedding photography businesses are struggling due to a variety of issues, service productivity has decreased. The purpose of this research is to measure and analyze the service productivity of 15 wedding photography businesses located in Khon Kaen province. In-depth semi-structured interviews were conducted to gather input and output variables over a one-month period from the 15 wedding photography businesses. The collected data were analyzed using Data Envelopment Analysis (DEA) with an input-oriented approach. The productivity measurement of the wedding photography businesses consists of 3 input variables and 2 output variables. The findings indicated that 6 wedding photography businesses were efficient under the CRS model, and 7 were efficient under the VRS model. The guideline from efficient wedding photography businesses is recommended to improve service productivity in the wedding photography business in terms of operations management by using the best practice units as a benchmark for inefficient DMUs. The results of this research are expected to benefit wedding photography businesses operating in similar contexts and contribute to the broader service sector for further study.

Translational science and related disciplines.

Translational research moves scientific discoveries and innovations across the development spectrum for a particular target or disease, trying to bridge in a multidisciplinary fashion the gap between laboratory scientific discoveries and practical, real-world applications in medicine and in healthcare. Translational research aims to move research findings across settings, specific languages, methodologies, and study designs, from laboratory to clinical practice and ultimately into community- and population-level health benefits. In contrast, translational science is a distinct field, which evolved over time toward a systematic study and practice of operationalizing the translation of content from one language, ecosystem, environment, contextual landscape, culture, discipline, area, or domain into another. It involves systematic and transdisciplinary integration of knowledge from basic science, clinical research and population science to improve human health, better longevity, and to ensure disease- and disability-free lives. Translational science often uses knowledge, operational frameworks, and specific capabilities borrowed from other specialties, disciplines, and fields such as operations management, implementation and dissemination science, quality improvement and management, project management, public health, intervention science, change management and leadership, decision science, design thinking, functional design, data science, communication and marketing science, etc. The main goal of this article is to open a series of thematic reviews in this journal, introducing the reader to the main definitions, contingencies, touchpoints, and overlapping areas between translational science and these related specialties, disciplines, and fields of study. Transdisciplinary capabilities borrowing from these related specialties can create a robust translational science machinery for health systems, research organizations, and innovation hubs.