Advanced Solutions Research Articles

How digital collaboration capability affects incremental and radical service innovation in manufacturing firms: the mediating role of organizational agility

PurposeDespite a growing number of manufacturing firms actively collaborating with ecosystem members aiming for advanced service solutions, it remains unclear how digital collaboration capability (DCC) affects incremental service innovation (ISI) and radical service innovation (RSI) in the context of ecosystems. Drawing on dynamic capabilities theory and resource orchestration theory, this study aims to investigate the impacts of DCC on ISI/RSI and examine the mediating role of organizational agility (OA).Design/methodology/approachIn this empirical paper, the authors obtained questionnaire data from 238 Chinese manufacturing firms embedded in the ecosystems. Then, the authors examined all the hypotheses through hierarchical regression analyses.FindingsThe empirical evidence indicates that DCC has a distinct effect on ISI and RSI. DCC positively affects ISI, whereas DCC shows an inverted U-shaped effect on RSI. In addition, OA mediates the positive effect of DCC on ISI within a certain range, whereas OA always mediates the inverted U-shaped effect of DCC on RSI.Originality/valueThis study enriches and extends existing research on DCC and service innovation by elucidating the heterogeneous effects of DCC on ISI/RSI and the complicated mediating role of OA. This study also has practical implications for manufacturing firms to revisit their collaboration capability and agility to boost ISI/RSI in the context of ecosystems.

Alginate-based edible coating with magnoliae essential oil/β-cyclodextrin inclusion complex for Kyoho grape preservation

To prolong the shelf life of Kyoho grapes at room temperature, an edible coating composed of sodium alginate (SA) containing an inclusion complex of Magnoliae essential oil (MEO) and β-cyclodextrin (β-CD) (SA/MEO@β-CD coating) was developed. Initially, the inclusion complex of MEO with β-CD was developed to enhance its thermal stability. MEO@β-CD was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). In addition, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) results show that β-CD can improve the thermal stability of MEO. The entrapment efficiency and loading efficiency were 82.56% and 8.8%. MEO@β-CD was added to the SA coating for preserving Kyoho grapes, while monitoring the enzyme activity, total bacterial count, and sensory characteristics of the grapes. Compared to the uncoated group, the SA/MEO@β-CD coated group exhibited a significant reduction in total color difference, decay rate, weight loss rate, enzyme activity, and total number of bacterial colonies, while also slowing the decline in hardness and total soluble solids. The effect of the SA/MEO@β-CD1 group was highly effective. Conclusively, the SA/MEO@β-CD coating preserves Kyoho grapes by reducing lipid oxidation and inhibiting microbial growth, demonstrating its potential as an advanced edible packaging solution for grape preservation.

2‐Oxazoline‐Based Polymer for Pharmaceutical Products Adsorption in Aqueous Media

ABSTRACTWe present the synthesis and comprehensive evaluation of a novel 2‐oxazoline‐based polymer (polyPhOx) functionalized with phenyl groups, designed for the efficient adsorption of pharmaceutical contaminants from aqueous solutions. Employing living polymerization techniques (CROP) with two distinct initiators, we optimize reaction parameters including time, temperature, and initiator concentration. The rigorous characterization of the obtained polymer is depicted as well as the demonstration of its significant binding capacity for various pharmaceuticals, exploiting π–π and electrostatic interactions to achieve superior adsorption efficiency. Adsorption experiments are performed using high concentrations of model pharmaceuticals, including aspirin, ibuprofen, metoclopramide, pyramidone, oestradiol, and indomethacin, in hydroalcoholic solutions. Initial investigations confirm polyPhOx's exceptional efficacy, particularly with hydrophobic compounds such as oestradiol, achieving adsorption capacities of up to 56.67 mg g−1. Further validation in real environmental samples highlights the polymer's practical applicability, showing substantial removal rates even under complex matrix conditions and bringing hope for effective water purification solutions. Remarkably, polyPhOx retains high adsorption performance after 10 cycles, underscoring its potential for sustainable and cost‐effective water purification. This study not only introduces a promising material for environmental remediation but also demonstrates its robustness and reusability, paving the way for advanced wastewater treatment solutions targeting persistent pharmaceutical pollutants.

Sustainable Water Resource Management

Pedrollo reveals more about its development of advanced and flexible solutions for wastewater management as the firm works towards becoming a key reference point in the water infrastructure sector

BEYOND THE HORIZON: FUTURE TECHNOLOGIES TRANSFORMING MARITIME NAVIGATION

The shipping industry is constantly evolving to meet today's demands and challenges. This article explores how innovations and trends in shipping are influencing the efficiency, sustainability and competitiveness of this vital sector of the global economy. Analysing technological transformations and emerging practices, the paper reveals how new technologies and cross-company collaborations are shaping the future of the maritime industry. The example of the BYD Explorer fleet, the first Ro-Ro vessel specialised in transporting electric cars, underlines the adaptability and innovation in response to the current needs of the automotive industry and sustainability requirements. At the same time, the collaboration between MOL and Bearing, which has led to the development of advanced solutions based on artificial intelligence, demonstrates the positive impact of innovation in optimising maritime operations.

Cross-domain autonomous driving visual segmentation based on enhanced target data learning

Within the broader context of Information and Communications Technology (ICT), the quest for reliable and scalable visual segmentation methods poses significant challenges, particularly in autonomous driving, where real-world scene complexity requires advanced solutions. To address data scarcity and improve segmentation performance, we propose a novel unsupervised domain adaptation (UDA) approach that enhances target domain learning. Our method introduces multiple perturbations consistency, leveraging spatial context within the target domain to improve recognition. By applying perturbations at input and feature levels and using a consistency loss, we enhance contextual learning. Additionally, a weight mapping technique reduces the impact of detrimental source domain information. Experimental results demonstrate that our approach outperforms baseline methods on the GTAV→Cityscapes and SYNTHIA→Cityscapes datasets.

Mixed- and multi-relative biological effectiveness model simultaneous optimization in carbon ion radiotherapy: A proof-of-concept

Background and purposeIn carbon ion radiotherapy (CIRT), different relative biological effectiveness (RBE) models have been used for calculating RBE-weighted dose (DRBE). Conversion between current RBE predictions and introduction of novel approaches remains a challenging task. Our aim is to introduce a framework considering multiple RBE models simultaneously during CIRT plan optimization, easing the translation between DRBE prescriptions. Materials and methodsAn in-house developed Monte Carlo treatment planning system was extended to incorporate the local effect model version I (LEM-I), the modified microdosimetric kinetic model (mMKM) and the MKM-derived Japanese biological model (NIRS-MKM). Four clinical cases (two head-and-neck and two prostate patients), initially optimized with LEM-I for both targets and organs at risk (OARs), underwent two further optimizations: to fulfill mMKM/NIRS-MKM-based target prescriptions (mixed-RBE approach) or to simultaneously consider two biological models within the target regions (multi-RBE approach). Both approaches retained LEM-I-derived dose constraints for OARs. ResultsThe developed optimization strategies have been successfully applied, fulfilling all the clinical criteria for the applied RBE models. One of the RBE models showed unfavorable dose distribution when not explicitly considered in the optimization, while multi-RBE model optimization allowed meeting dose objectives for the selected OARs for both models simultaneously. ConclusionsThe introduced optimization approaches allow for mixed- or multi-RBE optimization in CIRT through the selection of RBE models independently for each region of interest. This capability addresses challenges of adhering to multiple RBE frameworks and proposes an advanced solution for tailored patient treatment plans.

Mergers and Acquisitions in the Digital Age: The Role of Technology in Streamlining Financial Integration

Abstract: This comprehensive article explores the transformative role of technology in streamlining financial integration during mergers and acquisitions (M&A) in the digital age. It examines how advanced technological solutions, including cloud-based Enterprise Resource Planning (ERP) systems, automated reconciliation tools, and artificial intelligence (AI) and machine learning (ML) applications, are revolutionizing traditional M&A processes. The paper delves into the impact of these technologies on consolidating financial data, enabling real-time analysis, reducing manual errors, and providing predictive insights for risk assessment and opportunity identification. It also discusses best practices for successful financial integration, emphasizing the importance of pre-merger due diligence, phased integration approaches, and robust cybersecurity measures. Furthermore, the article investigates emerging trends and future prospects in technology-driven M&A, including the potential of blockchain, advanced analytics, and quantum computing to further enhance integration processes. Drawing on recent research and industry surveys, this study highlights the critical role of technology in realizing M&A potential, improving deal outcomes, and creating strategic value in an increasingly complex business environment. The findings underscore the necessity for organizations to embrace technological advancements to remain competitive in the evolving landscape of corporate mergers and acquisitions.

Understanding troll field operational practices contributing towards efficiency and sustainability

This paper explores the role of Baker Hughes in enhancing the efficiency of drilling operations at the Troll field in Norway and its contribution to the field's path toward net-zero emissions. The Troll field is a significant gas reservoir on the Norwegian Continental Shelf, crucial for Europe's energy supply. This study details the advanced drilling technologies and digital solutions provided by Baker Hughes that have optimized operations, reduced emissions, and supported the Troll field's sustainability objectives. Additionally, the paper presents a life cycle analysis (LCA) methodology to quantify the environmental impact of drilling activities, using the Troll field as a case study.

Proactive fraud defense: Machine learning’s evolving role in protecting against online fraud

As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in addressing these challenges by offering more advanced, scalable, and adaptable solutions for fraud detection and prevention. By analyzing key models such as Random Forest, Neural Networks, and Gradient Boosting, this paper highlights the strengths of machine learning in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions that enable a proactive approach to fraud prevention. Unlike rule-based systems that react after fraud has occurred, machine learning models continuously learn from new data, adapting to emerging fraud schemes and reducing false positives, which ultimately minimizes financial losses. This research emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries. Future developments in machine learning, including deep learning and hybrid models, are expected to further enhance the predictive accuracy and applicability of these systems, ensuring that organizations remain resilient in the face of new and emerging fraud tactics.

Challenges of Artificial Intelligence in Human Resource Management of an Organisation

Artificial Intelligence is a technology that enables and prepares the machines to think, grasp, and accomplishing the tasks which are previously carried out by humans. AI has been grown aggressively and exponentially in the past decade. Artificial intelligence is assisting IT companies for making better and faster decisions. This applies to the area of human resources as much as anything else. HR recruiters have carried out Artificial Intelligence software to expedite in recruitment process and create more competence in the overall selection process. The objective of this paper is to assess Artificial Intelligence in Human Resources Management. (HRM) and highlighted the challenges of artificial intelligence in Human Resources Management. Artificial intelligence helps in contributing multiple solutions for hiring managers including basic recruiting tools, intermediate applications and advanced AI solutions. These tools are generating the most efficient way for human resources to predict a candidate’s future success with their company. Artificial Intelligence (AI) is metamorphosing the human resources field altogether. The current study would scatter some light on the breakthroughs of Artificial Intelligence and their implications with respect to HRM.

The Impact of 3D Printing Technology on the Improvement of External Wall Thermal Efficiency—An Experimental Study

Three-dimensional printing technology continues to evolve, enabling new applications in manufacturing. Extensive research in the field of biomimetics underscores the significant impact of the internal geometry of building envelopes on their thermal performance. Although 3D printing holds great promise for improving thermal efficiency in construction, its full potential has yet to be realized, and the thermal performance of printed building components remains unexplored. The aim of this paper is to experimentally examine the thermal insulation characteristics of prototype cellular materials created using 3D additive manufacturing technologies (SLS and DLP). This study concentrates on exploring advanced thermal insulation solutions that could enhance the energy efficiency of buildings, cooling systems, appliances, or equipment. To this end, virtual models of sandwich composites with an open-cell foam core modeled after a Kelvin cell were created. They were characterized by a constant porosity of 0.95 and a pore diameter of the inner core of the composites of 6 mm. The independent variables included the different material from which the composites were made, the non-uniform number of layers in the composite (one, two, three, and five layers) and the total thickness of the composite (20, 40, 60, 80, and 100 mm). The impact of three independent parameters defining the prototype composite on its thermal insulation properties was assessed, including the heat flux (q) and the heat transfer coefficient (U). According to the experimental tests, a five-layer composite with a thickness of 100 mm made of soybean oil-based resin obtained the lowest coefficient with a value of U = 0.147 W/m2·K.

Nurses are never gone or forgotten: Breaking barriers to enhance nursing care with technology

In recent years, healthcare systems around the world have undergone significant transformation, and the integration of technology into nursing care is at the forefront of this evolution. From electronic health records (EHR) to telemedicine, technology is reshaping the way nurses deliver care, improving patient outcomes, and enhancing efficiency. However, as promising as these advancements are, there remain significant barriers that must be overcome to fully leverage the potential of technology in nursing. This editorial explores these challenges and how they can be addressed to pave the way for more effective, patient-centered care. One of the most significant barriers to the adoption of technology in nursing is the lack of adequate infrastructure. Many healthcare settings, particularly in low-resource environments, lack the necessary equipment, reliable internet connectivity, or even the technical support needed to implement advanced technological solutions. Without these foundational resources, even the most innovative tools cannot be effectively utilized, leaving nurses and patients at a disadvantage. Keywords: Healthcare systems; health records; nursing development; patient care; telemedicine; telehealth

Advancement of Nanofibers in Wound Healing: A Review

The field of wound healing is currently experiencing a ground-breaking transformation with the introduction of nanofiber technology. This groundbreaking discovery has the potential to revolutionize regenerative medicine and tissue engineering worldwide, effectively meeting the growing need for innovative and affordable healthcare solutions. This comprehensive review explores the evolution and utilization of nanofibers to enhance wound healing. Nanofibers, known for their ability to mimic the extracellular matrix of human tissue, play a crucial role in facilitating cell growth and wound repair. The review explores sophisticated techniques like electrospinning and the integration of bioactive agents such as antibiotics and growth factors with nanofibers. It emphasizes on the precision with which these nanofibers are customized to address specific therapeutic requirements. Additionally, the review sheds light on the use of both natural and synthetic polymers in crafting biocompatible nanofibers, which significantly reduces healing time while ensuring optimal aesthetic recovery, meeting the expectations of patients with minimal scarring. It emphasizes the synergy of expertise from materials science, biology,and clinical practices in propelling nanofiber-based therapies from the laboratory to the forefront of clinical care. Through this succinct overview, we aim to underscore the immense potential of nanofibers in transforming wound care. We are offering a glimpse into a future where effective and efficient healing is within reach, marking a remarkable leap forward in the global quest for advanced healthcare solutions.

Estimating the Effectiveness of some biological agents and Palm biochar in control of Dry bubble disease on the second flush of White mushroom Agaricus bisporus

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities.

Orthodontic Materials and Antimicrobial Nanomaterials

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities.

Solar energy prediction with synergistic adversarial energy forecasting system (Solar-SAFS): Harnessing advanced hybrid techniques

An effective solar forecasting is an important part of managing and enhancing the effectiveness of solar power systems. The purpose of this paper is to develop a distinct and smart energy prediction system for solar PV systems. An efficient Solar Synergistic Adversarial Energy Forecasting System (Solar-SAFS) has been developed, which greatly enhances the precision and reliability of solar power predictions. It combines various deep learning methods including Graph Convolutional Networks (GCNs), Variational Autoencoders (VAEs), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to effectively understand spatial and time-related patterns in solar data. The strength and accuracy of this model are increased by using adversarial training, which makes sure it gives reliable predictions in different situations. For making the hyper parameters better and to increase model's effectiveness and efficiency, we are using Grey-Oystercatcher Hybrid Optimization (GOHO) technique. The Solar-SAFS is validated and tested with different data sets including Fingrid and DSK solar. It shows that this method gives better results compared to current deep learning models. Our findings suggest that Solar-SAFS improves the accuracy and stability of solar energy prediction by a large margin, offering an advanced solution for managing renewable energy resources. Moreover, it significantly helps to obtain better predictions for solar energy production, which is a crucial need in this sector. It also encourages more efficient and dependable forecasting methods for renewable energy sources like sunlight. Compared to existing deep learning models, the Solar-SAFS demonstrates significantly lower error rates across multiple performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Bias Error (MBE).

A smart sponge with pressure–temperature dual-mode sensing for packaging and transportation

Conventional sponges are limited to providing mechanical protection and lack the capability to monitor the real-time status of transported object. Consequently, there is an urgent need to develop smart sponges that can simultaneously protect and monitor the status of objects during transportation. In this study, we have developed a poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) melamine sponge (PEDOT:PSS@MS) using a dip coating-centrifugation method. The PEDOT:PSS@MS functions as a dual-mode sensor for pressure and temperature. The sensor has a maximum linear pressure sensitivity of 0.411 kPa−1 within 42.7 kPa, and a fast recovery time of 46 ms. On the other hand, the sensor exhibits a linear temperature sensitivity of 9.42 μV/K via the Seebeck effect in the range of 30 to 80 °C. By integrating multiple PEDOT:PSS@MS units into a 3D-printed packing box, the pressure and temperature orientation distribution of the transported objects can be monitored in real-time. The development of these smart sponges enhances multimodal sensors for linear response performance, holding great potential in intelligent logistics security and advanced packaging solutions.

PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research.

Analysis of Financial Performances for CHKP: Comparison with QLYS, RPD and GEN

Due to the escalating cyber threats, the cybersecurity industry has experienced significant growth, necessitating a detailed financial performance study of market leaders to provide a comprehensive picture of company health and market position. This study investigates the financial performance of four leading cybersecurity companies: Check Point Software Technologies Ltd. (CHKP), Qualys Inc. (QLYS), Rapid7 Inc. (RPD), and Gen Digital Inc. (GEN) over the period from 2019 to 2024. The analysis reveals that Check Point consistently maintains high margins and stable profitability, making it the best investment among the four. In contrast, Qualys, Rapid 7, and Gen Digital show less competitive performance due to various operational and profitability challenges. The study acknowledges its limitations, including reliance on quantitative metrics and a limited timeframe, but underscores the promising prospects for the cybersecurity industry driven by the increasing need for advanced digital security solutions. These results provide valuable insights for investors and stakeholders in making informed decisions in the dynamic cybersecurity sector.