Search Results (140)

This study examined the impact of supply chain collaboration (SCC) on supply chain innovation (SCI) and sustainability performance in the context of manufacturing firms in Jordan. The study also investigated the mediating role of SCI in the relationship between different types of SCC and sustainability performance. SCC was represented by three types namely, customer collaboration, supplier collaboration, and internal collaboration. Data were collected using a structured questionnaire that was distributed to employees from numerous management levels in firms located in Jordan as a developing country. A total of 314 valid responses were obtained between December 2024 and March 2025. The data were analyzed using partial least squares structural equation modeling with using the SmartPLS software package. The results of the study revealed that customer, supplier, and internal collaboration significantly enhanced SCI. These three forms of SCC also improved sustainability performance. SCI was found to directly influence sustainability performance, confirming its role as a driver of sustainable outcomes. Moreover, SCI mediated the relationship between internal collaboration and sustainability performance. However, no mediating effects were found between customer or supplier collaboration and sustainability performance. The findings contribute to the resource-based view and dynamic capabilities theory by highlighting collaboration and innovation as critical pathways for achieving sustainable performance. The study offers managerial insights for manufacturing firms in Jordan, emphasizing the importance of strengthening collaboration with customers and suppliers, while also fostering internal innovation to embed sustainability into organizational practices. Full article

This study conceptualizes how artificial intelligence (AI)-based customer engagement strategies can shape consumers’ green purchasing intentions, focusing on the theorized roles of attitude and perceived risk toward green products as articulated in prior literature. Building on contemporary research in sustainable marketing and consumer psychology, the article proposes a conceptual framework in which AI-enabled engagement influences green purchase intention via attitudes, with perceived risk operating as a boundary condition that moderates these effects. To qualitatively substantiate the salience and practical relevance of these constructs, an exploratory sentiment analysis of Amazon reviews for green products was conducted to surface emotional responses, perceived value drivers, and behavioral cues. The review corpus predominantly reflects positive sentiment alongside mixed subjectivity and factual commentary, highlighting recurring decision factors such as product quality, packaging, sustainability claims, and price sensitivity. Consistent with literature, the evidence aligns with the view that personalization and transparency can bolster trust and more favorable attitudes, while perceived risks—spanning greenwashing concerns, cost, and performance doubts—remain obstacles to adoption. Crucially, the sentiment analysis is presented as illustrative and does not statistically test the proposed mediation or moderation pathways; rather, it offers qualitative support that complements the literature-based conceptual model. The study contributes by integrating insights from digital technologies, consumer psychology, and sustainable marketing to guide authentic, strategic engagement practices that can encourage eco-conscious behavior. Full article

The compound 3-((benzyloxy)carbonyl)bicyclo[1.1.1]pentane-1-carboxylic acid was successfully synthesized. High-quality crystals were obtained, and its X-ray structure was solved and refined by Hirshfeld atom refinement using custom aspherical scattering factors with the Olex2/NoSphereA2 package. Hydrogen bonding interactions lead to head-to-head carboxylic acid dimer formation. A positional disorder for the bridging H-atom was detected and modeled to two parts in a 0.85:0.15 ratio. Detailed comparison with a neutron diffraction study of benzoic acid at the same temperature (100 K) demonstrates that the E–H-bond distances in the title compound are in excellent agreement (differing less than 1%) and the displacement ellipsoids volumes to the model are also in excellent agreement to the neutron diffraction structure. Moreover, both the variation in refined disorder occupancy and differences in C=O and C–O lengths of the disordered carboxylic acids in the two structures track well with their dimer O···O separations. This is longer by 0.023 Å in the structure of the title compound than in that of benzoic acid. A database search was conducted and used for comparison of the title compound to other high-quality structures of bicyclo[1.1.1]pentane-containing species. Full article

This study investigates the determinants of service demand in the packaging and printing industry, identifying 19 key factors through expert evaluation. These factors were analyzed using the Importance–Satisfaction (I–S) Model to pinpoint areas requiring enhancement, with four elements classified within the improvement zone. Considering resource constraints, improvement priorities were established through a modified Risk Priority Number (RPN) framework derived from Failure Modes and Effects Analysis (FMEA), expressed as RPN = I × F × E. The highest-priority areas for improvement included product pricing, flexibility in meeting customer requirements, suppliers’ emergency response capabilities, and proactive communication regarding raw material price fluctuations. The findings indicate that consumers balance price against sustainability value, highlighting the necessity of setting prices that align with perceived value to sustain trust and meet expectations. Strengthening firms’ emergency response mechanisms and developing an online standard operating procedure (SOP) notification system for raw material price changes can enhance communication efficiency, increase transparency in pricing, and ultimately improve organizational competitiveness. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

This study addresses a documented gap in detailed, cost-effective, and performance-validated electric vehicle (EV) powertrain solutions. It presents the complete design, construction, and simulation-based validation of a high-efficiency electric powertrain for a Shell Eco-marathon Urban Concept vehicle. Novel contributions of this work include a unique drivetrain architecture: a BLDC motor with a modular two-stage chain drive and a custom lithium-ion battery pack. The design is optimized for compactness and reliability under stringent budget and packaging constraints. A comprehensive Simulink-based vehicle dynamics model was developed for robust validation. This model enabled the estimation of energy consumption, torque profiles, and battery State of Charge under realistic drive cycles. The system demonstrated a remarkably low energy consumption under competition conditions, signifying high efficiency with <50 Wh/km consumption and full compliance with technical regulations. Furthermore, the hardware is thoroughly documented with detailed build instructions, CAD models, and a full bill of materials. This promotes reproducibility. This research offers a validated, low-cost, and replicable electric powertrain. It provides a transferable framework for future Shell Eco-marathon teams and advances lightweight, cost-effective solutions for real-world low-speed electric mobility applications, such as micro-EVs and urban delivery vehicles. Full article

Objectives: This study presents a versatile, AI-guided workflow for the targeted isolation and characterization of prenylated flavonoids from Paulownia tomentosa (Thunb.) Steud. (Paulowniaceae). Methods: The approach integrates established extraction and chromatography-based fractionation protocols with LC-UV-HRMS/MS analysis and supervised machine-learning (ML) custom-trained classification models, which predict prenylated flavonoids from LC-HRMS/MS spectra based on the recently developed Python package AnnoMe (v1.0). Results: The workflow effectively reduced the chemical complexity of plant extracts and enabled efficient prioritization of fractions and compounds for targeted isolation. From the pre-fractionated plant extracts, 2687 features were detected, 42 were identified using reference standards, and 214 were annotated via spectra library matching (public and in-house). Furthermore, ML-trained classifiers predicted 1805 MS/MS spectra as derived from prenylated flavonoids. LC-UV-HRMS/MS data of the most abundant presumed prenyl-flavonoid candidates were manually inspected for coelution and annotated to provide dereplication. Based on this, one putative prenylated (C5) dihydroflavonol (1) and four geranylated (C10) flavanones (2–5) were selected and successfully isolated. Structural elucidation employed UV spectroscopy, HRMS, and 1D as well as 2D NMR spectroscopy. Compounds 1 and 5 were isolated from a natural source for the first time and were named 6-prenyl-4′-O-methyltaxifolin and 3′,4′-O-dimethylpaulodiplacone A, respectively. Conclusions: This study highlights the combination of machine learning with analytical techniques to streamline natural product discovery via MS/MS and AI-guided pre-selection, efficient prioritization, and characterization of prenylated flavonoids, paving the way for a broader application in metabolomics and further exploration of prenylated constituents across diverse plant species. Full article

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs). Full article

Effective detection of center-pivot irrigation systems is crucial in understanding agricultural activity and managing groundwater resources for sustainable uses, especially in semi-arid regions such as North Dakota, where irrigation primarily depends on groundwater resources. In this study, we have adopted YOLOv11 to detect the center-pivot irrigation systems using multiple remote sensing datasets, including Landsat 8, Sentinel-2, and NAIP (National Agriculture Imagery Program). We developed an ArcGIS custom tool to facilitate data preparation and large-scale model execution for YOLOv11, which was not included in the ArcGIS Pro deep learning package. YOLOv11 was compared against other popular deep learning model architectures such as U-Net, Faster R-CNN, and Mask R-CNN. YOLOv11, using Landsat 8 panchromatic data, achieved the highest detection accuracy (precision: 0.98; recall: 0.91; and F1-score: 0.94) among all tested datasets and models. Spatial autocorrelation and hotspot analysis revealed systematic prediction errors, suggesting a need to adjust training data regionally. Our research demonstrates the potential of deep learning in combination with GIS-based workflows for large-scale irrigation system analysis, adopting precision agricultural technologies for sustainable water resource management. Full article

Face recognition systems often falter when deployed in uncontrolled settings, grappling with low light, unexpected occlusions, motion blur, and the degradation of sensor signals. Most contemporary algorithms chase raw accuracy yet overlook the pragmatic need for uncertainty estimation and multispectral reasoning rolled into a single framework. This study introduces HUE-Net—a Human-centric, Uncertainty-aware, Event-fused Network—designed specifically to thrive under severe environmental stress. HUE-Net marries the visible RGB band with near-infrared (NIR) imagery and high-temporal-event data through an early-fusion pipeline, proven more responsive than serial approaches. A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. Central to the architecture is the perturbed multi-branch variational module, which distills probabilistic identity embeddings while delivering calibrated confidence scores. Complementing this, an Adaptive Spectral Attention mechanism dynamically reweights each stream to amplify the most reliable facial features in real time. Unlike previous efforts that compartmentalize uncertainty handling, spectral blending, or computational thrift, HUE-Net unites all three in a lightweight package. Benchmarks on the IJB-C and N-SpectralFace datasets illustrate that the system not only secures state-of-the-art accuracy but also exhibits unmatched spectral robustness and reliable probability calibration. The results indicate that HUE-Net is well-positioned for forensic missions and humanitarian scenarios where trustworthy identification cannot be deferred. Full article

Value chains are facing different challenges, caused by emerging technologies as well as Industry 4.0 and Industry 5.0 principles. In order to successfully deliver valuable products to their customers, firms have to adapt, transform, and continuously improve their operational processes. Digital technologies will enable digital supply chains which will be decentralized and composed of autonomous modules. Although the elements of the value chain are independent, this paper shows how they affect each other’s performance. In this study, a model which shows how human resources management impacts customers’ satisfaction is presented. Additionally, this model reveals direct and indirect relations between human resources management, processes, supply chain actors, and customers. The conducted study was based on the variance-based method, while the model was constructed using the “PLSPM” package in R software. Additionally, the confirmatory factor analysis was applied for assessing the construct constitution. Taking into account that “A chain is only as strong as its weakest link”, firms can use these findings to seek for performance indicators and problem causes across the whole value chain and not only in one of its elements. Full article

Background: In an increasingly digitized supply chain landscape, small and medium-sized enterprises (SMEs) face mounting challenges in regard to delivering differentiated and responsive customer experiences. This study investigates the role of Industrial Internet of Things-enabled coopetition networks (IIoT-CNs) in enhancing the customer experience and value cocreation among SMEs. Grounded in Service-Dominant Logic, this research explores how interfirm collaboration and real-time data integration influence key performance indicators (KPIs), including perceived product quality, delivery timeliness, packaging standards, and product performance. Methods: An experimental design involving SMEs in Portugal’s ornamental stone sector contrasts traditional operations with digitally integrated coopetition practices. Results: While individual KPI improvements were not statistically significant, regression analysis revealed a significant positive relationship between IIoT-CN participation and the overall customer experience. The reduced variance in the performance metrics further suggests increased consistency and reliability across the network. Conclusions: These findings highlight IIoT-CNs as a promising model for SME digital transformation, contingent on trust, interoperability, and collaborative governance. This study contributes empirical evidence and practical insights for advancing customer-centric innovation in SME-dominated supply chains. Full article

Service quality is important for the survival of all businesses, including the hospitality business. Service quality can be measured by a model referred to as SERVQUAL, which comprises five parameters, namely, tangibility, reliability, assurance, empathy, and responsiveness. It is very important to examine publications to ascertain trends in service quality in the hospitality industry during the previous decade (2014–2024). Data were collected from the Scopus database, the article search having yielded 876 documents. The eligibility criteria were as follows: papers had to be published between 2014 and 2024, had to be written in English, and were restricted to articles, conference papers, book chapters, and review papers. The collected data were analyzed with the biblioshiny package in RStudio. The results revealed that the journal with the highest number of articles published during the period under study was Sustainability (Switzerland). Hong Kong Polytechnic was the institution with the highest number of publications vis-à-vis service quality in the hospitality industry, followed by Bina Nusantara University and Eastern Mediterranean University. It is notable that customer satisfaction featured prominently in different clusters, which emphasizes the fact that service quality is targeted at satisfying customers. Full article

In this paper, a method of estimating the probability of susceptibility of a component on a circuit board to electromagnetic interference (EMI) is presented. The integrated circuit electromagnetic compatibility (IC EMC) standard IEC 62132-4 enables the assessment of the susceptibility of an IC by determining the forward power incident on each pin required to induce a malfunction. Although we focus on IC susceptibility, the method might be applied to other components and sub-circuits where the same information is known. Building upon a previously established numerical model capable of estimating the average coupled forward power at the end of a trace of a lossless PCB trace for a known load in a reverberant environment, this paper updates the model by incorporating PCB losses and utilizes the updated model to estimate the distribution of coupled forward power at the package pin over a number of boundary conditions in a reverberant field. Thus, the probability of failure can be predicted from the known component susceptibility level, the length, transmission line parameters, and the loading of the track to which it is attached. To validate this numerical model, the paper includes measurements obtained with a custom-designed RF IC detector, created for the purpose of measuring RF power coupled into the package pin via test PCB tracks. Full article

Physics-Informed Neural Networks (PINNs) have emerged as a powerful approach for integrating physical laws into a deep learning framework, offering enhanced capabilities for solving complex problems. Despite their potential, the practical implementation of PINNs presents significant challenges. This paper explores the application of PINNs to systems of ordinary differential equations (ODEs), focusing on two key challenges: the forward problem of solution approximation and the inverse problem of parameter estimation. We present three detailed case studies involving dynamical systems for tumor growth, gene expression, and the SIR (Susceptible, Infected, Recovered) model for disease spread. This paper outlines the core principles of PINNs and their integration with physical laws during neural network training. It details the steps involved in implementing PINNs, emphasizing the critical role of network architecture and hyperparameter tuning in achieving optimal performance. Additionally, we provide a Python package, ODE-PINN, to reproduce results for ODE-based systems. Our findings demonstrate that PINNs can yield accurate and consistent solutions, but their performance is highly sensitive to network architecture and hyperparameters tuning. These results underscore the need for customized configurations and robust optimization strategies. Overall, this study confirms the significant potential of PINNs to advance the understanding of dynamical systems in biology and epidemiology. Full article